autonomous mobile learning model of cloud education based

Research ArticleAutonomous Mobile Learning Model of Cloud Education Basedon Intelligent Algorithm of Wireless Network Communication

Lin Chen1 and Weiping Zhu 2

1School of Education Science, Chongqing Normal University, Chongqing, 401331 Chongqing, China2School of Humanities and Education, Nanchang Institute of Technology, Nanchang, 330044 Jiangxi, China

Correspondence should be addressed to Weiping Zhu; [email protected]

Received 5 August 2021; Accepted 4 September 2021; Published 13 October 2021

Academic Editor: Zhihan Lv

Copyright © 2021 Lin Chen and Weiping Zhu. This is an open access article distributed under the Creative Commons AttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work isproperly cited.

The rapid development of a new generation of information technology and its widespread application in colleges and universitieshave promoted profound changes in campus informatization. On the one hand, wireless campus networks as the maininfrastructure of digital campuses are gradually popularized in domestic colleges and universities, expanding the campusinformation network coverage. This article is aimed at studying the addition of intelligent algorithms to wireless networkcommunications to optimize and build autonomous mobile learning for cloud education. In order for children to communicatewith teachers face to face, no matter where they are, this article proposes how to reduce the energy loss in wirelesscommunication and find the best intelligent algorithm to realize the cloud education mobile learning platform. Experimentalresults show that the transmission delay of the wireless communication system of this method is significantly shorter than that ofthe other two methods, close to 5%, which speeds up the data transmission speed of the wireless communication system. It canbe implemented under time-sensitive conditions and has high practical application value. The wireless network communicationsystem of this method has a low packet loss rate and less wireless network data transmission errors, which is nearly 10% lowerthan the other two methods, thereby improving the data transmission power of the wireless communication network.

1. Introduction

1.1. Background. The applications of mobile learning andcloud education have been applied to the education of majorforeign universities. MU Elementary School, using Simon’scloud service, can obtain students’ computer informationthrough Weibo and provide students with services to com-municate and complete teaching tasks. Through the imple-mentation of the “Classroom 2000” (c2k) project inNorthern Ireland, more than 350,000 teachers and studentsin 1,200 primary schools can access the first “cloud” inEurope through wifi network services. Student educationprovides schools with high-quality online resources; teachersand student education can access and download variousresources through mobile terminals and computers. In thecontext of cloud computing technology, a “cloud” super-computer group has emerged, and service providers provideservices to users based on this group; it is a safe and fast

information storage method that allows users to quicklyprocess information in a secure network environment. Theapplication of mobile learning technology in the field ofeducation and learning has given birth to a new learningmethod under the background of cloud computing-mobilelearning. The American National Institute of Standardsand Technology is a standardization organization in theUnited States. It defines cloud computing as a new Internetcomputing method. It builds a resource pool through theInternet to provide users with real-time updates, flexibleexpansion, and virtualized resources, mainly based on infra-structure as a service platform and as a service and software,and other service models are presented. Our country hasbeen trying hard to try mobile learning applications. Thefirst is applied research in various studies. In 2001, theDepartment of Higher Education of the Ministry of Educa-tion proposed a pilot project for the construction of a mobilelearning platform and organized experts to carry out a

HindawiWireless Communications and Mobile ComputingVolume 2021, Article ID 1144767, 13 pageshttps://doi.org/10.1155/2021/1144767

https://orcid.org/0000-0002-7622-5295

https://creativecommons.org/licenses/by/4.0/

https://creativecommons.org/licenses/by/4.0/

https://doi.org/10.1155/2021/1144767

scientific research project on “Mobile Education Theory andPractice.” In 2002, some domestic schools began to build awireless school campus network. Students from HongKong’s Pui Ching Middle School can use their mobilephones to answer teacher’s questions in real time in theclassroom, improving the interactivity of learning. It is aninevitable product of cloud computing: one is the integrationof cloud computing memory, which provides a platform forintegrating massive learning resources, and two is cloudcomputing technology can effectively solve the problem of“information omission, insufficient storage, and limitations”of mobile learning media. Taiwan will also promote thedevelopment of mobile learning. With the continuousadvancement of Internet technology, mobile learning cannot only meet people’s needs at any time. The demand forlocal autonomous learning and abundant educationalresources will significantly reduce people’s learning costs.With the rapid development of wireless network communi-cation, we will find that our learning will become moreconvenient and effective.

1.2. Significance. Mobile learning activities in the cloud edu-cation platform can not only complete a series of actions in asequence and make them a continuous flow of activities butalso expand the field of activities and eliminate interferencefactors, thereby significantly improving learning efficiency.The development of information technology, especially thecontinuous development and progress of sensor technologyand computer technology, and the continuous enhancementand improvement of various sensor functions and the con-tinuous expansion of application fields make the workingsystem more energy-efficient, more efficient, smaller in size,and lower in cost. Node location information and collecteddata will lose some practical use value of network balance.With the continuous advancement of sensor network tech-nology, routing protocols based on location informationand various monitoring applications can be better developedand improved.

1.3. Related Work. Cloud education is to use the concept ofcloud computing to realize distributed management andjoint application of teaching resources based on cloud com-puting technology to meet the needs of teachers for reform-ing teaching methods and realizing teaching resources. Inpersonalized learning now, as long as you have a mobiledevice, you can realize online interactive learning and inter-active learning. In Wang et al.’s research, in order to preventsensor nodes from being easily captured, destroyed, orattacked in an open environment, which leads to the securityand accuracy of data transmission, they proposed a trust-based perception model. The ant colony optimization algo-rithm for secure routing in wireless sensor networks caneffectively identify and stay away from malicious nodes. Intheir research, although some problems have been solved,the research considerations are not very comprehensive,and there are still certain deficiencies. I hope that they canbe better improved in future life [1]. Lin et al. deal withWSN security from the adversarial perspective and studylow-cost and efficient algorithms to identify sensors in

