Research ArticleTASC-MADM Task Assignment in Spatial CrowdsourcingBased on Multiattribute Decision-Making

Yunhui Li 1 Liang Chang 2 Long Li 2 Xuguang Bao 2 and Tianlong Gu 3

1School of Information and Communication Guilin University of Electronic Technology Guilin 541004 China2Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin 541004 China3College of Information Science and Technology Jinan University Guangzhou 510000 China

Correspondence should be addressed to Tianlong Gu gutianlongjnueducn

Received 7 May 2021 Accepted 9 August 2021 Published 21 August 2021

Academic Editor Qing Yang

Copyright copy 2021 Yunhui Li et al )is is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

)e methodology formulating a reasonable task assignment to find the most suitable workers for a task and achieving the desiredobjectives is the most fundamental challenge in spatial crowdsourcing Many task assignment approaches have been proposed toimprove the quality of crowdsourcing results and the number of task assignment and to limit the budget and the travel costHowever these approaches have two shortcomings (1) these approaches are commonly based on the attributes influencing theresult of task assignment However different tasks may have different preferences for individual attributes (2) the performanceand efficiency of these approaches are expected to be improved further To address the above issues we proposed a task assignmentapproach in spatial crowdsourcing based on multiattribute decision-making (TASC-MADM) with the dual objectives of im-proving the performance as well as the efficiency Specifically the proposed approach jointly considers the attributes on the qualityof the worker and the distance between the worker and the task as well as the influence differences caused by the taskrsquos attributepreference Furthermore it can be extended flexibly to scenarios with more attributes We tested the proposed approach in a real-world dataset and a synthetic dataset )e proposed TASC-MADM approach was compared with the RB-TPSC and the Budget-TASC algorithm using the real dataset and the synthetic dataset the TASC-MADM approach yields better performance than theother two algorithms in the task assignment rate and the CPU cost

1 Introduction

Spatial crowdsourcing first introduced by Kazemi andShahabiin [1] refers to an economic and efficient solution toparticipation in completing tasks such as sensing tasks[2 3] )e popularity of mobile devices and advanced In-ternet technologies have made it a popular trend in per-forming spatial tasks [4 5] Unlike conventionalcrowdsourcing spatial crowdsourcing requires a worker totravel to a given location to perform a given task [6] Ex-amples of spatial crowdsourcing such as environmentalconditions and monitoring traffic flow at selected locations[7 8] crowdsourcing news reporting tasks [9] and naturaldisaster response [10] are likely to have spatial requirementsthat cannot be fulfilled remotely and require physical arrivalat the taskrsquos location Spatial crowdsourcing is becoming a

compelling paradigm for recruiting workers to perform thetasks However due to the openness of crowdsourcing thereare some core issues (1) how to guarantee the quality ofcrowdsourcing results and the number of tasks completed(2) how to control the cost such as the budget used and thetravel cost and (3) how to ensure the efficiency of taskcompleted All three core issues in spatial crowdsourcing areinvolved in task assignment )us the task assignment isconsidered as the most fundamental challenge in spatialcrowdsourcing [11]

)e task assignment approach is mainly based on someattributes affecting the performance of task assignments[12] such as the distances between workers and tasks andthe qualities of workers On one hand previous research hasshown that a taskrsquos distance from a worker affects thecrowdsourcing outcomes [13 14] Tasks that are further

HindawiSecurity and Communication NetworksVolume 2021 Article ID 5448397 14 pageshttpsdoiorg10115520215448397

away from workers are less likely to be completed becauseworkers tend to complete the nearby tasks Some task as-signment algorithms consider the locations of the tasks andworkers to maximize the total number of assigned tasks[1 14 15] On the other hand the quality of a worker isconsidered to positively affect the crowdsourcing resultrsquosquality [16] Some works evaluate a workerrsquos quality by usingdistance and reputation and then propose a task allocationapproach that balances the resultrsquos quality and the budgetutilization rate [16 17])e existing task assignment methodis based on the attributes that influence the results but ig-nores the different preferences of a task for different attri-butes For example monitoring traffic flow has strictrequirements on the location of a worker monitoring cli-mate may accept workers working in a slightly larger areasuch as a city and reporting news has limitations on both thelocation and quality of workers )erefore different tasksmay have different attribute preferences

Studies on spatial task assignment aim to allocate the taskto suitable workers for different objectives such as maximizingthe total amount of assigned tasks [1 18] minimizing the totaltravel cost of the allocated workers [16] and maximizing theoverall quality of crowdsourcing results under the budgetconstraint [19] Unfortunately these objectives conflict witheach other optimizing multiple goals simultaneously is es-pecially difficult Consider the following examples (a) in-creasing the total amount of tasks assigned is potentiallyachieved by relaxing the constraints on workers such as in-creasing the accepted task region in which the task is allowed tobe performed and lowering the threshold of workersrsquo credi-bility However these methods may lead to increased travelcosts or decreased quality of results (b) One way to reduce theuncertainty of crowdsourced data is to ask multiple workers tocomplete the same task and then aggregate the responses ofthose workers to get the result of the task However askingmultiple workers to complete the same task will increase thepayment and the latency )us a task assignment solutionmust involve a trade-off among various objectives

In short (1) the existing task assignment approach doesnot fully consider the taskrsquos attribute preference (2) aspecific task assignment approach usually achieves a certainobjective but fails to achieve several conflicting goals Toaddress the two problems we propose a flexible and efficienttask assignment approach in spatial crowdsourcing based onmultiattribute decision-making (TASC-MADM) whichtakes into account the distance attribute and the reputationattribute simultaneously as well as tasksrsquo different prefer-ences of attributes Our goal is to trade off the quality of theresult and the task allocation rate under the budget con-straint of the task

In this paper we advance the key contributions of ourresearch as follows

(i) Unlike existing work that simply focuses on somecritical attributes while ignoring the preferences ofdifferent tasks in this paper we collectively considerthe impact of distance attribute and worker qualityattribute on the crowdsourcing result as well astasksrsquo different preferences for attributes

(ii) We formulate the problem of task assignment inspatial crowdsourcing as a multiattribute decision-making (TASC-MADM) problem and propose anovel algorithm solving this problem )e linearweighted-evaluation method is used to rating thecandidate workers comprehensively which enablesa task to select the appropriate worker according toits preference for attributes Besides the proposedapproach allows achieving the objectives of maxi-mizing the total amount of assigned tasks or thequality of outcome by setting attribute weights

(iii) Although our algorithm is simple it performs wellBesides it can be flexibly extended to the situationwhere any number of attributes affects the crowd-sourcing result

)e rest of the paper is organized as follows Section 2presents related works on task assignment of spatialcrowdsourcing Section 3 formally defines problems in-volved in TASC-MADM Section 4 describes the proposedTASC-MADM approach )e performance evaluation anddiscussion of the TASC-MADM approach are conducted inSection 5 Finally Section 6 concludes the work and suggestssome directions for future studies

2 Related Work

Task assignment ie the intelligent matching of tasks withthe most appropriate workers is a fundamental challenge ofcrowdsourcing [20ndash22] Although there have been severalstudies on conventional crowdsourcing task allocation theycannot be directly applied to spatial crowdsourcing becausethe location of the spatial task and that of the workers arevital for the result of the spatial task assignment

Research on spatial crowdsourcing task allocation is stillin the early stage In the spatial task assignment area existingstudies have mainly concentrated on exploiting the attri-butes of the tasks and workers )ese attributes usuallyindicate the distance between the location of the workers andtasks the capacity (ie the maximum number of tasks that aworker is willingable to complete) and the quality of theworkers [21 23] etc Kazemi and Shahabi [1] utilized thespatial region R (ie a rectangle region in which the workeraccepts tasks) and the capacity of the workers maxT to assigneach worker to his nearby tasks )e greedy (GR) algorithmis presented to maximize the task assignment at each timeinstance However the greedy strategy cannot solve theglobal optimization problem To solve this problem heu-ristics are used to maximize the overall assignments Hencethey proposed the second strategy the least location entropypriority (LLEP) strategy A location located in an area withfew workers has low entropy Conversely a location locatedin a worker-density area has high entropy Obviously taskswith smaller location entropy are less likely to be completedby workers In the heuristic a higher priority is given to taskslocated in areas with smaller location entropy Furthermoretravel cost is a critical issue for spatial crowdsourcing Hightravel costs may prevent workers from participating in thetask and result in high costs for task requesters Hence they

2 Security and Communication Networks

proposed the third strategy the nearest neighbor priority(NNP) strategy Workers are assigned to tasks closer to themin preference which aims at maximizing the overall finishedtasks while reducing the workersrsquo travel cost wheneverpossible)e research of [1] aims to maximize the number oftask assignments while keeping the travel cost minimizedbut they assume that the worker does not reject tasksassigned to them and trusts the workers to be reliableHassan and Curry [24] consider the situation where theworker can reject a task propose a contextual bandit al-gorithm learning the possibility of task accepted by a workerto assign a worker with high possibility based on the spatiallocations of workers and tasks and aim tomaximize the totalamount of successful assignments

In addition to the locations of the tasks and workers andthe capacity of workers the quality of workers is anotherimportant attribute affecting the result of task allocationSome works incorporate the quality of workers in the as-signment process with the aim of controlling the quality [25]and cost for all completed tasks [16 19 26] In traditionalcrowdsourcing worker quality can be modeled by theworkerrsquos reputation [26ndash28] which may be a rating of theworkerrsquos past works or an evaluation of the workerrsquosknowledge ability confidence in completing tasks suc-cessfully etc A worker with a higher reputation is generallyperceived to be better at his work Cheng et al [29] con-sidered workersrsquo confidence in completing tasks successfullyand proposed the reliable-diversity-based spatial crowd-sourcing approach )e approach Budget-TASC [16] con-siders the number of workers in the task assignment andthinks that the distance of a worker from the task negativelyinfluences the quality of the crowdsourcing result [17 18]the reliability of the workers is given by a reputation functiondiscounted by the distance )en the task is assigned to theworker with the highest reliability to maximize the desiredquality of results obtained while the total budget is limitedHowever the task assignment rate of spatial crowdsourcingtasks is not considered RB-TPSC [17] presents a taskpackage assignment algorithm with aim of maximizing thedesired quality of the results from selected workers under alimited budget improving the number over all spatialcrowdsourcing tasks Besides Zhao et al [30] thought thatthe quality of task accomplishment is mostly related to theworkerrsquos preference for the task category

In this paper to improve the performance of task as-signment we present a novel efficient and flexible approachby jointly considering multiattributes and preferences

3 Problem Definition

In this section we introduce some basic concepts of spatialcrowdsourcing task assignment and then give the formaldefinitions For the convenience of the following descrip-tion we firstly list the symbols used in this paper see Table 1

We consider there are a set of workersW w1 w2 wi wm1113864 1113865 and a set of tasksT t1 t2 tj tn1113966 1113967 Subscripts i and j are the worker IDand task ID respectively A worker wi is represented as atuple of the form langli ri qirang where li langloni latirang is the

location of the work i loni and lati are the longitude andlatitude of the worker i respectively and ri represents thereputation of the worker i and qi is the task quota of theworker i A spatial crowdsourcing task tj is represented as atuple of the form ie tj langlj Rj Bjrang where lj langlonj latjrangis the location of the task j which is represented by alongitude-latitude coordinate lonj and latj are the longitudeand latitude of the task j respectively and Bj isin R+ is thelimited budget of the task j

Definition 1 (decision matrix for a task) Given a set of tasksTand a set of workers W Let Wj be the set of workers withinthe region of the radius R of the task tjϵT and f be thenumber of attributes that are considered when assigningtasks To facilitate the description of algorithms subse-quently the task and the worker are attached to the matrixcolumns)en the decision matrix of the task tj is shaped asDM

j

ntimes(f+2) where n |Wj|For example the worker wi isinWj the attributes involved

are the distance and workersrsquo reputation then the item sj

langj i dji rirang is included in DMj and dji represents thedistance between tj and wi

Setting the radius value of a task is one of the centralaspects of task assignment A very low radius value wouldresult in a low task completion rate because of the lack ofenough workers In contrast a very high radius value wouldhave no practical significance because of the unavailability ofworkers willing to travel a long distance to perform a taskPrevious studies suggest that the most acceptable distancefor the workers is 0ndash2 km [1 7 13] In practice someworkers may be tempted by the larger budget to performremote tasks In this paper we assume (1) a task with ahigher budget can select workers from a wider region and (2)some workers are willing to travel further for the higherrewards So the radius Rj is positively affected by the budgetBj and negatively affected by the extra allowance per kilo-meter β Let a workerrsquos accepted baseline distance be c in thecondition of a baseline payment P for a task When a taskrsquosbudget is less than the baseline payment P the task is notlikely to be accepted by any worker at this situation theradius is represented by a negative number because noworkers locate within a region of a taskrsquos negative radius)en the method in [16] is slightly changed to compute Rj

Rj

c +B

jminus P

β B

j geP

minus1 Bj ltP

⎧⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎩

(1)

Identifying the value of the parameter P is not ouremphasis We focus on proposing a flexible spatial taskassignment method that considers multiple attributes af-fecting the result in the task assignment and different re-quirement preferences for each attribute Hence we set P asthe lowest budget among the budgets of all tasks

Our goal is to select the worker with the highest rating ofcombined distance and reputation for a task However if theabove decision matrix is directly used to determine the task

Security and Communication Networks 3

assignment there are two problems Firstly the orders ofmagnitude of attributes are usually different owing to thedifferent natures of attributes If the original value is directlyused for rating the items the role of the attribute with thehigher value in the comprehensive rating will be highlightedand the role of the attribute with the lower value will berelatively weakened Secondly the distance is a cost-typeattribute and the reputation is a benefit-type attribute whichmeans the distance and the reputation have different in-fluence trend on ratings )erefore in order to ensure thereliability of the rating results it is necessary to normalizethe original data We adopt the linear proportional trans-formation method to normalize the distance and reputationas shown in the following equations

dji

maxiisinWj dji minus dji

maxiisinWj dji minus miniisinWj dji

maxiisinWj

dji ne miniisinWj

dji

1 otherwise

⎧⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎩

(2)

ri

riminus miniisinWj r

i

maxiisinWj riminus miniisinWj r

i max

iisinWjr

i ne miniisinWj

ri

1 otherwise

⎧⎪⎪⎪⎨

⎪⎪⎪⎩

(3)

Definition 2 (reward for a task) When a task is completedthe requester must offer rewards to the correspondingworker Let Ej represent the extra allowance for the task j

when it is completed by a worker without c )en the re-ward of the task j is expressed in the following equation

pj

P + Ej (4)

where Ej is related to the parameter β dji c )e farther aworker travels the more extra allowance he should get If theworker wi completes the task tj within the accepted baselinedistance c then Ej equals to 0 otherwise Ej is proportionalto the extra distance and the extra allowance so Ej iscomputed in the following equation [16]

Ej

β dji minus c1113872 1113873 dji gt c

0 dji le c

⎧⎨

⎩ (5)

)e parameter dji mentioned above involves the locationof the task and the worker We computed dji from the task tj

to the worker wi by the Haversine formula [31]

dji R times 2 times arcsin sin2latj minus lati

21113888 1113889 + cos latj1113872 1113873 times cos lati1113872 1113873 times sin2

lonj minus loni

21113888 11138891113888 1113889

12⎛⎝ ⎞⎠ (6)

where R refers to the earthrsquos radius [12]

Definition 3 (TASC-MADM problem) Given a set of tasksT and a set of workers W we assume that each task is

assigned to the optimal worker Let S s1 s2 sj sn1113864 1113865

represent the selected workers for all tasks sj langj i dji rirangsj isin DMj and TASC-MADM is to find sj such that thefollowing linear combination is optimized

Table 1 List of symbols

Symbol MeaningW )e set of workers W w1 w2 wi wm1113864 1113865

T The set of tasks T t1 t2 tj tn1113966 1113967

i Aworkerrsquos number i isin 1 2 m

j A spatial taskrsquos number j isin 1 2 n

li The location of the worker i

lj The location of the task j

Bj The budget of the task j

Rj The radius of the task j

ri The reputation of theworker i

dji The distance from the task j to theworker i

β The extra allowance per kilometerc Workerrsquos accepted baseline distanceP The baseline payment of a taskDMj The decisionmatrix of the task j

Ej The extra allowance for the task j

pj The reward for the task j

ai The amount of task assigned to theworker i

qi The task quota of theworker i

S The result of task assignment

4 Security and Communication Networks

maxiisinWj

scoreji1113872 1113873 maxiisinWj

w0 times dji + w1 times ri

1113872 1113873 (7)

where j and i are the matching task-worker pair andwv isin [0 1] is the attribute importance parameter1113936visin 01 wv 1

)e weights are usually determined objectively or sub-jectively)e entropy method is generally used to objectivelyget weighting of every attribute [32] However we hope thatthe task assignment operation can set the attribute weight toachieve different optimization goals So the weight valuesare set according to the requirement preference of the taskfor attributes If a task has more requirement preference fordistance more than that for reputation it can set w0 gt 05Otherwise w0 lt 05 )e setting w0 05 implies the fol-lowing situation among the workers with the same repu-tation the worker with a shorter distance has the priority tobe selected Similarly among the workers with the samedistance the worker with a higher reputation has the priorityto be selected

31 Complexity Analysis One task assignment of a singletask is to find the best worker among m workers it needs torepeat the assignment n times m times to complete all task as-signments so the problem is solvable in polynomial time

4 Assignment Protocol

We assume that the workers querying the tasks are willing toaccept the tasks )us assigning a task to a worker meansselecting the best worker with the highest comprehensiverating of distance and reputation In this section we willelaborate on our spatial task assignment algorithm

41 PreparingDecisionMatrix Preparing the taskrsquos decisionmatrix involves two steps Firstly we obtain the decisionmatrix DMj for the task j Each item of the decision matrixrepresents a candidate worker In this paper the attributesaffecting the result of a task assignment include the repu-tation of a worker and the distance from a task to a worker)e workers within the radius of the task j are a part of thetaskrsquos candidates excluding the workers who are assignedtasks more than their quota (Line 2ndash4) Secondly normalizethe decision matrix DMj by equations (2) and (3) (Line 5))e pseudocode obtaining and normalizing the decisionmatrix DMj for the task j is given by Algorithm 1

)e computational complexity of Algorithm 1 dependson the loop operation and the normalization operation )ecomplexities of these two operations are O(m) and O(|Wj|)respectively Since |Wj| is usually much less than |m| thetotal computational complexity is O(m)

42 TASC-MADM Approach As mentioned in Section 3 aspatial task should be assigned to the worker with the highestrating which optimizes the linear combination of the dis-tance and the reputation

Algorithm 2 is the pseudocode of the allocation methodnamely TASC-MADM algorithm which inputs a set ofworkers W a set of tasks T and the parameters P β c andreturns the best assignment result S containing task-and-worker assignment with the highest rating

Initially S is set to empty (Line 1) ai|i 12m 0 (ieeach worker has been assigned zero times) (Line 2) Nextfor each task tj calculate the radius and the distance to theworkers and obtain and normalize the decision matrixDMj by calling Algorithm 1 (Line 6) Next if the decisionmatrix has more than zero items compute the scores ofitems (Line 9) and simultaneously compute the reward pj

paid by the task tj to the worker wi (Line 10) For sub-sequent easy operation the itemrsquos score and the reward areassociated with other information including the tasknumber the worker number the distance and the repu-tation (Line 11) Next we sort the items descending byscores (Line 12) Intuitively the item with a higher rankindicates a better assignment than other assignments Fi-nally the task tj is assigned to the topmost worker (Line13ndash17)

)e assignment iterates for n rounds (Line 3) and finallyreturns the assignment result of all tasks (Line 18) In eachiteration the computational complexity is O(m) so the totalcomputational complexity is O(n times m)

5 Experiment Evaluation

In this section we tested the performance of our approachon both real and synthetic data

51 Metrics In the experiments we measured the perfor-mance of each approach according to the following metrics[23]

(1) Average task radius (δ) this metric measures theaverage spatial region size of the tasks which iscomputed as the average radius of all tasks

β 1

|T|1113944

j

Rj (8)

(2) Task assignment rate (η) this metric measures thealgorithmrsquos effectiveness in assigning several taskssuccessfully )e task assignment rate η is the per-centage of assigned tasks among the total amounts ofcrowded spatial tasks

η |S|

|T| (9)

(3) Average reputation of workers assigned tasks (ψ) thequality of crowdsourcing result is determined by theworkerrsquos quality which is modeled by the workerrsquosreputation So this metric evaluates the quality oftasks completed It is computed as the total repu-tation of selected workers divided by the number ofselected workers

Security and Communication Networks 5

ψ 1

|S|1113944iisinS

ri (10)

(4) Average distance traveled (ς) this metric measuresthe travel cost for workers when they complete theassigned tasks it is computed as the average distancetraveled by all the selected workers

ς 1

|S|1113944

langjirangisinSdji (11)

(5) Average budget utilization rate (ϕ) this metric is theaverage of the budget utilization for all assignedtasks )e budget utilization rate of each assignedtask is the ratio of the actual reward paid for the taskto the budget of that task

ϕ 1

|S|1113944jisinS

pj

Bj (12)

(6) Average reward (ω) this is the ratio of the totalreward for all the assigned tasks to the number ofassigned tasks

ω 1

|S|1113944jisinS

pj (13)

)e parameter δ is used to demonstrate the changing ofother metrics along with the average radius )e workersrsquoperspective would prefer to keep ς as low as possible )etask requestersrsquo perspective would prefer higher values of ηand ψ but lower values of ϕ and ω

52 Experimental Setting

521 Datasets

(i) Real Dataset We used a real dataset [33] from acrowdsourcing event by taking photos with the lo-cation information for 1877 workers and 835 tasks)ese tasks and workers were mainly obtained fromfour cities in China Foshan Guangzhou Shenzhenand Dongguan

(ii) Synthetic Dataset )e synthetic data were obtainedfrom a single day dataset from Gowalla in 2010which included 10956 tasks and 5087 workers

Input a set W the task j

Output the normalized decision matrix DMj for the task j

(1) DMj

(2) For each wi isinW(3) If dji leRj and ai lt qi(4) s

ji langj i dji rirang DMjlarrDMj cup s

ji1113966 1113967

(5) Normalize DMj by equations (2) and (3)(6) Return DMj

ALGORITHM 1 Obtain the decision matrix for a task

Input a set of workers W a set of tasks T parameters P β c

Output S which is the result of task assignment(1) S

(2) ai 0 i isin 1 2 m

(3) For each tj isin T(4) Calculate the radius Rj

(5) Calculate dji from the task j to each worker i

(6) Obtain and normalize the decision matrix DMj by calling Algorithm 1(7) If |DMj|gt 0(8) For iprime in range (|DMj|)(9) Compute the scoreji using equation (7)i DMj[iprime]i it is the number of the iprime th worker for the task tj(10) Compute the reward pj using equation (4)(11) s

ji s

ji append(langpj scorejirang)(ie s

ji langj i dji ri pj scorejirang)

(12) Sorted DMj descending by scores(13) For each item in DMj(14) If j in item(15) sj item ai+ 1(16) SlarrScup sj1113864 1113865

(17) Break(18) Return S

ALGORITHM 2 TASC-MADM algorithm

6 Security and Communication Networks

located in America )is dataset contains four at-tributes as follows task ID taskrsquos location workerID and workerrsquos location Reputations are generatedfollowing the uniform distribution of 0 sim 20000Budgets are generated following the uniform dis-tribution of 65 sim 85 )e task quotas of workers aregenerated following the uniform distribution of5 sim 10

522 Compared Algorithms )e RB-TPSC and Budget-TASC algorithms were selected as the baseline algorithmsbecause they are most closely related to TASC-MADM

(i) RB-TPSC is a task package assignment methodwhich aims at maximizing the number of tasksassigned within budget constraints Quality of resultsand travel costs are also being considered

(ii) Budget-TASC is a budget-aware spatial crowd-sourcing task assignment method which aims inmaximizing the total quality of tasks completedwithin budget constraints

We compared our TASC-MADM with RB-TPSC andBudget-TASC on both real and synthetic datasets

53 Results on Real Dataset )e first three experimentscompared TASC-MADM and RB-TPSC under differentsettings of parameters β c and Bj )e results are shown inFigures 1ndash3 For each parameter the experiment evaluatedthe average metrics of the two algorithms under 21 differentsettings We set the lowest budget of all tasks as P 65Moreover for the fairness in considering the effect of dis-tance and reputation the weight w0 05

First we compared TASC-MADM and RB-TPSC underdifferent β values )e results are shown in Figure 1 wherethe extra allowance per kilometer β varies from 0 sim 20monetary units in 1-unit increments c 05 and Bj isobtained from the dataset )e horizontal axis represents thedifferent settings of β while the vertical axis represents thedifferent values of the first six metrics As can be seen theaverage task radius δ is affected by the extra allowance perkilometer β (Figure 1(a)) δ is maximized when β 1 in-creases with β if βlt 1 and decreases with β if βgt 1 )echange in the trend of other metrics is consistent with theaverage task radius)is is because in a region with a smallerradius it is usually impossible to find enough workers withhigh reputations )us other metrics decrease with theaverage radius Compared with RB-TPSC TASC-MADMensures a high average task assignment rate (Figure 1(b))but reduces the average travel cost of workers (Figure 1(d))and saves the budget (Figures 1(e) and 1(f )) Besides ourresult shows that the average reputation of workers decreaseswith the average radius (Figure 1(c)) which is more realisticbecause there are fewer workers to choose from

Secondly we compared TASC-MADM and RB-TPSCunder different c values Figure 2 depicts the trend in whichthe above six metrics change when c the accepted distanceswithout extra remote allowance varies from 0 sim 2 km where

β 2 and Bj is obtained from the real dataset If cgt 05 theaverage radius increases with c and positively affects theabovementioned metrics except for ϕ and ω Our proposedmethod achieves a task assignment rate value of 9856(Figure 2(b)) but the maximum of the average distancetraveled the average reward and the average budget utili-zation rates are ς 103 km ω 6533 and ϕ 9493 re-spectively (Figures 2(d)ndash2(f)) Compared to the RB-TPSCmethod our method greatly decreases the average distancetraveled by the workers and the average reward offered bythe tasksrsquo requester while maintaining an equally high or agreater average task assignment rate Moreover it signifi-cantly improves the quality of crowdsourcing results becauseof the average reputation value of the selected workers in-creasing by about 250 (Figure 2(c))

)e third experiment compared TASC-MADM and RB-TPSC under different budgets (B) Figure 3 depicts how thefirst six metrics change while the tasksrsquo budget (B) variesfrom 90 to 110 in 1 increments where β 2 c 05Since the task with a greater budget should have moreworkers to choose from the average radius of the tasks ispositively affected by the tasksrsquo budget (Figure 3(a)) As thetask radius increases further the task assignment rate in-creases to 995 (Figure 3(b)) Compared to the RB-TPSCmethod our method decreases the average distance traveledfrom a range of 14sim25 km to a range of 08sim15 km(Figure 3(d)) but increases the average reputation by about147 (Figure 3(c)) and greatly decreases the average budgetutilization rate (Figure 3(e)) and the average reward((Figure 3(f))) More importantly with our method thegreater the number of candidate workers the lower theaverage reward offered by the requester and the higher is thequality of the crowdsourcing result In contrast RB-TPSCincreases the average reward continuously and decreases theaverage reputation to a stable value of around 500

)e fourth experiment compared TASC-MADM withRB-TPSC and Budget-TASC when radiuses were changedFor TASC-MADM and RB-TPSC we selected the averagemetrics of different c values In the above experiment thecomputed radius belongs to the interval [0 12] when c

varied from 0 sim 2 km and other parameters were fixed Wehave experimented with Budget-TASC in different radiusesvarying from 0 sim 12 km and then get the average of eachmetric For the Budget-TASC the other parameters were setas follows DC is set as the earthrsquos radius [10] PL 0 PH isset as the budget of the task and PM is set as the half of thebudget of the task Because we scaled the workerrsquos reputationto the interval [0 1] ThHM 075 ThML 05

)e results are shown in Table 2 TASC-MADM out-performs the baseline algorithms in all metrics except theaverage reputation For Budget-TASC PH is set as the taskrsquostotal budget which limits the task to high-quality workersbut also increases the average budget utilized

54 Results on Synthetic Dataset We repeated the first threeexperiments in Section 53 on the synthetic datasetFigures 4ndash6 illustrate the results of TASC-MADM and RB-TPSC Compared with RB-TPSC our algorithm greatly

Security and Communication Networks 7

005

115

225

335

445

5

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

adiu

s

RB-TPSCTASC-MADM

(a)

RB-TPSCTASC-MADM

0

02

04

06

08

1

12

0 2 4 6 8 10 12 14 16 18 20

Task

assig

nmen

t rat

e

(b)

RB-TPSCTASC-MADM

0

200

400

600

800

1000

1200

1400

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

eput

atio

n va

lue

(c)

RB-TPSCTASC-MADM

0

05

1

15

2

25

0 2 4 6 8 10 12 14 16 18 20

Aver

age d

istan

ce tr

avel

ed

(d)

RB-TPSCTASC-MADM

094

095

095

096

096

097

097

098

0 2 4 6 8 10 12 14 16 18 20

Aver

age b

udge

t util

izat

ion

rate

(e)

RB-TPSCTASC-MADM

6475

6500

6525

6550

6575

6600

6625

6650

6675

6700

0 2 4 6 8 10 12 14 16 18 20Av

erag

e rew

ard

(f )

Figure 1 Performance with an extra allowance per kilometer (β) (monetary unit) real dataset

25272931333537394143

00 02 04 06 08 10 12 14 16 18 20

Aver

age r

adiu

s

RB-TPSCTASC-MADM

(a)

RB-TPSCTASC-MADM

065

070

075

080

085

090

095

100

00 02 04 06 08 10 12 14 16 18 20

Task

assig

nmen

t

(b)

RB-TPSCTASC-MADM

0

200

400

600

800

1000

1200

1400

00 02 04 06 08 10 12 14 16 18 20

Aver

age r

eput

atio

n va

lue

(c)

Figure 2 Continued

8 Security and Communication Networks

improves the task assignment rate (Figures 4(b) 5(b) and6(b)) and the resultrsquos quality (Figures 4(c) 5(c) and 6(c))However for the synthetic dataset the task locates in a

slightly sparse scenario with few workers )en the highertask assignment rate implies the higher travel cost(Figures 4(d) 5(d) and 6(d)) Correspondingly the budgetused is increased (Figures 4(e) 4(f) 5(e) 5(f) 6(e) and6(f ))

Next based on the synthetic dataset we comparedTASC-MADM with RB-TPSC and Budget-TASC whenradiuses were changed For TASC-MADM and RB-TPSCwe selected the average metrics of different c values thecomputed radius approximately belongs to the interval [6 9]

