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Review ArticleA Survey of Abnormal Traffic Information Detection andTransmission Mechanisms in VSNs

Lingjuan Zhang,1 Deyun Gao,1 Chuan Heng Foh,2 Dong Yang,1 and Shuai Gao1

1 School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China2 Center for Communication Systems Research, University of Surrey, Guildford GU2 7XH, UK

Correspondence should be addressed to Deyun Gao; [email protected]

Received 15 December 2013; Accepted 17 February 2014; Published 29 May 2014

Academic Editor: Hai Vu

Copyright © 2014 Lingjuan Zhang et al.This is an open access article distributed under theCreative CommonsAttribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

With the continuously increasing number of vehicles, particularly in cities, there are more and more abnormal traffic eventsoccurring on road, which easily cause road congestion. In vehicular sensor networks (VSNs), with the intervehicle cooperativecommunication, vehicle nodes can detect the abnormal traffic events and disseminate the related information to the interestingvehicles or persons. This paper gives out a survey of the abnormal traffic information detection and transmission mechanisms inVSNs. Firstly, we overview the origin, characteristic, and application of VSNs. Meanwhile, the definition and features of trafficabnormal information are also introduced and presented. Then, we put more energy on elaborating the existing informationdetection and transmission mechanisms based on VSNs. Finally, the challenges and problems are discussed.

1. Introduction

1.1. What Are Vehicular Sensor Networks?

1.1.1. Origins and Definition. Recently, with the rapid devel-opment of various information technologies such as com-puter, communication, and microelectronics, a large amountof systems for different applications need to be graduallyemerged. And every new emerging application system is anintegrated body with multiple complex technologies.

For instance, under the support of microcomputer andembedded operating systems, the functions of smart onboarddevices become richer. They continuously improve the travelquality and driving efficiency from all aspects, so the numberof vehicles equipped with smart onboard devices increasesgreatly. For this reason, as a transport tool, vehicle begins toplay a new and important role.

Particularly, after the mobile ad hoc networks (MANETs)have been popularized in field of intelligent transportationsystem (ITS), the vehicles and roadside access points (APs)can connect with each other and form into a new applicationsystem—vehicular ad hoc networks (VANETs) [1]. It enrichesthe users’ driving life, by providing lots of services like

traffic information dissemination and inquiry, entertainmentresources sharing and downloading, and so on [2]. Compar-ing with MANETs, the vehicle node is more mobile and thenetwork connectivity is even worse in VANETs. In addition,MANET is based on no infrastructure, but VANET is hybridnetwork architecture, and the vehicle node could improve thenetwork connectivity using the roadside infrastructure.

Meanwhile, wireless sensor networks (WSNs) [3] areformed in a self-organizedmanner, by a large number of low-cost and low-power microelectronic sensors with percep-tion, computing, and wireless communication capabilities. InWSNs, all the nodes cooperative senses collect and processdata. Their intelligent and pervasive features provide a goodway for people accessing the physical world informationat any time and any place. However, early WSNs focus onmonitoring static objects like wild volcanomonitoring, forestmonitoring, and so forth. In order to collect the informationof moving objects, mobile sensor networks (MSNs) emerge[4]. In MSNs, many initial researches are around the wildlifeanimal migration monitoring and habits tracking, so theyhave some limitations [5–7]. But for the current popularpocket switched networks (PSNs) [8] or social networks [9],which are both formed by handhold devices, and the body

Hindawi Publishing CorporationInternational Journal of Distributed Sensor NetworksVolume 2014, Article ID 582761, 13 pageshttp://dx.doi.org/10.1155/2014/582761

2 International Journal of Distributed Sensor Networks

Early WSN

MSNBSN

PSN/SNVSN

Disaster detection Smart

building

Diseases preventation

Habits tracking

Health monitoring

Traffic/city monitoring

V2R

V2V

Static objects Moving animals Moving people Moving vehicles

· · ·

· · ·

· · ·

Forestmonitoring monitoring

Animalmigration

Figure 1: Various popular application systems.