WSN in the shortest time and at the lowest cost. It is recom-mended to convert the problem to a set covering problemand develop a greedy minimum cost node capture attackalgorithm (MCA) to reduce the cost of the attack. Extensivesimulations have been implemented to evaluate the perfor-mance of MCA and compare it with several related scenar-ios. The results showed that MCA reduced the cost ofdestroying WSN by 20%. Although the research has greatpractical value and broad prospects, there are technicalbarriers and great challenges [2]. Tang et al. improved theLow-Energy Adaptive Clustering Hierarchy (LEACH)protocol, making it suitable for energy harvesting wirelesssensor networks. However, there are still some problems inthe experimental part of the study, and it does not take intoaccount that there is a certain loss of energy [3]. Muhammadproposed a novel intelligent positioning algorithm that usesvariable distance beacon signals generated by changing thetransmission power of beacon nodes. The algorithm doesnot use any additional hardware resources for ranging andestimates the location by passively listening to beacon signalsusing only radio connections [4]. After summarizing itscharacteristics, it analyzes the possibility of combining itwith mobile education, proposes a new model of mobileeducation based on cloud computing, and conducts explor-atory research on the development of mobile education.Although the research is very forward-looking, there is stilla problem of insufficient technology [4]. Sengottuvelan andPrasath pointed out that a wireless sensor network (WSN)consists of a large number of independent nodes with sen-sors, wireless interfaces for communication, and limited pro-cessing and energy resources [5]. Research is very practical,but the framework constructed in this way requires a lot ofcost [5]. Alam and Saiyeda propose a solution that combinescloud technology and learning to obtain an optimized learn-ing system that will help students conduct self-assessmentand will use the cloud to provide their services. However,the experimental and analytical aspects are relatively simpleand not detailed enough [6]. Kati and Khan pointed out thatduring the period of the new coronavirus, many countrieshave adopted online classes in order not to delay studentsfrom attending classes. Although online teaching is widelyused, there are many challenges and security issues especiallywhen using education clouds for educational dissemination.There is no guarantee that students are listening carefullywhen they are studying online, and there are also relatedmobile device delays and technical problems. These are hugeproblems. He can only increase the children’s learning con-centration by increasing the questions in the classroom.Although their research is very forward-looking, there is stillno good experimental data to support [7].

1.4. Innovation. With the widespread construction of cam-pus wireless networks and the increasing popularity ofmobile terminals, mobile learning has become one of themost important learning methods for teachers and students,and they can learn anytime, anywhere. It does not meet theneeds of space-time mobile phones, massive resourceexchange, and differentiated terminal support, which createsobstacles for teachers to use. Based on the basic idea of cloud

2 Wireless Communications and Mobile Computing

computing, cloud training transmits all the informationfrom the client and realizes integrated management andresource planning. Based on the intelligent algorithm ofwireless network communication, it realizes the purpose ofautonomous mobile learning of cloud training model, whichis more and more convenient and more suitable for us. Theoptimization of wireless networks and intelligent algorithmsstrengthens the communication between teachers andstudents, thereby improving children’s learning efficiency.Through the combination of wireless network communica-tion and intelligent algorithms, intelligent algorithms canreduce energy consumption during signal propagation. Atthe technical level, by optimizing the signal of wireless net-work communication equipment, by helping to reduce theenergy consumption in the signal propagation process,ensuring that it can be transmitted farther and longer, aseries of updates are made to this by using new materialsand technologies developed. Wireless network communica-tion realizes the communication in different spaces but willlose a lot of energy in the connection, but the emergenceof intelligent algorithms perfectly fits wireless networkcommunication. With the combination of wireless networkcommunication and intelligent algorithms, cloud educationautonomous mobile learning will produce brilliant sparksin the education industry.

2. Methods and Related Concepts

2.1. Wireless Network Communication. In modern times,with the rapid development of science and engineering tech-nology, in the face of a large amount of data and informationrushing to us, collection, transmission, monitoring, analysis,wireless sensors, and their composed wireless sensor net-works have entered people’s field of vision and gained rapiddevelopment and attention. The wireless sensor networkintegrates microsmart sensor technology, embedded com-puting technology, wireless communication technology,and distributed information processing technology, real-time sensing and collecting information about the environ-ment or objects in the monitoring area. The developmentin the past two decades is mainly reflected in the continuousupgrading of hardware technology and the continuousmaturity of various network-related technologies, and goodresearch theoretical results and technical application resultshave been achieved. The function has attracted widespreadattention. The following shows the research proportions ofvarious directions in the number of publications in the data-base this year, and we can more intuitively observe the mainresearch directions of wireless network communication, asshown in Figure 1.

From Figure 1(a), we can see that there was moreresearch on new energy 15 years ago. Energy consumptionis an important issue that we need to solve before. It is thebasis for the development of other technologies, then net-work routing technology, and as can be seen from Figure1(b), for key technical issues such as positioning, in contin-uous practice, more and more problems follow one afteranother. At present, people are increasingly aware of theimportance of security and mobility issues. The intelligent

era based on the Internet of Things has come, and the devel-opment of science and technology continues to spread to thefield of life and production. As one of the key technologies,wireless sensor networks have received extensive attention.In response to the needs of the development of the times,some domestic scientific research institutes and university-related laboratories have carried out researches on the theoryand application of wireless sensor networks. The laborato-ries of many universities and other units across the countryhave carried out research on wireless sensor networks fromdifferent angles, and the results of academic and practicalapplications have increased sharply, which has led to a cli-max of research [8]. In the monitoring area, a large numberof sensor nodes are deployed to sense, collect, and monitorinformation in the designated area, and then, a certain routewas used to transmit to the collection node, then through theInternet or satellite media, and through wired or wirelesstransmission, and finally, the host computer of the monitor-ing system arrived. User-issued tasks are fed back to eachnode in the opposite transmission mode. In the entire datatransmission process, each sensor node must not only com-plete the task of collecting and forwarding data but also hasthe ability to connect to each other and communicate andlocate. The architecture of wireless network communicationis shown in Figure 2 [9].