RB-TPSCTASC-MADM

08

10

12

14

16

18

20

00 02 04 06 08 10 12 14 16 18 20

Aver

age d

istan

ce tr

avele

d

(d)

RB-TPSCTASC-MADM

09500

09525

09550

09575

09600

09625

09650

09675

09700

00 02 04 06 08 10 12 14 16 18 20

Aver

age b

udge

t util

izat

ion

rate

(e)

RB-TPSCTASC-MADM

650

655

660

665

670

675

680

685

00 02 04 06 08 10 12 14 16 18 20

Aver

age r

ewar

d

(f )

Figure 2 Performance with different accepted distances without extra remote allowance (c) (km) real dataset

0

1

2

3

4

5

6

7

Aver

age r

adiu

s

RB-TPSCTASC-MADM

090

092

094

096

098

100

102

104

106

108

110

(a)

RB-TPSCTASC-MADM

00

02

04

06

08

10

12

Task

assig

nmen

t rat

e

090

092

094

096

098

100

102

104

106

108

110

(b)

RB-TPSCTASC-MADM

0

500

1000

1500

2000

2500

3000

Aver

age r

eput

atio

n va

lue

090

092

094

096

098

100

102

104

106

108

110

(c)

RB-TPSCTASC-MADM

0

05

1

15

2

25

3

Aver

age d

istan

ce tr

avele

d

090

092

094

096

098

100

102

104

106

108

110

(d)

RB-TPSCTASC-MADM

086

088

09

092

094

096

098

Aver

age b

udge

t util

izat

ion

rate

090

092

094

096

098

100

102

104

106

108

110

(e)

RB-TPSCTASC-MADM

655660665670675680685690695

Aver

age r

ewar

d

090

092

094

096

098

100

102

104

106

108

110

(f )

Figure 3 Performance with an amplitude of variation in budget (B) real dataset

Table 2 Results under different approaches real dataset

Algorithms η () ψ ζ ϕ ωBudget-TASC 8567 139063 14 1 7761RB-TPSC 9395 52339 159 09645 6672TASC-MADM 9418 98746 078 09509 6598

Security and Communication Networks 9

02468

10121416

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

adiu

s

RB-TPSCTASC-MADM

(a)

RB-TPSCTASC-MADM

03

04

05

06

07

08

09

0 2 4 6 8 10 12 14 16 18 20

Task

assig

nmen

t rat

e(b)

RB-TPSCTASC-MADM

10000

10500

11000

11500

12000

12500

13000

13500

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

eput

atio

n va

lue

(c)

RB-TPSCTASC-MADM

00051015202530354045

0 2 4 6 8 10 12 14 16 18 20

Aver

age d

istan

ce tr

avel

ed

(d)

RB-TPSCTASC-MADM

084

085

086

087

088

089

09

0 2 4 6 8 10 12 14 16 18 20

Aver

age b

udge

t util

izat

ion

(e)

RB-TPSCTASC-MADM

650

655

660

665

670

675

680

685

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

ewar

d

(f )

Figure 4 Performance with an extra allowance per kilometer (β) (monetary unit) synthetic dataset

RB-TPSCTASC-MADM

60

65

70

75

80

85

90

95

00 02 04 06 08 10 12 14 16 18 20

Aver

age r

adiu

s

(a)

RB-TPSCTASC-MADM

065

067

069

071

073

075

077

079

00 02 04 06 08 10 12 14 16 18 20

Task

assig

nmen

t rat

e

(b)

RB-TPSCTASC-MADM

1000010250105001075011000112501150011750120001225012500

00 02 04 06 08 10 12 14 16 18 20

Aver

age r

eput

atio

n va

le

(c)

RB-TPSCTASC-MADM

100

120

140

160

180

200

220

00 02 04 06 08 10 12 14 16 18 20

Aver

age d

istan

ce tr

avele

d

(d)

RB-TPSCTASC-MADM

084085085086086087087088088089

00 02 04 06 08 10 12 14 16 18 20

Aver

age b

udge

t util

izat

ion

rate

(e)

RB-TPSCTASC-MADM

6600

6650

6700

6750

6800

6850

00 02 04 06 08 10 12 14 16 18 20

Aver

age r

ewar

d

(f )

Figure 5 Performance with different accepted distances without extra remote allowance (c) (km) synthetic dataset

10 Security and Communication Networks

(Figure 5(a)) So for Budget-TASC the radius varied from6 sim 9 km other parameters were set as in the fourthexperiment

)e experimental results (Table 3) show that TPSC-ADM obtains the highest task assignment rate but Budget-ASC achieves the highest quality and RB-TPSC spends thelowest budget

55 Effect of Taskrsquos Attribute Preference Our approachTASC-MADM enables the task to be assigned to satisfydemands for different goals If the resultrsquos quality is theprimary goal the task should select workers with a highreputation However if saving cost is the most concernedobjective the worker closer to the task should be chosenfirst Figure 7 shows the influence of tasksrsquo attributespreferences on the proposed approachrsquos performancew0 05 all targets are considered fairly w0 lt 05 the taskprefers the workerrsquos reputation to the distance the re-sultrsquos quality is significantly improved However thetravel cost and budget utilized are increased Whenw0 gt 05 the travel cost and the budget utilized are savedwhereas the resultrsquos quality is decreased Our approachcan maximize task assignment rate by setting w0 1 ormaximize the quality of workers selected by settingw0 0

56 Efficiency of Algorithms )e efficiency of the algorithmsis measured in CPU cost )e Budget-TASC algorithmrsquoscomputational complexity is O(n times m2) and that of RB-TPSC and TASC-MADM is O(n times m) we compare TASC-MADMwith RB-TPSC and Budget-TASC in CPU cost Eachof the programs runs 3 times 21 rounds )e average time perround is used to measure the algorithmrsquos efficiency Asshown in Figure 8 our approach significantly improves theefficiency of spatial task assignment

57 Summary of Experiment Results We summarized themajor finding as follows

(i) If the task is located in a worker-density area theproposed TASC-MADM approach exhibits betterresults than RB-TPSC It also performs better thanBudget-TASC on the metrics except for the qualityof workers

(ii) If the task is located in a worker-sparsity area theproposed TASC-MADM approach performs betterthan RB-TPSC in terms of the average assignmentrate and quality of workers But it leads to moretravel cost and budget utilized Budget-TASC ob-tains the best quality because it considers the qualityfirst

4

5

6

7

8

9

10

11

12Av

erag

e rad

ius

RB-TPSCTASC-MADM

090

092

094

096

098

100

102

104

106

108

110

(a)

RB-TPSCTASC-MADM

045

05

055

06

065

07

075

08

085

090

092

094

096

098

100

102

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108

110

Task

assig

nmen

t rat

e(b)

RB-TPSCTASC-MADM

090

092

094

096

098

100

102

104

106

108

110

10000

10500

11000

11500

12000

12500

13000

13500

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age r

eput

atio

n va

lue

(c)

RB-TPSCTASC-MADM

090

092

094

096

098

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102

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108

110

10

15

20

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35

Aver

age d

istan

ce tr

avel

ed

(d)

RB-TPSCTASC-MADM

090

092

094

096

098

100

102

104

106

108

110

076078080082084086088090

094092

Aver

age b

udge

t util

izat

ion

rate

(e)

RB-TPSCTASC-MADM

090

092

094

096

098

100

102

104

106

108

110

660066506700675068006850690069507000

71007050

Aver

age r

ewar

d

(f )

Figure 6 Performance with an amplitude of variation in budget (B) synthetic dataset

Security and Communication Networks 11

Table 3 Results under different approaches synthetic dataset

Algorithms η ψ ζ ϕ ωBudget-TASC 072743975 1367217 1417697 1 7758585RB-TPSC 072253468 1027882 1408169 0851329 6686784TPSC-ADM 074825306 1219453 1926809 0873916 6768671

0

5

10

15

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

adiu

s

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

(a)

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

03504

04505

05506

06507

07508

085

0 2 4 6 8 10 12 14 16 18 20

Task

assig

nmen

t rat

e

(b)

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

100001050011000115001200012500130001350014000

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

eput

atio

n va

lue

(c)

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

0

1

2

3

4

5

6

0 2 4 6 8 10 12 14 16 18 20

Aver

age d

istan

ce tr

avel

ed

(d)

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

084

085

086

087

088

089

09

091

0 2 4 6 8 10 12 14 16 18 20

Aver

age b

udge

t util

izat

ion

rate

(e)

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

650655660665670675680685690695700705

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

ewar

d

(f )

Figure 7 Effect of tasksrsquo attributes preferences on the TASC-MADM approach Performance with an extra allowance per kilometer (β)

(monetary unit) synthetic dataset

128 114

31961929

135

618

0

20

40

60

80

100

120

140

Synthetic Dataset Real Dataset

TASC-MADMRB-TPSCBudget-TASC

Figure 8 Average CPU cost

12 Security and Communication Networks

(iii) In the terms of CPU cost the proposed TASC-MADM approach is superior to the baselinealgorithms

58 Discussion )is section discusses the advantages andlimitations of the TASC-MADM approach )e advantagesare listed as follows

(1) Effective )e TASC-MADM approach improves thetask assignment rate By setting different attributeweights it can maximize the task assignment rate orthe quality of crowdsourcing results

(2) Efficient )e TASC-MADM approach enhancesefficiency because of computing simply

(3) Extensible )eoretically our method can be ex-tended to solving decision problems involving anynumber of attributes

)ere still exist the following limitations in the TASC-MADM approach

(1) Quality Quantification of Workers In this paper theworker quality is modeled as a reputation value toreflect the quality of crowdsourced results Inpractice the same workerrsquos quality may differ inspecific tasks How to quantify the quality of workersis not covered in this paper

(2) Efficiency of Indexing Records )e TASC-MADMapproach exhaustively searches all the records toidentify the candidate of a task which makes it lessefficient to get the decision matrix on large datasets

6 Conclusion

)is paper focuses on designing an efficient task assignmentapproach which can deal with the situation where tasks havedifferent require preferences for different task attributes toachieve different goals Our task assignment approach can beextended to scenarios containing any number of attributesIn addition to the distance and reputation other criteriasuch as the workersrsquo skills can be considered

As for future work more factors such as workersrsquowillingness to accept tasks and their quality differences indifferent professional fields are included in task assign-ments In addition improving the efficiency of indexingrecords to make the allocation scheme suitable for largedatasets is a valuable research topic

Data Availability

)e data of allocated sharing tasks are available from ChinaSociety for Industrial and Applied Mathematics 2017 re-trieved on September 2 2020 from httpwwwmcmeducnhtml_cnnode460baf68ab0ed0e1e557a0c79b1c4648html

Conflicts of Interest

)e authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

)e authors thank researchers working at Guangxi KeyLaboratory of Trusted Software especially Xuguang Bao andManli Zhu for their suggestion during the research andpreparation of the manuscript )is work was funded byGuangxi Key Laboratory of Trusted Software (Nokx201727) Project to Improve the Scientific Research BasicAbility of Middle-Aged and Young Teachers (No2019KY0226) Natural Science Foundation of China (Nos61966009 U1811264 and U1711263) and Natural ScienceFoundation of Guangxi Province (Nos2019GXNSFBA245049 2019GXNSFBA245059 and2018GXNSFDA281045)

References

[1] L Kazemi and C Shahabi ldquoGeoCrowd enabling query an-swering with spatial crowdsourcing GISrdquo in Proceedings ofthe ACM International Symposium on Advances in GeographicInformation Systems pp 189ndash198 CA USA November 2012

[2] J Xiong M Zhao M Z A Bhuiyan L Chen and Y TianldquoAn AI-enabled three-party game framework for guaranteeddata privacy in mobile edge crowdsensing of IoTrdquo IEEETransactions on Industrial Informatics vol 17 no 2pp 922ndash933 2021

[3] J Xiong R Ma L Chen et al ldquoA personalized privacyprotection framework for mobile crowdsensing in IIoTrdquo IEEETransactions on Industrial Informatics vol 16 no 6pp 4231ndash4241 2020

[4] DiDi Didi Chuxing Corporate Citizenship Report httpswwwdidiglobalcomaboutdidiresponsibility 2017

[5] J Xiong J Ren L Chen et al ldquoEnhancing privacy andavailability for data clustering in intelligent electrical serviceof IoTrdquo IEEE Internet of Bings Journal vol 6 no 2pp 1530ndash1540 2019

[6] C Zhao P Cheng L Chen X Lin and C Shahabi ldquoFair taskassignment in spatial crowdsourcingrdquo Proceedings of theVLDB Endowment vol 13 no 12 pp 2479ndash2492 2020

[7] M Venanzi A Rogers and N R Jennings ldquoCrowdsourcingspatial phenomena using trust-based heteroskedasticGaussian processesrdquo in Proceedings of the First Conference onHuman Computation and Crowdsourcing (HCOMP)pp 182ndash189 Palm Springs CA USA November 2013

[8] J Xiong R Bi M Zhao J Guo and Q Yang ldquoEdge-assistedprivacy-preserving raw data sharing framework for connectedautonomous vehiclesrdquo IEEE Wireless Communicationsvol 27 no 3 pp 24ndash30 2020

[9] B Van B V D Haak M Parks and M Castells ldquo)e futureof journalism networked journalism rethinking journalism inthe networked digital agerdquo International Journal of Com-munication vol 6 pp 2923ndash2938 2012

[10] M Zook M Graham S Taylor and S Gorman ldquoVolunteeredgeographic information and crowdsourcing disaster relief acase study of the Haitian earthquakerdquoWorld Medical umlHealthPolicy vol 2 no 2 pp 7ndash33 2010

[11] Y Tong L Chen and C Shahabi ldquoSpatial crowdsourcingchallenges techniques and applicationsrdquo Proceedings of theVLDB Endowment vol 10 pp 1988ndash1991 2017

[12] J Xiong X Chen Q Yang L Chen and Z Yao ldquoA task-oriented user selection incentive mechanism in edge-aidedmobile crowdsensingrdquo IEEE Transactions on Network Scienceand Engineering vol 7 no 4 pp 2347ndash2360 2020

Security and Communication Networks 13

proposed the third strategy the nearest neighbor priority(NNP) strategy Workers are assigned to tasks closer to themin preference which aims at maximizing the overall finishedtasks while reducing the workersrsquo travel cost wheneverpossible)e research of [1] aims to maximize the number oftask assignments while keeping the travel cost minimizedbut they assume that the worker does not reject tasksassigned to them and trusts the workers to be reliableHassan and Curry [24] consider the situation where theworker can reject a task propose a contextual bandit al-gorithm learning the possibility of task accepted by a workerto assign a worker with high possibility based on the spatiallocations of workers and tasks and aim tomaximize the totalamount of successful assignments

In addition to the locations of the tasks and workers andthe capacity of workers the quality of workers is anotherimportant attribute affecting the result of task allocationSome works incorporate the quality of workers in the as-signment process with the aim of controlling the quality [25]and cost for all completed tasks [16 19 26] In traditionalcrowdsourcing worker quality can be modeled by theworkerrsquos reputation [26ndash28] which may be a rating of theworkerrsquos past works or an evaluation of the workerrsquosknowledge ability confidence in completing tasks suc-cessfully etc A worker with a higher reputation is generallyperceived to be better at his work Cheng et al [29] con-sidered workersrsquo confidence in completing tasks successfullyand proposed the reliable-diversity-based spatial crowd-sourcing approach )e approach Budget-TASC [16] con-siders the number of workers in the task assignment andthinks that the distance of a worker from the task negativelyinfluences the quality of the crowdsourcing result [17 18]the reliability of the workers is given by a reputation functiondiscounted by the distance )en the task is assigned to theworker with the highest reliability to maximize the desiredquality of results obtained while the total budget is limitedHowever the task assignment rate of spatial crowdsourcingtasks is not considered RB-TPSC [17] presents a taskpackage assignment algorithm with aim of maximizing thedesired quality of the results from selected workers under alimited budget improving the number over all spatialcrowdsourcing tasks Besides Zhao et al [30] thought thatthe quality of task accomplishment is mostly related to theworkerrsquos preference for the task category

In this paper to improve the performance of task as-signment we present a novel efficient and flexible approachby jointly considering multiattributes and preferences

3 Problem Definition

In this section we introduce some basic concepts of spatialcrowdsourcing task assignment and then give the formaldefinitions For the convenience of the following descrip-tion we firstly list the symbols used in this paper see Table 1

We consider there are a set of workersW w1 w2 wi wm1113864 1113865 and a set of tasksT t1 t2 tj tn1113966 1113967 Subscripts i and j are the worker IDand task ID respectively A worker wi is represented as atuple of the form langli ri qirang where li langloni latirang is the

location of the work i loni and lati are the longitude andlatitude of the worker i respectively and ri represents thereputation of the worker i and qi is the task quota of theworker i A spatial crowdsourcing task tj is represented as atuple of the form ie tj langlj Rj Bjrang where lj langlonj latjrangis the location of the task j which is represented by alongitude-latitude coordinate lonj and latj are the longitudeand latitude of the task j respectively and Bj isin R+ is thelimited budget of the task j

Definition 1 (decision matrix for a task) Given a set of tasksTand a set of workers W Let Wj be the set of workers withinthe region of the radius R of the task tjϵT and f be thenumber of attributes that are considered when assigningtasks To facilitate the description of algorithms subse-quently the task and the worker are attached to the matrixcolumns)en the decision matrix of the task tj is shaped asDM

j

ntimes(f+2) where n |Wj|For example the worker wi isinWj the attributes involved

are the distance and workersrsquo reputation then the item sj

langj i dji rirang is included in DMj and dji represents thedistance between tj and wi

Setting the radius value of a task is one of the centralaspects of task assignment A very low radius value wouldresult in a low task completion rate because of the lack ofenough workers In contrast a very high radius value wouldhave no practical significance because of the unavailability ofworkers willing to travel a long distance to perform a taskPrevious studies suggest that the most acceptable distancefor the workers is 0ndash2 km [1 7 13] In practice someworkers may be tempted by the larger budget to performremote tasks In this paper we assume (1) a task with ahigher budget can select workers from a wider region and (2)some workers are willing to travel further for the higherrewards So the radius Rj is positively affected by the budgetBj and negatively affected by the extra allowance per kilo-meter β Let a workerrsquos accepted baseline distance be c in thecondition of a baseline payment P for a task When a taskrsquosbudget is less than the baseline payment P the task is notlikely to be accepted by any worker at this situation theradius is represented by a negative number because noworkers locate within a region of a taskrsquos negative radius)en the method in [16] is slightly changed to compute Rj

Rj

c +B

jminus P

β B

j geP

minus1 Bj ltP

⎧⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎩

(1)

Identifying the value of the parameter P is not ouremphasis We focus on proposing a flexible spatial taskassignment method that considers multiple attributes af-fecting the result in the task assignment and different re-quirement preferences for each attribute Hence we set P asthe lowest budget among the budgets of all tasks

Our goal is to select the worker with the highest rating ofcombined distance and reputation for a task However if theabove decision matrix is directly used to determine the task

Security and Communication Networks 3

assignment there are two problems Firstly the orders ofmagnitude of attributes are usually different owing to thedifferent natures of attributes If the original value is directlyused for rating the items the role of the attribute with thehigher value in the comprehensive rating will be highlightedand the role of the attribute with the lower value will berelatively weakened Secondly the distance is a cost-typeattribute and the reputation is a benefit-type attribute whichmeans the distance and the reputation have different in-fluence trend on ratings )erefore in order to ensure thereliability of the rating results it is necessary to normalizethe original data We adopt the linear proportional trans-formation method to normalize the distance and reputationas shown in the following equations

dji

maxiisinWj dji minus dji

maxiisinWj dji minus miniisinWj dji

maxiisinWj

dji ne miniisinWj

dji

1 otherwise

⎧⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎩

(2)

ri

riminus miniisinWj r

i

maxiisinWj riminus miniisinWj r

i max

iisinWjr

i ne miniisinWj

ri

1 otherwise

⎧⎪⎪⎪⎨

⎪⎪⎪⎩

(3)

Definition 2 (reward for a task) When a task is completedthe requester must offer rewards to the correspondingworker Let Ej represent the extra allowance for the task j

when it is completed by a worker without c )en the re-ward of the task j is expressed in the following equation

pj

P + Ej (4)

where Ej is related to the parameter β dji c )e farther aworker travels the more extra allowance he should get If theworker wi completes the task tj within the accepted baselinedistance c then Ej equals to 0 otherwise Ej is proportionalto the extra distance and the extra allowance so Ej iscomputed in the following equation [16]

Ej

β dji minus c1113872 1113873 dji gt c

0 dji le c

⎧⎨

⎩ (5)

)e parameter dji mentioned above involves the locationof the task and the worker We computed dji from the task tj

to the worker wi by the Haversine formula [31]

dji R times 2 times arcsin sin2latj minus lati

21113888 1113889 + cos latj1113872 1113873 times cos lati1113872 1113873 times sin2

lonj minus loni

21113888 11138891113888 1113889

12⎛⎝ ⎞⎠ (6)

where R refers to the earthrsquos radius [12]

Definition 3 (TASC-MADM problem) Given a set of tasksT and a set of workers W we assume that each task is

assigned to the optimal worker Let S s1 s2 sj sn1113864 1113865

represent the selected workers for all tasks sj langj i dji rirangsj isin DMj and TASC-MADM is to find sj such that thefollowing linear combination is optimized

Table 1 List of symbols

Symbol MeaningW )e set of workers W w1 w2 wi wm1113864 1113865

T The set of tasks T t1 t2 tj tn1113966 1113967

i Aworkerrsquos number i isin 1 2 m

j A spatial taskrsquos number j isin 1 2 n

li The location of the worker i

lj The location of the task j

Bj The budget of the task j

Rj The radius of the task j

ri The reputation of theworker i

dji The distance from the task j to theworker i

β The extra allowance per kilometerc Workerrsquos accepted baseline distanceP The baseline payment of a taskDMj The decisionmatrix of the task j

Ej The extra allowance for the task j

pj The reward for the task j

ai The amount of task assigned to theworker i

qi The task quota of theworker i

S The result of task assignment

4 Security and Communication Networks

maxiisinWj

scoreji1113872 1113873 maxiisinWj

w0 times dji + w1 times ri

1113872 1113873 (7)

where j and i are the matching task-worker pair andwv isin [0 1] is the attribute importance parameter1113936visin 01 wv 1

)e weights are usually determined objectively or sub-jectively)e entropy method is generally used to objectivelyget weighting of every attribute [32] However we hope thatthe task assignment operation can set the attribute weight toachieve different optimization goals So the weight valuesare set according to the requirement preference of the taskfor attributes If a task has more requirement preference fordistance more than that for reputation it can set w0 gt 05Otherwise w0 lt 05 )e setting w0 05 implies the fol-lowing situation among the workers with the same repu-tation the worker with a shorter distance has the priority tobe selected Similarly among the workers with the samedistance the worker with a higher reputation has the priorityto be selected

31 Complexity Analysis One task assignment of a singletask is to find the best worker among m workers it needs torepeat the assignment n times m times to complete all task as-signments so the problem is solvable in polynomial time

4 Assignment Protocol

We assume that the workers querying the tasks are willing toaccept the tasks )us assigning a task to a worker meansselecting the best worker with the highest comprehensiverating of distance and reputation In this section we willelaborate on our spatial task assignment algorithm

41 PreparingDecisionMatrix Preparing the taskrsquos decisionmatrix involves two steps Firstly we obtain the decisionmatrix DMj for the task j Each item of the decision matrixrepresents a candidate worker In this paper the attributesaffecting the result of a task assignment include the repu-tation of a worker and the distance from a task to a worker)e workers within the radius of the task j are a part of thetaskrsquos candidates excluding the workers who are assignedtasks more than their quota (Line 2ndash4) Secondly normalizethe decision matrix DMj by equations (2) and (3) (Line 5))e pseudocode obtaining and normalizing the decisionmatrix DMj for the task j is given by Algorithm 1

)e computational complexity of Algorithm 1 dependson the loop operation and the normalization operation )ecomplexities of these two operations are O(m) and O(|Wj|)respectively Since |Wj| is usually much less than |m| thetotal computational complexity is O(m)

42 TASC-MADM Approach As mentioned in Section 3 aspatial task should be assigned to the worker with the highestrating which optimizes the linear combination of the dis-tance and the reputation

Algorithm 2 is the pseudocode of the allocation methodnamely TASC-MADM algorithm which inputs a set ofworkers W a set of tasks T and the parameters P β c andreturns the best assignment result S containing task-and-worker assignment with the highest rating

Initially S is set to empty (Line 1) ai|i 12m 0 (ieeach worker has been assigned zero times) (Line 2) Nextfor each task tj calculate the radius and the distance to theworkers and obtain and normalize the decision matrixDMj by calling Algorithm 1 (Line 6) Next if the decisionmatrix has more than zero items compute the scores ofitems (Line 9) and simultaneously compute the reward pj

paid by the task tj to the worker wi (Line 10) For sub-sequent easy operation the itemrsquos score and the reward areassociated with other information including the tasknumber the worker number the distance and the repu-tation (Line 11) Next we sort the items descending byscores (Line 12) Intuitively the item with a higher rankindicates a better assignment than other assignments Fi-nally the task tj is assigned to the topmost worker (Line13ndash17)

)e assignment iterates for n rounds (Line 3) and finallyreturns the assignment result of all tasks (Line 18) In eachiteration the computational complexity is O(m) so the totalcomputational complexity is O(n times m)

5 Experiment Evaluation

In this section we tested the performance of our approachon both real and synthetic data

51 Metrics In the experiments we measured the perfor-mance of each approach according to the following metrics[23]

(1) Average task radius (δ) this metric measures theaverage spatial region size of the tasks which iscomputed as the average radius of all tasks

β 1

|T|1113944

j

Rj (8)

(2) Task assignment rate (η) this metric measures thealgorithmrsquos effectiveness in assigning several taskssuccessfully )e task assignment rate η is the per-centage of assigned tasks among the total amounts ofcrowded spatial tasks

η |S|

|T| (9)

(3) Average reputation of workers assigned tasks (ψ) thequality of crowdsourcing result is determined by theworkerrsquos quality which is modeled by the workerrsquosreputation So this metric evaluates the quality oftasks completed It is computed as the total repu-tation of selected workers divided by the number ofselected workers

Security and Communication Networks 5

ψ 1

|S|1113944iisinS

ri (10)

(4) Average distance traveled (ς) this metric measuresthe travel cost for workers when they complete theassigned tasks it is computed as the average distancetraveled by all the selected workers

ς 1

|S|1113944

langjirangisinSdji (11)

(5) Average budget utilization rate (ϕ) this metric is theaverage of the budget utilization for all assignedtasks )e budget utilization rate of each assignedtask is the ratio of the actual reward paid for the taskto the budget of that task

ϕ 1

|S|1113944jisinS

pj

Bj (12)

(6) Average reward (ω) this is the ratio of the totalreward for all the assigned tasks to the number ofassigned tasks

ω 1

|S|1113944jisinS

pj (13)

)e parameter δ is used to demonstrate the changing ofother metrics along with the average radius )e workersrsquoperspective would prefer to keep ς as low as possible )etask requestersrsquo perspective would prefer higher values of ηand ψ but lower values of ϕ and ω

52 Experimental Setting

521 Datasets

(i) Real Dataset We used a real dataset [33] from acrowdsourcing event by taking photos with the lo-cation information for 1877 workers and 835 tasks)ese tasks and workers were mainly obtained fromfour cities in China Foshan Guangzhou Shenzhenand Dongguan

(ii) Synthetic Dataset )e synthetic data were obtainedfrom a single day dataset from Gowalla in 2010which included 10956 tasks and 5087 workers

Input a set W the task j

Output the normalized decision matrix DMj for the task j

(1) DMj

(2) For each wi isinW(3) If dji leRj and ai lt qi(4) s

ji langj i dji rirang DMjlarrDMj cup s

ji1113966 1113967

(5) Normalize DMj by equations (2) and (3)(6) Return DMj

ALGORITHM 1 Obtain the decision matrix for a task

Input a set of workers W a set of tasks T parameters P β c

Output S which is the result of task assignment(1) S

(2) ai 0 i isin 1 2 m

(3) For each tj isin T(4) Calculate the radius Rj

(5) Calculate dji from the task j to each worker i

(6) Obtain and normalize the decision matrix DMj by calling Algorithm 1(7) If |DMj|gt 0(8) For iprime in range (|DMj|)(9) Compute the scoreji using equation (7)i DMj[iprime]i it is the number of the iprime th worker for the task tj(10) Compute the reward pj using equation (4)(11) s

ji s

ji append(langpj scorejirang)(ie s

ji langj i dji ri pj scorejirang)

(12) Sorted DMj descending by scores(13) For each item in DMj(14) If j in item(15) sj item ai+ 1(16) SlarrScup sj1113864 1113865

(17) Break(18) Return S

ALGORITHM 2 TASC-MADM algorithm

6 Security and Communication Networks

located in America )is dataset contains four at-tributes as follows task ID taskrsquos location workerID and workerrsquos location Reputations are generatedfollowing the uniform distribution of 0 sim 20000Budgets are generated following the uniform dis-tribution of 65 sim 85 )e task quotas of workers aregenerated following the uniform distribution of5 sim 10

522 Compared Algorithms )e RB-TPSC and Budget-TASC algorithms were selected as the baseline algorithmsbecause they are most closely related to TASC-MADM

(i) RB-TPSC is a task package assignment methodwhich aims at maximizing the number of tasksassigned within budget constraints Quality of resultsand travel costs are also being considered

(ii) Budget-TASC is a budget-aware spatial crowd-sourcing task assignment method which aims inmaximizing the total quality of tasks completedwithin budget constraints

We compared our TASC-MADM with RB-TPSC andBudget-TASC on both real and synthetic datasets

53 Results on Real Dataset )e first three experimentscompared TASC-MADM and RB-TPSC under differentsettings of parameters β c and Bj )e results are shown inFigures 1ndash3 For each parameter the experiment evaluatedthe average metrics of the two algorithms under 21 differentsettings We set the lowest budget of all tasks as P 65Moreover for the fairness in considering the effect of dis-tance and reputation the weight w0 05

First we compared TASC-MADM and RB-TPSC underdifferent β values )e results are shown in Figure 1 wherethe extra allowance per kilometer β varies from 0 sim 20monetary units in 1-unit increments c 05 and Bj isobtained from the dataset )e horizontal axis represents thedifferent settings of β while the vertical axis represents thedifferent values of the first six metrics As can be seen theaverage task radius δ is affected by the extra allowance perkilometer β (Figure 1(a)) δ is maximized when β 1 in-creases with β if βlt 1 and decreases with β if βgt 1 )echange in the trend of other metrics is consistent with theaverage task radius)is is because in a region with a smallerradius it is usually impossible to find enough workers withhigh reputations )us other metrics decrease with theaverage radius Compared with RB-TPSC TASC-MADMensures a high average task assignment rate (Figure 1(b))but reduces the average travel cost of workers (Figure 1(d))and saves the budget (Figures 1(e) and 1(f )) Besides ourresult shows that the average reputation of workers decreaseswith the average radius (Figure 1(c)) which is more realisticbecause there are fewer workers to choose from