RSU

RSU

RSU

RSUInternet

WiMAX/3G/LTEwireless base station

GNNS/GPS

Vehicle-to-vehicleVehicle-to-RSURSU-to-RSU

Ethernet wired linkCellular wireless link

Figure 2: The popular architecture of the vehicular sensor networks.

sensor networks (BSNs) [10, 11], which is formed by multiplebody carryingmedical sensor nodes, theirmonitoring objectsare moving people. They are other manifestations of MSNsand can be used to prevent diseases andmonitor health.Thesevarious popular application systems can be seen in Figure 1.

When the moving objects refer to vehicles, the concept ofvehicular sensor networks (VSNs) naturally appears [12, 13].In short, VSN is one typical application of MSNs in fieldof vehicles, which is composed of smart onboard sensornodes and road side units (RSUs) [14]. There exists two

communication modes: vehicle-to-vehicle (V2V) communi-cation and vehicle-to-RSU (V2R) communication [15, 16],as shown in Figure 2. Additionally, RSUs can be connectedby pass-by mobile vehicles [17]. Some more powerful RSUsaccess Internet by wired cable, optical fiber, or utilizing thewireless base stations (WiMAX, 3G, LTE) operated by serviceprovider. Comparing with VANETs, VSNs can make up thedrawbacks for lack of front sensor information input functionand enrich the network transmission contents, relying on itsunique perceived advantage.

International Journal of Distributed Sensor Networks 3

1.1.2. Main Characteristics. Different from the traditionalWSNs, VSN mainly has the following characteristics.

Intermittent Connectivity. Due to the high mobility of vehi-cle nodes and the interference of wireless communicationobstacles like city buildings, trees, and so forth, the networktopology of VSNs is highly dynamic and the link durationtime is quite short. Thus, the network connectivity is worse.Furthermore, there is no fixed end-to-end path betweenvehicle nodes.

Predicable Movement. Since vehicles are limited to move onroad, constrained by the road topology and the front vehicles,the movements of vehicle nodes are predicable.

Enhanced Transmission and Processing Ability. For the low-cost and low-power WSN nodes, their energy, storage space,transmission range, and processing speed are limited. ButVSN nodes have no strict hardware conditions limitation, sotheir data transmission range and processing performancesare enhanced.

Node Selfishness. The number of vehicles moving on cityroad is large, for some reasons like privacy protection orenergy saving need, part of vehicle nodes are unwilling toparticipate in data forwarding. So, the vehicle nodes in VSNshave selfishness.

Network Openness. Because vehicle nodes are not always“online”, large number of nodes would frequently join or leavethe network. When vehicles are parked on the street, somevehicle nodes will be “offline” temporarily; meanwhile, othernew vehicle nodes will be “online”.Thus, openness is the basicfeature of VSNs.

Unbalanced Network Topology. Influenced by the road topol-ogy and traffic condition, for the same road segments atdifferent time or the different road segments at the sametime, the distribution of vehicle nodes is uneven and changesgreatly. Thus, the network density switches in sparse anddense states frequently. In addition, the regional networkpartition phenomenon may produce “information island”effect [18] in a time period.

1.1.3. Application and Projects. VSN has extremely importantresearch position and broad application prospects in ITS.After induction and summary, most applications focus on thefollowing aspects.

Ensure the Driving Safety [19]. Vehicle nodes can sense muchdangerous information on road, such as surface gatheredwater, icy pavement, visual obstacles under weather of densefog, large falling objects on highway, emergency braking, andcar breakdown. With the cooperative intervehicle communi-cation technology in VSNs, the above dangerous informationcan be broadcasted to the vehicles behind to avoid carcollision and ensure the driving safety.

Reduce the Driving Time. According to the real-time trafficinformation gathered from mobile vehicle nodes, the future

Table 1: Projects and application.

Project Name Safety Efficiency ServiceTraffic view [24] √ √

FleetNet [25] √ √

CarTALK 2000 [26] √ √

CarTel [27] √ √

MobEyes [28] √ √

UMass diselNet [29] √

Group cooperative driving [30] √

SAFESPOT [31] √

Network on wheels [32] √

VII Plan [33] √

PReVENT [34] √

973 programme √ √ √

NSFC [35] √ √ √

road conditions can be estimated. Through identifying thetraffic abnormal information, vehicle nodes can assist driverto bypass congested roads and optimize the travel routes.Thus, it saves the travel time.