2.2. Cloud Education Autonomous Mobile Learning Model.As an important support for mobile learning, cloud educa-tion has irreplaceable advantages over Moodle, Bio, andother network platforms. These unique advantages providefavorable conditions for the development of mobile learningand fully demonstrate the charm of mobile learning. Stu-dents’ learning interest and strong learning interest makelearning more active. The virtual learning environment pro-vided by cloud education can separate teachers and studentsin time and space. Students can choose the time, place, andcontent of learning according to their needs. Based on theadvantages of the cloud learning platform, we have built astand-alone machine based on the cloud learning platform;the mobile learning model is shown in Figure 3 [10].

The process model of mobile learning activities based onthe cloud education platform mainly includes three stages.The first is the initial analysis stage of mobile learning activ-ities. This is a very important stage of mobile learningdesign. According to the analyzed learning content and stu-dent characteristics, determine whether the mobile learningmode is suitable for development. The second is the mobilelearning activity design stage under cloud education. Thisstage includes the planning of mobile learning activity tasks,the establishment of its organizational form, and the designof activity processes and activity criteria and activity evalua-tion design. The third stage is the mobile learning environ-ment design stage under the cloud education platform.This part of the work mainly includes related cloud hard-ware and cloud software ecological design and cloudhumanistic ecological design.

Our model provides students with learning planningbefore, during, and after class. Before class, students canlog in to the system to obtain the corresponding learning

3Wireless Communications and Mobile Computing

Network protocol

Position

Energy

System

structu

reReli

abilit

yW

ireles

s case

Data fu

sion

Safety

810

15

7

2.5

6.5 6

2

02468

10121416

Prop

ortio

n

Research direction

(a)

7.512.5

1010

155

444

Network protocolRoutingPosition

EnergyMobility

Node coverageReliability

MACNode placement

Research direction

Prop

ortio

n

(b)

Figure 1: (a) Distribution map of wireless network research before 2015. (b) Distribution map of current wireless network research directions.

Externalnetwork

Managementcontrol center

Sensor node

Monitoringarea

Sink node

Figure 2: The architecture of wireless network communication.

Student 1mobile login



Online courseselection

Individual self-learningsystem

Resource center

Study plan Broadcastingsystem

Homework

Online Q & A

Evaluation

Teacher and studentassessment

Self assessment

Onlineassessment

Puzzle game

Before class In class After class

Interact

News

Interact

Figure 3: Autonomous mobile learning model for cloud education.


materials and make sufficient preclass preparations. We canalso choose according to our hobbies. In curriculum, interestis the child’s first teacher and will also provide children withsome news at home and abroad, so that children can learnabout real-time news [11]. In the classroom, children donot need to carry heavy schoolbags to school. They canobtain the corresponding information based on mobiledevices. When they encounter problems, they can askteachers and classmates for help online at any time, whichcan help children online. For Q&A, after class, children nolonger have to worry about homework and forgetting thetrouble. Our homework can be submitted anywhere. Thepremise is that there is a network. Some homework can becontrolled by the teacher through the system. Studentsanswer and fill in online, and this can provide timely feed-back on the evaluation results to students and teachers, sothat they can recognize their shortcomings and strengthsin a timely manner and deepen their impression of the learn-ing content. Educational classes provided by cloud educationlearning game of the game allow students to increase theirknowledge and improve their learning skills in the game.The most important thing is that they can learn happily.Mobile learning must have three characteristics. The firstimportant thing is to have digital teaching content, and thesecond is to use mobile media as learning tools, such asmobile phones, tablets, computers, and TVs, and teachersand students must be on a separated status; this is the crite-rion for mobile learning, as shown in Figure 4 [12].

For the application of future wireless network communi-cation intelligent algorithms in the autonomous mobilelearning model of cloud education, they will bring moreintelligence and speed to our learning environment and con-sume less energy, and there will be less energy consumptionbetween students and teachers. Communication is notlimited to space. In this era of communication, no matterwhere we are, we can always pick up mobile devices to com-municate with our teachers or students.

2.3. Intelligent Algorithm. The swarm intelligence algorithmis created by researchers based on the self-organizing behav-ior of social organisms, mathematically modeling swarmintelligence, and reconstructing this model with computers[13]. The collective foraging process of ant colonies and birdcolonies reflects the limited ability of individuals [14]. How-ever, complex tasks can be completed through cooperationand division of labor by grouping into groups [15]. Soinspired by this, it provides new ideas for solving complexoptimization problems. According to the behavior of antcolonies and bird colonies, the researchers, respectively,proposed an ant colony optimization algorithm and anotherrepresentative particle swarm optimization algorithmthrough simulation analysis. The continuous in-depth devel-opment of the two has laid a good foundation for the futuredevelopment of swarm intelligence, the basis of [16]. Apply-ing it to cloud education, autonomous mobile learning willgive people a completely new feeling, and the actual changesare shown in Figure 5 [17]. The advantage of intelligentalgorithms and other algorithms is that they can help thiscloud education learning model to perform a certain optimi-

zation process, help to make the wireless network transmis-sion process faster, and reduce certain network delays.

Under the cloud education mobile autonomous learningmodel of wireless network communication, we can not onlylearn some courses in school, if we do not hear clearly, wecan also watch related videos according to the courses wehave learned after returning home. There are also somevideo teachings and so on that we are interested in. Wecan come into contact with anything we are interested in,and we can learn by looking for related teaching videos,which not only deepens our learning content but alsoimproves our skill strengthening.

PSO is an evolutionary computing technology based onswarm intelligence, which solves the global optimal solutionof iterative optimization problems. It finds the global opti-mum by following the optimal value currently searched.This algorithm has attracted the attention of academia dueto its advantages such as easy implementation, high accu-racy, and fast convergence and has demonstrated its superi-ority in solving practical problems. The algorithm principleis simple. Compared with genetic algorithms, there are nocomplex functions such as hybridization and mutation. Theyare just particles looking for the best particles in the solutionspace. Compared with other evolutionary algorithms, theyhave the advantage of being easy to apply and do not requirecomplex models to solve optimization problems [18]. Sincethe field of evolutionary computing was proposed. As shownin Table 1, according to the number of commonly usedsearch libraries, it can be seen that a large number of librar-ies and research results have emerged in a short period ofmore than ten years.