Secondly we compared TASC-MADM and RB-TPSCunder different c values Figure 2 depicts the trend in whichthe above six metrics change when c the accepted distanceswithout extra remote allowance varies from 0 sim 2 km where

β 2 and Bj is obtained from the real dataset If cgt 05 theaverage radius increases with c and positively affects theabovementioned metrics except for ϕ and ω Our proposedmethod achieves a task assignment rate value of 9856(Figure 2(b)) but the maximum of the average distancetraveled the average reward and the average budget utili-zation rates are ς 103 km ω 6533 and ϕ 9493 re-spectively (Figures 2(d)ndash2(f)) Compared to the RB-TPSCmethod our method greatly decreases the average distancetraveled by the workers and the average reward offered bythe tasksrsquo requester while maintaining an equally high or agreater average task assignment rate Moreover it signifi-cantly improves the quality of crowdsourcing results becauseof the average reputation value of the selected workers in-creasing by about 250 (Figure 2(c))

)e third experiment compared TASC-MADM and RB-TPSC under different budgets (B) Figure 3 depicts how thefirst six metrics change while the tasksrsquo budget (B) variesfrom 90 to 110 in 1 increments where β 2 c 05Since the task with a greater budget should have moreworkers to choose from the average radius of the tasks ispositively affected by the tasksrsquo budget (Figure 3(a)) As thetask radius increases further the task assignment rate in-creases to 995 (Figure 3(b)) Compared to the RB-TPSCmethod our method decreases the average distance traveledfrom a range of 14sim25 km to a range of 08sim15 km(Figure 3(d)) but increases the average reputation by about147 (Figure 3(c)) and greatly decreases the average budgetutilization rate (Figure 3(e)) and the average reward((Figure 3(f))) More importantly with our method thegreater the number of candidate workers the lower theaverage reward offered by the requester and the higher is thequality of the crowdsourcing result In contrast RB-TPSCincreases the average reward continuously and decreases theaverage reputation to a stable value of around 500

)e fourth experiment compared TASC-MADM withRB-TPSC and Budget-TASC when radiuses were changedFor TASC-MADM and RB-TPSC we selected the averagemetrics of different c values In the above experiment thecomputed radius belongs to the interval [0 12] when c

varied from 0 sim 2 km and other parameters were fixed Wehave experimented with Budget-TASC in different radiusesvarying from 0 sim 12 km and then get the average of eachmetric For the Budget-TASC the other parameters were setas follows DC is set as the earthrsquos radius [10] PL 0 PH isset as the budget of the task and PM is set as the half of thebudget of the task Because we scaled the workerrsquos reputationto the interval [0 1] ThHM 075 ThML 05

)e results are shown in Table 2 TASC-MADM out-performs the baseline algorithms in all metrics except theaverage reputation For Budget-TASC PH is set as the taskrsquostotal budget which limits the task to high-quality workersbut also increases the average budget utilized

54 Results on Synthetic Dataset We repeated the first threeexperiments in Section 53 on the synthetic datasetFigures 4ndash6 illustrate the results of TASC-MADM and RB-TPSC Compared with RB-TPSC our algorithm greatly

Security and Communication Networks 7

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erag

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Figure 1 Performance with an extra allowance per kilometer (β) (monetary unit) real dataset

25272931333537394143

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Figure 2 Continued

8 Security and Communication Networks

improves the task assignment rate (Figures 4(b) 5(b) and6(b)) and the resultrsquos quality (Figures 4(c) 5(c) and 6(c))However for the synthetic dataset the task locates in a

slightly sparse scenario with few workers )en the highertask assignment rate implies the higher travel cost(Figures 4(d) 5(d) and 6(d)) Correspondingly the budgetused is increased (Figures 4(e) 4(f) 5(e) 5(f) 6(e) and6(f ))

Next based on the synthetic dataset we comparedTASC-MADM with RB-TPSC and Budget-TASC whenradiuses were changed For TASC-MADM and RB-TPSCwe selected the average metrics of different c values thecomputed radius approximately belongs to the interval [6 9]

RB-TPSCTASC-MADM

08

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(f )

Figure 2 Performance with different accepted distances without extra remote allowance (c) (km) real dataset

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655660665670675680685690695

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ewar

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110

(f )

Figure 3 Performance with an amplitude of variation in budget (B) real dataset

Table 2 Results under different approaches real dataset

Algorithms η () ψ ζ ϕ ωBudget-TASC 8567 139063 14 1 7761RB-TPSC 9395 52339 159 09645 6672TASC-MADM 9418 98746 078 09509 6598

Security and Communication Networks 9

02468

10121416

0 2 4 6 8 10 12 14 16 18 20

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age r

adiu

s

RB-TPSCTASC-MADM

(a)

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Figure 4 Performance with an extra allowance per kilometer (β) (monetary unit) synthetic dataset

RB-TPSCTASC-MADM

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084085085086086087087088088089

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ewar

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(f )

Figure 5 Performance with different accepted distances without extra remote allowance (c) (km) synthetic dataset

10 Security and Communication Networks

(Figure 5(a)) So for Budget-TASC the radius varied from6 sim 9 km other parameters were set as in the fourthexperiment

)e experimental results (Table 3) show that TPSC-ADM obtains the highest task assignment rate but Budget-ASC achieves the highest quality and RB-TPSC spends thelowest budget

55 Effect of Taskrsquos Attribute Preference Our approachTASC-MADM enables the task to be assigned to satisfydemands for different goals If the resultrsquos quality is theprimary goal the task should select workers with a highreputation However if saving cost is the most concernedobjective the worker closer to the task should be chosenfirst Figure 7 shows the influence of tasksrsquo attributespreferences on the proposed approachrsquos performancew0 05 all targets are considered fairly w0 lt 05 the taskprefers the workerrsquos reputation to the distance the re-sultrsquos quality is significantly improved However thetravel cost and budget utilized are increased Whenw0 gt 05 the travel cost and the budget utilized are savedwhereas the resultrsquos quality is decreased Our approachcan maximize task assignment rate by setting w0 1 ormaximize the quality of workers selected by settingw0 0

56 Efficiency of Algorithms )e efficiency of the algorithmsis measured in CPU cost )e Budget-TASC algorithmrsquoscomputational complexity is O(n times m2) and that of RB-TPSC and TASC-MADM is O(n times m) we compare TASC-MADMwith RB-TPSC and Budget-TASC in CPU cost Eachof the programs runs 3 times 21 rounds )e average time perround is used to measure the algorithmrsquos efficiency Asshown in Figure 8 our approach significantly improves theefficiency of spatial task assignment

57 Summary of Experiment Results We summarized themajor finding as follows

(i) If the task is located in a worker-density area theproposed TASC-MADM approach exhibits betterresults than RB-TPSC It also performs better thanBudget-TASC on the metrics except for the qualityof workers

(ii) If the task is located in a worker-sparsity area theproposed TASC-MADM approach performs betterthan RB-TPSC in terms of the average assignmentrate and quality of workers But it leads to moretravel cost and budget utilized Budget-TASC ob-tains the best quality because it considers the qualityfirst

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660066506700675068006850690069507000

71007050

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age r

ewar

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(f )

Figure 6 Performance with an amplitude of variation in budget (B) synthetic dataset

Security and Communication Networks 11

Table 3 Results under different approaches synthetic dataset

Algorithms η ψ ζ ϕ ωBudget-TASC 072743975 1367217 1417697 1 7758585RB-TPSC 072253468 1027882 1408169 0851329 6686784TPSC-ADM 074825306 1219453 1926809 0873916 6768671

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adiu

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650655660665670675680685690695700705

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age r

ewar

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(f )

Figure 7 Effect of tasksrsquo attributes preferences on the TASC-MADM approach Performance with an extra allowance per kilometer (β)

(monetary unit) synthetic dataset

128 114

31961929

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Synthetic Dataset Real Dataset

TASC-MADMRB-TPSCBudget-TASC

Figure 8 Average CPU cost

12 Security and Communication Networks

(iii) In the terms of CPU cost the proposed TASC-MADM approach is superior to the baselinealgorithms

58 Discussion )is section discusses the advantages andlimitations of the TASC-MADM approach )e advantagesare listed as follows

(1) Effective )e TASC-MADM approach improves thetask assignment rate By setting different attributeweights it can maximize the task assignment rate orthe quality of crowdsourcing results

(2) Efficient )e TASC-MADM approach enhancesefficiency because of computing simply

(3) Extensible )eoretically our method can be ex-tended to solving decision problems involving anynumber of attributes

)ere still exist the following limitations in the TASC-MADM approach

(1) Quality Quantification of Workers In this paper theworker quality is modeled as a reputation value toreflect the quality of crowdsourced results Inpractice the same workerrsquos quality may differ inspecific tasks How to quantify the quality of workersis not covered in this paper

(2) Efficiency of Indexing Records )e TASC-MADMapproach exhaustively searches all the records toidentify the candidate of a task which makes it lessefficient to get the decision matrix on large datasets

6 Conclusion

)is paper focuses on designing an efficient task assignmentapproach which can deal with the situation where tasks havedifferent require preferences for different task attributes toachieve different goals Our task assignment approach can beextended to scenarios containing any number of attributesIn addition to the distance and reputation other criteriasuch as the workersrsquo skills can be considered

As for future work more factors such as workersrsquowillingness to accept tasks and their quality differences indifferent professional fields are included in task assign-ments In addition improving the efficiency of indexingrecords to make the allocation scheme suitable for largedatasets is a valuable research topic

Data Availability

)e data of allocated sharing tasks are available from ChinaSociety for Industrial and Applied Mathematics 2017 re-trieved on September 2 2020 from httpwwwmcmeducnhtml_cnnode460baf68ab0ed0e1e557a0c79b1c4648html

Conflicts of Interest

)e authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

)e authors thank researchers working at Guangxi KeyLaboratory of Trusted Software especially Xuguang Bao andManli Zhu for their suggestion during the research andpreparation of the manuscript )is work was funded byGuangxi Key Laboratory of Trusted Software (Nokx201727) Project to Improve the Scientific Research BasicAbility of Middle-Aged and Young Teachers (No2019KY0226) Natural Science Foundation of China (Nos61966009 U1811264 and U1711263) and Natural ScienceFoundation of Guangxi Province (Nos2019GXNSFBA245049 2019GXNSFBA245059 and2018GXNSFDA281045)

References

[1] L Kazemi and C Shahabi ldquoGeoCrowd enabling query an-swering with spatial crowdsourcing GISrdquo in Proceedings ofthe ACM International Symposium on Advances in GeographicInformation Systems pp 189ndash198 CA USA November 2012

[2] J Xiong M Zhao M Z A Bhuiyan L Chen and Y TianldquoAn AI-enabled three-party game framework for guaranteeddata privacy in mobile edge crowdsensing of IoTrdquo IEEETransactions on Industrial Informatics vol 17 no 2pp 922ndash933 2021

[3] J Xiong R Ma L Chen et al ldquoA personalized privacyprotection framework for mobile crowdsensing in IIoTrdquo IEEETransactions on Industrial Informatics vol 16 no 6pp 4231ndash4241 2020

[4] DiDi Didi Chuxing Corporate Citizenship Report httpswwwdidiglobalcomaboutdidiresponsibility 2017

[5] J Xiong J Ren L Chen et al ldquoEnhancing privacy andavailability for data clustering in intelligent electrical serviceof IoTrdquo IEEE Internet of Bings Journal vol 6 no 2pp 1530ndash1540 2019

[6] C Zhao P Cheng L Chen X Lin and C Shahabi ldquoFair taskassignment in spatial crowdsourcingrdquo Proceedings of theVLDB Endowment vol 13 no 12 pp 2479ndash2492 2020

[7] M Venanzi A Rogers and N R Jennings ldquoCrowdsourcingspatial phenomena using trust-based heteroskedasticGaussian processesrdquo in Proceedings of the First Conference onHuman Computation and Crowdsourcing (HCOMP)pp 182ndash189 Palm Springs CA USA November 2013

[8] J Xiong R Bi M Zhao J Guo and Q Yang ldquoEdge-assistedprivacy-preserving raw data sharing framework for connectedautonomous vehiclesrdquo IEEE Wireless Communicationsvol 27 no 3 pp 24ndash30 2020

[9] B Van B V D Haak M Parks and M Castells ldquo)e futureof journalism networked journalism rethinking journalism inthe networked digital agerdquo International Journal of Com-munication vol 6 pp 2923ndash2938 2012

[10] M Zook M Graham S Taylor and S Gorman ldquoVolunteeredgeographic information and crowdsourcing disaster relief acase study of the Haitian earthquakerdquoWorld Medical umlHealthPolicy vol 2 no 2 pp 7ndash33 2010

[11] Y Tong L Chen and C Shahabi ldquoSpatial crowdsourcingchallenges techniques and applicationsrdquo Proceedings of theVLDB Endowment vol 10 pp 1988ndash1991 2017

[12] J Xiong X Chen Q Yang L Chen and Z Yao ldquoA task-oriented user selection incentive mechanism in edge-aidedmobile crowdsensingrdquo IEEE Transactions on Network Scienceand Engineering vol 7 no 4 pp 2347ndash2360 2020

Security and Communication Networks 13

assignment there are two problems Firstly the orders ofmagnitude of attributes are usually different owing to thedifferent natures of attributes If the original value is directlyused for rating the items the role of the attribute with thehigher value in the comprehensive rating will be highlightedand the role of the attribute with the lower value will berelatively weakened Secondly the distance is a cost-typeattribute and the reputation is a benefit-type attribute whichmeans the distance and the reputation have different in-fluence trend on ratings )erefore in order to ensure thereliability of the rating results it is necessary to normalizethe original data We adopt the linear proportional trans-formation method to normalize the distance and reputationas shown in the following equations

dji

maxiisinWj dji minus dji

maxiisinWj dji minus miniisinWj dji

maxiisinWj

dji ne miniisinWj

dji

1 otherwise

⎧⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎩

(2)

ri

riminus miniisinWj r

i

maxiisinWj riminus miniisinWj r

i max

iisinWjr

i ne miniisinWj

ri

1 otherwise

⎧⎪⎪⎪⎨

⎪⎪⎪⎩

(3)

Definition 2 (reward for a task) When a task is completedthe requester must offer rewards to the correspondingworker Let Ej represent the extra allowance for the task j

when it is completed by a worker without c )en the re-ward of the task j is expressed in the following equation

pj

P + Ej (4)

where Ej is related to the parameter β dji c )e farther aworker travels the more extra allowance he should get If theworker wi completes the task tj within the accepted baselinedistance c then Ej equals to 0 otherwise Ej is proportionalto the extra distance and the extra allowance so Ej iscomputed in the following equation [16]

Ej

β dji minus c1113872 1113873 dji gt c

0 dji le c

⎧⎨

⎩ (5)

)e parameter dji mentioned above involves the locationof the task and the worker We computed dji from the task tj

to the worker wi by the Haversine formula [31]

dji R times 2 times arcsin sin2latj minus lati

21113888 1113889 + cos latj1113872 1113873 times cos lati1113872 1113873 times sin2

lonj minus loni

21113888 11138891113888 1113889

12⎛⎝ ⎞⎠ (6)

where R refers to the earthrsquos radius [12]

Definition 3 (TASC-MADM problem) Given a set of tasksT and a set of workers W we assume that each task is

assigned to the optimal worker Let S s1 s2 sj sn1113864 1113865

represent the selected workers for all tasks sj langj i dji rirangsj isin DMj and TASC-MADM is to find sj such that thefollowing linear combination is optimized

Table 1 List of symbols

Symbol MeaningW )e set of workers W w1 w2 wi wm1113864 1113865

T The set of tasks T t1 t2 tj tn1113966 1113967

i Aworkerrsquos number i isin 1 2 m

j A spatial taskrsquos number j isin 1 2 n

li The location of the worker i

lj The location of the task j

Bj The budget of the task j

Rj The radius of the task j

ri The reputation of theworker i

dji The distance from the task j to theworker i

β The extra allowance per kilometerc Workerrsquos accepted baseline distanceP The baseline payment of a taskDMj The decisionmatrix of the task j

Ej The extra allowance for the task j

pj The reward for the task j

ai The amount of task assigned to theworker i

qi The task quota of theworker i

S The result of task assignment

4 Security and Communication Networks

maxiisinWj

scoreji1113872 1113873 maxiisinWj

w0 times dji + w1 times ri

1113872 1113873 (7)

where j and i are the matching task-worker pair andwv isin [0 1] is the attribute importance parameter1113936visin 01 wv 1

)e weights are usually determined objectively or sub-jectively)e entropy method is generally used to objectivelyget weighting of every attribute [32] However we hope thatthe task assignment operation can set the attribute weight toachieve different optimization goals So the weight valuesare set according to the requirement preference of the taskfor attributes If a task has more requirement preference fordistance more than that for reputation it can set w0 gt 05Otherwise w0 lt 05 )e setting w0 05 implies the fol-lowing situation among the workers with the same repu-tation the worker with a shorter distance has the priority tobe selected Similarly among the workers with the samedistance the worker with a higher reputation has the priorityto be selected

31 Complexity Analysis One task assignment of a singletask is to find the best worker among m workers it needs torepeat the assignment n times m times to complete all task as-signments so the problem is solvable in polynomial time

4 Assignment Protocol

We assume that the workers querying the tasks are willing toaccept the tasks )us assigning a task to a worker meansselecting the best worker with the highest comprehensiverating of distance and reputation In this section we willelaborate on our spatial task assignment algorithm

41 PreparingDecisionMatrix Preparing the taskrsquos decisionmatrix involves two steps Firstly we obtain the decisionmatrix DMj for the task j Each item of the decision matrixrepresents a candidate worker In this paper the attributesaffecting the result of a task assignment include the repu-tation of a worker and the distance from a task to a worker)e workers within the radius of the task j are a part of thetaskrsquos candidates excluding the workers who are assignedtasks more than their quota (Line 2ndash4) Secondly normalizethe decision matrix DMj by equations (2) and (3) (Line 5))e pseudocode obtaining and normalizing the decisionmatrix DMj for the task j is given by Algorithm 1

)e computational complexity of Algorithm 1 dependson the loop operation and the normalization operation )ecomplexities of these two operations are O(m) and O(|Wj|)respectively Since |Wj| is usually much less than |m| thetotal computational complexity is O(m)

42 TASC-MADM Approach As mentioned in Section 3 aspatial task should be assigned to the worker with the highestrating which optimizes the linear combination of the dis-tance and the reputation

Algorithm 2 is the pseudocode of the allocation methodnamely TASC-MADM algorithm which inputs a set ofworkers W a set of tasks T and the parameters P β c andreturns the best assignment result S containing task-and-worker assignment with the highest rating

Initially S is set to empty (Line 1) ai|i 12m 0 (ieeach worker has been assigned zero times) (Line 2) Nextfor each task tj calculate the radius and the distance to theworkers and obtain and normalize the decision matrixDMj by calling Algorithm 1 (Line 6) Next if the decisionmatrix has more than zero items compute the scores ofitems (Line 9) and simultaneously compute the reward pj

paid by the task tj to the worker wi (Line 10) For sub-sequent easy operation the itemrsquos score and the reward areassociated with other information including the tasknumber the worker number the distance and the repu-tation (Line 11) Next we sort the items descending byscores (Line 12) Intuitively the item with a higher rankindicates a better assignment than other assignments Fi-nally the task tj is assigned to the topmost worker (Line13ndash17)

)e assignment iterates for n rounds (Line 3) and finallyreturns the assignment result of all tasks (Line 18) In eachiteration the computational complexity is O(m) so the totalcomputational complexity is O(n times m)

5 Experiment Evaluation

In this section we tested the performance of our approachon both real and synthetic data

51 Metrics In the experiments we measured the perfor-mance of each approach according to the following metrics[23]

(1) Average task radius (δ) this metric measures theaverage spatial region size of the tasks which iscomputed as the average radius of all tasks

β 1

|T|1113944

j

Rj (8)

(2) Task assignment rate (η) this metric measures thealgorithmrsquos effectiveness in assigning several taskssuccessfully )e task assignment rate η is the per-centage of assigned tasks among the total amounts ofcrowded spatial tasks

η |S|

|T| (9)

(3) Average reputation of workers assigned tasks (ψ) thequality of crowdsourcing result is determined by theworkerrsquos quality which is modeled by the workerrsquosreputation So this metric evaluates the quality oftasks completed It is computed as the total repu-tation of selected workers divided by the number ofselected workers

Security and Communication Networks 5

ψ 1

|S|1113944iisinS

ri (10)

(4) Average distance traveled (ς) this metric measuresthe travel cost for workers when they complete theassigned tasks it is computed as the average distancetraveled by all the selected workers

ς 1

|S|1113944

langjirangisinSdji (11)

(5) Average budget utilization rate (ϕ) this metric is theaverage of the budget utilization for all assignedtasks )e budget utilization rate of each assignedtask is the ratio of the actual reward paid for the taskto the budget of that task

ϕ 1

|S|1113944jisinS

pj

Bj (12)

(6) Average reward (ω) this is the ratio of the totalreward for all the assigned tasks to the number ofassigned tasks

ω 1

|S|1113944jisinS

pj (13)

)e parameter δ is used to demonstrate the changing ofother metrics along with the average radius )e workersrsquoperspective would prefer to keep ς as low as possible )etask requestersrsquo perspective would prefer higher values of ηand ψ but lower values of ϕ and ω

52 Experimental Setting

521 Datasets

(i) Real Dataset We used a real dataset [33] from acrowdsourcing event by taking photos with the lo-cation information for 1877 workers and 835 tasks)ese tasks and workers were mainly obtained fromfour cities in China Foshan Guangzhou Shenzhenand Dongguan

(ii) Synthetic Dataset )e synthetic data were obtainedfrom a single day dataset from Gowalla in 2010which included 10956 tasks and 5087 workers

Input a set W the task j

Output the normalized decision matrix DMj for the task j

(1) DMj

(2) For each wi isinW(3) If dji leRj and ai lt qi(4) s

ji langj i dji rirang DMjlarrDMj cup s

ji1113966 1113967

(5) Normalize DMj by equations (2) and (3)(6) Return DMj

ALGORITHM 1 Obtain the decision matrix for a task

Input a set of workers W a set of tasks T parameters P β c

Output S which is the result of task assignment(1) S

(2) ai 0 i isin 1 2 m

(3) For each tj isin T(4) Calculate the radius Rj

(5) Calculate dji from the task j to each worker i

(6) Obtain and normalize the decision matrix DMj by calling Algorithm 1(7) If |DMj|gt 0(8) For iprime in range (|DMj|)(9) Compute the scoreji using equation (7)i DMj[iprime]i it is the number of the iprime th worker for the task tj(10) Compute the reward pj using equation (4)(11) s

ji s

ji append(langpj scorejirang)(ie s

ji langj i dji ri pj scorejirang)

(12) Sorted DMj descending by scores(13) For each item in DMj(14) If j in item(15) sj item ai+ 1(16) SlarrScup sj1113864 1113865

(17) Break(18) Return S

ALGORITHM 2 TASC-MADM algorithm

6 Security and Communication Networks

located in America )is dataset contains four at-tributes as follows task ID taskrsquos location workerID and workerrsquos location Reputations are generatedfollowing the uniform distribution of 0 sim 20000Budgets are generated following the uniform dis-tribution of 65 sim 85 )e task quotas of workers aregenerated following the uniform distribution of5 sim 10

522 Compared Algorithms )e RB-TPSC and Budget-TASC algorithms were selected as the baseline algorithmsbecause they are most closely related to TASC-MADM

(i) RB-TPSC is a task package assignment methodwhich aims at maximizing the number of tasksassigned within budget constraints Quality of resultsand travel costs are also being considered

(ii) Budget-TASC is a budget-aware spatial crowd-sourcing task assignment method which aims inmaximizing the total quality of tasks completedwithin budget constraints

We compared our TASC-MADM with RB-TPSC andBudget-TASC on both real and synthetic datasets

53 Results on Real Dataset )e first three experimentscompared TASC-MADM and RB-TPSC under differentsettings of parameters β c and Bj )e results are shown inFigures 1ndash3 For each parameter the experiment evaluatedthe average metrics of the two algorithms under 21 differentsettings We set the lowest budget of all tasks as P 65Moreover for the fairness in considering the effect of dis-tance and reputation the weight w0 05

First we compared TASC-MADM and RB-TPSC underdifferent β values )e results are shown in Figure 1 wherethe extra allowance per kilometer β varies from 0 sim 20monetary units in 1-unit increments c 05 and Bj isobtained from the dataset )e horizontal axis represents thedifferent settings of β while the vertical axis represents thedifferent values of the first six metrics As can be seen theaverage task radius δ is affected by the extra allowance perkilometer β (Figure 1(a)) δ is maximized when β 1 in-creases with β if βlt 1 and decreases with β if βgt 1 )echange in the trend of other metrics is consistent with theaverage task radius)is is because in a region with a smallerradius it is usually impossible to find enough workers withhigh reputations )us other metrics decrease with theaverage radius Compared with RB-TPSC TASC-MADMensures a high average task assignment rate (Figure 1(b))but reduces the average travel cost of workers (Figure 1(d))and saves the budget (Figures 1(e) and 1(f )) Besides ourresult shows that the average reputation of workers decreaseswith the average radius (Figure 1(c)) which is more realisticbecause there are fewer workers to choose from

Secondly we compared TASC-MADM and RB-TPSCunder different c values Figure 2 depicts the trend in whichthe above six metrics change when c the accepted distanceswithout extra remote allowance varies from 0 sim 2 km where

β 2 and Bj is obtained from the real dataset If cgt 05 theaverage radius increases with c and positively affects theabovementioned metrics except for ϕ and ω Our proposedmethod achieves a task assignment rate value of 9856(Figure 2(b)) but the maximum of the average distancetraveled the average reward and the average budget utili-zation rates are ς 103 km ω 6533 and ϕ 9493 re-spectively (Figures 2(d)ndash2(f)) Compared to the RB-TPSCmethod our method greatly decreases the average distancetraveled by the workers and the average reward offered bythe tasksrsquo requester while maintaining an equally high or agreater average task assignment rate Moreover it signifi-cantly improves the quality of crowdsourcing results becauseof the average reputation value of the selected workers in-creasing by about 250 (Figure 2(c))

)e third experiment compared TASC-MADM and RB-TPSC under different budgets (B) Figure 3 depicts how thefirst six metrics change while the tasksrsquo budget (B) variesfrom 90 to 110 in 1 increments where β 2 c 05Since the task with a greater budget should have moreworkers to choose from the average radius of the tasks ispositively affected by the tasksrsquo budget (Figure 3(a)) As thetask radius increases further the task assignment rate in-creases to 995 (Figure 3(b)) Compared to the RB-TPSCmethod our method decreases the average distance traveledfrom a range of 14sim25 km to a range of 08sim15 km(Figure 3(d)) but increases the average reputation by about147 (Figure 3(c)) and greatly decreases the average budgetutilization rate (Figure 3(e)) and the average reward((Figure 3(f))) More importantly with our method thegreater the number of candidate workers the lower theaverage reward offered by the requester and the higher is thequality of the crowdsourcing result In contrast RB-TPSCincreases the average reward continuously and decreases theaverage reputation to a stable value of around 500

)e fourth experiment compared TASC-MADM withRB-TPSC and Budget-TASC when radiuses were changedFor TASC-MADM and RB-TPSC we selected the averagemetrics of different c values In the above experiment thecomputed radius belongs to the interval [0 12] when c

varied from 0 sim 2 km and other parameters were fixed Wehave experimented with Budget-TASC in different radiusesvarying from 0 sim 12 km and then get the average of eachmetric For the Budget-TASC the other parameters were setas follows DC is set as the earthrsquos radius [10] PL 0 PH isset as the budget of the task and PM is set as the half of thebudget of the task Because we scaled the workerrsquos reputationto the interval [0 1] ThHM 075 ThML 05

)e results are shown in Table 2 TASC-MADM out-performs the baseline algorithms in all metrics except theaverage reputation For Budget-TASC PH is set as the taskrsquostotal budget which limits the task to high-quality workersbut also increases the average budget utilized

54 Results on Synthetic Dataset We repeated the first threeexperiments in Section 53 on the synthetic datasetFigures 4ndash6 illustrate the results of TASC-MADM and RB-TPSC Compared with RB-TPSC our algorithm greatly

Security and Communication Networks 7

005

115

225

335

445

5

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

adiu

s

RB-TPSCTASC-MADM

(a)

RB-TPSCTASC-MADM

0

02

04

06

08

1

12

0 2 4 6 8 10 12 14 16 18 20

Task

assig

nmen

t rat

e

(b)

RB-TPSCTASC-MADM

0

200

400

600

800

1000

1200

1400

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

eput

atio

n va

lue

(c)

RB-TPSCTASC-MADM

0

05

1

15

2

25

0 2 4 6 8 10 12 14 16 18 20

Aver

age d

istan

ce tr

avel

ed

(d)

RB-TPSCTASC-MADM

094

095

095

096

096

097

097

098

0 2 4 6 8 10 12 14 16 18 20

Aver

age b

udge

t util

izat

ion

rate

(e)

RB-TPSCTASC-MADM

6475

6500

6525

6550

6575

6600

6625

6650

6675

6700

0 2 4 6 8 10 12 14 16 18 20Av

erag

e rew

ard

(f )

Figure 1 Performance with an extra allowance per kilometer (β) (monetary unit) real dataset

25272931333537394143

00 02 04 06 08 10 12 14 16 18 20

Aver

age r

adiu

s

RB-TPSCTASC-MADM

(a)

RB-TPSCTASC-MADM

065

070

075

080

085

090

095

100

00 02 04 06 08 10 12 14 16 18 20

Task

assig

nmen

t

(b)

RB-TPSCTASC-MADM

0

200

400

600

800

1000

1200

1400

00 02 04 06 08 10 12 14 16 18 20

Aver

age r

eput

atio

n va

lue

(c)

Figure 2 Continued

8 Security and Communication Networks

improves the task assignment rate (Figures 4(b) 5(b) and6(b)) and the resultrsquos quality (Figures 4(c) 5(c) and 6(c))However for the synthetic dataset the task locates in a

slightly sparse scenario with few workers )en the highertask assignment rate implies the higher travel cost(Figures 4(d) 5(d) and 6(d)) Correspondingly the budgetused is increased (Figures 4(e) 4(f) 5(e) 5(f) 6(e) and6(f ))

Next based on the synthetic dataset we comparedTASC-MADM with RB-TPSC and Budget-TASC whenradiuses were changed For TASC-MADM and RB-TPSCwe selected the average metrics of different c values thecomputed radius approximately belongs to the interval [6 9]