Improve the Driving Services. Recently, more and moredrivers begin to concern on diverse driving services, suchas travel office, file download, free chatting, and entertain-ment resources sharing. Specifically, drivers could downloaddesired text files or video slices using RSUs in VSNs. Bylearning the concept of social network, drivers could chator search the nearby parking, hotels, discount shopping,and other life information. Also, they can share all kindsof interested entertainment resources like popular music,sports, news, and so forth.

VSN also has great applied value in urban environmentmonitoring [20], traffic management and scheduling, vehicletracking [21], carpooling and vehicle calling [22], emergencydisaster relief [23], and so on.

Over the years, the research about VSNs has gainedthe favor of many academic communities and large well-known companies. And theNational Science andTechnologyDepartments provide adequate funding to finance thoseprojects.The typical projects and their applications have beenlisted in Table 1.

1.2. What Is Traffic Abnormal Event?

1.2.1. Definition and Characteristics. On city road, some un-expected and sudden accidents often happen, like car col-lision, car turnover, car breakdown, falling objects, severeweather, large-scale commercial activities, temporary roadcontrol, and so forth. They not only affect the normal trafficflows, but also easily result in decreased road capacity. Thus,we name them as traffic abnormal events. In Figure 3, itshows the difference of traffic flows on roads in normal andabnormal states.

Based on our observations, the traffic abnormal events oncity road mainly have the following characteristics.


Upstream road segment Event source Downstream road segment

Road surfaceVehicles

Abnormalstate

stateNormal

Figure 3: The difference of traffic flows on roads in normal and abnormal states.

Physical variable

Physical variable

Extract

Extract

Information variable

Traffic abnormalevent

Traffic abnormal

information

information

Road congestionRoad congestionevent

Deduce

Deduce

Produce Produce

Information variable

RegenerateRegenerate

Figure 4: The logical deduction relationship between event andinformation.

Suddenness and Uncertainty. Whatever the event time or thehappening place, the occurrence of traffic abnormal event hasthe features of suddenness and uncertainty.

Danger and Importance. There is a certain danger in trafficabnormal event. Fast and accurate traffic abnormal informa-tion is more beneficial to improve the road safety and reducethe economic losses of drivers.

Diversity and Complex Causes. Any complex and changeable,objective and subjective factors will affect the city trafficconditions.

Derivative and Chain Effect. Once the traffic abnormal eventhappens on city road, secondary accidents and road conges-tion events are easily derived. And it can be seen that thereexists a chain effect between them.

Diffusibility and Persistency. The traffic abnormal event willnot disappear immediately; it will persist for a period time. Ifit is not timely dealt with or disposed of properly, more roadsegments will be congested along with the event diffusion.The event impact areawill expand to farther regions and showdifferent evolution trends.

Expressive and Detectable. The physical variables of trafficabnormal event and secondary road congestion event (suchas speed, picture, and direction) can be detected by sensorsand expressed as information. In Figure 4, it shows the logicaldeduction relationship among traffic abnormal event, roadcongestion event, traffic abnormal information, and roadcongestion information.

2. Motivation and Background

2.1. Why Study the Traffic Abnormal Information Detectionand Transmission Mechanism? Traffic abnormal event is themain factor reducing the city road capacity and service level.When the event status is light, road congestion problem willarise. When the event status is serious, people’s lives andproperty safety will be threatened. Meanwhile, the first taskof constructing Smart City is to detect the traffic congestionpoints on city road and improve the ability responding toemergency events for roads. Thus, utilizing VSNs technologyto achieve interconnection between moving vehicle nodes isvery helpful to improve the intelligent degree of city road andalleviate the growing issues of traffic congestion [36, 37].

The traffic abnormal event will bring great changes intraffic flow, which also can be reflected in the mutation ofphysical parameters. With the onboard nodes in VSNs, theroad environment information is sensed and collected. Basedon these data, drivers can extract and deduce the trafficabnormal information. For the traffic abnormal information,if it could be transmitted to the event impact area or potentialinformation subscribers, drivers will adjust their travel routesto shy away the road congestion. So, it is necessary andmean-ingful to study the traffic abnormal information detection andtransmission mechanisms based on VSNs.

2.2. Traffic Abnormal Information Detection Based on VSNs.Now, most of the related research work about the trafficabnormal information detection mechanisms is based onVSNs and mainly focuses on the incident detection and roadcongestion detection.