The particle swarm algorithm was first proposed by twoAmerican scientists based on the process of flocking birds tofind the best foraging area. As an intelligent algorithm, PSOsimulates the process of optimal decision-making. Similar tothe human decision-making process, before you make achoice, will you be affected by your own experience (localoptimal) and the experience of people around you (globaloptimal)? In the same way, the initial position of each birdis random during the foraging process of a group of birds.Of course, it is not known where the best foraging point is,and the flight direction of each bird is also random. It canbe considered that in the early stage of foraging, the trajec-tory of the bird flock is chaotic. Over time, the birds in ran-dom positions learn from each other and share foraginginformation in the flock, combining his own experienceand the information sent by his companions in the processof foraging to estimate the value of the food at the currentlocation. Based on this search method, particle swarm opti-mization (PSO) came into being.

When the wireless signal propagates in free space andhas an unobstructed straight path, the following formulacan be used to calculate the distance R between the transmit-ting node and the receiving node after the measurement datais obtained.

p rð Þ = pkGKβ2

4πð Þ2r2 : ð1Þ


For the description of some parameters in the above for-mula, PK is the power of the transmitter,pr is the receivedpower at the distance r, r is the distance, β is the wavelength,and the units are all meters; Gt and Gr are the transmittingantenna and the receiving antenna, respectively. With this,

we can calculate the distance between the transmitting pointand the receiving point through a certain calculation.

The PSO algorithm has been widely used but there arealso many problems. In order to solve these problems andin order to strengthen the search ability and convergenceof PSO, some predecessors have introduced ω in the for-mula. The research shows that when ω is small, it narrowsthe network search range of the algorithm, and the powerfullocal search ability can improve the search accuracy. Balan-cing search accuracy and convergence speed is the focus ofparticle swarm optimization. The types of linearly decreas-ing inertia weights are

v = vmax‐vmax‐vmin

tmax× t: ð2Þ

In the above formula, vmax is the maximum value of iner-tia weight, vmin is the minimum value of inertia weight, tmaxis the maximum number of iterations, and t is the currentnumber of iterations. The linear inertia weight is globallyoptimized in the early stage of the particle swarm, and afterfinding the approximate position of the optimal solution, alocal search is performed, which effectively improves the

Digital learningcontent

The teacher andthe student are in astate of separation

Mobile media asa learning tool

Figure 4: Judgment criteria for mobile learning.

Encoder

MSS

Teachingstaff HDMI

Videocamera

LSS, DMSLi

vestr

eam

ing

Mobile phone

Ondemand

Figure 5: Online education live broadcast.

Table 1: Data statistics of particle swarm optimization algorithm inthe literature library.

Publication year Records Percentage

2008 300 3.20%

2009 400 6.20%

2010 445 7.10%

2011 523 7.50%

2012 625 8.60%

2013 800 9%

2014 654 9.50%

2015 911 10.23%

2016 240 15.66%

2017 1318 18.66%

2018 1097 19.60%


convergence speed and accuracy at the same time [19].However, when the linearly decreasing weight solves com-plex problems, it is easy to ignore the population character-istics, the convergence speed slows down, and the optimalsolution cannot be found, and it is easy to fall into the localoptimum. Therefore, in order to balance the global searchand the local search and solve the local optimal problem, thispaper proposes a nonlinear adaptive inertia weight formula:

v kð Þ =vmax −

vmax − 1/2ð Þvmin1/2ð Þtmax

⋅ k,

12 vmax − vmin

� �⋅ e− k− 1/2ð Þtmaxð Þ + vmin,

8>>><>>>:

k ≤12 tmax,

k > 12 tmax:

ð3Þ

In the above formula, vmax is the maximum value of iner-tia weight, vmin is the minimum value of inertia weight, andtmax is the maximum number of iterations, which is the cur-rent number of iterations. In the algorithm search process,the inertia weight of the particle swarm decreases linearlyin the early stage ðk ≤ ð1/2ÞtmaxÞ, and the particle swarmcan often find the approximate position of the optimal solu-tion quickly, which can prevent the particle swarm from fall-ing into the local optimum in the early stage; in the late stageof the particle swarm ðk > ð1/2ÞtmaxÞ, the inertia weightdecreases exponentially, which improves the search accuracyof the particle swarm to find the optimal solution more accu-rately. The improved inertia weight can adaptively changenonlinearly during the optimization process of the particleswarm. The high-speed optimization of the particle swarmin the early stage improves the convergence speed and atthe same time improves the search accuracy of the networkin the later stage and avoids the particle swarm from fallinginto the local optimum.

In the process of algorithm optimization, the initial stageshould focus on global optimization, focusing on self-learning ability; in the later stage, the particle optimizationrange should be narrowed to quickly approach the globaloptimal, focusing on social learning ability [20]. The valueof the learning factor c1 first becomes larger and thensmaller, and the value of the learning factor c2 first becomessmaller and then larger. Based on this improved method tosearch for the global optimum of the network, improve theconvergence speed of the algorithm, prevent the algorithmfrom falling into local freedom, and use the cosine algorithmto improve the learning factor.

c1 = j1 − j2ð Þ ∗ cos π

2t

tmax

� �� + j2,

c2 = j3‐j4ð Þ ∗ cos π

2t

tmax

� �� + j4:

ð4Þ

In the above formula, j1, j2, j3, and j4 are the startingvalue and final value of c1 and c2, respectively. After manyexperiments, we get j1 = 3:5, j2 = 0:5, j3 = 2:5, and j4 = 0:5.When the algorithm is used, do it right.