RB-TPSCTASC-MADM

08

10

12

14

16

18

20

00 02 04 06 08 10 12 14 16 18 20

Aver

age d

istan

ce tr

avele

d

(d)

RB-TPSCTASC-MADM

09500

09525

09550

09575

09600

09625

09650

09675

09700

00 02 04 06 08 10 12 14 16 18 20

Aver

age b

udge

t util

izat

ion

rate

(e)

RB-TPSCTASC-MADM

650

655

660

665

670

675

680

685

00 02 04 06 08 10 12 14 16 18 20

Aver

age r

ewar

d

(f )

Figure 2 Performance with different accepted distances without extra remote allowance (c) (km) real dataset

0

1

2

3

4

5

6

7

Aver

age r

adiu

s

RB-TPSCTASC-MADM

090

092

094

096

098

100

102

104

106

108

110

(a)

RB-TPSCTASC-MADM

00

02

04

06

08

10

12

Task

assig

nmen

t rat

e

090

092

094

096

098

100

102

104

106

108

110

(b)

RB-TPSCTASC-MADM

0

500

1000

1500

2000

2500

3000

Aver

age r

eput

atio

n va

lue

090

092

094

096

098

100

102

104

106

108

110

(c)

RB-TPSCTASC-MADM

0

05

1

15

2

25

3

Aver

age d

istan

ce tr

avele

d

090

092

094

096

098

100

102

104

106

108

110

(d)

RB-TPSCTASC-MADM

086

088

09

092

094

096

098

Aver

age b

udge

t util

izat

ion

rate

090

092

094

096

098

100

102

104

106

108

110

(e)

RB-TPSCTASC-MADM

655660665670675680685690695

Aver

age r

ewar

d

090

092

094

096

098

100

102

104

106

108

110

(f )

Figure 3 Performance with an amplitude of variation in budget (B) real dataset

Table 2 Results under different approaches real dataset

Algorithms η () ψ ζ ϕ ωBudget-TASC 8567 139063 14 1 7761RB-TPSC 9395 52339 159 09645 6672TASC-MADM 9418 98746 078 09509 6598

Security and Communication Networks 9

02468

10121416

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

adiu

s

RB-TPSCTASC-MADM

(a)

RB-TPSCTASC-MADM

03

04

05

06

07

08

09

0 2 4 6 8 10 12 14 16 18 20

Task

assig

nmen

t rat

e(b)

RB-TPSCTASC-MADM

10000

10500

11000

11500

12000

12500

13000

13500

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

eput

atio

n va

lue

(c)

RB-TPSCTASC-MADM

00051015202530354045

0 2 4 6 8 10 12 14 16 18 20

Aver

age d

istan

ce tr

avel

ed

(d)

RB-TPSCTASC-MADM

084

085

086

087

088

089

09

0 2 4 6 8 10 12 14 16 18 20

Aver

age b

udge

t util

izat

ion

(e)

RB-TPSCTASC-MADM

650

655

660

665

670

675

680

685

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

ewar

d

(f )

Figure 4 Performance with an extra allowance per kilometer (β) (monetary unit) synthetic dataset

RB-TPSCTASC-MADM

60

65

70

75

80

85

90

95

00 02 04 06 08 10 12 14 16 18 20

Aver

age r

adiu

s

(a)

RB-TPSCTASC-MADM

065

067

069

071

073

075

077

079

00 02 04 06 08 10 12 14 16 18 20

Task

assig

nmen

t rat

e

(b)

RB-TPSCTASC-MADM

1000010250105001075011000112501150011750120001225012500

00 02 04 06 08 10 12 14 16 18 20

Aver

age r

eput

atio

n va

le

(c)

RB-TPSCTASC-MADM

100

120

140

160

180

200

220

00 02 04 06 08 10 12 14 16 18 20

Aver

age d

istan

ce tr

avele

d

(d)

RB-TPSCTASC-MADM

084085085086086087087088088089

00 02 04 06 08 10 12 14 16 18 20

Aver

age b

udge

t util

izat

ion

rate

(e)

RB-TPSCTASC-MADM

6600

6650

6700

6750

6800

6850

00 02 04 06 08 10 12 14 16 18 20

Aver

age r

ewar

d

(f )

Figure 5 Performance with different accepted distances without extra remote allowance (c) (km) synthetic dataset

10 Security and Communication Networks

(Figure 5(a)) So for Budget-TASC the radius varied from6 sim 9 km other parameters were set as in the fourthexperiment

)e experimental results (Table 3) show that TPSC-ADM obtains the highest task assignment rate but Budget-ASC achieves the highest quality and RB-TPSC spends thelowest budget

55 Effect of Taskrsquos Attribute Preference Our approachTASC-MADM enables the task to be assigned to satisfydemands for different goals If the resultrsquos quality is theprimary goal the task should select workers with a highreputation However if saving cost is the most concernedobjective the worker closer to the task should be chosenfirst Figure 7 shows the influence of tasksrsquo attributespreferences on the proposed approachrsquos performancew0 05 all targets are considered fairly w0 lt 05 the taskprefers the workerrsquos reputation to the distance the re-sultrsquos quality is significantly improved However thetravel cost and budget utilized are increased Whenw0 gt 05 the travel cost and the budget utilized are savedwhereas the resultrsquos quality is decreased Our approachcan maximize task assignment rate by setting w0 1 ormaximize the quality of workers selected by settingw0 0

56 Efficiency of Algorithms )e efficiency of the algorithmsis measured in CPU cost )e Budget-TASC algorithmrsquoscomputational complexity is O(n times m2) and that of RB-TPSC and TASC-MADM is O(n times m) we compare TASC-MADMwith RB-TPSC and Budget-TASC in CPU cost Eachof the programs runs 3 times 21 rounds )e average time perround is used to measure the algorithmrsquos efficiency Asshown in Figure 8 our approach significantly improves theefficiency of spatial task assignment

57 Summary of Experiment Results We summarized themajor finding as follows

(i) If the task is located in a worker-density area theproposed TASC-MADM approach exhibits betterresults than RB-TPSC It also performs better thanBudget-TASC on the metrics except for the qualityof workers

(ii) If the task is located in a worker-sparsity area theproposed TASC-MADM approach performs betterthan RB-TPSC in terms of the average assignmentrate and quality of workers But it leads to moretravel cost and budget utilized Budget-TASC ob-tains the best quality because it considers the qualityfirst

4

5

6

7

8

9

10

11

12Av

erag

e rad

ius

RB-TPSCTASC-MADM

090

092

094

096

098

100

102

104

106

108

110

(a)

RB-TPSCTASC-MADM

045

05

055

06

065

07

075

08

085

090

092

094

096

098

100

102

104

106

108

110

Task

assig

nmen

t rat

e(b)

RB-TPSCTASC-MADM

090

092

094

096

098

100

102

104

106

108

110

10000

10500

11000

11500

12000

12500

13000

13500

Aver

age r

eput

atio

n va

lue

(c)

RB-TPSCTASC-MADM

090

092

094

096

098

100

102

104

106

108

110

10

15

20

25

30

35

Aver

age d

istan

ce tr

avel

ed

(d)

RB-TPSCTASC-MADM

090

092

094

096

098

100

102

104

106

108

110

076078080082084086088090

094092

Aver

age b

udge

t util

izat

ion

rate

(e)

RB-TPSCTASC-MADM

090

092

094

096

098

100

102

104

106

108

110

660066506700675068006850690069507000

71007050

Aver

age r

ewar

d

(f )

Figure 6 Performance with an amplitude of variation in budget (B) synthetic dataset

Security and Communication Networks 11

Table 3 Results under different approaches synthetic dataset

Algorithms η ψ ζ ϕ ωBudget-TASC 072743975 1367217 1417697 1 7758585RB-TPSC 072253468 1027882 1408169 0851329 6686784TPSC-ADM 074825306 1219453 1926809 0873916 6768671

0

5

10

15

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

adiu

s

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

(a)

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

03504

04505

05506

06507

07508

085

0 2 4 6 8 10 12 14 16 18 20

Task

assig

nmen

t rat

e

(b)

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

100001050011000115001200012500130001350014000

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

eput

atio

n va

lue

(c)

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

0

1

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3

4

5

6

0 2 4 6 8 10 12 14 16 18 20

Aver

age d

istan

ce tr

avel

ed

(d)

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

084

085

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087

088

089

09

091

0 2 4 6 8 10 12 14 16 18 20

Aver

age b

udge

t util

izat

ion

rate

(e)

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

650655660665670675680685690695700705

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

ewar

d

(f )

Figure 7 Effect of tasksrsquo attributes preferences on the TASC-MADM approach Performance with an extra allowance per kilometer (β)

(monetary unit) synthetic dataset

128 114

31961929

135

618

0

20

40

60

80

100

120

140

Synthetic Dataset Real Dataset

TASC-MADMRB-TPSCBudget-TASC

Figure 8 Average CPU cost

12 Security and Communication Networks

(iii) In the terms of CPU cost the proposed TASC-MADM approach is superior to the baselinealgorithms

58 Discussion )is section discusses the advantages andlimitations of the TASC-MADM approach )e advantagesare listed as follows

(1) Effective )e TASC-MADM approach improves thetask assignment rate By setting different attributeweights it can maximize the task assignment rate orthe quality of crowdsourcing results

(2) Efficient )e TASC-MADM approach enhancesefficiency because of computing simply

(3) Extensible )eoretically our method can be ex-tended to solving decision problems involving anynumber of attributes

)ere still exist the following limitations in the TASC-MADM approach

(1) Quality Quantification of Workers In this paper theworker quality is modeled as a reputation value toreflect the quality of crowdsourced results Inpractice the same workerrsquos quality may differ inspecific tasks How to quantify the quality of workersis not covered in this paper

(2) Efficiency of Indexing Records )e TASC-MADMapproach exhaustively searches all the records toidentify the candidate of a task which makes it lessefficient to get the decision matrix on large datasets

6 Conclusion

)is paper focuses on designing an efficient task assignmentapproach which can deal with the situation where tasks havedifferent require preferences for different task attributes toachieve different goals Our task assignment approach can beextended to scenarios containing any number of attributesIn addition to the distance and reputation other criteriasuch as the workersrsquo skills can be considered

As for future work more factors such as workersrsquowillingness to accept tasks and their quality differences indifferent professional fields are included in task assign-ments In addition improving the efficiency of indexingrecords to make the allocation scheme suitable for largedatasets is a valuable research topic

Data Availability

)e data of allocated sharing tasks are available from ChinaSociety for Industrial and Applied Mathematics 2017 re-trieved on September 2 2020 from httpwwwmcmeducnhtml_cnnode460baf68ab0ed0e1e557a0c79b1c4648html

Conflicts of Interest

)e authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

)e authors thank researchers working at Guangxi KeyLaboratory of Trusted Software especially Xuguang Bao andManli Zhu for their suggestion during the research andpreparation of the manuscript )is work was funded byGuangxi Key Laboratory of Trusted Software (Nokx201727) Project to Improve the Scientific Research BasicAbility of Middle-Aged and Young Teachers (No2019KY0226) Natural Science Foundation of China (Nos61966009 U1811264 and U1711263) and Natural ScienceFoundation of Guangxi Province (Nos2019GXNSFBA245049 2019GXNSFBA245059 and2018GXNSFDA281045)

References

[1] L Kazemi and C Shahabi ldquoGeoCrowd enabling query an-swering with spatial crowdsourcing GISrdquo in Proceedings ofthe ACM International Symposium on Advances in GeographicInformation Systems pp 189ndash198 CA USA November 2012

[2] J Xiong M Zhao M Z A Bhuiyan L Chen and Y TianldquoAn AI-enabled three-party game framework for guaranteeddata privacy in mobile edge crowdsensing of IoTrdquo IEEETransactions on Industrial Informatics vol 17 no 2pp 922ndash933 2021

[3] J Xiong R Ma L Chen et al ldquoA personalized privacyprotection framework for mobile crowdsensing in IIoTrdquo IEEETransactions on Industrial Informatics vol 16 no 6pp 4231ndash4241 2020

[4] DiDi Didi Chuxing Corporate Citizenship Report httpswwwdidiglobalcomaboutdidiresponsibility 2017

[5] J Xiong J Ren L Chen et al ldquoEnhancing privacy andavailability for data clustering in intelligent electrical serviceof IoTrdquo IEEE Internet of Bings Journal vol 6 no 2pp 1530ndash1540 2019

[6] C Zhao P Cheng L Chen X Lin and C Shahabi ldquoFair taskassignment in spatial crowdsourcingrdquo Proceedings of theVLDB Endowment vol 13 no 12 pp 2479ndash2492 2020

[7] M Venanzi A Rogers and N R Jennings ldquoCrowdsourcingspatial phenomena using trust-based heteroskedasticGaussian processesrdquo in Proceedings of the First Conference onHuman Computation and Crowdsourcing (HCOMP)pp 182ndash189 Palm Springs CA USA November 2013

[8] J Xiong R Bi M Zhao J Guo and Q Yang ldquoEdge-assistedprivacy-preserving raw data sharing framework for connectedautonomous vehiclesrdquo IEEE Wireless Communicationsvol 27 no 3 pp 24ndash30 2020

[9] B Van B V D Haak M Parks and M Castells ldquo)e futureof journalism networked journalism rethinking journalism inthe networked digital agerdquo International Journal of Com-munication vol 6 pp 2923ndash2938 2012

[10] M Zook M Graham S Taylor and S Gorman ldquoVolunteeredgeographic information and crowdsourcing disaster relief acase study of the Haitian earthquakerdquoWorld Medical umlHealthPolicy vol 2 no 2 pp 7ndash33 2010

[11] Y Tong L Chen and C Shahabi ldquoSpatial crowdsourcingchallenges techniques and applicationsrdquo Proceedings of theVLDB Endowment vol 10 pp 1988ndash1991 2017

[12] J Xiong X Chen Q Yang L Chen and Z Yao ldquoA task-oriented user selection incentive mechanism in edge-aidedmobile crowdsensingrdquo IEEE Transactions on Network Scienceand Engineering vol 7 no 4 pp 2347ndash2360 2020

Security and Communication Networks 13

maxiisinWj

scoreji1113872 1113873 maxiisinWj

w0 times dji + w1 times ri

1113872 1113873 (7)

where j and i are the matching task-worker pair andwv isin [0 1] is the attribute importance parameter1113936visin 01 wv 1

)e weights are usually determined objectively or sub-jectively)e entropy method is generally used to objectivelyget weighting of every attribute [32] However we hope thatthe task assignment operation can set the attribute weight toachieve different optimization goals So the weight valuesare set according to the requirement preference of the taskfor attributes If a task has more requirement preference fordistance more than that for reputation it can set w0 gt 05Otherwise w0 lt 05 )e setting w0 05 implies the fol-lowing situation among the workers with the same repu-tation the worker with a shorter distance has the priority tobe selected Similarly among the workers with the samedistance the worker with a higher reputation has the priorityto be selected

31 Complexity Analysis One task assignment of a singletask is to find the best worker among m workers it needs torepeat the assignment n times m times to complete all task as-signments so the problem is solvable in polynomial time

4 Assignment Protocol

We assume that the workers querying the tasks are willing toaccept the tasks )us assigning a task to a worker meansselecting the best worker with the highest comprehensiverating of distance and reputation In this section we willelaborate on our spatial task assignment algorithm

41 PreparingDecisionMatrix Preparing the taskrsquos decisionmatrix involves two steps Firstly we obtain the decisionmatrix DMj for the task j Each item of the decision matrixrepresents a candidate worker In this paper the attributesaffecting the result of a task assignment include the repu-tation of a worker and the distance from a task to a worker)e workers within the radius of the task j are a part of thetaskrsquos candidates excluding the workers who are assignedtasks more than their quota (Line 2ndash4) Secondly normalizethe decision matrix DMj by equations (2) and (3) (Line 5))e pseudocode obtaining and normalizing the decisionmatrix DMj for the task j is given by Algorithm 1

)e computational complexity of Algorithm 1 dependson the loop operation and the normalization operation )ecomplexities of these two operations are O(m) and O(|Wj|)respectively Since |Wj| is usually much less than |m| thetotal computational complexity is O(m)

42 TASC-MADM Approach As mentioned in Section 3 aspatial task should be assigned to the worker with the highestrating which optimizes the linear combination of the dis-tance and the reputation

Algorithm 2 is the pseudocode of the allocation methodnamely TASC-MADM algorithm which inputs a set ofworkers W a set of tasks T and the parameters P β c andreturns the best assignment result S containing task-and-worker assignment with the highest rating

Initially S is set to empty (Line 1) ai|i 12m 0 (ieeach worker has been assigned zero times) (Line 2) Nextfor each task tj calculate the radius and the distance to theworkers and obtain and normalize the decision matrixDMj by calling Algorithm 1 (Line 6) Next if the decisionmatrix has more than zero items compute the scores ofitems (Line 9) and simultaneously compute the reward pj

paid by the task tj to the worker wi (Line 10) For sub-sequent easy operation the itemrsquos score and the reward areassociated with other information including the tasknumber the worker number the distance and the repu-tation (Line 11) Next we sort the items descending byscores (Line 12) Intuitively the item with a higher rankindicates a better assignment than other assignments Fi-nally the task tj is assigned to the topmost worker (Line13ndash17)

)e assignment iterates for n rounds (Line 3) and finallyreturns the assignment result of all tasks (Line 18) In eachiteration the computational complexity is O(m) so the totalcomputational complexity is O(n times m)

5 Experiment Evaluation

In this section we tested the performance of our approachon both real and synthetic data

51 Metrics In the experiments we measured the perfor-mance of each approach according to the following metrics[23]

(1) Average task radius (δ) this metric measures theaverage spatial region size of the tasks which iscomputed as the average radius of all tasks

β 1

|T|1113944

j

Rj (8)

(2) Task assignment rate (η) this metric measures thealgorithmrsquos effectiveness in assigning several taskssuccessfully )e task assignment rate η is the per-centage of assigned tasks among the total amounts ofcrowded spatial tasks

η |S|

|T| (9)

(3) Average reputation of workers assigned tasks (ψ) thequality of crowdsourcing result is determined by theworkerrsquos quality which is modeled by the workerrsquosreputation So this metric evaluates the quality oftasks completed It is computed as the total repu-tation of selected workers divided by the number ofselected workers

Security and Communication Networks 5

ψ 1

|S|1113944iisinS

ri (10)

(4) Average distance traveled (ς) this metric measuresthe travel cost for workers when they complete theassigned tasks it is computed as the average distancetraveled by all the selected workers

ς 1

|S|1113944

langjirangisinSdji (11)

(5) Average budget utilization rate (ϕ) this metric is theaverage of the budget utilization for all assignedtasks )e budget utilization rate of each assignedtask is the ratio of the actual reward paid for the taskto the budget of that task

ϕ 1

|S|1113944jisinS

pj

Bj (12)

(6) Average reward (ω) this is the ratio of the totalreward for all the assigned tasks to the number ofassigned tasks

ω 1

|S|1113944jisinS

pj (13)

)e parameter δ is used to demonstrate the changing ofother metrics along with the average radius )e workersrsquoperspective would prefer to keep ς as low as possible )etask requestersrsquo perspective would prefer higher values of ηand ψ but lower values of ϕ and ω

52 Experimental Setting

521 Datasets

(i) Real Dataset We used a real dataset [33] from acrowdsourcing event by taking photos with the lo-cation information for 1877 workers and 835 tasks)ese tasks and workers were mainly obtained fromfour cities in China Foshan Guangzhou Shenzhenand Dongguan

(ii) Synthetic Dataset )e synthetic data were obtainedfrom a single day dataset from Gowalla in 2010which included 10956 tasks and 5087 workers

Input a set W the task j

Output the normalized decision matrix DMj for the task j

(1) DMj

(2) For each wi isinW(3) If dji leRj and ai lt qi(4) s

ji langj i dji rirang DMjlarrDMj cup s

ji1113966 1113967

(5) Normalize DMj by equations (2) and (3)(6) Return DMj

ALGORITHM 1 Obtain the decision matrix for a task

Input a set of workers W a set of tasks T parameters P β c

Output S which is the result of task assignment(1) S

(2) ai 0 i isin 1 2 m

(3) For each tj isin T(4) Calculate the radius Rj

(5) Calculate dji from the task j to each worker i

(6) Obtain and normalize the decision matrix DMj by calling Algorithm 1(7) If |DMj|gt 0(8) For iprime in range (|DMj|)(9) Compute the scoreji using equation (7)i DMj[iprime]i it is the number of the iprime th worker for the task tj(10) Compute the reward pj using equation (4)(11) s

ji s

ji append(langpj scorejirang)(ie s

ji langj i dji ri pj scorejirang)

(12) Sorted DMj descending by scores(13) For each item in DMj(14) If j in item(15) sj item ai+ 1(16) SlarrScup sj1113864 1113865

(17) Break(18) Return S

ALGORITHM 2 TASC-MADM algorithm

6 Security and Communication Networks

located in America )is dataset contains four at-tributes as follows task ID taskrsquos location workerID and workerrsquos location Reputations are generatedfollowing the uniform distribution of 0 sim 20000Budgets are generated following the uniform dis-tribution of 65 sim 85 )e task quotas of workers aregenerated following the uniform distribution of5 sim 10

522 Compared Algorithms )e RB-TPSC and Budget-TASC algorithms were selected as the baseline algorithmsbecause they are most closely related to TASC-MADM

(i) RB-TPSC is a task package assignment methodwhich aims at maximizing the number of tasksassigned within budget constraints Quality of resultsand travel costs are also being considered

(ii) Budget-TASC is a budget-aware spatial crowd-sourcing task assignment method which aims inmaximizing the total quality of tasks completedwithin budget constraints

We compared our TASC-MADM with RB-TPSC andBudget-TASC on both real and synthetic datasets

53 Results on Real Dataset )e first three experimentscompared TASC-MADM and RB-TPSC under differentsettings of parameters β c and Bj )e results are shown inFigures 1ndash3 For each parameter the experiment evaluatedthe average metrics of the two algorithms under 21 differentsettings We set the lowest budget of all tasks as P 65Moreover for the fairness in considering the effect of dis-tance and reputation the weight w0 05

First we compared TASC-MADM and RB-TPSC underdifferent β values )e results are shown in Figure 1 wherethe extra allowance per kilometer β varies from 0 sim 20monetary units in 1-unit increments c 05 and Bj isobtained from the dataset )e horizontal axis represents thedifferent settings of β while the vertical axis represents thedifferent values of the first six metrics As can be seen theaverage task radius δ is affected by the extra allowance perkilometer β (Figure 1(a)) δ is maximized when β 1 in-creases with β if βlt 1 and decreases with β if βgt 1 )echange in the trend of other metrics is consistent with theaverage task radius)is is because in a region with a smallerradius it is usually impossible to find enough workers withhigh reputations )us other metrics decrease with theaverage radius Compared with RB-TPSC TASC-MADMensures a high average task assignment rate (Figure 1(b))but reduces the average travel cost of workers (Figure 1(d))and saves the budget (Figures 1(e) and 1(f )) Besides ourresult shows that the average reputation of workers decreaseswith the average radius (Figure 1(c)) which is more realisticbecause there are fewer workers to choose from

Secondly we compared TASC-MADM and RB-TPSCunder different c values Figure 2 depicts the trend in whichthe above six metrics change when c the accepted distanceswithout extra remote allowance varies from 0 sim 2 km where

β 2 and Bj is obtained from the real dataset If cgt 05 theaverage radius increases with c and positively affects theabovementioned metrics except for ϕ and ω Our proposedmethod achieves a task assignment rate value of 9856(Figure 2(b)) but the maximum of the average distancetraveled the average reward and the average budget utili-zation rates are ς 103 km ω 6533 and ϕ 9493 re-spectively (Figures 2(d)ndash2(f)) Compared to the RB-TPSCmethod our method greatly decreases the average distancetraveled by the workers and the average reward offered bythe tasksrsquo requester while maintaining an equally high or agreater average task assignment rate Moreover it signifi-cantly improves the quality of crowdsourcing results becauseof the average reputation value of the selected workers in-creasing by about 250 (Figure 2(c))

)e third experiment compared TASC-MADM and RB-TPSC under different budgets (B) Figure 3 depicts how thefirst six metrics change while the tasksrsquo budget (B) variesfrom 90 to 110 in 1 increments where β 2 c 05Since the task with a greater budget should have moreworkers to choose from the average radius of the tasks ispositively affected by the tasksrsquo budget (Figure 3(a)) As thetask radius increases further the task assignment rate in-creases to 995 (Figure 3(b)) Compared to the RB-TPSCmethod our method decreases the average distance traveledfrom a range of 14sim25 km to a range of 08sim15 km(Figure 3(d)) but increases the average reputation by about147 (Figure 3(c)) and greatly decreases the average budgetutilization rate (Figure 3(e)) and the average reward((Figure 3(f))) More importantly with our method thegreater the number of candidate workers the lower theaverage reward offered by the requester and the higher is thequality of the crowdsourcing result In contrast RB-TPSCincreases the average reward continuously and decreases theaverage reputation to a stable value of around 500

)e fourth experiment compared TASC-MADM withRB-TPSC and Budget-TASC when radiuses were changedFor TASC-MADM and RB-TPSC we selected the averagemetrics of different c values In the above experiment thecomputed radius belongs to the interval [0 12] when c

varied from 0 sim 2 km and other parameters were fixed Wehave experimented with Budget-TASC in different radiusesvarying from 0 sim 12 km and then get the average of eachmetric For the Budget-TASC the other parameters were setas follows DC is set as the earthrsquos radius [10] PL 0 PH isset as the budget of the task and PM is set as the half of thebudget of the task Because we scaled the workerrsquos reputationto the interval [0 1] ThHM 075 ThML 05

)e results are shown in Table 2 TASC-MADM out-performs the baseline algorithms in all metrics except theaverage reputation For Budget-TASC PH is set as the taskrsquostotal budget which limits the task to high-quality workersbut also increases the average budget utilized

54 Results on Synthetic Dataset We repeated the first threeexperiments in Section 53 on the synthetic datasetFigures 4ndash6 illustrate the results of TASC-MADM and RB-TPSC Compared with RB-TPSC our algorithm greatly

Security and Communication Networks 7

005

115

225

335

445

5

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

adiu

s

RB-TPSCTASC-MADM

(a)

RB-TPSCTASC-MADM

0

02

04

06

08

1

12

0 2 4 6 8 10 12 14 16 18 20

Task

assig

nmen

t rat

e

(b)

RB-TPSCTASC-MADM

0

200

400

600

800

1000

1200

1400

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

eput

atio

n va

lue

(c)

RB-TPSCTASC-MADM

0

05

1

15

2

25

0 2 4 6 8 10 12 14 16 18 20

Aver

age d

istan

ce tr

avel

ed

(d)

RB-TPSCTASC-MADM

094

095

095

096

096

097

097

098

0 2 4 6 8 10 12 14 16 18 20

Aver

age b

udge

t util

izat

ion

rate

(e)

RB-TPSCTASC-MADM

6475

6500

6525

6550

6575

6600

6625

6650

6675

6700

0 2 4 6 8 10 12 14 16 18 20Av

erag

e rew

ard

(f )

Figure 1 Performance with an extra allowance per kilometer (β) (monetary unit) real dataset

25272931333537394143

00 02 04 06 08 10 12 14 16 18 20

Aver

age r

adiu

s

RB-TPSCTASC-MADM

(a)

RB-TPSCTASC-MADM

065

070

075

080

085

090

095

100

00 02 04 06 08 10 12 14 16 18 20

Task

assig

nmen

t

(b)

RB-TPSCTASC-MADM

0

200

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600

800

1000

1200

1400

00 02 04 06 08 10 12 14 16 18 20

Aver

age r

eput

atio

n va

lue

(c)

Figure 2 Continued

8 Security and Communication Networks

improves the task assignment rate (Figures 4(b) 5(b) and6(b)) and the resultrsquos quality (Figures 4(c) 5(c) and 6(c))However for the synthetic dataset the task locates in a

slightly sparse scenario with few workers )en the highertask assignment rate implies the higher travel cost(Figures 4(d) 5(d) and 6(d)) Correspondingly the budgetused is increased (Figures 4(e) 4(f) 5(e) 5(f) 6(e) and6(f ))

Next based on the synthetic dataset we comparedTASC-MADM with RB-TPSC and Budget-TASC whenradiuses were changed For TASC-MADM and RB-TPSCwe selected the average metrics of different c values thecomputed radius approximately belongs to the interval [6 9]

RB-TPSCTASC-MADM

08

10

12

14

16

18

20

00 02 04 06 08 10 12 14 16 18 20

Aver

age d

istan

ce tr

avele

d

(d)

RB-TPSCTASC-MADM

09500

09525

09550

09575

09600

09625

09650

09675

09700

00 02 04 06 08 10 12 14 16 18 20

Aver

age b

udge

t util

izat

ion

rate

(e)

RB-TPSCTASC-MADM

650

655

660

665

670

675

680

685

00 02 04 06 08 10 12 14 16 18 20

Aver

age r

ewar

d

(f )

Figure 2 Performance with different accepted distances without extra remote allowance (c) (km) real dataset

0

1

2

3

4

5

6

7

Aver

age r

adiu

s

RB-TPSCTASC-MADM

090

092

094

096

098

100

102

104

106

108

110

(a)

RB-TPSCTASC-MADM

00

02

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08

10

12

Task

assig

nmen

t rat

e

090

092

094

096

098

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(b)

RB-TPSCTASC-MADM

0

500

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2500

3000

Aver

age r

eput

atio

n va

lue

090

092

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096

098

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110

(c)

RB-TPSCTASC-MADM

0

05

1

15

2

25

3

Aver

age d

istan

ce tr

avele

d

090

092

094

096

098

100

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110

(d)

RB-TPSCTASC-MADM

086

088

09

092

094

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Aver

age b

udge

t util

izat

ion

rate

090

092

094

096

098

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(e)

RB-TPSCTASC-MADM

655660665670675680685690695

Aver

age r

ewar

d

090

092

094

096

098

100

102

104

106

108

110

(f )

Figure 3 Performance with an amplitude of variation in budget (B) real dataset

Table 2 Results under different approaches real dataset

Algorithms η () ψ ζ ϕ ωBudget-TASC 8567 139063 14 1 7761RB-TPSC 9395 52339 159 09645 6672TASC-MADM 9418 98746 078 09509 6598

Security and Communication Networks 9

02468

10121416

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

adiu

s

RB-TPSCTASC-MADM

(a)

RB-TPSCTASC-MADM

03

04

05

06

07

08

09

0 2 4 6 8 10 12 14 16 18 20

Task

assig

nmen

t rat

e(b)

RB-TPSCTASC-MADM

10000

10500

11000

11500

12000

12500

13000

13500

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

eput

atio

n va

lue

(c)