2.2.1. Incident Detection. Incident detection puts moreenergy on perceiving and preventing traffic incident eventin advance, so as to assist building an in car safety warningand driving system. As for the technology implementation,the e-NOTIFY system [38] uses the on board units (OBUs)to gather and process dangerous information and make finaldecisions. The NOTICE system [39] firstly embeds sensorbelts in the road at regular intervals then detects the abnormalevent based on the communication between sensor belts. Andeach belt consists of a collection of piezoelectric pressure sen-sors, a simple aggregation and fusion engine, and a few smalltransceivers. In [40], a novel probabilistic automatic incidentdetection technique is presented for nondense traffic flowbased on Bayesian theory. And it utilizes the communicationbetween RSUs and sensor belts to detect the incidents.


2.2.2. Road Congestion Detection. The traditional road con-gestion detection mechanisms mainly rely on traffic flowsobservation and auto congestion identification (ACI) algo-rithms. To be specific, large-scale infrastructure-based sen-sors are deployed at cross-roads to gather traffic flow data,such as inductive loop detector [41], video camera, andradio frequency identification receiver. Traffic control centers(TCC) or RSUs play the role of sink nodes and are responsiblefor detecting congestion information by running ACI algo-rithms.

In [42], based on the correlation coefficient of stationarycoil sensor data collected from different crossroads, randomvariable matrix and complex network model are created.Through observing the eigenvalue changes of randommatrix,the fluctuations of traffic flows on some road segments aredetected. Combined with the cluster analysis method, theposition and scale of traffic congestion can be obtained. In[43], based on the recorded live video streams or pictures, theproposed MORYNE system evaluates the surrounding trafficinformation by adopting video graphical analysis (VGA)method. After processing this traffic information, the roadcongestion information is extracted by TCC.

Other typical information preprocessing techniques alsoinclude: congestion identification based on gain amplification[44], congestion identification based on wavelet analysis[45], congestion prediction based on pheromonemodel [46],and congestion detection based on pattern recognition [47].Obviously, those centralized TCC-based detecting methodshave high detection overhead and long response delay.

In order to gather real time and dynamic traffic flowdata, some scholars proposed the distributed congestiondetection mechanism based on VSNs. For instance, Bauza etal. raise the cooperative traffic congestion detection (CoTEC)system in [48]. It uses onboard sensors to transmit andreceive CAM packets to estimate the surrounding trafficdensity. By applying six LOS road service levels supported bySkycomp platform and the fuzzy theory, the vehicle nodescan implement the local road congestion detection by thecooperative intervehicle communication technique.

At the same time, the information fusion techniquehas also been applied into road congestion detection basedon VSNs. Generally, it is used to extract the congestionfeature information or generate the final result of congestiondetection. For instance, the traffic view system utilizes thethreshold-based message aggregation method to removeredundancy atomic information and extract the congestionfeature information. Due to the fixed threshold values con-straints, the number of aggregation message pairs is limited.So, the information fusion efficiency is low.

In [49], the SOTIS system proposes the road segment-based message aggregation method. All the messages withthe same road segment ID can be aggregated together.Since the length of road segment is fixed, it is hard toachieve a good tradeoff between the information fusionefficiency and the information detection accuracy. In [50],the Street Smart system proposes the cluster structure-basedmessage aggregation method. It requires that the networktopology to be completely connected. Because of the dynamicchangeable cluster structures, the information maintenance

cost is quite large in VSNs. In [51], Dietzel et al. propose astructureless message aggregationmethod based on the fuzzylogic algorithm. Although it can reduce the maintenancecost of dynamic structure, the computation overhead is stillconsiderable. In [52], Liu and Chigan design a parallel-styleSLMA method based on decision-level information fusion.And each vehicle node has an opportunity to become thedecision maker. Therefore, it can avoid the interference ofuncertain and inaccurate sensor data.

Additionally, with the help of the audio receiver unitsmounted on street lamp post [53], the GPS system embeddedin drivers’ smartphone [54], or the E-SAR airborne radarsensors [55], the road congestion information detection alsocan be realized.

2.3. Traffic Abnormal Information TransmissionBased on VSNs

2.3.1. DSRC-Based One-Hop Transmission. Dedicated shortrange communication (DSRC) is the dedicated wireless com-munication technique in vehicular environment. It is basedon the IEEE 802.11p standard [56]. Compared with conven-tional cellular WiMax or GPRS communication, DSRC hasthe advantages of low cost, high speed, low delay, and highreliability.