2.4. Optimal Number of Cluster Heads. In the algorithm per-formance comparison of wireless sensor networks, clusteringcan effectively reduce network energy consumption andimprove performance. Assuming that there are g nodes inan m ×m network, the number of cluster heads is b, so theprobability of a node being elected as a cluster head is g/b,the number of nodes distributed in each cluster is ðg/bÞ‐1,and the number of nodes in each cluster is JJ. In the calcula-tion, the energy consumed by the base station during signaltransmission is

E = gb− 1

� �× eel × l + g

b× EDA × l + Eel × l + ηam × r4to × l

� �:

ð5Þ

In the above formula, rto is the distance from the clusterhead to the base station. When a node transmits data to thecluster head, it consumes a certain amount of energy.

Eme = l × Eel + l × ηf × r2to: ð6Þ

In the above formula, rto is the distance from the node tothe cluster head. Assuming that each node is pðx, yÞ, itsexpectation is

E r2to�

=∬ x2 + y2� �

p x, yð Þdxdy =∬ x2 + y2� � b

m2 dxdy =m2

2bπ :

ð7Þ

The total energy consumed is

Eto = bECH = l × 2g − bð Þ × Eel +m2

2bπ g‐bð Þηf + edag + bηamr4to �

:

ð8Þ

Solve the first derivative of E with respect to b, and makeit equal to zero, so that we can get

ηam × r4to‐ηf ×g2π × m2

b2= 0: ð9Þ

Solve the above equation to get

b =ffiffiffiffiffiffiffiηfηam

r⋅

ffiffiffiffiffiffig2π

r⋅mr2to

: ð10Þ

The decision factor f1 is based on the network energyconsumption, the decision factor f2 is based on the remain-ing energy of the cluster head, the decision factor f3 is basedon the distance between the cluster head and the nodes inthe cluster, and the decision factor f4 is based on the distancebetween the cluster head and the base station. According tothe previous energy consumption model, we can get the totalenergy consumption of network communication as


Eto = 〠g

g=1l × 2 cbj j − 1ð Þ × Eel + r2to cbj j − 1ð Þηf + cbj jeda + ηamr

4to

� :

ð11Þ

Then, we can get the power consumption judgmentfactor of the network node as

f1 =1g〠g

g=1l × 2 cbj j − 1ð Þ × Eel + r2to cbj j − 1ð Þηf + cbj jeda + ηamr

4to

� :

ð12Þ

In the definition of decision factor f2, first sum up theremaining energy of all particle nodes in the algorithm,and divide it by the energy of the cluster head. The smallerthe value obtained, the greater and better the possibility ofbeing selected as the cluster head.

f2 =∑g

i=1E

∑bb=1e chð Þ

: ð13Þ

The definition of decision factor f3 is to calculate themaximum average Euclidean distance between a commonnode and its cluster head according to the node position.The smaller the value, the better the compactness.

f3 = 〠g

i=1〠b

b=1

d ni, chð ÞCP,K : ð14Þ

f4is the minimum distance between the node and thebase station. The smaller the distance from the base station,the lower the communication energy consumption.

f4 = min df chi, bsð Þg: ð15Þ

In the above formula, dðchi, bsÞ is the distance from the ith cluster head to the base station. Based on the above fourdecision factors, the decision function constructed in thisarticle is

cost = βf1 + αf2 + χf3 + γf4: ð16Þ

f1, f2, f3, and f4 are the decision factors of this algorithmwhen selecting cluster heads. The specific physical meaningsof the four factors are as described above. The parametersα,β,λ, andη, respectively, represent the weight of each decisionfactor in the fitness function,α β γ ηε½0, 1�,α + β + λ + η = 1,and the weights of the four decision-making factors can beadjusted according to actual needs [21]. According to thedefinition of fitness function, a relatively small fitness func-tion value can ensure the selection of the optimal clusterhead. The smaller the fitness function of the particle, the lessthe energy consumption of the network, the more theremaining energy of the cluster head node, the better thecompactness within the cluster, and the better the energyconsumption of the cluster head communication. Therefore,the goal of improving the particle swarm algorithm is to

select the node with the smallest fitness function value asthe cluster head, optimize the selection of the cluster head,and reduce network energy consumption.

In the initial stage of the algorithm, each node randomlygenerates a random number μ, 0 < μ < 1, and sets the thresh-oldTfor electing cluster heads,μ < T ; the node is elected asthe candidate cluster head. In the improvement of thethreshold T , the influence of the global network node istaken into consideration, and comprehensive considerationis given to node energy, density, and distance from the basestation which are three factors.

T nð Þp

1‐p d ∗mi 1/pð Þð Þ ⋅eie0

⋅di‐dmin

dmax‐dmin, n ∈ g,

0, n ∉ g,

8><>:

Qi =nei ið Þalinetali

,

xij h + 1ð Þ = xij hð Þ + vij h + 1ð Þ:ð17Þ

In the above formula, there is a probability that a net-work node is elected as a cluster head in normal times, wherep is the probability that a network node is elected as a clusterhead, r is the number of running rounds of the network atthis time, and G is the set of nodes that are elected as clusterheads in the most recent 1/p round. E0 and Ei are the initialenergy and current energy of the node, respectively; dmaxand dmin are the longest and shortest distances between thenetwork node and the sink node, d is the distance betweenthe node and the base station, and Qi is the node density.Nei is the number of neighbor nodes of node i, and Net isthe number of surviving nodes in the network. The candi-date cluster head set is constructed through the determina-tion of the candidate cluster heads. The candidate clusterheads only unilaterally determine the energy and locationinformation of the nodes. The energy consumption of thenodes and the global network information are not consid-ered, and the election of the algorithm cannot be optimized.There is a need to optimize the election of the networkcandidate cluster head [22].

Test experiments and practice have proved that thequantum particle swarm optimization algorithm shows goodsolving characteristics. The improved algorithm adds parti-cles with quantum behavior, breaking the limitations ofparticle motion speed and position, so the particle swarmoptimization algorithm with quantum behavior shows. Inaddition to the excellent features that the particle swarmoptimization algorithm cannot match, many aspects havebeen improved. In order to meet this exponential increasein data throughput, heterogeneous networks will achievebetter performance in terms of increasing network capacity,increasing coverage, and improving spectrum efficiency. Atthe same time, the unprecedented rapid development ofwireless communication technology is shown in Figure 6.