RB-TPSCTASC-MADM

00051015202530354045

0 2 4 6 8 10 12 14 16 18 20

Aver

age d

istan

ce tr

avel

ed

(d)

RB-TPSCTASC-MADM

084

085

086

087

088

089

09

0 2 4 6 8 10 12 14 16 18 20

Aver

age b

udge

t util

izat

ion

(e)

RB-TPSCTASC-MADM

650

655

660

665

670

675

680

685

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

ewar

d

(f )

Figure 4 Performance with an extra allowance per kilometer (β) (monetary unit) synthetic dataset

RB-TPSCTASC-MADM

60

65

70

75

80

85

90

95

00 02 04 06 08 10 12 14 16 18 20

Aver

age r

adiu

s

(a)

RB-TPSCTASC-MADM

065

067

069

071

073

075

077

079

00 02 04 06 08 10 12 14 16 18 20

Task

assig

nmen

t rat

e

(b)

RB-TPSCTASC-MADM

1000010250105001075011000112501150011750120001225012500

00 02 04 06 08 10 12 14 16 18 20

Aver

age r

eput

atio

n va

le

(c)

RB-TPSCTASC-MADM

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220

00 02 04 06 08 10 12 14 16 18 20

Aver

age d

istan

ce tr

avele

d

(d)

RB-TPSCTASC-MADM

084085085086086087087088088089

00 02 04 06 08 10 12 14 16 18 20

Aver

age b

udge

t util

izat

ion

rate

(e)

RB-TPSCTASC-MADM

6600

6650

6700

6750

6800

6850

00 02 04 06 08 10 12 14 16 18 20

Aver

age r

ewar

d

(f )

Figure 5 Performance with different accepted distances without extra remote allowance (c) (km) synthetic dataset

10 Security and Communication Networks

(Figure 5(a)) So for Budget-TASC the radius varied from6 sim 9 km other parameters were set as in the fourthexperiment

)e experimental results (Table 3) show that TPSC-ADM obtains the highest task assignment rate but Budget-ASC achieves the highest quality and RB-TPSC spends thelowest budget

55 Effect of Taskrsquos Attribute Preference Our approachTASC-MADM enables the task to be assigned to satisfydemands for different goals If the resultrsquos quality is theprimary goal the task should select workers with a highreputation However if saving cost is the most concernedobjective the worker closer to the task should be chosenfirst Figure 7 shows the influence of tasksrsquo attributespreferences on the proposed approachrsquos performancew0 05 all targets are considered fairly w0 lt 05 the taskprefers the workerrsquos reputation to the distance the re-sultrsquos quality is significantly improved However thetravel cost and budget utilized are increased Whenw0 gt 05 the travel cost and the budget utilized are savedwhereas the resultrsquos quality is decreased Our approachcan maximize task assignment rate by setting w0 1 ormaximize the quality of workers selected by settingw0 0

56 Efficiency of Algorithms )e efficiency of the algorithmsis measured in CPU cost )e Budget-TASC algorithmrsquoscomputational complexity is O(n times m2) and that of RB-TPSC and TASC-MADM is O(n times m) we compare TASC-MADMwith RB-TPSC and Budget-TASC in CPU cost Eachof the programs runs 3 times 21 rounds )e average time perround is used to measure the algorithmrsquos efficiency Asshown in Figure 8 our approach significantly improves theefficiency of spatial task assignment

57 Summary of Experiment Results We summarized themajor finding as follows

(i) If the task is located in a worker-density area theproposed TASC-MADM approach exhibits betterresults than RB-TPSC It also performs better thanBudget-TASC on the metrics except for the qualityof workers

(ii) If the task is located in a worker-sparsity area theproposed TASC-MADM approach performs betterthan RB-TPSC in terms of the average assignmentrate and quality of workers But it leads to moretravel cost and budget utilized Budget-TASC ob-tains the best quality because it considers the qualityfirst

4

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erag

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ius

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(a)

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assig

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10500

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lue

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age d

istan

ce tr

avel

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(d)

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092

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110

076078080082084086088090

094092

Aver

age b

udge

t util

izat

ion

rate

(e)

RB-TPSCTASC-MADM

090

092

094

096

098

100

102

104

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108

110

660066506700675068006850690069507000

71007050

Aver

age r

ewar

d

(f )

Figure 6 Performance with an amplitude of variation in budget (B) synthetic dataset

Security and Communication Networks 11

Table 3 Results under different approaches synthetic dataset

Algorithms η ψ ζ ϕ ωBudget-TASC 072743975 1367217 1417697 1 7758585RB-TPSC 072253468 1027882 1408169 0851329 6686784TPSC-ADM 074825306 1219453 1926809 0873916 6768671

0

5

10

15

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

adiu

s

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

(a)

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

03504

04505

05506

06507

07508

085

0 2 4 6 8 10 12 14 16 18 20

Task

assig

nmen

t rat

e

(b)

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

100001050011000115001200012500130001350014000

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

eput

atio

n va

lue

(c)

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

0

1

2

3

4

5

6

0 2 4 6 8 10 12 14 16 18 20

Aver

age d

istan

ce tr

avel

ed

(d)

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

084

085

086

087

088

089

09

091

0 2 4 6 8 10 12 14 16 18 20

Aver

age b

udge

t util

izat

ion

rate

(e)

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

650655660665670675680685690695700705

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

ewar

d

(f )

Figure 7 Effect of tasksrsquo attributes preferences on the TASC-MADM approach Performance with an extra allowance per kilometer (β)

(monetary unit) synthetic dataset

128 114

31961929

135

618

0

20

40

60

80

100

120

140

Synthetic Dataset Real Dataset

TASC-MADMRB-TPSCBudget-TASC

Figure 8 Average CPU cost

12 Security and Communication Networks

(iii) In the terms of CPU cost the proposed TASC-MADM approach is superior to the baselinealgorithms

58 Discussion )is section discusses the advantages andlimitations of the TASC-MADM approach )e advantagesare listed as follows

(1) Effective )e TASC-MADM approach improves thetask assignment rate By setting different attributeweights it can maximize the task assignment rate orthe quality of crowdsourcing results

(2) Efficient )e TASC-MADM approach enhancesefficiency because of computing simply

(3) Extensible )eoretically our method can be ex-tended to solving decision problems involving anynumber of attributes

)ere still exist the following limitations in the TASC-MADM approach

(1) Quality Quantification of Workers In this paper theworker quality is modeled as a reputation value toreflect the quality of crowdsourced results Inpractice the same workerrsquos quality may differ inspecific tasks How to quantify the quality of workersis not covered in this paper

(2) Efficiency of Indexing Records )e TASC-MADMapproach exhaustively searches all the records toidentify the candidate of a task which makes it lessefficient to get the decision matrix on large datasets

6 Conclusion

)is paper focuses on designing an efficient task assignmentapproach which can deal with the situation where tasks havedifferent require preferences for different task attributes toachieve different goals Our task assignment approach can beextended to scenarios containing any number of attributesIn addition to the distance and reputation other criteriasuch as the workersrsquo skills can be considered

As for future work more factors such as workersrsquowillingness to accept tasks and their quality differences indifferent professional fields are included in task assign-ments In addition improving the efficiency of indexingrecords to make the allocation scheme suitable for largedatasets is a valuable research topic

Data Availability

)e data of allocated sharing tasks are available from ChinaSociety for Industrial and Applied Mathematics 2017 re-trieved on September 2 2020 from httpwwwmcmeducnhtml_cnnode460baf68ab0ed0e1e557a0c79b1c4648html

Conflicts of Interest

)e authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

)e authors thank researchers working at Guangxi KeyLaboratory of Trusted Software especially Xuguang Bao andManli Zhu for their suggestion during the research andpreparation of the manuscript )is work was funded byGuangxi Key Laboratory of Trusted Software (Nokx201727) Project to Improve the Scientific Research BasicAbility of Middle-Aged and Young Teachers (No2019KY0226) Natural Science Foundation of China (Nos61966009 U1811264 and U1711263) and Natural ScienceFoundation of Guangxi Province (Nos2019GXNSFBA245049 2019GXNSFBA245059 and2018GXNSFDA281045)

References

[1] L Kazemi and C Shahabi ldquoGeoCrowd enabling query an-swering with spatial crowdsourcing GISrdquo in Proceedings ofthe ACM International Symposium on Advances in GeographicInformation Systems pp 189ndash198 CA USA November 2012

[2] J Xiong M Zhao M Z A Bhuiyan L Chen and Y TianldquoAn AI-enabled three-party game framework for guaranteeddata privacy in mobile edge crowdsensing of IoTrdquo IEEETransactions on Industrial Informatics vol 17 no 2pp 922ndash933 2021

[3] J Xiong R Ma L Chen et al ldquoA personalized privacyprotection framework for mobile crowdsensing in IIoTrdquo IEEETransactions on Industrial Informatics vol 16 no 6pp 4231ndash4241 2020

[4] DiDi Didi Chuxing Corporate Citizenship Report httpswwwdidiglobalcomaboutdidiresponsibility 2017

[5] J Xiong J Ren L Chen et al ldquoEnhancing privacy andavailability for data clustering in intelligent electrical serviceof IoTrdquo IEEE Internet of Bings Journal vol 6 no 2pp 1530ndash1540 2019

[6] C Zhao P Cheng L Chen X Lin and C Shahabi ldquoFair taskassignment in spatial crowdsourcingrdquo Proceedings of theVLDB Endowment vol 13 no 12 pp 2479ndash2492 2020

[7] M Venanzi A Rogers and N R Jennings ldquoCrowdsourcingspatial phenomena using trust-based heteroskedasticGaussian processesrdquo in Proceedings of the First Conference onHuman Computation and Crowdsourcing (HCOMP)pp 182ndash189 Palm Springs CA USA November 2013

[8] J Xiong R Bi M Zhao J Guo and Q Yang ldquoEdge-assistedprivacy-preserving raw data sharing framework for connectedautonomous vehiclesrdquo IEEE Wireless Communicationsvol 27 no 3 pp 24ndash30 2020

[9] B Van B V D Haak M Parks and M Castells ldquo)e futureof journalism networked journalism rethinking journalism inthe networked digital agerdquo International Journal of Com-munication vol 6 pp 2923ndash2938 2012

[10] M Zook M Graham S Taylor and S Gorman ldquoVolunteeredgeographic information and crowdsourcing disaster relief acase study of the Haitian earthquakerdquoWorld Medical umlHealthPolicy vol 2 no 2 pp 7ndash33 2010

[11] Y Tong L Chen and C Shahabi ldquoSpatial crowdsourcingchallenges techniques and applicationsrdquo Proceedings of theVLDB Endowment vol 10 pp 1988ndash1991 2017

[12] J Xiong X Chen Q Yang L Chen and Z Yao ldquoA task-oriented user selection incentive mechanism in edge-aidedmobile crowdsensingrdquo IEEE Transactions on Network Scienceand Engineering vol 7 no 4 pp 2347ndash2360 2020

Security and Communication Networks 13

ψ 1

|S|1113944iisinS

ri (10)

(4) Average distance traveled (ς) this metric measuresthe travel cost for workers when they complete theassigned tasks it is computed as the average distancetraveled by all the selected workers

ς 1

|S|1113944

langjirangisinSdji (11)

(5) Average budget utilization rate (ϕ) this metric is theaverage of the budget utilization for all assignedtasks )e budget utilization rate of each assignedtask is the ratio of the actual reward paid for the taskto the budget of that task

ϕ 1

|S|1113944jisinS

pj

Bj (12)

(6) Average reward (ω) this is the ratio of the totalreward for all the assigned tasks to the number ofassigned tasks

ω 1

|S|1113944jisinS

pj (13)

)e parameter δ is used to demonstrate the changing ofother metrics along with the average radius )e workersrsquoperspective would prefer to keep ς as low as possible )etask requestersrsquo perspective would prefer higher values of ηand ψ but lower values of ϕ and ω

52 Experimental Setting

521 Datasets

(i) Real Dataset We used a real dataset [33] from acrowdsourcing event by taking photos with the lo-cation information for 1877 workers and 835 tasks)ese tasks and workers were mainly obtained fromfour cities in China Foshan Guangzhou Shenzhenand Dongguan

(ii) Synthetic Dataset )e synthetic data were obtainedfrom a single day dataset from Gowalla in 2010which included 10956 tasks and 5087 workers

Input a set W the task j

Output the normalized decision matrix DMj for the task j

(1) DMj

(2) For each wi isinW(3) If dji leRj and ai lt qi(4) s

ji langj i dji rirang DMjlarrDMj cup s

ji1113966 1113967

(5) Normalize DMj by equations (2) and (3)(6) Return DMj

ALGORITHM 1 Obtain the decision matrix for a task

Input a set of workers W a set of tasks T parameters P β c

Output S which is the result of task assignment(1) S

(2) ai 0 i isin 1 2 m

(3) For each tj isin T(4) Calculate the radius Rj

(5) Calculate dji from the task j to each worker i

(6) Obtain and normalize the decision matrix DMj by calling Algorithm 1(7) If |DMj|gt 0(8) For iprime in range (|DMj|)(9) Compute the scoreji using equation (7)i DMj[iprime]i it is the number of the iprime th worker for the task tj(10) Compute the reward pj using equation (4)(11) s

ji s

ji append(langpj scorejirang)(ie s

ji langj i dji ri pj scorejirang)

(12) Sorted DMj descending by scores(13) For each item in DMj(14) If j in item(15) sj item ai+ 1(16) SlarrScup sj1113864 1113865

(17) Break(18) Return S

ALGORITHM 2 TASC-MADM algorithm

6 Security and Communication Networks

located in America )is dataset contains four at-tributes as follows task ID taskrsquos location workerID and workerrsquos location Reputations are generatedfollowing the uniform distribution of 0 sim 20000Budgets are generated following the uniform dis-tribution of 65 sim 85 )e task quotas of workers aregenerated following the uniform distribution of5 sim 10

522 Compared Algorithms )e RB-TPSC and Budget-TASC algorithms were selected as the baseline algorithmsbecause they are most closely related to TASC-MADM

(i) RB-TPSC is a task package assignment methodwhich aims at maximizing the number of tasksassigned within budget constraints Quality of resultsand travel costs are also being considered

(ii) Budget-TASC is a budget-aware spatial crowd-sourcing task assignment method which aims inmaximizing the total quality of tasks completedwithin budget constraints

We compared our TASC-MADM with RB-TPSC andBudget-TASC on both real and synthetic datasets

53 Results on Real Dataset )e first three experimentscompared TASC-MADM and RB-TPSC under differentsettings of parameters β c and Bj )e results are shown inFigures 1ndash3 For each parameter the experiment evaluatedthe average metrics of the two algorithms under 21 differentsettings We set the lowest budget of all tasks as P 65Moreover for the fairness in considering the effect of dis-tance and reputation the weight w0 05

First we compared TASC-MADM and RB-TPSC underdifferent β values )e results are shown in Figure 1 wherethe extra allowance per kilometer β varies from 0 sim 20monetary units in 1-unit increments c 05 and Bj isobtained from the dataset )e horizontal axis represents thedifferent settings of β while the vertical axis represents thedifferent values of the first six metrics As can be seen theaverage task radius δ is affected by the extra allowance perkilometer β (Figure 1(a)) δ is maximized when β 1 in-creases with β if βlt 1 and decreases with β if βgt 1 )echange in the trend of other metrics is consistent with theaverage task radius)is is because in a region with a smallerradius it is usually impossible to find enough workers withhigh reputations )us other metrics decrease with theaverage radius Compared with RB-TPSC TASC-MADMensures a high average task assignment rate (Figure 1(b))but reduces the average travel cost of workers (Figure 1(d))and saves the budget (Figures 1(e) and 1(f )) Besides ourresult shows that the average reputation of workers decreaseswith the average radius (Figure 1(c)) which is more realisticbecause there are fewer workers to choose from

Secondly we compared TASC-MADM and RB-TPSCunder different c values Figure 2 depicts the trend in whichthe above six metrics change when c the accepted distanceswithout extra remote allowance varies from 0 sim 2 km where

β 2 and Bj is obtained from the real dataset If cgt 05 theaverage radius increases with c and positively affects theabovementioned metrics except for ϕ and ω Our proposedmethod achieves a task assignment rate value of 9856(Figure 2(b)) but the maximum of the average distancetraveled the average reward and the average budget utili-zation rates are ς 103 km ω 6533 and ϕ 9493 re-spectively (Figures 2(d)ndash2(f)) Compared to the RB-TPSCmethod our method greatly decreases the average distancetraveled by the workers and the average reward offered bythe tasksrsquo requester while maintaining an equally high or agreater average task assignment rate Moreover it signifi-cantly improves the quality of crowdsourcing results becauseof the average reputation value of the selected workers in-creasing by about 250 (Figure 2(c))

)e third experiment compared TASC-MADM and RB-TPSC under different budgets (B) Figure 3 depicts how thefirst six metrics change while the tasksrsquo budget (B) variesfrom 90 to 110 in 1 increments where β 2 c 05Since the task with a greater budget should have moreworkers to choose from the average radius of the tasks ispositively affected by the tasksrsquo budget (Figure 3(a)) As thetask radius increases further the task assignment rate in-creases to 995 (Figure 3(b)) Compared to the RB-TPSCmethod our method decreases the average distance traveledfrom a range of 14sim25 km to a range of 08sim15 km(Figure 3(d)) but increases the average reputation by about147 (Figure 3(c)) and greatly decreases the average budgetutilization rate (Figure 3(e)) and the average reward((Figure 3(f))) More importantly with our method thegreater the number of candidate workers the lower theaverage reward offered by the requester and the higher is thequality of the crowdsourcing result In contrast RB-TPSCincreases the average reward continuously and decreases theaverage reputation to a stable value of around 500

)e fourth experiment compared TASC-MADM withRB-TPSC and Budget-TASC when radiuses were changedFor TASC-MADM and RB-TPSC we selected the averagemetrics of different c values In the above experiment thecomputed radius belongs to the interval [0 12] when c

varied from 0 sim 2 km and other parameters were fixed Wehave experimented with Budget-TASC in different radiusesvarying from 0 sim 12 km and then get the average of eachmetric For the Budget-TASC the other parameters were setas follows DC is set as the earthrsquos radius [10] PL 0 PH isset as the budget of the task and PM is set as the half of thebudget of the task Because we scaled the workerrsquos reputationto the interval [0 1] ThHM 075 ThML 05

)e results are shown in Table 2 TASC-MADM out-performs the baseline algorithms in all metrics except theaverage reputation For Budget-TASC PH is set as the taskrsquostotal budget which limits the task to high-quality workersbut also increases the average budget utilized

54 Results on Synthetic Dataset We repeated the first threeexperiments in Section 53 on the synthetic datasetFigures 4ndash6 illustrate the results of TASC-MADM and RB-TPSC Compared with RB-TPSC our algorithm greatly

Security and Communication Networks 7

005

115

225

335

445

5

0 2 4 6 8 10 12 14 16 18 20

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age r

adiu

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RB-TPSCTASC-MADM

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RB-TPSCTASC-MADM

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12

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age r

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05

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25

0 2 4 6 8 10 12 14 16 18 20

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age d

istan

ce tr

avel

ed

(d)

RB-TPSCTASC-MADM

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0 2 4 6 8 10 12 14 16 18 20

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age b

udge

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RB-TPSCTASC-MADM

6475

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6525

6550

6575

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6625

6650

6675

6700

0 2 4 6 8 10 12 14 16 18 20Av

erag

e rew

ard

(f )

Figure 1 Performance with an extra allowance per kilometer (β) (monetary unit) real dataset

25272931333537394143

00 02 04 06 08 10 12 14 16 18 20

Aver

age r

adiu

s

RB-TPSCTASC-MADM

(a)

RB-TPSCTASC-MADM

065

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095

100

00 02 04 06 08 10 12 14 16 18 20

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assig

nmen

t

(b)

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0

200

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1200

1400

00 02 04 06 08 10 12 14 16 18 20

Aver

age r

eput

atio

n va

lue

(c)

Figure 2 Continued

8 Security and Communication Networks

improves the task assignment rate (Figures 4(b) 5(b) and6(b)) and the resultrsquos quality (Figures 4(c) 5(c) and 6(c))However for the synthetic dataset the task locates in a

slightly sparse scenario with few workers )en the highertask assignment rate implies the higher travel cost(Figures 4(d) 5(d) and 6(d)) Correspondingly the budgetused is increased (Figures 4(e) 4(f) 5(e) 5(f) 6(e) and6(f ))

Next based on the synthetic dataset we comparedTASC-MADM with RB-TPSC and Budget-TASC whenradiuses were changed For TASC-MADM and RB-TPSCwe selected the average metrics of different c values thecomputed radius approximately belongs to the interval [6 9]

RB-TPSCTASC-MADM

08

10

12

14

16

18

20

00 02 04 06 08 10 12 14 16 18 20

Aver

age d

istan

ce tr

avele

d

(d)

RB-TPSCTASC-MADM

09500

09525

09550

09575

09600

09625

09650

09675

09700

00 02 04 06 08 10 12 14 16 18 20

Aver

age b

udge

t util

izat

ion

rate

(e)

RB-TPSCTASC-MADM

650

655

660

665

670

675

680

685

00 02 04 06 08 10 12 14 16 18 20

Aver

age r

ewar

d

(f )

Figure 2 Performance with different accepted distances without extra remote allowance (c) (km) real dataset

0

1

2

3

4

5

6

7

Aver

age r

adiu

s

RB-TPSCTASC-MADM

090

092

094

096

098

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102

104

106

108

110

(a)

RB-TPSCTASC-MADM

00

02

04

06

08

10

12

Task

assig

nmen

t rat

e

090

092

094

096

098

100

102

104

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108

110

(b)

RB-TPSCTASC-MADM

0

500

1000

1500

2000

2500

3000

Aver

age r

eput

atio

n va

lue

090

092

094

096

098

100

102

104

106

108

110

(c)

RB-TPSCTASC-MADM

0

05

1

15

2

25

3

Aver

age d

istan

ce tr

avele

d

090

092

094

096

098

100

102

104

106

108

110

(d)

RB-TPSCTASC-MADM

086

088

09

092

094

096

098

Aver

age b

udge

t util

izat

ion

rate

090

092

094

096

098

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102

104

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108

110

(e)

RB-TPSCTASC-MADM

655660665670675680685690695

Aver

age r

ewar

d

090

092

094

096

098

100

102

104

106

108

110

(f )

Figure 3 Performance with an amplitude of variation in budget (B) real dataset

Table 2 Results under different approaches real dataset

Algorithms η () ψ ζ ϕ ωBudget-TASC 8567 139063 14 1 7761RB-TPSC 9395 52339 159 09645 6672TASC-MADM 9418 98746 078 09509 6598

Security and Communication Networks 9

02468

10121416

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

adiu

s

RB-TPSCTASC-MADM

(a)

RB-TPSCTASC-MADM

03

04

05

06

07

08

09

0 2 4 6 8 10 12 14 16 18 20

Task

assig

nmen

t rat

e(b)

RB-TPSCTASC-MADM

10000

10500

11000

11500

12000

12500

13000

13500

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

eput

atio

n va

lue

(c)

RB-TPSCTASC-MADM

00051015202530354045

0 2 4 6 8 10 12 14 16 18 20

Aver

age d

istan

ce tr

avel

ed

(d)

RB-TPSCTASC-MADM

084

085

086

087

088

089

09

0 2 4 6 8 10 12 14 16 18 20

Aver

age b

udge

t util

izat

ion

(e)

RB-TPSCTASC-MADM

650

655

660

665

670

675

680

685

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

ewar

d

(f )

Figure 4 Performance with an extra allowance per kilometer (β) (monetary unit) synthetic dataset

RB-TPSCTASC-MADM

60

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age r

adiu

s

(a)

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067

069

071

073

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077

079

00 02 04 06 08 10 12 14 16 18 20

Task

assig

nmen

t rat

e

(b)

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1000010250105001075011000112501150011750120001225012500

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age r

eput

atio

n va

le

(c)

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age d

istan

ce tr

avele

d

(d)

RB-TPSCTASC-MADM

084085085086086087087088088089

00 02 04 06 08 10 12 14 16 18 20

Aver

age b

udge

t util

izat

ion

rate

(e)

RB-TPSCTASC-MADM

6600

6650

6700

6750

6800

6850

00 02 04 06 08 10 12 14 16 18 20

Aver

age r

ewar

d

(f )

Figure 5 Performance with different accepted distances without extra remote allowance (c) (km) synthetic dataset

10 Security and Communication Networks

(Figure 5(a)) So for Budget-TASC the radius varied from6 sim 9 km other parameters were set as in the fourthexperiment

)e experimental results (Table 3) show that TPSC-ADM obtains the highest task assignment rate but Budget-ASC achieves the highest quality and RB-TPSC spends thelowest budget

55 Effect of Taskrsquos Attribute Preference Our approachTASC-MADM enables the task to be assigned to satisfydemands for different goals If the resultrsquos quality is theprimary goal the task should select workers with a highreputation However if saving cost is the most concernedobjective the worker closer to the task should be chosenfirst Figure 7 shows the influence of tasksrsquo attributespreferences on the proposed approachrsquos performancew0 05 all targets are considered fairly w0 lt 05 the taskprefers the workerrsquos reputation to the distance the re-sultrsquos quality is significantly improved However thetravel cost and budget utilized are increased Whenw0 gt 05 the travel cost and the budget utilized are savedwhereas the resultrsquos quality is decreased Our approachcan maximize task assignment rate by setting w0 1 ormaximize the quality of workers selected by settingw0 0

56 Efficiency of Algorithms )e efficiency of the algorithmsis measured in CPU cost )e Budget-TASC algorithmrsquoscomputational complexity is O(n times m2) and that of RB-TPSC and TASC-MADM is O(n times m) we compare TASC-MADMwith RB-TPSC and Budget-TASC in CPU cost Eachof the programs runs 3 times 21 rounds )e average time perround is used to measure the algorithmrsquos efficiency Asshown in Figure 8 our approach significantly improves theefficiency of spatial task assignment

57 Summary of Experiment Results We summarized themajor finding as follows

(i) If the task is located in a worker-density area theproposed TASC-MADM approach exhibits betterresults than RB-TPSC It also performs better thanBudget-TASC on the metrics except for the qualityof workers

(ii) If the task is located in a worker-sparsity area theproposed TASC-MADM approach performs betterthan RB-TPSC in terms of the average assignmentrate and quality of workers But it leads to moretravel cost and budget utilized Budget-TASC ob-tains the best quality because it considers the qualityfirst

4

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ius

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(a)

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assig

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10000

10500

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age r

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age d

istan

ce tr

avel

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(d)

RB-TPSCTASC-MADM

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092

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098

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110

076078080082084086088090

094092

Aver

age b

udge

t util

izat

ion

rate

(e)

RB-TPSCTASC-MADM

090

092

094

096

098

100

102

104

106

108

110

660066506700675068006850690069507000

71007050

Aver

age r

ewar

d

(f )

Figure 6 Performance with an amplitude of variation in budget (B) synthetic dataset

Security and Communication Networks 11

Table 3 Results under different approaches synthetic dataset

Algorithms η ψ ζ ϕ ωBudget-TASC 072743975 1367217 1417697 1 7758585RB-TPSC 072253468 1027882 1408169 0851329 6686784TPSC-ADM 074825306 1219453 1926809 0873916 6768671

0

5

10

15

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

adiu

s

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

(a)

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

03504

04505

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085

0 2 4 6 8 10 12 14 16 18 20

Task

assig

nmen

t rat

e

(b)

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

100001050011000115001200012500130001350014000

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

eput

atio

n va

lue

(c)

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

0

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6

0 2 4 6 8 10 12 14 16 18 20

Aver

age d

istan

ce tr

avel

ed

(d)

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

084

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09

091

0 2 4 6 8 10 12 14 16 18 20

Aver

age b

udge

t util

izat

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rate

(e)

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

650655660665670675680685690695700705

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

ewar

d

(f )

Figure 7 Effect of tasksrsquo attributes preferences on the TASC-MADM approach Performance with an extra allowance per kilometer (β)

(monetary unit) synthetic dataset

128 114

31961929

135

618

0

20

40

60

80

100

120

140

Synthetic Dataset Real Dataset

TASC-MADMRB-TPSCBudget-TASC

Figure 8 Average CPU cost

12 Security and Communication Networks

(iii) In the terms of CPU cost the proposed TASC-MADM approach is superior to the baselinealgorithms

58 Discussion )is section discusses the advantages andlimitations of the TASC-MADM approach )e advantagesare listed as follows

(1) Effective )e TASC-MADM approach improves thetask assignment rate By setting different attributeweights it can maximize the task assignment rate orthe quality of crowdsourcing results

(2) Efficient )e TASC-MADM approach enhancesefficiency because of computing simply

(3) Extensible )eoretically our method can be ex-tended to solving decision problems involving anynumber of attributes

)ere still exist the following limitations in the TASC-MADM approach

(1) Quality Quantification of Workers In this paper theworker quality is modeled as a reputation value toreflect the quality of crowdsourced results Inpractice the same workerrsquos quality may differ inspecific tasks How to quantify the quality of workersis not covered in this paper

(2) Efficiency of Indexing Records )e TASC-MADMapproach exhaustively searches all the records toidentify the candidate of a task which makes it lessefficient to get the decision matrix on large datasets

6 Conclusion

)is paper focuses on designing an efficient task assignmentapproach which can deal with the situation where tasks havedifferent require preferences for different task attributes toachieve different goals Our task assignment approach can beextended to scenarios containing any number of attributesIn addition to the distance and reputation other criteriasuch as the workersrsquo skills can be considered

As for future work more factors such as workersrsquowillingness to accept tasks and their quality differences indifferent professional fields are included in task assign-ments In addition improving the efficiency of indexingrecords to make the allocation scheme suitable for largedatasets is a valuable research topic

Data Availability

)e data of allocated sharing tasks are available from ChinaSociety for Industrial and Applied Mathematics 2017 re-trieved on September 2 2020 from httpwwwmcmeducnhtml_cnnode460baf68ab0ed0e1e557a0c79b1c4648html

Conflicts of Interest

)e authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

)e authors thank researchers working at Guangxi KeyLaboratory of Trusted Software especially Xuguang Bao andManli Zhu for their suggestion during the research andpreparation of the manuscript )is work was funded byGuangxi Key Laboratory of Trusted Software (Nokx201727) Project to Improve the Scientific Research BasicAbility of Middle-Aged and Young Teachers (No2019KY0226) Natural Science Foundation of China (Nos61966009 U1811264 and U1711263) and Natural ScienceFoundation of Guangxi Province (Nos2019GXNSFBA245049 2019GXNSFBA245059 and2018GXNSFDA281045)

References

[1] L Kazemi and C Shahabi ldquoGeoCrowd enabling query an-swering with spatial crowdsourcing GISrdquo in Proceedings ofthe ACM International Symposium on Advances in GeographicInformation Systems pp 189ndash198 CA USA November 2012