DSRC is capable of delivering 27Mbps data rate by usinga two-way line-of-sight radio and provides seven 10MHzwide channels at the 5.9GHz licensed band. As shown inFigure 5, channel 178 is the control channel (CCH), which isrestricted to safety communication only. Other two channelsat the edges of the spectrum are reserved for future advancedaccident avoidance applications and high-powered publicsafety usages. The rest are four service channels (SCHs) andare available for both safety and nonsafety usage [57].

2.3.2. Routing-Based Multihop Transmission. According tothe transmission strategy, the existingmultihop routing tech-nologies in VSNs can be classified into broadcast, multicast,and unicast. From the perspective of routing information, allthe multihop routing protocols can be divided into topology-based routing, location-based routing, DTN-based routing,and hybrid-style routing. The detailed classification resultshave been shown in Figure 6.

(a) Transmission Strategy

(i) Broadcast. “Broadcast” aims at delivering information toall vehicle nodes. The traditional flooding-based broadcastmethods easily cause broadcast storm problems. To addressthis issue, most broadcast protocols in VSNs focus onreducing message redundancy and optimizing the selectionof rebroadcast nodes.

For example, a content-based dissemination protocolfor VANETs is proposed in [58], which defines an event-related degree parameter to indicate the encounter proba-bility between vehicle node and event. The last broadcastvehicle node selects the neighbor node that has the highestevent-related degree value to be the next rebroadcast node.


Maximum EIRP

33dBm 33dBm 33dBm 44.8dBm 23dBm 23dBm 40dBm

5MHz 10MHz 10MHz 10MHz 10MHz 10MHz 10MHz 10MHz

Reserved Safety SafetySafety/service

Safety/service

Safety/service

Safety/service

Control channel

High power, long range

Merged into 20MHzservice channel

Merged into 20MHzservice channel

20MHz 20MHz

5.925

(GH

z)

5.850

(GH

z) Accident avoidance, safety of life

CH172 CH174 CH176 CH178 CH180 CH182 CH184

CH175 CH181

Figure 5: Channel assignment in range of 5.850GHz–5.925GHz.

Socialforwarding

Information transmission based on the multihop routing

Transmission strategy Routing information

Broadcast Multicast Unicast Topology DTN Hybrid

QoS aware Prediction aware Environment aware

Cluster-basedrouting

Geocast-basedrouting

Request-basedrouting

Knowledge-basedforwarding

Nonknowledge-basedforwarding

Location

Figure 6: The classification of multihop routing technology in VSNs.

In this way, the event information can only be transmittedto potential users, so the total number of rebroadcast nodesdecreases. In [59], a cross layer broadcast protocol for mul-tihop emergency message dissemination is proposed basedon the handshaking mechanism. In order to restrict the totalnumber of rebroadcast nodes in the whole network, it opti-mizes the selection of next rebroadcast node by comparingsome information like distance, speed, channel condition,and so forth. Moreover, the handshaking mechanism canprevent much unnecessary information rebroadcasting oper-ations.

In [60], the authors propose a fast and reliable emergencymessage dissemination mechanism. It divides the road intomultiple small segments of equal areas according to thevehicle density. The minimal waiting time slot is assigned tothe vehicle node located in the farthest road segment. Thisidea is repeatedly used to handle the broadcast storms, but itstill may produce the spatial broadcast storm problem [61].To this, the idea of ring-based road division is first raised in[62], where the width of each ring is computed based on thedensity of neighbor vehicles.The vehicle nodes in the farthestring will be assigned the shortest waiting time.

(ii) Multicast. “Multicast” aims at delivering informationto multiple vehicle nodes. It includes cluster-based routingand geographic broadcast (geocast) routing. In cluster-based

routing, the objects of multicast are all the nodes in eachcluster structure. And the formation of cluster structure isbased on the vehicle ID, the relative mobility between vehi-cles, or the composite information of vehicles. For example,the COIN [63] protocol utilizes the mobility, position, andbehaviors of vehicle nodes to build the cluster structure.