3. Contrast Experimental Design

3.1. Experimental Environment. In order to test the wirelesschannel allocation performance of the combined intelligentalgorithm, Matlab 2016 was selected as the experimentalplatform. The parameters of the wireless network and thecombined intelligent algorithm are defined as follows: thenumber of nodes is 40, the communication radius is 100m,the interference radius is 30m, the total number of availablechannels is 24, the population is 100 for the genetic algo-rithm, the maximum evolutionary number of the geneticalgorithm is 1000, the population number of the swarm opti-mization algorithm is 40, and the maximum number of iter-ations of the particle swarm optimization algorithm is 500,δ = 0:95, C1 = 1:95, andC2 = 1:5. Select the wireless channelallocation method in literature [9] and bibliography [13]for benchmark testing.

For genetic algorithm, it is difficult to obtain the globaloptimal wireless network channel allocation plan when solv-ing the wireless network channel allocation problem, andthe particle swarm optimization algorithm for the wirelessnetwork channel allocation problem solves the slower prob-lem, and the combination of them is improved. Thethroughput of the wireless network, the network delay,and the packet loss rate of data transmission are smaller,and the communication performance of the wireless net-work is significantly improved.

The wireless network channel allocation process ismainly divided into three stages: data collection, wirelessnetwork channel allocation method design, and wireless net-work channel allocation implementation. Among them, datacollection is used to collect wireless network-related param-eters and working environment data to provide data for thewireless network channel allocation method; and the wire-less network channel allocation method design mainly deter-

mines the optimal wireless network channel allocation planand finally obtains the wireless network channel. The alloca-tion scheme is applied in the specific wireless network com-munication process, so the design of the wireless networkchannel allocation method is the most critical.

3.2. Throughput Comparison of Wireless NetworkCommunication Systems. Throughput mainly describes theamount of data received by the wireless network communi-cation system in a unit of time. The performance changecurve of the wireless network communication system underdifferent simulation times and three wireless network chan-nel allocation methods is shown in Figure 7 [23]. It showsthat the wireless channel allocation method used in thispaper is relatively stable to the changes in the performanceof the wireless communication system; it shows that the

Femto

MBSPico BS

Femto

Femto

Relay

D2D

Relay

Figure 6: Multilayer heterogeneous network architecture model.

600

700

800

900

1000

1100

1200

5 10 15 20 25 30 35 40 45 50

Thro

ughp

ut

Simulation time

Intelligent algorithm

Literature 9 methodLiterature 13 method

Figure 7: The change curve of the throughput of the wirelessnetwork communication system.


performance of the wireless network communication systemis unstable. At the same time, the network communicationsystem of the wireless channel allocation method in thispaper is significantly higher than that of the literature [9]and literature (the wireless communication system of themethod in 13) shows that the wireless network distributionsystem implemented by this method makes it more mean-ingful to make full use of the wireless network and increasethe utilization rate of the wireless network.

3.3. Problems in the Process of Wireless NetworkTransmission. In the same experimental environment, thetransmission delay curves of the wireless network communi-cation system with different simulation times and threewireless channel allocation methods are shown in Figure 8.Point (a) shows that the transmission delay of this methodof wireless communication network is significantly shorterthan that of literature [9] and literature [13], which is almost5%. Accelerated data transmission the wireless networkcommunication system is fast, can be applied to time-sensitive occasions, and has high practical application value.It can be seen from point (b) that the packet loss rate of thewireless communication network in reference [9] and refer-ence [13] is generally high, indicating that the wirelessnetwork is subject to great interference, and the probabilityof network congestion is high and wireless channel alloca-tion is unreasonable. This wireless network communicationmethod has a low packet loss rate and fewer wirelessnetwork data transmission errors, which is nearly 10% lowerthan the other two methods. This improves the wireless datatransmission capacity of the communication network.

Through reading other documents, it is found that thepacket loss rate of their wireless communication network isgenerally high, but this wireless network communicationmethod in this article has a low packet loss rate and fewerwireless network data transmission errors, which is moreenergy-efficient and faster than the market. By about 15%,the wireless data transmission capacity of the communica-tion network has been improved.

4. Discussion of Experimental Results

4.1. Energy Consumption during Signal Transmission. Asshown in Figure 9, when the distance between the transmit-ting end and the receiving end of the user changes from 10mto 100m, the total energy efficiency of D2D users under bothmethods increases with the increase of the distance betweenTX and RX in D2D users and reduces. This is because as thedistance between TX and RX increases, the path lossincreases, resulting in greater power consumption, whichwill have a serious impact on the energy effect of D2D com-munication. At the same time, we can see that the RR algo-rithm proposed in this chapter is better than the TMalgorithm in terms of energy efficiency under the QoSrequirements of the two cell users. When the minimum raterequirement for cell users increases, the gap between the RRalgorithm and the TM algorithm in terms of D2D commu-nication energy efficiency will be reduced. This is becausewhen Rc is large, in order to avoid crosslayer interference,the transmission power of D2D users will be strictly limited[24]. Through the RR algorithm with intelligent algorithm,we found that they can reduce energy consumption bynearly 10% without the help of intelligent algorithm in theprocess of wireless network propagation, which is abovethe same distance., We also found in the TM algorithm thatwith the blessing of intelligent algorithms, only the energyconsumption has been reduced by 7% [25, 26].