[2] J Xiong M Zhao M Z A Bhuiyan L Chen and Y TianldquoAn AI-enabled three-party game framework for guaranteeddata privacy in mobile edge crowdsensing of IoTrdquo IEEETransactions on Industrial Informatics vol 17 no 2pp 922ndash933 2021

[3] J Xiong R Ma L Chen et al ldquoA personalized privacyprotection framework for mobile crowdsensing in IIoTrdquo IEEETransactions on Industrial Informatics vol 16 no 6pp 4231ndash4241 2020

[4] DiDi Didi Chuxing Corporate Citizenship Report httpswwwdidiglobalcomaboutdidiresponsibility 2017

[5] J Xiong J Ren L Chen et al ldquoEnhancing privacy andavailability for data clustering in intelligent electrical serviceof IoTrdquo IEEE Internet of Bings Journal vol 6 no 2pp 1530ndash1540 2019

[6] C Zhao P Cheng L Chen X Lin and C Shahabi ldquoFair taskassignment in spatial crowdsourcingrdquo Proceedings of theVLDB Endowment vol 13 no 12 pp 2479ndash2492 2020

[7] M Venanzi A Rogers and N R Jennings ldquoCrowdsourcingspatial phenomena using trust-based heteroskedasticGaussian processesrdquo in Proceedings of the First Conference onHuman Computation and Crowdsourcing (HCOMP)pp 182ndash189 Palm Springs CA USA November 2013

[8] J Xiong R Bi M Zhao J Guo and Q Yang ldquoEdge-assistedprivacy-preserving raw data sharing framework for connectedautonomous vehiclesrdquo IEEE Wireless Communicationsvol 27 no 3 pp 24ndash30 2020

[9] B Van B V D Haak M Parks and M Castells ldquo)e futureof journalism networked journalism rethinking journalism inthe networked digital agerdquo International Journal of Com-munication vol 6 pp 2923ndash2938 2012

[10] M Zook M Graham S Taylor and S Gorman ldquoVolunteeredgeographic information and crowdsourcing disaster relief acase study of the Haitian earthquakerdquoWorld Medical umlHealthPolicy vol 2 no 2 pp 7ndash33 2010

[11] Y Tong L Chen and C Shahabi ldquoSpatial crowdsourcingchallenges techniques and applicationsrdquo Proceedings of theVLDB Endowment vol 10 pp 1988ndash1991 2017

[12] J Xiong X Chen Q Yang L Chen and Z Yao ldquoA task-oriented user selection incentive mechanism in edge-aidedmobile crowdsensingrdquo IEEE Transactions on Network Scienceand Engineering vol 7 no 4 pp 2347ndash2360 2020

Security and Communication Networks 13

located in America )is dataset contains four at-tributes as follows task ID taskrsquos location workerID and workerrsquos location Reputations are generatedfollowing the uniform distribution of 0 sim 20000Budgets are generated following the uniform dis-tribution of 65 sim 85 )e task quotas of workers aregenerated following the uniform distribution of5 sim 10

522 Compared Algorithms )e RB-TPSC and Budget-TASC algorithms were selected as the baseline algorithmsbecause they are most closely related to TASC-MADM

(i) RB-TPSC is a task package assignment methodwhich aims at maximizing the number of tasksassigned within budget constraints Quality of resultsand travel costs are also being considered

(ii) Budget-TASC is a budget-aware spatial crowd-sourcing task assignment method which aims inmaximizing the total quality of tasks completedwithin budget constraints

We compared our TASC-MADM with RB-TPSC andBudget-TASC on both real and synthetic datasets

53 Results on Real Dataset )e first three experimentscompared TASC-MADM and RB-TPSC under differentsettings of parameters β c and Bj )e results are shown inFigures 1ndash3 For each parameter the experiment evaluatedthe average metrics of the two algorithms under 21 differentsettings We set the lowest budget of all tasks as P 65Moreover for the fairness in considering the effect of dis-tance and reputation the weight w0 05

First we compared TASC-MADM and RB-TPSC underdifferent β values )e results are shown in Figure 1 wherethe extra allowance per kilometer β varies from 0 sim 20monetary units in 1-unit increments c 05 and Bj isobtained from the dataset )e horizontal axis represents thedifferent settings of β while the vertical axis represents thedifferent values of the first six metrics As can be seen theaverage task radius δ is affected by the extra allowance perkilometer β (Figure 1(a)) δ is maximized when β 1 in-creases with β if βlt 1 and decreases with β if βgt 1 )echange in the trend of other metrics is consistent with theaverage task radius)is is because in a region with a smallerradius it is usually impossible to find enough workers withhigh reputations )us other metrics decrease with theaverage radius Compared with RB-TPSC TASC-MADMensures a high average task assignment rate (Figure 1(b))but reduces the average travel cost of workers (Figure 1(d))and saves the budget (Figures 1(e) and 1(f )) Besides ourresult shows that the average reputation of workers decreaseswith the average radius (Figure 1(c)) which is more realisticbecause there are fewer workers to choose from

Secondly we compared TASC-MADM and RB-TPSCunder different c values Figure 2 depicts the trend in whichthe above six metrics change when c the accepted distanceswithout extra remote allowance varies from 0 sim 2 km where

β 2 and Bj is obtained from the real dataset If cgt 05 theaverage radius increases with c and positively affects theabovementioned metrics except for ϕ and ω Our proposedmethod achieves a task assignment rate value of 9856(Figure 2(b)) but the maximum of the average distancetraveled the average reward and the average budget utili-zation rates are ς 103 km ω 6533 and ϕ 9493 re-spectively (Figures 2(d)ndash2(f)) Compared to the RB-TPSCmethod our method greatly decreases the average distancetraveled by the workers and the average reward offered bythe tasksrsquo requester while maintaining an equally high or agreater average task assignment rate Moreover it signifi-cantly improves the quality of crowdsourcing results becauseof the average reputation value of the selected workers in-creasing by about 250 (Figure 2(c))

)e third experiment compared TASC-MADM and RB-TPSC under different budgets (B) Figure 3 depicts how thefirst six metrics change while the tasksrsquo budget (B) variesfrom 90 to 110 in 1 increments where β 2 c 05Since the task with a greater budget should have moreworkers to choose from the average radius of the tasks ispositively affected by the tasksrsquo budget (Figure 3(a)) As thetask radius increases further the task assignment rate in-creases to 995 (Figure 3(b)) Compared to the RB-TPSCmethod our method decreases the average distance traveledfrom a range of 14sim25 km to a range of 08sim15 km(Figure 3(d)) but increases the average reputation by about147 (Figure 3(c)) and greatly decreases the average budgetutilization rate (Figure 3(e)) and the average reward((Figure 3(f))) More importantly with our method thegreater the number of candidate workers the lower theaverage reward offered by the requester and the higher is thequality of the crowdsourcing result In contrast RB-TPSCincreases the average reward continuously and decreases theaverage reputation to a stable value of around 500

)e fourth experiment compared TASC-MADM withRB-TPSC and Budget-TASC when radiuses were changedFor TASC-MADM and RB-TPSC we selected the averagemetrics of different c values In the above experiment thecomputed radius belongs to the interval [0 12] when c

varied from 0 sim 2 km and other parameters were fixed Wehave experimented with Budget-TASC in different radiusesvarying from 0 sim 12 km and then get the average of eachmetric For the Budget-TASC the other parameters were setas follows DC is set as the earthrsquos radius [10] PL 0 PH isset as the budget of the task and PM is set as the half of thebudget of the task Because we scaled the workerrsquos reputationto the interval [0 1] ThHM 075 ThML 05

)e results are shown in Table 2 TASC-MADM out-performs the baseline algorithms in all metrics except theaverage reputation For Budget-TASC PH is set as the taskrsquostotal budget which limits the task to high-quality workersbut also increases the average budget utilized

54 Results on Synthetic Dataset We repeated the first threeexperiments in Section 53 on the synthetic datasetFigures 4ndash6 illustrate the results of TASC-MADM and RB-TPSC Compared with RB-TPSC our algorithm greatly

Security and Communication Networks 7

005

115

225

335

445

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0 2 4 6 8 10 12 14 16 18 20

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age r

adiu

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t rat

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RB-TPSCTASC-MADM

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age r

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25

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age d

istan

ce tr

avel

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age b

udge

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6475

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6525

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6575

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6625

6650

6675

6700

0 2 4 6 8 10 12 14 16 18 20Av

erag

e rew

ard

(f )

Figure 1 Performance with an extra allowance per kilometer (β) (monetary unit) real dataset

25272931333537394143

00 02 04 06 08 10 12 14 16 18 20

Aver

age r

adiu

s

RB-TPSCTASC-MADM

(a)

RB-TPSCTASC-MADM

065

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00 02 04 06 08 10 12 14 16 18 20

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assig

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1400

00 02 04 06 08 10 12 14 16 18 20

Aver

age r

eput

atio

n va

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(c)

Figure 2 Continued

8 Security and Communication Networks

improves the task assignment rate (Figures 4(b) 5(b) and6(b)) and the resultrsquos quality (Figures 4(c) 5(c) and 6(c))However for the synthetic dataset the task locates in a

slightly sparse scenario with few workers )en the highertask assignment rate implies the higher travel cost(Figures 4(d) 5(d) and 6(d)) Correspondingly the budgetused is increased (Figures 4(e) 4(f) 5(e) 5(f) 6(e) and6(f ))

Next based on the synthetic dataset we comparedTASC-MADM with RB-TPSC and Budget-TASC whenradiuses were changed For TASC-MADM and RB-TPSCwe selected the average metrics of different c values thecomputed radius approximately belongs to the interval [6 9]

RB-TPSCTASC-MADM

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00 02 04 06 08 10 12 14 16 18 20

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age d

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avele

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09500

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00 02 04 06 08 10 12 14 16 18 20

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age b

udge

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650

655

660

665

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675

680

685

00 02 04 06 08 10 12 14 16 18 20

Aver

age r

ewar

d

(f )

Figure 2 Performance with different accepted distances without extra remote allowance (c) (km) real dataset

0

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7

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age r

adiu

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RB-TPSCTASC-MADM

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(a)

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assig

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e

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(b)

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age r

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(c)

RB-TPSCTASC-MADM

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25

3

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age d

istan

ce tr

avele

d

090

092

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(d)

RB-TPSCTASC-MADM

086

088

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092

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Aver

age b

udge

t util

izat

ion

rate

090

092

094

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098

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(e)

RB-TPSCTASC-MADM

655660665670675680685690695

Aver

age r

ewar

d

090

092

094

096

098

100

102

104

106

108

110

(f )

Figure 3 Performance with an amplitude of variation in budget (B) real dataset

Table 2 Results under different approaches real dataset

Algorithms η () ψ ζ ϕ ωBudget-TASC 8567 139063 14 1 7761RB-TPSC 9395 52339 159 09645 6672TASC-MADM 9418 98746 078 09509 6598

Security and Communication Networks 9

02468

10121416

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

adiu

s

RB-TPSCTASC-MADM

(a)

RB-TPSCTASC-MADM

03

04

05

06

07

08

09

0 2 4 6 8 10 12 14 16 18 20

Task

assig

nmen

t rat

e(b)

RB-TPSCTASC-MADM

10000

10500

11000

11500

12000

12500

13000

13500

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

eput

atio

n va

lue

(c)

RB-TPSCTASC-MADM

00051015202530354045

0 2 4 6 8 10 12 14 16 18 20

Aver

age d

istan

ce tr

avel

ed

(d)

RB-TPSCTASC-MADM

084

085

086

087

088

089

09

0 2 4 6 8 10 12 14 16 18 20

Aver

age b

udge

t util

izat

ion

(e)

RB-TPSCTASC-MADM

650

655

660

665

670

675

680

685

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

ewar

d

(f )

Figure 4 Performance with an extra allowance per kilometer (β) (monetary unit) synthetic dataset

RB-TPSCTASC-MADM

60

65

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80

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90

95

00 02 04 06 08 10 12 14 16 18 20

Aver

age r

adiu

s

(a)

RB-TPSCTASC-MADM

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067

069

071

073

075

077

079

00 02 04 06 08 10 12 14 16 18 20

Task

assig

nmen

t rat

e

(b)

RB-TPSCTASC-MADM

1000010250105001075011000112501150011750120001225012500

00 02 04 06 08 10 12 14 16 18 20

Aver

age r

eput

atio

n va

le

(c)

RB-TPSCTASC-MADM

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120

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160

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220

00 02 04 06 08 10 12 14 16 18 20

Aver

age d

istan

ce tr

avele

d

(d)

RB-TPSCTASC-MADM

084085085086086087087088088089

00 02 04 06 08 10 12 14 16 18 20

Aver

age b

udge

t util

izat

ion

rate

(e)

RB-TPSCTASC-MADM

6600

6650

6700

6750

6800

6850

00 02 04 06 08 10 12 14 16 18 20

Aver

age r

ewar

d

(f )

Figure 5 Performance with different accepted distances without extra remote allowance (c) (km) synthetic dataset

10 Security and Communication Networks

(Figure 5(a)) So for Budget-TASC the radius varied from6 sim 9 km other parameters were set as in the fourthexperiment

)e experimental results (Table 3) show that TPSC-ADM obtains the highest task assignment rate but Budget-ASC achieves the highest quality and RB-TPSC spends thelowest budget

55 Effect of Taskrsquos Attribute Preference Our approachTASC-MADM enables the task to be assigned to satisfydemands for different goals If the resultrsquos quality is theprimary goal the task should select workers with a highreputation However if saving cost is the most concernedobjective the worker closer to the task should be chosenfirst Figure 7 shows the influence of tasksrsquo attributespreferences on the proposed approachrsquos performancew0 05 all targets are considered fairly w0 lt 05 the taskprefers the workerrsquos reputation to the distance the re-sultrsquos quality is significantly improved However thetravel cost and budget utilized are increased Whenw0 gt 05 the travel cost and the budget utilized are savedwhereas the resultrsquos quality is decreased Our approachcan maximize task assignment rate by setting w0 1 ormaximize the quality of workers selected by settingw0 0

56 Efficiency of Algorithms )e efficiency of the algorithmsis measured in CPU cost )e Budget-TASC algorithmrsquoscomputational complexity is O(n times m2) and that of RB-TPSC and TASC-MADM is O(n times m) we compare TASC-MADMwith RB-TPSC and Budget-TASC in CPU cost Eachof the programs runs 3 times 21 rounds )e average time perround is used to measure the algorithmrsquos efficiency Asshown in Figure 8 our approach significantly improves theefficiency of spatial task assignment

57 Summary of Experiment Results We summarized themajor finding as follows

(i) If the task is located in a worker-density area theproposed TASC-MADM approach exhibits betterresults than RB-TPSC It also performs better thanBudget-TASC on the metrics except for the qualityof workers

(ii) If the task is located in a worker-sparsity area theproposed TASC-MADM approach performs betterthan RB-TPSC in terms of the average assignmentrate and quality of workers But it leads to moretravel cost and budget utilized Budget-TASC ob-tains the best quality because it considers the qualityfirst

4

5

6

7

8

9

10

11

12Av

erag

e rad

ius

RB-TPSCTASC-MADM

090

092

094

096

098

100

102

104

106

108

110

(a)

RB-TPSCTASC-MADM

045

05

055

06

065

07

075

08

085

090

092

094

096

098

100

102

104

106

108

110

Task

assig

nmen

t rat

e(b)

RB-TPSCTASC-MADM

090

092

094

096

098

100

102

104

106

108

110

10000

10500

11000

11500

12000

12500

13000

13500

Aver

age r

eput

atio

n va

lue

(c)

RB-TPSCTASC-MADM

090

092

094

096

098

100

102

104

106

108

110

10

15

20

25

30

35

Aver

age d

istan

ce tr

avel

ed

(d)

RB-TPSCTASC-MADM

090

092

094

096

098

100

102

104

106

108

110

076078080082084086088090

094092

Aver

age b

udge

t util

izat

ion

rate

(e)

RB-TPSCTASC-MADM

090

092

094

096

098

100

102

104

106

108

110

660066506700675068006850690069507000

71007050

Aver

age r

ewar

d

(f )

Figure 6 Performance with an amplitude of variation in budget (B) synthetic dataset

Security and Communication Networks 11

Table 3 Results under different approaches synthetic dataset

Algorithms η ψ ζ ϕ ωBudget-TASC 072743975 1367217 1417697 1 7758585RB-TPSC 072253468 1027882 1408169 0851329 6686784TPSC-ADM 074825306 1219453 1926809 0873916 6768671

0

5

10

15

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

adiu

s

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

(a)

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

03504

04505

05506

06507

07508

085

0 2 4 6 8 10 12 14 16 18 20

Task

assig

nmen

t rat

e

(b)

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

100001050011000115001200012500130001350014000

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

eput

atio

n va

lue

(c)

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

0

1

2

3

4

5

6

0 2 4 6 8 10 12 14 16 18 20

Aver

age d

istan

ce tr

avel

ed

(d)

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

084

085

086

087

088

089

09

091

0 2 4 6 8 10 12 14 16 18 20

Aver

age b

udge

t util

izat

ion

rate

(e)

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

650655660665670675680685690695700705

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

ewar

d

(f )

Figure 7 Effect of tasksrsquo attributes preferences on the TASC-MADM approach Performance with an extra allowance per kilometer (β)

(monetary unit) synthetic dataset

128 114

31961929

135

618

0

20

40

60

80

100

120

140

Synthetic Dataset Real Dataset

TASC-MADMRB-TPSCBudget-TASC

Figure 8 Average CPU cost

12 Security and Communication Networks

(iii) In the terms of CPU cost the proposed TASC-MADM approach is superior to the baselinealgorithms

58 Discussion )is section discusses the advantages andlimitations of the TASC-MADM approach )e advantagesare listed as follows

(1) Effective )e TASC-MADM approach improves thetask assignment rate By setting different attributeweights it can maximize the task assignment rate orthe quality of crowdsourcing results

(2) Efficient )e TASC-MADM approach enhancesefficiency because of computing simply

(3) Extensible )eoretically our method can be ex-tended to solving decision problems involving anynumber of attributes

)ere still exist the following limitations in the TASC-MADM approach

(1) Quality Quantification of Workers In this paper theworker quality is modeled as a reputation value toreflect the quality of crowdsourced results Inpractice the same workerrsquos quality may differ inspecific tasks How to quantify the quality of workersis not covered in this paper

(2) Efficiency of Indexing Records )e TASC-MADMapproach exhaustively searches all the records toidentify the candidate of a task which makes it lessefficient to get the decision matrix on large datasets

6 Conclusion

)is paper focuses on designing an efficient task assignmentapproach which can deal with the situation where tasks havedifferent require preferences for different task attributes toachieve different goals Our task assignment approach can beextended to scenarios containing any number of attributesIn addition to the distance and reputation other criteriasuch as the workersrsquo skills can be considered

As for future work more factors such as workersrsquowillingness to accept tasks and their quality differences indifferent professional fields are included in task assign-ments In addition improving the efficiency of indexingrecords to make the allocation scheme suitable for largedatasets is a valuable research topic

Data Availability

)e data of allocated sharing tasks are available from ChinaSociety for Industrial and Applied Mathematics 2017 re-trieved on September 2 2020 from httpwwwmcmeducnhtml_cnnode460baf68ab0ed0e1e557a0c79b1c4648html

Conflicts of Interest

)e authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

)e authors thank researchers working at Guangxi KeyLaboratory of Trusted Software especially Xuguang Bao andManli Zhu for their suggestion during the research andpreparation of the manuscript )is work was funded byGuangxi Key Laboratory of Trusted Software (Nokx201727) Project to Improve the Scientific Research BasicAbility of Middle-Aged and Young Teachers (No2019KY0226) Natural Science Foundation of China (Nos61966009 U1811264 and U1711263) and Natural ScienceFoundation of Guangxi Province (Nos2019GXNSFBA245049 2019GXNSFBA245059 and2018GXNSFDA281045)

References

[1] L Kazemi and C Shahabi ldquoGeoCrowd enabling query an-swering with spatial crowdsourcing GISrdquo in Proceedings ofthe ACM International Symposium on Advances in GeographicInformation Systems pp 189ndash198 CA USA November 2012

[2] J Xiong M Zhao M Z A Bhuiyan L Chen and Y TianldquoAn AI-enabled three-party game framework for guaranteeddata privacy in mobile edge crowdsensing of IoTrdquo IEEETransactions on Industrial Informatics vol 17 no 2pp 922ndash933 2021

[3] J Xiong R Ma L Chen et al ldquoA personalized privacyprotection framework for mobile crowdsensing in IIoTrdquo IEEETransactions on Industrial Informatics vol 16 no 6pp 4231ndash4241 2020

[4] DiDi Didi Chuxing Corporate Citizenship Report httpswwwdidiglobalcomaboutdidiresponsibility 2017

[5] J Xiong J Ren L Chen et al ldquoEnhancing privacy andavailability for data clustering in intelligent electrical serviceof IoTrdquo IEEE Internet of Bings Journal vol 6 no 2pp 1530ndash1540 2019

[6] C Zhao P Cheng L Chen X Lin and C Shahabi ldquoFair taskassignment in spatial crowdsourcingrdquo Proceedings of theVLDB Endowment vol 13 no 12 pp 2479ndash2492 2020

[7] M Venanzi A Rogers and N R Jennings ldquoCrowdsourcingspatial phenomena using trust-based heteroskedasticGaussian processesrdquo in Proceedings of the First Conference onHuman Computation and Crowdsourcing (HCOMP)pp 182ndash189 Palm Springs CA USA November 2013

[8] J Xiong R Bi M Zhao J Guo and Q Yang ldquoEdge-assistedprivacy-preserving raw data sharing framework for connectedautonomous vehiclesrdquo IEEE Wireless Communicationsvol 27 no 3 pp 24ndash30 2020

[9] B Van B V D Haak M Parks and M Castells ldquo)e futureof journalism networked journalism rethinking journalism inthe networked digital agerdquo International Journal of Com-munication vol 6 pp 2923ndash2938 2012

[10] M Zook M Graham S Taylor and S Gorman ldquoVolunteeredgeographic information and crowdsourcing disaster relief acase study of the Haitian earthquakerdquoWorld Medical umlHealthPolicy vol 2 no 2 pp 7ndash33 2010

[11] Y Tong L Chen and C Shahabi ldquoSpatial crowdsourcingchallenges techniques and applicationsrdquo Proceedings of theVLDB Endowment vol 10 pp 1988ndash1991 2017

[12] J Xiong X Chen Q Yang L Chen and Z Yao ldquoA task-oriented user selection incentive mechanism in edge-aidedmobile crowdsensingrdquo IEEE Transactions on Network Scienceand Engineering vol 7 no 4 pp 2347ndash2360 2020

Security and Communication Networks 13

005

115

225

335

445

5

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

adiu

s

RB-TPSCTASC-MADM

(a)

RB-TPSCTASC-MADM

0

02

04

06

08

1

12

0 2 4 6 8 10 12 14 16 18 20

Task

assig

nmen

t rat

e

(b)

RB-TPSCTASC-MADM

0

200

400

600

800

1000

1200

1400

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

eput

atio

n va

lue

(c)

RB-TPSCTASC-MADM

0

05

1

15

2

25

0 2 4 6 8 10 12 14 16 18 20

Aver

age d

istan

ce tr

avel

ed

(d)

RB-TPSCTASC-MADM

094

095

095

096

096

097

097

098

0 2 4 6 8 10 12 14 16 18 20

Aver

age b

udge

t util

izat

ion

rate

(e)

RB-TPSCTASC-MADM

6475

6500

6525

6550

6575

6600

6625

6650

6675

6700

0 2 4 6 8 10 12 14 16 18 20Av

erag

e rew

ard

(f )

Figure 1 Performance with an extra allowance per kilometer (β) (monetary unit) real dataset

25272931333537394143

00 02 04 06 08 10 12 14 16 18 20

Aver

age r

adiu

s

RB-TPSCTASC-MADM

(a)

RB-TPSCTASC-MADM

065

070

075

080

085

090

095

100

00 02 04 06 08 10 12 14 16 18 20

Task

assig

nmen

t

(b)

RB-TPSCTASC-MADM

0

200

400

600

800

1000

1200

1400

00 02 04 06 08 10 12 14 16 18 20

Aver

age r

eput

atio

n va

lue

(c)

Figure 2 Continued

8 Security and Communication Networks

improves the task assignment rate (Figures 4(b) 5(b) and6(b)) and the resultrsquos quality (Figures 4(c) 5(c) and 6(c))However for the synthetic dataset the task locates in a

slightly sparse scenario with few workers )en the highertask assignment rate implies the higher travel cost(Figures 4(d) 5(d) and 6(d)) Correspondingly the budgetused is increased (Figures 4(e) 4(f) 5(e) 5(f) 6(e) and6(f ))

Next based on the synthetic dataset we comparedTASC-MADM with RB-TPSC and Budget-TASC whenradiuses were changed For TASC-MADM and RB-TPSCwe selected the average metrics of different c values thecomputed radius approximately belongs to the interval [6 9]

RB-TPSCTASC-MADM

08

10

12

14

16

18

20

00 02 04 06 08 10 12 14 16 18 20

Aver

age d

istan

ce tr

avele

d

(d)

RB-TPSCTASC-MADM

09500

09525

09550

09575

09600

09625

09650

09675

09700

00 02 04 06 08 10 12 14 16 18 20

Aver

age b

udge

t util

izat

ion

rate

(e)

RB-TPSCTASC-MADM

650

655

660

665

670

675

680

685

00 02 04 06 08 10 12 14 16 18 20

Aver

age r

ewar

d

(f )

Figure 2 Performance with different accepted distances without extra remote allowance (c) (km) real dataset

0

1

2

3

4

5

6

7

Aver

age r

adiu

s

RB-TPSCTASC-MADM

090

092

094

096

098

100

102

104

106

108

110

(a)

RB-TPSCTASC-MADM

00

02

04

06

08

10

12

Task

assig

nmen

t rat

e

090

092

094

096

098

100

102

104

106

108

110

(b)

RB-TPSCTASC-MADM

0

500

1000

1500

2000

2500

3000

Aver

age r

eput

atio

n va

lue

090

092

094

096

098

100

102

104

106

108

110

(c)

RB-TPSCTASC-MADM

0

05

1

15

2

25

3

Aver

age d

istan

ce tr

avele

d

090

092

094

096

098

100

102

104

106

108

110

(d)

RB-TPSCTASC-MADM

086

088

09

092

094

096

098

Aver

age b

udge

t util

izat

ion

rate

090

092

094

096

098

100

102

104

106

108

110

(e)

RB-TPSCTASC-MADM

655660665670675680685690695

Aver

age r

ewar

d

090

092

094

096

098

100

102

104

106

108

110

(f )

Figure 3 Performance with an amplitude of variation in budget (B) real dataset

Table 2 Results under different approaches real dataset

Algorithms η () ψ ζ ϕ ωBudget-TASC 8567 139063 14 1 7761RB-TPSC 9395 52339 159 09645 6672TASC-MADM 9418 98746 078 09509 6598

Security and Communication Networks 9

02468

10121416

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

adiu

s

RB-TPSCTASC-MADM

(a)

RB-TPSCTASC-MADM

03

04

05

06

07

08

09

0 2 4 6 8 10 12 14 16 18 20

Task

assig

nmen

t rat

e(b)

RB-TPSCTASC-MADM

10000

10500

11000

11500

12000

12500

13000

13500

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

eput

atio

n va

lue

(c)

RB-TPSCTASC-MADM

00051015202530354045

0 2 4 6 8 10 12 14 16 18 20

Aver

age d

istan

ce tr

avel

ed

(d)

RB-TPSCTASC-MADM

084

085

086

087

088

089

09

0 2 4 6 8 10 12 14 16 18 20

Aver

age b

udge

t util

izat

ion

(e)

RB-TPSCTASC-MADM

650

655

660

665

670

675

680

685

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

ewar

d

(f )

Figure 4 Performance with an extra allowance per kilometer (β) (monetary unit) synthetic dataset

RB-TPSCTASC-MADM

60

65

70

75

80

85

90

95

00 02 04 06 08 10 12 14 16 18 20

Aver

age r

adiu

s

(a)

RB-TPSCTASC-MADM

065

067

069

071

073

075

077

079

00 02 04 06 08 10 12 14 16 18 20

Task

assig

nmen

t rat

e

(b)

RB-TPSCTASC-MADM

1000010250105001075011000112501150011750120001225012500

00 02 04 06 08 10 12 14 16 18 20

Aver

age r

eput

atio

n va

le

(c)

RB-TPSCTASC-MADM

100

120

140

160

180

200

220

00 02 04 06 08 10 12 14 16 18 20

Aver

age d

istan

ce tr

avele

d

(d)

RB-TPSCTASC-MADM

084085085086086087087088088089

00 02 04 06 08 10 12 14 16 18 20

Aver

age b

udge

t util

izat

ion

rate

(e)

RB-TPSCTASC-MADM

6600

6650

6700

6750

6800

6850

00 02 04 06 08 10 12 14 16 18 20

Aver

age r

ewar

d

(f )

Figure 5 Performance with different accepted distances without extra remote allowance (c) (km) synthetic dataset

10 Security and Communication Networks

(Figure 5(a)) So for Budget-TASC the radius varied from6 sim 9 km other parameters were set as in the fourthexperiment

)e experimental results (Table 3) show that TPSC-ADM obtains the highest task assignment rate but Budget-ASC achieves the highest quality and RB-TPSC spends thelowest budget

55 Effect of Taskrsquos Attribute Preference Our approachTASC-MADM enables the task to be assigned to satisfydemands for different goals If the resultrsquos quality is theprimary goal the task should select workers with a highreputation However if saving cost is the most concernedobjective the worker closer to the task should be chosenfirst Figure 7 shows the influence of tasksrsquo attributespreferences on the proposed approachrsquos performancew0 05 all targets are considered fairly w0 lt 05 the taskprefers the workerrsquos reputation to the distance the re-sultrsquos quality is significantly improved However thetravel cost and budget utilized are increased Whenw0 gt 05 the travel cost and the budget utilized are savedwhereas the resultrsquos quality is decreased Our approachcan maximize task assignment rate by setting w0 1 ormaximize the quality of workers selected by settingw0 0

56 Efficiency of Algorithms )e efficiency of the algorithmsis measured in CPU cost )e Budget-TASC algorithmrsquoscomputational complexity is O(n times m2) and that of RB-TPSC and TASC-MADM is O(n times m) we compare TASC-MADMwith RB-TPSC and Budget-TASC in CPU cost Eachof the programs runs 3 times 21 rounds )e average time perround is used to measure the algorithmrsquos efficiency Asshown in Figure 8 our approach significantly improves theefficiency of spatial task assignment

57 Summary of Experiment Results We summarized themajor finding as follows

(i) If the task is located in a worker-density area theproposed TASC-MADM approach exhibits betterresults than RB-TPSC It also performs better thanBudget-TASC on the metrics except for the qualityof workers