In geocast routing, the objects of multicast are all nodesin the targeted geographic area, which is also called the zoneof relevance (ZOR). To reduce the communication overheadand avoid the network congestion, the zone of forwarding(ZOF) is designed to limit the flooding range. At first, themessage is transmitted to the ZOF by the unicast routing.When the message has arrived in ZOR, it will be delivered toall the other vehicle nodes by the flooding method.The mostclassic geocast routing protocols include IVG [64], cachedgeocast [65], and abiding geocast [66].

In [67], the Mapcast protocol is proposed based on themutual positions of nodes and the CSMA/CA MAC mech-anisms. It extends the intuitive concepts of Geo-Broadcasttowards a more precise idea of Map-Driven forwarding,where the real life road topology leads the forwardingpolicies.

(iii) Unicast. “Unicast” aims at delivering information to thespecified vehicle node. It is widely applied in the data gath-ering and publish/subscribe systems. Actually, data gathering


can be thought of as a special publish/subscribe system initi-ated by the sink node. For instance, Palazzi et al. exploit thebase station (BS) to create request packets and continuouslybroadcast them in BS coverage area [68]. Then, those vehiclenodes driven into BS coverage area and received requestpackets will become specified target vehicle nodes. They areresponsible for gathering and replying interest informationfor BS.

In [69], Shafiee et al. propose a request-adaptive packetdissemination mechanism (RPDM). Firstly, it asks the sub-scribe node to disseminate the request packets until findingthe target nodes conforming to the return condition (RC).Then, the reply mechanism is triggered by target nodes. Toexpand the environment sensing area, RPDM designs moreflexible RC structure, which can be adaptive to different RCtypes and delay conditions.

(b) Routing Information

(i) Topology-Based Routing. Generally, the topology-basedrouting refers to the traditional MANET routing, whichdefine the data forwarding rules based on the link infor-mation stored in routing tables. They mainly include thefollowing.

(1) Proactive Routing. It requires the node to broadcastand exchange the routing information packets peri-odically, thereby discover the routing initiatively. Inaddition, each node has to maintain all the nodes’routing information. To reflect the changes of thenetwork topology, the routing tables also need updat-ing frequently. For the high mobile vehicle nodesin VSNs, the proactive routing will spend too muchcommunication cost. The typical proactive routingprotocols include: DSDV [70], OLSR [71], and FSR[72].

(2) On-Demand Routing. It contains two procedures:routing discovery and routing maintenance. More-over, their topology and routing tables are built ondemand; that is, only when the source node finds thatthere exists no path to destination node, the routingdiscovery procedure will be started. The typical on-demand routing protocols include: AODV [73], DSR[74], and TORA [75].

(3) Hybrid Topology Routing. It aims to minimize therouting control overhead of proactive routing and therouting discovery time of on-demand routing. It splitsthe whole network into two zones: inside zone andoutside zone. Then, each node maintains the insidezone routing by the proactive routing and discoversthe outside zone routing by the on-demand routing.The typical hybrid routing protocols include: ZRP[76] and ZHLS [77].

(ii) Location-Based Routing. In location-based routing, eachnode must be aware of its position information, so it canforward data to the optimal neighbor node that is closest tothe destination node. However, the optimal neighbor nodedoes not always exist. To solve this problem, lots of protocols

are proposed, such asGPSR [78], GyTAR [79], andRIRP [80].But, for the reason that they assume that lots of nodes havethe communication opportunity and ignore the issue of thenetwork nonconnectivity, they are more suitable to the highdensity VSNs environment.

(iii) DTN Routing. Due to the high mobility and unevendistribution of vehicles, the network topology is dynamiclychangeable and the end-to-end connection is hard to keepin VSNs. Moreover, the low market penetration of DSRCdevices leads to more sparse and low network connectivity.To enhance the reliability of information transmission, VSNswidely adopt the DTN (delay tolerant network) routingmechanism. The main idea of DTN routing is store andforward. It can be classified into the following.

(1) Knowledge-Based Routing. It utilizes the knowledgeinformation to forward data, such as the link state,history encounter information, movement pattern,and network topology. The common knowledge-based routing methods include the following.