4.2. The Purpose of Using Cloud Education by Students. Thesource of the experimental data is a questionnaire survey of550 issued for each class of a certain university. The ques-tionnaire stars used are drawn based on the results of thequestionnaire survey we have obtained. In order to under-stand how students use various electronic device cloud edu-cation platforms for learning at different levels, because wefelt that the data obtained was not true enough, we adoptedoffline and distributed some questionnaires in the cafeteriasof various schools in the university city for correspondingrecovery. The recovery rate reached 100%. A total of 550questionnaires were collected, and 480 valid questionnaires

00.5

11.5

22.5

33.5

4

5 10 15 20 25 30

Del

ay (s

)

Simulation time (s)

Intelligent algorithmLiterature 9 methodLiterature 13 method

(a)

0

0.2

0.4

0.6

0.8

1

5 10 15 20 25 30

Pack

et lo

ss ra

te (%

)

Simulation time (s)

Intelligent algorithmLiterature 9 methodLiterature 13 method

(b)

Figure 8: (a) Transmission delay variation curve of wireless network communication system. (b) The packet loss rate of the wirelessnetwork communication system changes with time.


were collected. The effective rate is 87.28%. According tothe returned questionnaire, it can be clearly seen fromFigure 10(a) that 80% of the survey subjects are undergradu-ates (384), 10% are master students (48), and 9% are doctoralstudents (48 people); 1% is other personnel (5 people); 98%of the students own laptops, among which the holding rateof notebooks for masters and doctoral students is 100%,and the holding rate of iPad reaches 95%. We can see fromFigure 10(b) that the purpose of students participating inmobile learning is not consistent, basically considering indi-vidual needs (note: this data excludes nonlearning purposebehavior), but mainly concentrated in three aspects, 28% ofstudents. It is believed that mobile learning can acquire newknowledge and assist professional learning in the classroomenvironment. 37% of students use mobile learning to assistin exams, as a means of preparing for exams, especially for

time users such as review of final exams and college Englishtest bands 4 and 6. Moreover, 33% of the students usedmobile learning for teacher-student exchanges and discus-sions, 2% of the students used mobile learning for other pur-poses such as entertainment and emotional communication[27], as shown in Figure 10.

5. Conclusions

The learning that cloud education will bring to us will bewhen we are at home, turn on the computer or mobilephone, and wait for any mobile terminal device that canaccess the Internet, and timely check the missing and fillvacancies of what we learned on the day, summarize, andreview; on the bus, complete the day’s assignments whichare automatically submitted; while traveling or doing other

28

30

32

34

36

38

40

42

10 20 30 40 50 60 70 80

Ener

gy co

nsum

ptio

n

Distance

rr12tm12

(a)

24

25

26

27

28

29

30

31

32

10 20 30 40 50 60 70 80

Ener

gy co

nsum

ptio

n

Distance

rr15tm15

(b)

Figure 9: (a) The relationship between propagation energy consumption rr12 and tm12 and distance. (b) The relationship betweenpropagation energy consumption rr15 and tm15 and distance.

0102030405060708090

Number of surveys

Undergrad

uatePost

graduate

PhD studen

tOther

perso

nnel

(a)

05

Profess

ional

studay

Auxiliar

y prep

aratio

nCom

municatio

n

Other

10152025303540

(b)

Figure 10: (a) Educational qualifications of investigators. (b) Purpose of participating in mobile learning.


things while listening to teaching videos, or learning the lat-est information, we will be in the cloud campus, the smartcampus of the cloud classroom. It can help us enrich ourknowledge through cloud education.

The American Annual Education Technology Reportpoints out that globalization, networking, and digitizationhave brought about a big explosion of knowledge, and peo-ple’s learning time and learning space are extending infi-nitely. The study time broke through the fixed hours ofstudy or work and began to expand to sleep and lifetime.The learning space also began to break through the bound-aries of the school and gradually expanded to the society.Future education must be a knowledge dissemination modethat meets the needs of all kinds of learners and supportsmultiple learning methods. Use advanced information tech-nology to build a modern education and teaching environ-ment and realize the integration of people and campus,people and information system, and campus entity andinformation system, so as to improve teaching methods,promote scientific research, improve management efficiency,and improve service level. As new technologies, new ideas,and new applications emerge, cloud education has manyadvantages. However, we should also see that before anynew thing matures, there will always be more or less defects,and cloud education is no exception. Mobile learning is con-venient and fast, but it is difficult for learners to concentratefor a long time. We can split the learning content into smallknowledge points to suit learners to quickly learn in a shorttime. In addition, rich resources are a major advantage ofcloud education. However, massive learning resources canmake learners lose or feel at a loss. Then, when we build anetwork resource library, we must fully consider the needsof users and use the resources most appropriately. The wayof organization is presented to students, allowing them totruly experience the convenience and speed brought by“cloud computing” technology to resource retrieval. Howto make cloud education play its true value and advantages;realize anytime, anywhere; and maximize the benefits, tomake up for the lack of resources and support services forextracurricular learning, we need to further explore. Thedevelopment of cloud education is the general trend. It isbelieved that cloud education will play a huge role inpromoting the development of mobile learning and bringdisruptive changes to mobile learning. Perhaps, in the nearfuture, the learning we will see will be as follows: when thelearner is at home, turning on the computer or mobilephone and waiting for any mobile terminal device that canaccess the Internet and timely checking the missing and fill-ing vacancies of the content of the day, summing up, andreviewing; in the bus and in the car, complete the day’shomework and automatically submit it; while traveling ordoing other things, listen to the instructional video, or learnthe latest information; we will be in the cloud campus, thesmart campus of the cloud classroom. I believe that we willbecome more and more intelligent in the future.

Data Availability

No data were used to support this study.

Conflicts of Interest

The authors state that this article has no conflict of interest.

Acknowledgments

This work was supported by the project of higher educationteaching reform of Chongqing Municipal Education Com-mission in 2018: Research on the Development, Construc-tion and Application of Series of Online Courses forTeacher Education (183040). This work was supported byEducation Department of Jiangxi Province Science andTechnology Research Project-Indoor Positioning Researchfor Educational Robots (GJJ202119).

References

[1] Y. Wang, M. Zhang, and W. Shu, “An emerging intelligentoptimization algorithm based on trust sensing model for wire-less sensor networks,” Eurasip Journal on Wireless Communi-cations & Networking, vol. 2018, no. 1, pp. 145–148, 2018.

[2] C. Lin, G. Wu, T. Qiu, and J. Deng, “A low-cost node captureattack algorithm for wireless sensor networks,” InternationalJournal of Communication Systems, vol. 29, no. 7, pp. 1251–1268, 2016.