(ii) If the task is located in a worker-sparsity area theproposed TASC-MADM approach performs betterthan RB-TPSC in terms of the average assignmentrate and quality of workers But it leads to moretravel cost and budget utilized Budget-TASC ob-tains the best quality because it considers the qualityfirst

4

5

6

7

8

9

10

11

12Av

erag

e rad

ius

RB-TPSCTASC-MADM

090

092

094

096

098

100

102

104

106

108

110

(a)

RB-TPSCTASC-MADM

045

05

055

06

065

07

075

08

085

090

092

094

096

098

100

102

104

106

108

110

Task

assig

nmen

t rat

e(b)

RB-TPSCTASC-MADM

090

092

094

096

098

100

102

104

106

108

110

10000

10500

11000

11500

12000

12500

13000

13500

Aver

age r

eput

atio

n va

lue

(c)

RB-TPSCTASC-MADM

090

092

094

096

098

100

102

104

106

108

110

10

15

20

25

30

35

Aver

age d

istan

ce tr

avel

ed

(d)

RB-TPSCTASC-MADM

090

092

094

096

098

100

102

104

106

108

110

076078080082084086088090

094092

Aver

age b

udge

t util

izat

ion

rate

(e)

RB-TPSCTASC-MADM

090

092

094

096

098

100

102

104

106

108

110

660066506700675068006850690069507000

71007050

Aver

age r

ewar

d

(f )

Figure 6 Performance with an amplitude of variation in budget (B) synthetic dataset

Security and Communication Networks 11

Table 3 Results under different approaches synthetic dataset

Algorithms η ψ ζ ϕ ωBudget-TASC 072743975 1367217 1417697 1 7758585RB-TPSC 072253468 1027882 1408169 0851329 6686784TPSC-ADM 074825306 1219453 1926809 0873916 6768671

0

5

10

15

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

adiu

s

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

(a)

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

03504

04505

05506

06507

07508

085

0 2 4 6 8 10 12 14 16 18 20

Task

assig

nmen

t rat

e

(b)

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

100001050011000115001200012500130001350014000

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

eput

atio

n va

lue

(c)

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

0

1

2

3

4

5

6

0 2 4 6 8 10 12 14 16 18 20

Aver

age d

istan

ce tr

avel

ed

(d)

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

084

085

086

087

088

089

09

091

0 2 4 6 8 10 12 14 16 18 20

Aver

age b

udge

t util

izat

ion

rate

(e)

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

650655660665670675680685690695700705

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

ewar

d

(f )

Figure 7 Effect of tasksrsquo attributes preferences on the TASC-MADM approach Performance with an extra allowance per kilometer (β)

(monetary unit) synthetic dataset

128 114

31961929

135

618

0

20

40

60

80

100

120

140

Synthetic Dataset Real Dataset

TASC-MADMRB-TPSCBudget-TASC

Figure 8 Average CPU cost

12 Security and Communication Networks

(iii) In the terms of CPU cost the proposed TASC-MADM approach is superior to the baselinealgorithms

58 Discussion )is section discusses the advantages andlimitations of the TASC-MADM approach )e advantagesare listed as follows

(1) Effective )e TASC-MADM approach improves thetask assignment rate By setting different attributeweights it can maximize the task assignment rate orthe quality of crowdsourcing results

(2) Efficient )e TASC-MADM approach enhancesefficiency because of computing simply

(3) Extensible )eoretically our method can be ex-tended to solving decision problems involving anynumber of attributes

)ere still exist the following limitations in the TASC-MADM approach

(1) Quality Quantification of Workers In this paper theworker quality is modeled as a reputation value toreflect the quality of crowdsourced results Inpractice the same workerrsquos quality may differ inspecific tasks How to quantify the quality of workersis not covered in this paper

(2) Efficiency of Indexing Records )e TASC-MADMapproach exhaustively searches all the records toidentify the candidate of a task which makes it lessefficient to get the decision matrix on large datasets

6 Conclusion

)is paper focuses on designing an efficient task assignmentapproach which can deal with the situation where tasks havedifferent require preferences for different task attributes toachieve different goals Our task assignment approach can beextended to scenarios containing any number of attributesIn addition to the distance and reputation other criteriasuch as the workersrsquo skills can be considered

As for future work more factors such as workersrsquowillingness to accept tasks and their quality differences indifferent professional fields are included in task assign-ments In addition improving the efficiency of indexingrecords to make the allocation scheme suitable for largedatasets is a valuable research topic

Data Availability

)e data of allocated sharing tasks are available from ChinaSociety for Industrial and Applied Mathematics 2017 re-trieved on September 2 2020 from httpwwwmcmeducnhtml_cnnode460baf68ab0ed0e1e557a0c79b1c4648html

Conflicts of Interest

)e authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

)e authors thank researchers working at Guangxi KeyLaboratory of Trusted Software especially Xuguang Bao andManli Zhu for their suggestion during the research andpreparation of the manuscript )is work was funded byGuangxi Key Laboratory of Trusted Software (Nokx201727) Project to Improve the Scientific Research BasicAbility of Middle-Aged and Young Teachers (No2019KY0226) Natural Science Foundation of China (Nos61966009 U1811264 and U1711263) and Natural ScienceFoundation of Guangxi Province (Nos2019GXNSFBA245049 2019GXNSFBA245059 and2018GXNSFDA281045)

References

[1] L Kazemi and C Shahabi ldquoGeoCrowd enabling query an-swering with spatial crowdsourcing GISrdquo in Proceedings ofthe ACM International Symposium on Advances in GeographicInformation Systems pp 189ndash198 CA USA November 2012

[2] J Xiong M Zhao M Z A Bhuiyan L Chen and Y TianldquoAn AI-enabled three-party game framework for guaranteeddata privacy in mobile edge crowdsensing of IoTrdquo IEEETransactions on Industrial Informatics vol 17 no 2pp 922ndash933 2021

[3] J Xiong R Ma L Chen et al ldquoA personalized privacyprotection framework for mobile crowdsensing in IIoTrdquo IEEETransactions on Industrial Informatics vol 16 no 6pp 4231ndash4241 2020

[4] DiDi Didi Chuxing Corporate Citizenship Report httpswwwdidiglobalcomaboutdidiresponsibility 2017

[5] J Xiong J Ren L Chen et al ldquoEnhancing privacy andavailability for data clustering in intelligent electrical serviceof IoTrdquo IEEE Internet of Bings Journal vol 6 no 2pp 1530ndash1540 2019

[6] C Zhao P Cheng L Chen X Lin and C Shahabi ldquoFair taskassignment in spatial crowdsourcingrdquo Proceedings of theVLDB Endowment vol 13 no 12 pp 2479ndash2492 2020

[7] M Venanzi A Rogers and N R Jennings ldquoCrowdsourcingspatial phenomena using trust-based heteroskedasticGaussian processesrdquo in Proceedings of the First Conference onHuman Computation and Crowdsourcing (HCOMP)pp 182ndash189 Palm Springs CA USA November 2013

[8] J Xiong R Bi M Zhao J Guo and Q Yang ldquoEdge-assistedprivacy-preserving raw data sharing framework for connectedautonomous vehiclesrdquo IEEE Wireless Communicationsvol 27 no 3 pp 24ndash30 2020

[9] B Van B V D Haak M Parks and M Castells ldquo)e futureof journalism networked journalism rethinking journalism inthe networked digital agerdquo International Journal of Com-munication vol 6 pp 2923ndash2938 2012

[10] M Zook M Graham S Taylor and S Gorman ldquoVolunteeredgeographic information and crowdsourcing disaster relief acase study of the Haitian earthquakerdquoWorld Medical umlHealthPolicy vol 2 no 2 pp 7ndash33 2010

[11] Y Tong L Chen and C Shahabi ldquoSpatial crowdsourcingchallenges techniques and applicationsrdquo Proceedings of theVLDB Endowment vol 10 pp 1988ndash1991 2017

[12] J Xiong X Chen Q Yang L Chen and Z Yao ldquoA task-oriented user selection incentive mechanism in edge-aidedmobile crowdsensingrdquo IEEE Transactions on Network Scienceand Engineering vol 7 no 4 pp 2347ndash2360 2020

Security and Communication Networks 13

improves the task assignment rate (Figures 4(b) 5(b) and6(b)) and the resultrsquos quality (Figures 4(c) 5(c) and 6(c))However for the synthetic dataset the task locates in a

slightly sparse scenario with few workers )en the highertask assignment rate implies the higher travel cost(Figures 4(d) 5(d) and 6(d)) Correspondingly the budgetused is increased (Figures 4(e) 4(f) 5(e) 5(f) 6(e) and6(f ))

Next based on the synthetic dataset we comparedTASC-MADM with RB-TPSC and Budget-TASC whenradiuses were changed For TASC-MADM and RB-TPSCwe selected the average metrics of different c values thecomputed radius approximately belongs to the interval [6 9]

RB-TPSCTASC-MADM

08

10

12

14

16

18

20

00 02 04 06 08 10 12 14 16 18 20

Aver

age d

istan

ce tr

avele

d

(d)

RB-TPSCTASC-MADM

09500

09525

09550

09575

09600

09625

09650

09675

09700

00 02 04 06 08 10 12 14 16 18 20

Aver

age b

udge

t util

izat

ion

rate

(e)

RB-TPSCTASC-MADM

650

655

660

665

670

675

680

685

00 02 04 06 08 10 12 14 16 18 20

Aver

age r

ewar

d

(f )

Figure 2 Performance with different accepted distances without extra remote allowance (c) (km) real dataset

0

1

2

3

4

5

6

7

Aver

age r

adiu

s

RB-TPSCTASC-MADM

090

092

094

096

098

100

102

104

106

108

110

(a)

RB-TPSCTASC-MADM

00

02

04

06

08

10

12

Task

assig

nmen

t rat

e

090

092

094

096

098

100

102

104

106

108

110

(b)

RB-TPSCTASC-MADM

0

500

1000

1500

2000

2500

3000

Aver

age r

eput

atio

n va

lue

090

092

094

096

098

100

102

104

106

108

110

(c)

RB-TPSCTASC-MADM

0

05

1

15

2

25

3

Aver

age d

istan

ce tr

avele

d

090

092

094

096

098

100

102

104

106

108

110

(d)

RB-TPSCTASC-MADM

086

088

09

092

094

096

098

Aver

age b

udge

t util

izat

ion

rate

090

092

094

096

098

100

102

104

106

108

110

(e)

RB-TPSCTASC-MADM

655660665670675680685690695

Aver

age r

ewar

d

090

092

094

096

098

100

102

104

106

108

110

(f )

Figure 3 Performance with an amplitude of variation in budget (B) real dataset

Table 2 Results under different approaches real dataset

Algorithms η () ψ ζ ϕ ωBudget-TASC 8567 139063 14 1 7761RB-TPSC 9395 52339 159 09645 6672TASC-MADM 9418 98746 078 09509 6598

Security and Communication Networks 9

02468

10121416

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

adiu

s

RB-TPSCTASC-MADM

(a)

RB-TPSCTASC-MADM

03

04

05

06

07

08

09

0 2 4 6 8 10 12 14 16 18 20

Task

assig

nmen

t rat

e(b)

RB-TPSCTASC-MADM

10000

10500

11000

11500

12000

12500

13000

13500

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

eput

atio

n va

lue

(c)

RB-TPSCTASC-MADM

00051015202530354045

0 2 4 6 8 10 12 14 16 18 20

Aver

age d

istan

ce tr

avel

ed

(d)

RB-TPSCTASC-MADM

084

085

086

087

088

089

09

0 2 4 6 8 10 12 14 16 18 20

Aver

age b

udge

t util

izat

ion

(e)

RB-TPSCTASC-MADM

650

655

660

665

670

675

680

685

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

ewar

d

(f )

Figure 4 Performance with an extra allowance per kilometer (β) (monetary unit) synthetic dataset

RB-TPSCTASC-MADM

60

65

70

75

80

85

90

95

00 02 04 06 08 10 12 14 16 18 20

Aver

age r

adiu

s

(a)

RB-TPSCTASC-MADM

065

067

069

071

073

075

077

079

00 02 04 06 08 10 12 14 16 18 20

Task

assig

nmen

t rat

e

(b)

RB-TPSCTASC-MADM

1000010250105001075011000112501150011750120001225012500

00 02 04 06 08 10 12 14 16 18 20

Aver

age r

eput

atio

n va

le

(c)

RB-TPSCTASC-MADM

100

120

140

160

180

200

220

00 02 04 06 08 10 12 14 16 18 20

Aver

age d

istan

ce tr

avele

d

(d)

RB-TPSCTASC-MADM

084085085086086087087088088089

00 02 04 06 08 10 12 14 16 18 20

Aver

age b

udge

t util

izat

ion

rate

(e)

RB-TPSCTASC-MADM

6600

6650

6700

6750

6800

6850

00 02 04 06 08 10 12 14 16 18 20

Aver

age r

ewar

d

(f )

Figure 5 Performance with different accepted distances without extra remote allowance (c) (km) synthetic dataset

10 Security and Communication Networks

(Figure 5(a)) So for Budget-TASC the radius varied from6 sim 9 km other parameters were set as in the fourthexperiment

)e experimental results (Table 3) show that TPSC-ADM obtains the highest task assignment rate but Budget-ASC achieves the highest quality and RB-TPSC spends thelowest budget

55 Effect of Taskrsquos Attribute Preference Our approachTASC-MADM enables the task to be assigned to satisfydemands for different goals If the resultrsquos quality is theprimary goal the task should select workers with a highreputation However if saving cost is the most concernedobjective the worker closer to the task should be chosenfirst Figure 7 shows the influence of tasksrsquo attributespreferences on the proposed approachrsquos performancew0 05 all targets are considered fairly w0 lt 05 the taskprefers the workerrsquos reputation to the distance the re-sultrsquos quality is significantly improved However thetravel cost and budget utilized are increased Whenw0 gt 05 the travel cost and the budget utilized are savedwhereas the resultrsquos quality is decreased Our approachcan maximize task assignment rate by setting w0 1 ormaximize the quality of workers selected by settingw0 0

56 Efficiency of Algorithms )e efficiency of the algorithmsis measured in CPU cost )e Budget-TASC algorithmrsquoscomputational complexity is O(n times m2) and that of RB-TPSC and TASC-MADM is O(n times m) we compare TASC-MADMwith RB-TPSC and Budget-TASC in CPU cost Eachof the programs runs 3 times 21 rounds )e average time perround is used to measure the algorithmrsquos efficiency Asshown in Figure 8 our approach significantly improves theefficiency of spatial task assignment

57 Summary of Experiment Results We summarized themajor finding as follows

(i) If the task is located in a worker-density area theproposed TASC-MADM approach exhibits betterresults than RB-TPSC It also performs better thanBudget-TASC on the metrics except for the qualityof workers

(ii) If the task is located in a worker-sparsity area theproposed TASC-MADM approach performs betterthan RB-TPSC in terms of the average assignmentrate and quality of workers But it leads to moretravel cost and budget utilized Budget-TASC ob-tains the best quality because it considers the qualityfirst

4

5

6

7

8

9

10

11

12Av

erag

e rad

ius

RB-TPSCTASC-MADM

090

092

094

096

098

100

102

104

106

108

110

(a)

RB-TPSCTASC-MADM

045

05

055

06

065

07

075

08

085

090

092

094

096

098

100

102

104

106

108

110

Task

assig

nmen

t rat

e(b)

RB-TPSCTASC-MADM

090

092

094

096

098

100

102

104

106

108

110

10000

10500

11000

11500

12000

12500

13000

13500

Aver

age r

eput

atio

n va

lue

(c)

RB-TPSCTASC-MADM

090

092

094

096

098

100

102

104

106

108

110

10

15

20

25

30

35

Aver

age d

istan

ce tr

avel

ed

(d)

RB-TPSCTASC-MADM

090

092

094

096

098

100

102

104

106

108

110

076078080082084086088090

094092

Aver

age b

udge

t util

izat

ion

rate

(e)

RB-TPSCTASC-MADM

090

092

094

096

098

100

102

104

106

108

110

660066506700675068006850690069507000

71007050

Aver

age r

ewar

d

(f )

Figure 6 Performance with an amplitude of variation in budget (B) synthetic dataset

Security and Communication Networks 11

Table 3 Results under different approaches synthetic dataset

Algorithms η ψ ζ ϕ ωBudget-TASC 072743975 1367217 1417697 1 7758585RB-TPSC 072253468 1027882 1408169 0851329 6686784TPSC-ADM 074825306 1219453 1926809 0873916 6768671

0

5

10

15

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

adiu

s

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

(a)

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

03504

04505

05506

06507

07508

085

0 2 4 6 8 10 12 14 16 18 20

Task

assig

nmen

t rat

e

(b)

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

100001050011000115001200012500130001350014000

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

eput

atio

n va

lue

(c)

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

0

1

2

3

4

5

6

0 2 4 6 8 10 12 14 16 18 20

Aver

age d

istan

ce tr

avel

ed

(d)

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

084

085

086

087

088

089

09

091

0 2 4 6 8 10 12 14 16 18 20

Aver

age b

udge

t util

izat

ion

rate

(e)

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

650655660665670675680685690695700705

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

ewar

d

(f )

Figure 7 Effect of tasksrsquo attributes preferences on the TASC-MADM approach Performance with an extra allowance per kilometer (β)

(monetary unit) synthetic dataset

128 114

31961929

135

618

0

20

40

60

80

100

120

140

Synthetic Dataset Real Dataset

TASC-MADMRB-TPSCBudget-TASC

Figure 8 Average CPU cost

12 Security and Communication Networks

(iii) In the terms of CPU cost the proposed TASC-MADM approach is superior to the baselinealgorithms

58 Discussion )is section discusses the advantages andlimitations of the TASC-MADM approach )e advantagesare listed as follows

(1) Effective )e TASC-MADM approach improves thetask assignment rate By setting different attributeweights it can maximize the task assignment rate orthe quality of crowdsourcing results

(2) Efficient )e TASC-MADM approach enhancesefficiency because of computing simply

(3) Extensible )eoretically our method can be ex-tended to solving decision problems involving anynumber of attributes

)ere still exist the following limitations in the TASC-MADM approach

(1) Quality Quantification of Workers In this paper theworker quality is modeled as a reputation value toreflect the quality of crowdsourced results Inpractice the same workerrsquos quality may differ inspecific tasks How to quantify the quality of workersis not covered in this paper

(2) Efficiency of Indexing Records )e TASC-MADMapproach exhaustively searches all the records toidentify the candidate of a task which makes it lessefficient to get the decision matrix on large datasets

6 Conclusion

)is paper focuses on designing an efficient task assignmentapproach which can deal with the situation where tasks havedifferent require preferences for different task attributes toachieve different goals Our task assignment approach can beextended to scenarios containing any number of attributesIn addition to the distance and reputation other criteriasuch as the workersrsquo skills can be considered

As for future work more factors such as workersrsquowillingness to accept tasks and their quality differences indifferent professional fields are included in task assign-ments In addition improving the efficiency of indexingrecords to make the allocation scheme suitable for largedatasets is a valuable research topic

Data Availability

)e data of allocated sharing tasks are available from ChinaSociety for Industrial and Applied Mathematics 2017 re-trieved on September 2 2020 from httpwwwmcmeducnhtml_cnnode460baf68ab0ed0e1e557a0c79b1c4648html

Conflicts of Interest

)e authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

)e authors thank researchers working at Guangxi KeyLaboratory of Trusted Software especially Xuguang Bao andManli Zhu for their suggestion during the research andpreparation of the manuscript )is work was funded byGuangxi Key Laboratory of Trusted Software (Nokx201727) Project to Improve the Scientific Research BasicAbility of Middle-Aged and Young Teachers (No2019KY0226) Natural Science Foundation of China (Nos61966009 U1811264 and U1711263) and Natural ScienceFoundation of Guangxi Province (Nos2019GXNSFBA245049 2019GXNSFBA245059 and2018GXNSFDA281045)

References

[1] L Kazemi and C Shahabi ldquoGeoCrowd enabling query an-swering with spatial crowdsourcing GISrdquo in Proceedings ofthe ACM International Symposium on Advances in GeographicInformation Systems pp 189ndash198 CA USA November 2012

[2] J Xiong M Zhao M Z A Bhuiyan L Chen and Y TianldquoAn AI-enabled three-party game framework for guaranteeddata privacy in mobile edge crowdsensing of IoTrdquo IEEETransactions on Industrial Informatics vol 17 no 2pp 922ndash933 2021

[3] J Xiong R Ma L Chen et al ldquoA personalized privacyprotection framework for mobile crowdsensing in IIoTrdquo IEEETransactions on Industrial Informatics vol 16 no 6pp 4231ndash4241 2020

[4] DiDi Didi Chuxing Corporate Citizenship Report httpswwwdidiglobalcomaboutdidiresponsibility 2017

[5] J Xiong J Ren L Chen et al ldquoEnhancing privacy andavailability for data clustering in intelligent electrical serviceof IoTrdquo IEEE Internet of Bings Journal vol 6 no 2pp 1530ndash1540 2019

[6] C Zhao P Cheng L Chen X Lin and C Shahabi ldquoFair taskassignment in spatial crowdsourcingrdquo Proceedings of theVLDB Endowment vol 13 no 12 pp 2479ndash2492 2020

[7] M Venanzi A Rogers and N R Jennings ldquoCrowdsourcingspatial phenomena using trust-based heteroskedasticGaussian processesrdquo in Proceedings of the First Conference onHuman Computation and Crowdsourcing (HCOMP)pp 182ndash189 Palm Springs CA USA November 2013

[8] J Xiong R Bi M Zhao J Guo and Q Yang ldquoEdge-assistedprivacy-preserving raw data sharing framework for connectedautonomous vehiclesrdquo IEEE Wireless Communicationsvol 27 no 3 pp 24ndash30 2020

[9] B Van B V D Haak M Parks and M Castells ldquo)e futureof journalism networked journalism rethinking journalism inthe networked digital agerdquo International Journal of Com-munication vol 6 pp 2923ndash2938 2012

[10] M Zook M Graham S Taylor and S Gorman ldquoVolunteeredgeographic information and crowdsourcing disaster relief acase study of the Haitian earthquakerdquoWorld Medical umlHealthPolicy vol 2 no 2 pp 7ndash33 2010

[11] Y Tong L Chen and C Shahabi ldquoSpatial crowdsourcingchallenges techniques and applicationsrdquo Proceedings of theVLDB Endowment vol 10 pp 1988ndash1991 2017

[12] J Xiong X Chen Q Yang L Chen and Z Yao ldquoA task-oriented user selection incentive mechanism in edge-aidedmobile crowdsensingrdquo IEEE Transactions on Network Scienceand Engineering vol 7 no 4 pp 2347ndash2360 2020

Security and Communication Networks 13

02468

10121416

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

adiu

s

RB-TPSCTASC-MADM

(a)

RB-TPSCTASC-MADM

03

04

05

06

07

08

09

0 2 4 6 8 10 12 14 16 18 20

Task

assig

nmen

t rat

e(b)

RB-TPSCTASC-MADM

10000

10500

11000

11500

12000

12500

13000

13500

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

eput

atio

n va

lue

(c)

RB-TPSCTASC-MADM

00051015202530354045

0 2 4 6 8 10 12 14 16 18 20

Aver

age d

istan

ce tr

avel

ed

(d)

RB-TPSCTASC-MADM

084

085

086

087

088

089

09

0 2 4 6 8 10 12 14 16 18 20

Aver

age b

udge

t util

izat

ion

(e)

RB-TPSCTASC-MADM

650

655

660

665

670

675

680

685

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

ewar

d

(f )

Figure 4 Performance with an extra allowance per kilometer (β) (monetary unit) synthetic dataset

RB-TPSCTASC-MADM

60

65

70

75

80

85

90

95

00 02 04 06 08 10 12 14 16 18 20

Aver

age r

adiu

s

(a)

RB-TPSCTASC-MADM

065

067

069

071

073

075

077

079

00 02 04 06 08 10 12 14 16 18 20

Task

assig

nmen

t rat

e

(b)

RB-TPSCTASC-MADM

1000010250105001075011000112501150011750120001225012500

00 02 04 06 08 10 12 14 16 18 20

Aver

age r

eput

atio

n va

le

(c)

RB-TPSCTASC-MADM

100

120

140

160

180

200

220

00 02 04 06 08 10 12 14 16 18 20

Aver

age d

istan

ce tr

avele

d

(d)

RB-TPSCTASC-MADM

084085085086086087087088088089

00 02 04 06 08 10 12 14 16 18 20

Aver

age b

udge

t util

izat

ion

rate

(e)

RB-TPSCTASC-MADM

6600

6650

6700

6750

6800

6850

00 02 04 06 08 10 12 14 16 18 20

Aver

age r

ewar

d

(f )

Figure 5 Performance with different accepted distances without extra remote allowance (c) (km) synthetic dataset

10 Security and Communication Networks

(Figure 5(a)) So for Budget-TASC the radius varied from6 sim 9 km other parameters were set as in the fourthexperiment

)e experimental results (Table 3) show that TPSC-ADM obtains the highest task assignment rate but Budget-ASC achieves the highest quality and RB-TPSC spends thelowest budget

55 Effect of Taskrsquos Attribute Preference Our approachTASC-MADM enables the task to be assigned to satisfydemands for different goals If the resultrsquos quality is theprimary goal the task should select workers with a highreputation However if saving cost is the most concernedobjective the worker closer to the task should be chosenfirst Figure 7 shows the influence of tasksrsquo attributespreferences on the proposed approachrsquos performancew0 05 all targets are considered fairly w0 lt 05 the taskprefers the workerrsquos reputation to the distance the re-sultrsquos quality is significantly improved However thetravel cost and budget utilized are increased Whenw0 gt 05 the travel cost and the budget utilized are savedwhereas the resultrsquos quality is decreased Our approachcan maximize task assignment rate by setting w0 1 ormaximize the quality of workers selected by settingw0 0

56 Efficiency of Algorithms )e efficiency of the algorithmsis measured in CPU cost )e Budget-TASC algorithmrsquoscomputational complexity is O(n times m2) and that of RB-TPSC and TASC-MADM is O(n times m) we compare TASC-MADMwith RB-TPSC and Budget-TASC in CPU cost Eachof the programs runs 3 times 21 rounds )e average time perround is used to measure the algorithmrsquos efficiency Asshown in Figure 8 our approach significantly improves theefficiency of spatial task assignment

57 Summary of Experiment Results We summarized themajor finding as follows

(i) If the task is located in a worker-density area theproposed TASC-MADM approach exhibits betterresults than RB-TPSC It also performs better thanBudget-TASC on the metrics except for the qualityof workers

(ii) If the task is located in a worker-sparsity area theproposed TASC-MADM approach performs betterthan RB-TPSC in terms of the average assignmentrate and quality of workers But it leads to moretravel cost and budget utilized Budget-TASC ob-tains the best quality because it considers the qualityfirst

4

5

6

7

8

9

10

11

12Av

erag

e rad

ius

RB-TPSCTASC-MADM

090

092

094

096

098

100

102

104

106

108

110

(a)

RB-TPSCTASC-MADM

045

05

055

06

065

07

075

08

085

090

092

094

096

098

100

102

104

106

108

110

Task

assig

nmen

t rat

e(b)

RB-TPSCTASC-MADM

090

092

094

096

098

100

102

104

106

108

110

10000

10500

11000

11500

12000

12500

13000

13500

Aver

age r

eput

atio

n va

lue

(c)

RB-TPSCTASC-MADM

090

092

094

096

098

100

102

104

106

108

110

10

15

20

25

30

35

Aver

age d

istan

ce tr

avel

ed

(d)

RB-TPSCTASC-MADM

090

092

094

096

098

100

102

104

106

108

110

076078080082084086088090

094092

Aver

age b

udge

t util

izat

ion

rate

(e)

RB-TPSCTASC-MADM

090

092

094

096

098

100

102

104

106

108

110

660066506700675068006850690069507000

71007050

Aver

age r

ewar

d

(f )

Figure 6 Performance with an amplitude of variation in budget (B) synthetic dataset

Security and Communication Networks 11

Table 3 Results under different approaches synthetic dataset

Algorithms η ψ ζ ϕ ωBudget-TASC 072743975 1367217 1417697 1 7758585RB-TPSC 072253468 1027882 1408169 0851329 6686784TPSC-ADM 074825306 1219453 1926809 0873916 6768671

0

5

10

15

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

adiu

s

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

(a)

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

03504

04505

05506

06507

07508

085

0 2 4 6 8 10 12 14 16 18 20

Task

assig

nmen

t rat

e

(b)

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

100001050011000115001200012500130001350014000

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

eput

atio

n va

lue

(c)

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

0

1

2

3

4

5

6

0 2 4 6 8 10 12 14 16 18 20

Aver

age d

istan

ce tr

avel

ed

(d)

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

084

085

086

087

088

089

09

091

0 2 4 6 8 10 12 14 16 18 20

Aver

age b

udge

t util

izat

ion

rate

(e)

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

650655660665670675680685690695700705

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

ewar

d

(f )

Figure 7 Effect of tasksrsquo attributes preferences on the TASC-MADM approach Performance with an extra allowance per kilometer (β)

(monetary unit) synthetic dataset

128 114

31961929

135

618

0

20

40

60

80

100

120

140

Synthetic Dataset Real Dataset

TASC-MADMRB-TPSCBudget-TASC

Figure 8 Average CPU cost

12 Security and Communication Networks

(iii) In the terms of CPU cost the proposed TASC-MADM approach is superior to the baselinealgorithms

58 Discussion )is section discusses the advantages andlimitations of the TASC-MADM approach )e advantagesare listed as follows

(1) Effective )e TASC-MADM approach improves thetask assignment rate By setting different attributeweights it can maximize the task assignment rate orthe quality of crowdsourcing results

(2) Efficient )e TASC-MADM approach enhancesefficiency because of computing simply

(3) Extensible )eoretically our method can be ex-tended to solving decision problems involving anynumber of attributes

)ere still exist the following limitations in the TASC-MADM approach

(1) Quality Quantification of Workers In this paper theworker quality is modeled as a reputation value toreflect the quality of crowdsourced results Inpractice the same workerrsquos quality may differ inspecific tasks How to quantify the quality of workersis not covered in this paper

(2) Efficiency of Indexing Records )e TASC-MADMapproach exhaustively searches all the records toidentify the candidate of a task which makes it lessefficient to get the decision matrix on large datasets

6 Conclusion

)is paper focuses on designing an efficient task assignmentapproach which can deal with the situation where tasks havedifferent require preferences for different task attributes toachieve different goals Our task assignment approach can beextended to scenarios containing any number of attributesIn addition to the distance and reputation other criteriasuch as the workersrsquo skills can be considered