(a) QoS-Aware Forwarding. Most of the QoS-awareforwarding mechanisms aim to minimize theinformation transmission delay. Moreover, theconstraint objects are two mobile vehicle nodesor two static roadside units. If the constraintobjects are vehicle nodes, the whole networkcan be seen as a connected graph with weights,where the weight of each edge denotes thetransmission delay of each link. To guaran-tee the QoS performance, the node will takethe minimum source-to-destination informa-tion transmission delay as the optimal objectand find the optimal transmission route byalgorithms like Dijsktra. The typical example isthe MEDR protocol [81]. Additionally, QoSBee[82] utilizes the pheromone, produced by beeslooking for food, to assist in finding the optimalroute, thereby ensure the transmission delaycondition. For the delay minimization betweenroadside units, the probability that the databundles in queue head could be released to theroadside vehicles is computed. Since the highprobability indicates a low transmission delay,the vehicle node with the lowest transmissiondelay is in charge of forwarding data.The typicalexamples are PBRS-BBR [83] and PBRS [84]protocols.

(b) Prediction-Aware Forwarding. It mainly utilizesthe historical statistics, such as encounter timeor frequency, movement trajectory of vehiclenodes, to predict the future encounter probabil-ity between the vehicle node and the destina-tion node. The neighbor node with the largestencounter probabilitywill be selected to forwarddata. While the protocols of PROPHET [85],Spray and Focus [86], EBR [87], PER [88], andCCSDV [89] use the history encounter informa-tion to predict the encounter probability, TMA


[90] and PTDD [91] use the history movementtrajectory to predict.

(c) Environment-Aware Forwarding. It originatesfrom the CAR routing [92], which is basedon the various environmental information toforward data. For example, the SCAR [93]is based on the history neighbor perception,the MobySpace [94] and VADD [95] are bothbased on the movement pattern perception, theORWAR [96] is based on the link time win-dow perception, the RDAR [97] and DRADG[98] are based on the distance perception, theMaxprop [29] and RAPID [99] are based onthe buffer space perception, and the TADs [100]is based on the traffic pattern and road layoutperception.

(2) Non Knowledge-Based Routing. It forwards data with-out any knowledge. Meanwhile, the forwarding be-havior is random and blind. The typical routing pro-tocols include flooding [101], epidemic [102], directtransmission [103], and Spray and Wait [104].

(3) Social Routing. It forwards data with the socialrelationships or behaviors of drivers. For instance,LABLE [105], BUBBLE [106], and PepleRank [107]are proposed based on the social relationship. AndPLBR [108], ESPR [109], CSI [110], andHARP [111] areproposed based on the social behaviors.

(iv) Hybrid-Style Routing. The hybrid-style routing integratesthe topology-based routing, location-based routing, andDTN routing. For instance, the GeoDTN+Nav system [112]integrates the navigation information with the DTN routing;the GPSR-DTN system [113] integrates the location informa-tion with the DTN routing; and the DARCC system [114]integrates the geographic information with the encounterinformation.

3. Challenges and Problems

3.1.The Node Selfishness. Due to the enhancement of energy-saving and environmental protection awareness, more andmore vehicles are required to minimize the energy consump-tion. For traditional motor vehicles, minimizing the energyconsumption is beneficial to reduce the cities exhaust emis-sions. For power driven or green energy vehicles, minimizingthe energy consumption is beneficial to reduce the energycost and maximize the mileage.

In VSNs, the vehicle nodes have no strict energy con-straints, so many data gathering protocols rarely considerthe energy consumption problem. Driven by the interests ofmaximizing the successful data transfers, the data forwardingoperations are easily concentrated on some more “excellent”vehicle nodes. As a result of the overhigh energy consump-tion, the energy consumption is unfair among all the vehiclenodes.

To save energy, more and more vehicle nodes are unwill-ing to participate in forwarding data. It can be seen that some

vehicle nodes are selfish. And the selfishness of vehicle nodeswill lead to low reliability of data transmission.

3.2. Redundant Atomic Message. Road congestion informa-tion detection aims at supplementing the abnormal trafficinformation content. For safety considerations, the car man-ufacturers like to equip various sensor nodes on vehicles. Asa result, a huge number of similar and redundant atomicmessages are generated in VSNs. The atomic message canexpress themorphological information, but cannot reflect theevent essence. Thus, the existence of huge atomic messageswill waste limited bandwidth resources. Meanwhile, theywill obstruct and delay the congestion information detectionprocedure.