[3] C. Tang, Q. Tan, and Y. Han, “An energy harvesting awarerouting algorithm for hierarchical clustering wireless sensornetworks,” KSII Transactions on Internet and Information Sys-tems(TIIS), vol. 2, pp. 504–521, 2016.

[4] F.-I.-A. Muhammad, “Intelligent energy efficient localizationusing variable range beacons in industrial wireless sensor net-works,” IEEE Transactions on Industrial Informatics, vol. 12,no. 6, pp. 2206–2216, 2016.

[5] P. Sengottuvelan and N. Prasath, “BAFSA: breeding artificialfish swarm algorithm for optimal cluster head selection inwireless sensor networks,”Wireless Personal Communications,vol. 94, no. 4, 2017.

[6] Y. Xu, “Simulation of optimal selection algorithm for wirelesssensor cluster head node Bayesian statistical network,” Journalof Intelligent and Fuzzy Systems, vol. 1, pp. 1–9, 2021.

[7] S. A. Kati and M. A. Khan, “Challenges and issues of E-learning using education cloud – a review in context ofCOVID-19 pandemic,” International Journal of ComputerApplications Technology and Research, vol. 10, no. 5,pp. 106–110, 2021.

[8] S. Li, B. Duo, X. Yuan, Y.-C. Liang, and M. Di Renzo, “Recon-figurable intelligent surface assisted UAV communication:joint trajectory design and passive beamforming,” IEEE Wire-less Communications Letters, vol. 9, no. 5, pp. 716–720, 2020.

[9] X. Yang, D. Yang, and S. Aue, “Intelligent encryption algo-rithm for network communication parallel data of informationrelease terminal,” Journal of Intelligent & Fuzzy Systems,vol. 35, no. 4, pp. 4245–4255, 2018.

[10] S. Sasirekha and S. Swamynathan, “Cluster-chain mobile agentrouting algorithm for efficient data aggregation in wireless sen-sor network,” Journal of Communications & Networks, vol. 19,no. 4, pp. 392–401, 2017.

[11] K. Soga, S. Hara, M. Motoki, and S. Sasaki, “The landmarkdetection method using SURF and Q-learning for an autono-mous mobile robot,” Ieej Transactions on Electronics Informa-tion & Systems, vol. 137, no. 9, pp. 1248–1257, 2017.


[12] Y. Wang, J. Yue, Y. Dong, and Z. Hu, “Review on kernel basedtarget tracking for autonomous driving,” Journal of Informa-tion Processing, vol. 24, no. 1, pp. 49–63, 2016.

[13] X. T. Truong, T. D. Hong, and D. N. Cong, “An efficient nav-igation framework for autonomous mobile robots in dynamicenvironments using learning algorithms,” Journal of ComputerScience and Cybernetics, vol. 33, no. 2, pp. 107–118, 2018.

[14] T. Takebayashi, R. Miyagusuku, and K. Ozaki, “Developmentof magnetic-based navigation by constructing maps usingmachine learning for autonomous mobile robots in real envi-ronments,” Sensors, vol. 21, no. 12, pp. 3972–3974, 2021.

[15] Q. Ailing, “A study on college English autonomous learningmodel based on ESP theory,” Agro Food Industry Hi Tech,vol. 28, no. 1, pp. 904–907, 2017.

[16] S. Ghosh, A. Pal, S. Jaiswal, K. C. Santosh, N. Das, andM. Nasipuri, “SegFast-V2: semantic image segmentation withless parameters in deep learning for autonomous driving,”International Journal of Machine Learning and Cybernetics,vol. 10, no. 11, pp. 3145–3154, 2019.

[17] J. Zhai and J. Bi, “Empirical research of college students’ learn-ing autonomy from the perspective of intelligent mobile learn-ing platform,” Boletin Tecnico/Technical Bulletin, vol. 55, no. 8,pp. 266–273, 2017.

[18] H. M. Sabi, F.-M. E. Uzoka, K. Langmia, and F. N. Njeh, “Con-ceptualizing a model for adoption of cloud computing in edu-cation,” International Journal of Information Management,vol. 36, no. 2, pp. 183–191, 2016.

[19] I. W. Park and J. Han, “Teachers' views on the use of robotsand cloud services in education for sustainable development,”Cluster Computing, vol. 19, no. 2, pp. 987–999, 2016.

[20] F. Karim and G. Rampersad, “Cloud computing in educationin developing countries,” Computer & Information Science,vol. 10, no. 2, pp. 87–96, 2017.

[21] H. Lin, “The construction of mobile education in cloud com-puting,” Procedia Computer Science, vol. 183, no. 8, pp. 14–17, 2021.

[22] G. Hu, Y. Yang, and L. Li, “Education technology cloud plat-form framework establishment and security,” Journal of Com-puter and Communications, vol. 4, no. 7, pp. 7–14, 2016.

[23] M. A. Alam and A. Saiyeda, “A cloud based solution for smarteducation,” International Journal of Smart Education andUrban Society, vol. 11, no. 2, pp. 28–37, 2020.

[24] A. Dadamuxamedov, X. Mavlyuda, and J. Turdali, “Cloudtechnologies in Islamic education institutions,” ACADEMICIAAn International Multidisciplinary Research Journal, vol. 10,no. 8, pp. 542–546, 2020.

[25] Z. Lv, “The security of Internet of drones,” Computer Commu-nications, vol. 148, pp. 208–214, 2019.

[26] A. Zl, A. Dc, A. Rl, and B. Aa, “Artificial intelligence for secur-ing industrial-based cyber-physical systems,” Future Genera-tion Computer Systems, vol. 117, pp. 291–298, 2021.

[27] G. Yan and R. Sha, “Autonomous learning of foreign languagebased on facial emotion recognition and cloud computing,”Journal of Intelligent and Fuzzy Systems, vol. 11, pp. 1–12,2020.


autonomous mobile learning model of cloud education based

Documents