As for future work more factors such as workersrsquowillingness to accept tasks and their quality differences indifferent professional fields are included in task assign-ments In addition improving the efficiency of indexingrecords to make the allocation scheme suitable for largedatasets is a valuable research topic

Data Availability

)e data of allocated sharing tasks are available from ChinaSociety for Industrial and Applied Mathematics 2017 re-trieved on September 2 2020 from httpwwwmcmeducnhtml_cnnode460baf68ab0ed0e1e557a0c79b1c4648html

Conflicts of Interest

)e authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

)e authors thank researchers working at Guangxi KeyLaboratory of Trusted Software especially Xuguang Bao andManli Zhu for their suggestion during the research andpreparation of the manuscript )is work was funded byGuangxi Key Laboratory of Trusted Software (Nokx201727) Project to Improve the Scientific Research BasicAbility of Middle-Aged and Young Teachers (No2019KY0226) Natural Science Foundation of China (Nos61966009 U1811264 and U1711263) and Natural ScienceFoundation of Guangxi Province (Nos2019GXNSFBA245049 2019GXNSFBA245059 and2018GXNSFDA281045)

References

[1] L Kazemi and C Shahabi ldquoGeoCrowd enabling query an-swering with spatial crowdsourcing GISrdquo in Proceedings ofthe ACM International Symposium on Advances in GeographicInformation Systems pp 189ndash198 CA USA November 2012

[2] J Xiong M Zhao M Z A Bhuiyan L Chen and Y TianldquoAn AI-enabled three-party game framework for guaranteeddata privacy in mobile edge crowdsensing of IoTrdquo IEEETransactions on Industrial Informatics vol 17 no 2pp 922ndash933 2021

[3] J Xiong R Ma L Chen et al ldquoA personalized privacyprotection framework for mobile crowdsensing in IIoTrdquo IEEETransactions on Industrial Informatics vol 16 no 6pp 4231ndash4241 2020

[4] DiDi Didi Chuxing Corporate Citizenship Report httpswwwdidiglobalcomaboutdidiresponsibility 2017

[5] J Xiong J Ren L Chen et al ldquoEnhancing privacy andavailability for data clustering in intelligent electrical serviceof IoTrdquo IEEE Internet of Bings Journal vol 6 no 2pp 1530ndash1540 2019

[6] C Zhao P Cheng L Chen X Lin and C Shahabi ldquoFair taskassignment in spatial crowdsourcingrdquo Proceedings of theVLDB Endowment vol 13 no 12 pp 2479ndash2492 2020

[7] M Venanzi A Rogers and N R Jennings ldquoCrowdsourcingspatial phenomena using trust-based heteroskedasticGaussian processesrdquo in Proceedings of the First Conference onHuman Computation and Crowdsourcing (HCOMP)pp 182ndash189 Palm Springs CA USA November 2013

[8] J Xiong R Bi M Zhao J Guo and Q Yang ldquoEdge-assistedprivacy-preserving raw data sharing framework for connectedautonomous vehiclesrdquo IEEE Wireless Communicationsvol 27 no 3 pp 24ndash30 2020

[9] B Van B V D Haak M Parks and M Castells ldquo)e futureof journalism networked journalism rethinking journalism inthe networked digital agerdquo International Journal of Com-munication vol 6 pp 2923ndash2938 2012

[10] M Zook M Graham S Taylor and S Gorman ldquoVolunteeredgeographic information and crowdsourcing disaster relief acase study of the Haitian earthquakerdquoWorld Medical umlHealthPolicy vol 2 no 2 pp 7ndash33 2010

[11] Y Tong L Chen and C Shahabi ldquoSpatial crowdsourcingchallenges techniques and applicationsrdquo Proceedings of theVLDB Endowment vol 10 pp 1988ndash1991 2017

[12] J Xiong X Chen Q Yang L Chen and Z Yao ldquoA task-oriented user selection incentive mechanism in edge-aidedmobile crowdsensingrdquo IEEE Transactions on Network Scienceand Engineering vol 7 no 4 pp 2347ndash2360 2020

Security and Communication Networks 13

(Figure 5(a)) So for Budget-TASC the radius varied from6 sim 9 km other parameters were set as in the fourthexperiment

)e experimental results (Table 3) show that TPSC-ADM obtains the highest task assignment rate but Budget-ASC achieves the highest quality and RB-TPSC spends thelowest budget

55 Effect of Taskrsquos Attribute Preference Our approachTASC-MADM enables the task to be assigned to satisfydemands for different goals If the resultrsquos quality is theprimary goal the task should select workers with a highreputation However if saving cost is the most concernedobjective the worker closer to the task should be chosenfirst Figure 7 shows the influence of tasksrsquo attributespreferences on the proposed approachrsquos performancew0 05 all targets are considered fairly w0 lt 05 the taskprefers the workerrsquos reputation to the distance the re-sultrsquos quality is significantly improved However thetravel cost and budget utilized are increased Whenw0 gt 05 the travel cost and the budget utilized are savedwhereas the resultrsquos quality is decreased Our approachcan maximize task assignment rate by setting w0 1 ormaximize the quality of workers selected by settingw0 0

56 Efficiency of Algorithms )e efficiency of the algorithmsis measured in CPU cost )e Budget-TASC algorithmrsquoscomputational complexity is O(n times m2) and that of RB-TPSC and TASC-MADM is O(n times m) we compare TASC-MADMwith RB-TPSC and Budget-TASC in CPU cost Eachof the programs runs 3 times 21 rounds )e average time perround is used to measure the algorithmrsquos efficiency Asshown in Figure 8 our approach significantly improves theefficiency of spatial task assignment

57 Summary of Experiment Results We summarized themajor finding as follows

(i) If the task is located in a worker-density area theproposed TASC-MADM approach exhibits betterresults than RB-TPSC It also performs better thanBudget-TASC on the metrics except for the qualityof workers

(ii) If the task is located in a worker-sparsity area theproposed TASC-MADM approach performs betterthan RB-TPSC in terms of the average assignmentrate and quality of workers But it leads to moretravel cost and budget utilized Budget-TASC ob-tains the best quality because it considers the qualityfirst

4

5

6

7

8

9

10

11

12Av

erag

e rad

ius

RB-TPSCTASC-MADM

090

092

094

096

098

100

102

104

106

108

110

(a)

RB-TPSCTASC-MADM

045

05

055

06

065

07

075

08

085

090

092

094

096

098

100

102

104

106

108

110

Task

assig

nmen

t rat

e(b)

RB-TPSCTASC-MADM

090

092

094

096

098

100

102

104

106

108

110

10000

10500

11000

11500

12000

12500

13000

13500

Aver

age r

eput

atio

n va

lue

(c)

RB-TPSCTASC-MADM

090

092

094

096

098

100

102

104

106

108

110

10

15

20

25

30

35

Aver

age d

istan

ce tr

avel

ed

(d)

RB-TPSCTASC-MADM

090

092

094

096

098

100

102

104

106

108

110

076078080082084086088090

094092

Aver

age b

udge

t util

izat

ion

rate

(e)

RB-TPSCTASC-MADM

090

092

094

096

098

100

102

104

106

108

110

660066506700675068006850690069507000

71007050

Aver

age r

ewar

d

(f )

Figure 6 Performance with an amplitude of variation in budget (B) synthetic dataset

Security and Communication Networks 11

Table 3 Results under different approaches synthetic dataset

Algorithms η ψ ζ ϕ ωBudget-TASC 072743975 1367217 1417697 1 7758585RB-TPSC 072253468 1027882 1408169 0851329 6686784TPSC-ADM 074825306 1219453 1926809 0873916 6768671

0

5

10

15

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

adiu

s

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

(a)

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

03504

04505

05506

06507

07508

085

0 2 4 6 8 10 12 14 16 18 20

Task

assig

nmen

t rat

e

(b)

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

100001050011000115001200012500130001350014000

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

eput

atio

n va

lue

(c)

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

0

1

2

3

4

5

6

0 2 4 6 8 10 12 14 16 18 20

Aver

age d

istan

ce tr

avel

ed

(d)

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

084

085

086

087

088

089

09

091

0 2 4 6 8 10 12 14 16 18 20

Aver

age b

udge

t util

izat

ion

rate

(e)

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

650655660665670675680685690695700705

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

ewar

d

(f )

Figure 7 Effect of tasksrsquo attributes preferences on the TASC-MADM approach Performance with an extra allowance per kilometer (β)

(monetary unit) synthetic dataset

128 114

31961929

135

618

0

20

40

60

80

100

120

140

Synthetic Dataset Real Dataset

TASC-MADMRB-TPSCBudget-TASC

Figure 8 Average CPU cost

12 Security and Communication Networks

(iii) In the terms of CPU cost the proposed TASC-MADM approach is superior to the baselinealgorithms

58 Discussion )is section discusses the advantages andlimitations of the TASC-MADM approach )e advantagesare listed as follows

(1) Effective )e TASC-MADM approach improves thetask assignment rate By setting different attributeweights it can maximize the task assignment rate orthe quality of crowdsourcing results

(2) Efficient )e TASC-MADM approach enhancesefficiency because of computing simply

(3) Extensible )eoretically our method can be ex-tended to solving decision problems involving anynumber of attributes

)ere still exist the following limitations in the TASC-MADM approach

(1) Quality Quantification of Workers In this paper theworker quality is modeled as a reputation value toreflect the quality of crowdsourced results Inpractice the same workerrsquos quality may differ inspecific tasks How to quantify the quality of workersis not covered in this paper

(2) Efficiency of Indexing Records )e TASC-MADMapproach exhaustively searches all the records toidentify the candidate of a task which makes it lessefficient to get the decision matrix on large datasets

6 Conclusion

)is paper focuses on designing an efficient task assignmentapproach which can deal with the situation where tasks havedifferent require preferences for different task attributes toachieve different goals Our task assignment approach can beextended to scenarios containing any number of attributesIn addition to the distance and reputation other criteriasuch as the workersrsquo skills can be considered

As for future work more factors such as workersrsquowillingness to accept tasks and their quality differences indifferent professional fields are included in task assign-ments In addition improving the efficiency of indexingrecords to make the allocation scheme suitable for largedatasets is a valuable research topic

Data Availability

)e data of allocated sharing tasks are available from ChinaSociety for Industrial and Applied Mathematics 2017 re-trieved on September 2 2020 from httpwwwmcmeducnhtml_cnnode460baf68ab0ed0e1e557a0c79b1c4648html

Conflicts of Interest

)e authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

)e authors thank researchers working at Guangxi KeyLaboratory of Trusted Software especially Xuguang Bao andManli Zhu for their suggestion during the research andpreparation of the manuscript )is work was funded byGuangxi Key Laboratory of Trusted Software (Nokx201727) Project to Improve the Scientific Research BasicAbility of Middle-Aged and Young Teachers (No2019KY0226) Natural Science Foundation of China (Nos61966009 U1811264 and U1711263) and Natural ScienceFoundation of Guangxi Province (Nos2019GXNSFBA245049 2019GXNSFBA245059 and2018GXNSFDA281045)

References

[1] L Kazemi and C Shahabi ldquoGeoCrowd enabling query an-swering with spatial crowdsourcing GISrdquo in Proceedings ofthe ACM International Symposium on Advances in GeographicInformation Systems pp 189ndash198 CA USA November 2012

[2] J Xiong M Zhao M Z A Bhuiyan L Chen and Y TianldquoAn AI-enabled three-party game framework for guaranteeddata privacy in mobile edge crowdsensing of IoTrdquo IEEETransactions on Industrial Informatics vol 17 no 2pp 922ndash933 2021

[3] J Xiong R Ma L Chen et al ldquoA personalized privacyprotection framework for mobile crowdsensing in IIoTrdquo IEEETransactions on Industrial Informatics vol 16 no 6pp 4231ndash4241 2020

[4] DiDi Didi Chuxing Corporate Citizenship Report httpswwwdidiglobalcomaboutdidiresponsibility 2017

[5] J Xiong J Ren L Chen et al ldquoEnhancing privacy andavailability for data clustering in intelligent electrical serviceof IoTrdquo IEEE Internet of Bings Journal vol 6 no 2pp 1530ndash1540 2019

[6] C Zhao P Cheng L Chen X Lin and C Shahabi ldquoFair taskassignment in spatial crowdsourcingrdquo Proceedings of theVLDB Endowment vol 13 no 12 pp 2479ndash2492 2020

[7] M Venanzi A Rogers and N R Jennings ldquoCrowdsourcingspatial phenomena using trust-based heteroskedasticGaussian processesrdquo in Proceedings of the First Conference onHuman Computation and Crowdsourcing (HCOMP)pp 182ndash189 Palm Springs CA USA November 2013

[8] J Xiong R Bi M Zhao J Guo and Q Yang ldquoEdge-assistedprivacy-preserving raw data sharing framework for connectedautonomous vehiclesrdquo IEEE Wireless Communicationsvol 27 no 3 pp 24ndash30 2020

[9] B Van B V D Haak M Parks and M Castells ldquo)e futureof journalism networked journalism rethinking journalism inthe networked digital agerdquo International Journal of Com-munication vol 6 pp 2923ndash2938 2012

[10] M Zook M Graham S Taylor and S Gorman ldquoVolunteeredgeographic information and crowdsourcing disaster relief acase study of the Haitian earthquakerdquoWorld Medical umlHealthPolicy vol 2 no 2 pp 7ndash33 2010

[11] Y Tong L Chen and C Shahabi ldquoSpatial crowdsourcingchallenges techniques and applicationsrdquo Proceedings of theVLDB Endowment vol 10 pp 1988ndash1991 2017

[12] J Xiong X Chen Q Yang L Chen and Z Yao ldquoA task-oriented user selection incentive mechanism in edge-aidedmobile crowdsensingrdquo IEEE Transactions on Network Scienceand Engineering vol 7 no 4 pp 2347ndash2360 2020

Security and Communication Networks 13

Table 3 Results under different approaches synthetic dataset

Algorithms η ψ ζ ϕ ωBudget-TASC 072743975 1367217 1417697 1 7758585RB-TPSC 072253468 1027882 1408169 0851329 6686784TPSC-ADM 074825306 1219453 1926809 0873916 6768671

0

5

10

15

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

adiu

s

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

(a)

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

03504

04505

05506

06507

07508

085

0 2 4 6 8 10 12 14 16 18 20

Task

assig

nmen

t rat

e

(b)

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

100001050011000115001200012500130001350014000

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

eput

atio

n va

lue

(c)

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

0

1

2

3

4

5

6

0 2 4 6 8 10 12 14 16 18 20

Aver

age d

istan

ce tr

avel

ed

(d)

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

084

085

086

087

088

089

09

091

0 2 4 6 8 10 12 14 16 18 20

Aver

age b

udge

t util

izat

ion

rate

(e)

TASM-MADMw0=01TASM-MADMw0=05TASM-MADMw0=09

650655660665670675680685690695700705

0 2 4 6 8 10 12 14 16 18 20

Aver

age r

ewar

d

(f )

Figure 7 Effect of tasksrsquo attributes preferences on the TASC-MADM approach Performance with an extra allowance per kilometer (β)

(monetary unit) synthetic dataset

128 114

31961929

135

618

0

20

40

60

80

100

120

140

Synthetic Dataset Real Dataset

TASC-MADMRB-TPSCBudget-TASC

Figure 8 Average CPU cost

12 Security and Communication Networks

(iii) In the terms of CPU cost the proposed TASC-MADM approach is superior to the baselinealgorithms

58 Discussion )is section discusses the advantages andlimitations of the TASC-MADM approach )e advantagesare listed as follows

(1) Effective )e TASC-MADM approach improves thetask assignment rate By setting different attributeweights it can maximize the task assignment rate orthe quality of crowdsourcing results

(2) Efficient )e TASC-MADM approach enhancesefficiency because of computing simply

(3) Extensible )eoretically our method can be ex-tended to solving decision problems involving anynumber of attributes

)ere still exist the following limitations in the TASC-MADM approach

(1) Quality Quantification of Workers In this paper theworker quality is modeled as a reputation value toreflect the quality of crowdsourced results Inpractice the same workerrsquos quality may differ inspecific tasks How to quantify the quality of workersis not covered in this paper

(2) Efficiency of Indexing Records )e TASC-MADMapproach exhaustively searches all the records toidentify the candidate of a task which makes it lessefficient to get the decision matrix on large datasets

6 Conclusion

)is paper focuses on designing an efficient task assignmentapproach which can deal with the situation where tasks havedifferent require preferences for different task attributes toachieve different goals Our task assignment approach can beextended to scenarios containing any number of attributesIn addition to the distance and reputation other criteriasuch as the workersrsquo skills can be considered

As for future work more factors such as workersrsquowillingness to accept tasks and their quality differences indifferent professional fields are included in task assign-ments In addition improving the efficiency of indexingrecords to make the allocation scheme suitable for largedatasets is a valuable research topic

Data Availability

)e data of allocated sharing tasks are available from ChinaSociety for Industrial and Applied Mathematics 2017 re-trieved on September 2 2020 from httpwwwmcmeducnhtml_cnnode460baf68ab0ed0e1e557a0c79b1c4648html

Conflicts of Interest

)e authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

)e authors thank researchers working at Guangxi KeyLaboratory of Trusted Software especially Xuguang Bao andManli Zhu for their suggestion during the research andpreparation of the manuscript )is work was funded byGuangxi Key Laboratory of Trusted Software (Nokx201727) Project to Improve the Scientific Research BasicAbility of Middle-Aged and Young Teachers (No2019KY0226) Natural Science Foundation of China (Nos61966009 U1811264 and U1711263) and Natural ScienceFoundation of Guangxi Province (Nos2019GXNSFBA245049 2019GXNSFBA245059 and2018GXNSFDA281045)

References

[1] L Kazemi and C Shahabi ldquoGeoCrowd enabling query an-swering with spatial crowdsourcing GISrdquo in Proceedings ofthe ACM International Symposium on Advances in GeographicInformation Systems pp 189ndash198 CA USA November 2012

[2] J Xiong M Zhao M Z A Bhuiyan L Chen and Y TianldquoAn AI-enabled three-party game framework for guaranteeddata privacy in mobile edge crowdsensing of IoTrdquo IEEETransactions on Industrial Informatics vol 17 no 2pp 922ndash933 2021

[3] J Xiong R Ma L Chen et al ldquoA personalized privacyprotection framework for mobile crowdsensing in IIoTrdquo IEEETransactions on Industrial Informatics vol 16 no 6pp 4231ndash4241 2020

[4] DiDi Didi Chuxing Corporate Citizenship Report httpswwwdidiglobalcomaboutdidiresponsibility 2017

[5] J Xiong J Ren L Chen et al ldquoEnhancing privacy andavailability for data clustering in intelligent electrical serviceof IoTrdquo IEEE Internet of Bings Journal vol 6 no 2pp 1530ndash1540 2019

[6] C Zhao P Cheng L Chen X Lin and C Shahabi ldquoFair taskassignment in spatial crowdsourcingrdquo Proceedings of theVLDB Endowment vol 13 no 12 pp 2479ndash2492 2020

[7] M Venanzi A Rogers and N R Jennings ldquoCrowdsourcingspatial phenomena using trust-based heteroskedasticGaussian processesrdquo in Proceedings of the First Conference onHuman Computation and Crowdsourcing (HCOMP)pp 182ndash189 Palm Springs CA USA November 2013

[8] J Xiong R Bi M Zhao J Guo and Q Yang ldquoEdge-assistedprivacy-preserving raw data sharing framework for connectedautonomous vehiclesrdquo IEEE Wireless Communicationsvol 27 no 3 pp 24ndash30 2020

[9] B Van B V D Haak M Parks and M Castells ldquo)e futureof journalism networked journalism rethinking journalism inthe networked digital agerdquo International Journal of Com-munication vol 6 pp 2923ndash2938 2012

[10] M Zook M Graham S Taylor and S Gorman ldquoVolunteeredgeographic information and crowdsourcing disaster relief acase study of the Haitian earthquakerdquoWorld Medical umlHealthPolicy vol 2 no 2 pp 7ndash33 2010

[11] Y Tong L Chen and C Shahabi ldquoSpatial crowdsourcingchallenges techniques and applicationsrdquo Proceedings of theVLDB Endowment vol 10 pp 1988ndash1991 2017

[12] J Xiong X Chen Q Yang L Chen and Z Yao ldquoA task-oriented user selection incentive mechanism in edge-aidedmobile crowdsensingrdquo IEEE Transactions on Network Scienceand Engineering vol 7 no 4 pp 2347ndash2360 2020

Security and Communication Networks 13

(iii) In the terms of CPU cost the proposed TASC-MADM approach is superior to the baselinealgorithms

58 Discussion )is section discusses the advantages andlimitations of the TASC-MADM approach )e advantagesare listed as follows

(1) Effective )e TASC-MADM approach improves thetask assignment rate By setting different attributeweights it can maximize the task assignment rate orthe quality of crowdsourcing results

(2) Efficient )e TASC-MADM approach enhancesefficiency because of computing simply

(3) Extensible )eoretically our method can be ex-tended to solving decision problems involving anynumber of attributes

)ere still exist the following limitations in the TASC-MADM approach

(1) Quality Quantification of Workers In this paper theworker quality is modeled as a reputation value toreflect the quality of crowdsourced results Inpractice the same workerrsquos quality may differ inspecific tasks How to quantify the quality of workersis not covered in this paper

(2) Efficiency of Indexing Records )e TASC-MADMapproach exhaustively searches all the records toidentify the candidate of a task which makes it lessefficient to get the decision matrix on large datasets

6 Conclusion

)is paper focuses on designing an efficient task assignmentapproach which can deal with the situation where tasks havedifferent require preferences for different task attributes toachieve different goals Our task assignment approach can beextended to scenarios containing any number of attributesIn addition to the distance and reputation other criteriasuch as the workersrsquo skills can be considered

As for future work more factors such as workersrsquowillingness to accept tasks and their quality differences indifferent professional fields are included in task assign-ments In addition improving the efficiency of indexingrecords to make the allocation scheme suitable for largedatasets is a valuable research topic

Data Availability

)e data of allocated sharing tasks are available from ChinaSociety for Industrial and Applied Mathematics 2017 re-trieved on September 2 2020 from httpwwwmcmeducnhtml_cnnode460baf68ab0ed0e1e557a0c79b1c4648html

Conflicts of Interest

)e authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

)e authors thank researchers working at Guangxi KeyLaboratory of Trusted Software especially Xuguang Bao andManli Zhu for their suggestion during the research andpreparation of the manuscript )is work was funded byGuangxi Key Laboratory of Trusted Software (Nokx201727) Project to Improve the Scientific Research BasicAbility of Middle-Aged and Young Teachers (No2019KY0226) Natural Science Foundation of China (Nos61966009 U1811264 and U1711263) and Natural ScienceFoundation of Guangxi Province (Nos2019GXNSFBA245049 2019GXNSFBA245059 and2018GXNSFDA281045)

References

[1] L Kazemi and C Shahabi ldquoGeoCrowd enabling query an-swering with spatial crowdsourcing GISrdquo in Proceedings ofthe ACM International Symposium on Advances in GeographicInformation Systems pp 189ndash198 CA USA November 2012

[2] J Xiong M Zhao M Z A Bhuiyan L Chen and Y TianldquoAn AI-enabled three-party game framework for guaranteeddata privacy in mobile edge crowdsensing of IoTrdquo IEEETransactions on Industrial Informatics vol 17 no 2pp 922ndash933 2021

[3] J Xiong R Ma L Chen et al ldquoA personalized privacyprotection framework for mobile crowdsensing in IIoTrdquo IEEETransactions on Industrial Informatics vol 16 no 6pp 4231ndash4241 2020

[4] DiDi Didi Chuxing Corporate Citizenship Report httpswwwdidiglobalcomaboutdidiresponsibility 2017

[5] J Xiong J Ren L Chen et al ldquoEnhancing privacy andavailability for data clustering in intelligent electrical serviceof IoTrdquo IEEE Internet of Bings Journal vol 6 no 2pp 1530ndash1540 2019

[6] C Zhao P Cheng L Chen X Lin and C Shahabi ldquoFair taskassignment in spatial crowdsourcingrdquo Proceedings of theVLDB Endowment vol 13 no 12 pp 2479ndash2492 2020

[7] M Venanzi A Rogers and N R Jennings ldquoCrowdsourcingspatial phenomena using trust-based heteroskedasticGaussian processesrdquo in Proceedings of the First Conference onHuman Computation and Crowdsourcing (HCOMP)pp 182ndash189 Palm Springs CA USA November 2013

[8] J Xiong R Bi M Zhao J Guo and Q Yang ldquoEdge-assistedprivacy-preserving raw data sharing framework for connectedautonomous vehiclesrdquo IEEE Wireless Communicationsvol 27 no 3 pp 24ndash30 2020

[9] B Van B V D Haak M Parks and M Castells ldquo)e futureof journalism networked journalism rethinking journalism inthe networked digital agerdquo International Journal of Com-munication vol 6 pp 2923ndash2938 2012

[10] M Zook M Graham S Taylor and S Gorman ldquoVolunteeredgeographic information and crowdsourcing disaster relief acase study of the Haitian earthquakerdquoWorld Medical umlHealthPolicy vol 2 no 2 pp 7ndash33 2010

[11] Y Tong L Chen and C Shahabi ldquoSpatial crowdsourcingchallenges techniques and applicationsrdquo Proceedings of theVLDB Endowment vol 10 pp 1988ndash1991 2017

[12] J Xiong X Chen Q Yang L Chen and Z Yao ldquoA task-oriented user selection incentive mechanism in edge-aidedmobile crowdsensingrdquo IEEE Transactions on Network Scienceand Engineering vol 7 no 4 pp 2347ndash2360 2020

Security and Communication Networks 13

[13] F Alt A Shirazi A Schmidt U Kramer and Z NawazldquoLocation-based crowdsourcing extending crowdsourcing tothe real worldrdquo in Proceedings of the 6th Nordic Conference onHuman-Computer Interaction pp 13ndash22 Reykjavik IcelandOctober 2010

[14] L Kazemi C Shahabi and L Chen ldquoGeoTruCrowd trust-worthy query answering with spatial crowdsourcingrdquo inProceedings of the ACM International Symposium on Ad-vances in Geographic Information Systems pp 314ndash323Orlando Florida USA November 2013

[15] U ul Hassan and E Curry ldquoEfficient task assignment forspatial crowdsourcing a combinatorial fractional optimiza-tion approach with semi-bandit learningrdquo Expert Systemswith Applications vol 58 pp 36ndash56 2016

[16] C Miao H Yu Z Shen and C Leung ldquoBalancing quality andbudget considerations in mobile crowdsourcingrdquo DecisionSupport Systems vol 90 pp 56ndash64 2016

[17] P Wu E W T Ngai Y Wu and Y Wu ldquoToward a real-timeand budget-aware task package allocation in spatial crowd-sourcingrdquo Decision Support Systems vol 110 pp 107ndash1172018

[18] H To C Shahabi and L Kazemi ldquoA server-assigned spatialcrowdsourcing frameworkrdquo ACM Transactions on SpatialAlgorithms and Systems vol 1 no 1 pp 1ndash28 2015

[19] L T )anh T D Huynh A Rosenfeld S Ramchun andN R Jennings ldquoBudgetFIx budget limited crowdsourcing forinterdependent task allocation with quality guarantees 13thinternational conference on autonomous agents and multi-agent systemsrdquo AAMAS 2014 vol 1 pp 477ndash484 2014

[20] U Hassan and E Curry ldquoA capability requirements approachfor predicting worker performance in crowdsourcingrdquo vol 1pp 429ndash437 in Proceedings of the 2013 9th InternationalConference on Collaborative Computing Networking Appli-cations and Worksharing (CollaborateCom) vol 1 IEEEComputer Society Austin TX USA October 2013

[21] H To G Gabriel and C Shahabi ldquoA framework for pro-tecting worker location privacy in spatial crowdsourcingrdquoProceedings of the VLDB Endow vol 7 no 10 pp 919ndash9302014

[22] U U Hassan S OrsquoRiain and E Curry E_ects of ExpertiseAssessment on the Quality of Task Routing in HumanComputation httpsdoiorg1014236ewicsohuman201322013

[23] Y Tong Z Zhou Y Zeng L Chen and C Shahabi ldquoSpatialcrowdsourcing a surveyrdquo VLDB JOURNAL vol 1 pp 217ndash250 2020

[24] U U Hassan and E Curry ldquoA multi-armed bandit approachto online spatial task assignmentrdquo in Proceedings of the 2014IEEE 11th Intl Conf on Ubiquitous Intelligence and Computingand 2014 IEEE 11th Intl Conf on Autonomic and TrustedComputing and 2014 IEEE 14th Intl Conf on Scalable Com-puting and Communications and its Associated Workshopspp 212ndash219 IEEE Bali Indonesia December 2014

[25] Y Zeng Y Tong L Chen and Z Zhou ldquoLatency-Orientedtask completion via spatial crowdsourcingrdquo in Proceedings ofthe 2018 IEEE 34th International Conference on Data Engi-neering (ICDE) pp 317ndash328 IEEE Computer Society ParisFrance April 2018

[26] L Tran H To L Fan and C Shahabi ldquoA real-time frameworkfor task assignment in h spatial crowdsourcingrdquo ACMTransactions on Intelligent Systems and Technology vol 9no 3 pp 1ndash26 2018

[27] Z Shen Y Han C Miao and J Weng ldquoTrust-based webservice selection in virtual communitiesrdquo Web Intelligenceand Agent Systems vol 9 pp 227ndash238 2011

[28] Y Han S Liu A Kot C Miao and C Leung ldquoDynamicwitness selection for trustworthy distributed cooperativesensing in cognitive radio networksrdquo in Proceedings of theInternational Conference on Communication TechnologyProceedings ICCT pp 1ndash6 Baku Azerbaijan October 2011

[29] P Cheng X Lian Z Chen et al ldquoReliable diversity basedspatial crowdsourcing by moving workersrdquo Proceedings of theVLDB Endow vol 8 no 10 pp 1022ndash1033 2015

[30] Y Zhao J Xia G Liu et al ldquoPreference-aware task assign-ment in spatial crowdsourcingrdquo in Proceedings of the BeBirty-Bird AAAI Conference on Arti_cial Intelligence(AAAI-19) Honolulu HI USA February 2019

[31] C Veness Calculate Distance and Bearing between TwoLatitudeLongitude Points Using Haversine Formula inJavaScript httpwwwmovable-typecoukscriptslatlonghtml 2002-2020

[32] M A Mohammed H A Karrar S Alaa et al ldquoBenchmarkingmethodology for selection of optimal covid-19 diagnosticmodel based on entropy and topsis methodsrdquo IEEE Accessvol 8 2020

[33] China Society for Industrial and Applied MathematicsldquoDataset the data of allocated sharing tasksrdquo 2017 httpwwwmcmeducnhtml_cnnode460baf68ab0ed0e1e557a0c79b1c4648html

14 Security and Communication Networks