Due to the limit of single-level information fusionmethod and the existence of uncertain environment data,even if the feature-level information fusion method canreduce the redundancy of atomic messages and improve theinformation detection efficiency, the accuracy and consis-tency of congestion information detection cannot be guaran-teed. Needless to say, inaccurate road congestion informationwill break the normal traffic order and severely reducethe travel quality. Therefore, how to realize the rapid andreliable road congestion information detection is the primaryproblem of the traffic abnormal information dissemination.

3.3. Multiregion Geocasting Demands. Because of the dif-fusibility and persistency of traffic abnormal event, its eventimpact area changes as time increases. According to the traffictheory, the event impact area starts from the road segmentwhere the event happens and expands to multiple roadsegments based on the rule of “parallel to the first, after theupstream”.Thus, the traffic abnormal information needs to bedisseminated to multiple geographical regions. However, thetraditional geocasting methods only consider the geocastingproblem oriented one fixed geographical region.

To solve the multiregion geocasting problem, the tradi-tional geocasting protocols create multiple event messages.Each message is affiliated to one geographical region. Sinceall the multiple geographical regions take the event source asthe center and extend toward the upstream, the tree-basedgeocasting paths are easily repeated. In addition, previousresearch on geocast protocols pays little attention for infor-mationmaintenance cost. InWSNs, the nodes in target regionare fixed, so it is unnecessary to consider the informationmaintenance issue. However, the high mobility of vehiclesnodes in VSNs makes the information dissemination objectsunstable. Specifically, when some vehicles are moving intothe target region, other vehicles are moving out of the targetregion. With the frequent moving behaviors of vehicles, thecommunication overhead is large. Furthermore, it will bringabout another severe problem that drivers may receive sameinformation or miss event information. Therefore, it is quitesignificant to guarantee the new vehicle nodes obtaining theevent information in a reliable and timely manner whilepaying lower information maintenance cost.


3.4. Interregional Information Subscription. With the help ofRSUs, the traffic abnormal information subscription servicein VSNs has been greatly improved. But the scope andnumber of the RSUs’ deployment are limited, and theirdistribution is also uneven. For example, RSUs are deployeddensely on main roads in city, while the distribution ofRUSs on suburban roads is sparse, sometimes there is noRSU. Additionally, most RSUs play the role of informationrelay station; only a part of RSUs have the function ofaccessing Internet, so not all the RSUs can communicationwith each other. Furthermore, some high speed wirelesscommunication interfaces (like 3G, LTE etc.) need to pay offadditional fees. Meanwhile, the huge information subscrip-tion requirements will put a heavier burden on the networkinfrastructure. To deal with this issue, the researchers try toexcavate the information transmission capability of vehiclenodes, thereby provide a traffic abnormal information sub-scription mechanism based on the V2V communication.

Different from the commercial advertising and enter-tainment information, the traffic abnormal information sub-scription claims a high probability of successful informa-tion transmission. However, the large geographical spacebetween the subscription node and the target node shows aninterregional characteristic. And the uneven distribution ofvehicle nodes leads to worse network connectivity.Therefore,it has presented a big challenge to interregional informationtransmission based on the V2V communication in VSNs.

4. Conclusion

In this paper, we first explain the formation of VSNs anddescribe its network characteristics, applications, and somerelated research projects. Then, the definition and the mainfeatures of the traffic abnormal information are introduced.Following that, we provide the main reasons of studyingthe traffic abnormal information detection and transmissionmechanisms in VSNs. Simultaneously, the current abnormalinformation detection mechanisms are depicted in detail,respectively. According to the transmission hops, we dividethe existing information transmission mechanisms into one-hop DSRC-based transmission and multihop routing-basedtransmission. In particular, the multihop routing-basedtransmission mechanisms are summarized and discussedfrom the view of information transmission strategies androuting information. Finally, the challenges and problems infuture are presented. And they can be seen as the valuablereference for future research directions and issues.

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper.

Acknowledgments

The authors gratefully acknowledge the support of theNational Natural Science Foundation of China (NSFC)(61272504, 61201204, and 61100217), the support of Beijing

Natural Science Foundation (4122057), the support of theNational S&T Major Program (2012ZX03005003), the sup-port by Research Fund for the Doctoral Program of HigherEducation of China under Grant no. 20130009110010, andthe Fundamental Research Funds for the Central Universities(2014JBM006).

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