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JOURNAL OF LATEX CLASS FILES, VOL. XX, NO. XX, XX 2018 1

Vehicular Edge Computing and Networking: ASurvey

Lei Liu, Student Member, IEEE, Chen Chen, Senior Member, IEEE, Qingqi Pei, Senior Member, IEEE, SabitaMaharjan, Member, IEEE, and Yan Zhang, Senior Member, IEEE

Abstract—As one key enabler of Intelligent TransportationSystem (ITS), Vehicular Ad Hoc Network (VANET) has receivedremarkable interest from academia and industry. The emergingvehicular applications and the exponential growing data havenaturally led to the increased needs of communication, com-putation and storage resources, and also to strict performancerequirements on response time and network bandwidth. In orderto deal with these challenges, Mobile Edge Computing (MEC)is regarded as a promising solution. MEC pushes powerfulcomputational and storage capacities from the remote cloudto the edge of networks in close proximity of vehicular users,which enables low latency and reduced bandwidth consumption.Driven by the benefits of MEC, many efforts have been devotedto integrating vehicular networks into MEC, thereby forming anovel paradigm named as Vehicular Edge Computing (VEC).In this paper, we provide a comprehensive survey of state-of-art research on VEC. First of all, we provide an overviewof VEC, including the introduction, architecture, key enablers,advantages, challenges as well as several attractive applicationscenarios. Then, we describe several typical research topics whereVEC is applied. After that, we present a careful literature reviewon existing research work in VEC by classification. Finally,we identify open research issues and discuss future researchdirections.

Index Terms—Vehicular Ad Hoc Network, Mobile Edge Com-puting, Vehicular Edge Computing, Computation Offloading,Caching.

I. INTRODUCTION

Due to the rapid pace of industrialization and urbaniza-tion, Intelligent Transportation System (ITS) has receivedincreasing interest from academia and industry. By integratinginformation and communication technologies [1], [2], [3], [4],[5], [6], ITS plays an important role in enhancing road safetyand improving traffic efficiency. Vehicular Ad Hoc Network(VANET) [7] is a key enabler in ITS. Being a special kind ofMobile Ad Hoc Networks (MANETs), VANETs are comprisedof two basic elements:vehicles and Road-Side Units (RSUs)[8], [9], as shown in Fig. 1. Vehicles are equipped withcommunication devices, which enables short-range wirelesstransportation. RSUs are distributed along the road to be con-nected to the backbone network for the purpose of facilitating

Lei Liu, Chen Chen and Qingqi Pei are with the State KeyLaboratory of Integrated Service Networks, Xidian University, Xi’an710071, China(e-mail:[email protected];[email protected];[email protected]).

Sabita Maharjan is with Simula Metropolitan Center for Digital Engineeringand University of Oslo, Norway (email: [email protected]).

Yan Zhang is with the Department of Informatics, University of Oslo,Norway (email: [email protected]).

Manuscript received XX, 2018; revised XX, 2018.

network access. Data communication in VANETs can be real-ized in two models: Vehicle-to-Vehicle (V2V) and Vehicle-to-RSU (V2R) [10], [11]. Using the two communication models,vehicular networks support an array of applications, whichinclude three main categories: 1) road safety applications (e.g.,lowering the risk of accidents); 2) traffic efficiency applications(e.g., reducing travel time and alleviating traffic congestion; 3)value-added applications ( e.g., providing infotainment, pathplanning and internet access).

The rapid development of vehicular networks will facilitatewide use of smart vehicles, which enables a large number ofvarious types of applications [12], [13]. The implementation ofthese applications call for enormous resources for data storageand processing. Constrained by the limited computation andcommunication capacities, vehicles cannot readily satisfy theincreasing resource demands of such applications, speciallythose with intensive computation and strict delay requirements.In order to tackle these issues, Mobile Cloud Computing(MCC) is widely regarded as a promising solution [14].With the combination of computation and communicationtechnologies, MCC allows to run application services of usersat the remote cloud. Thus, MCC can bring the followingbenefits for users: 1) reduce energy consumption; 2) enablesophisticated services; 3) provide huge storage capacity. Therehave been several survey articles on MCC with differentfocuses [14], [15], [16]. In [14], the definition, architectureand application of MCC are introduced, and the existing issuesand corresponding approaches are presented. The work in [15]discusses applications, challenges and opportunities of MCC.The taxonomy and comparison of state-of-the-art authentica-tion schemes regrading MCC are presented in [16]. In orderto enhance road safety and improve travel comfort, significantamount of effort has been denoted to integrating MCC withvehicular networks [17]. The authors in [18] propose a cloud-supported gateway model to seamlessly access the internet inITS. By this model, the user experience can be improved. AVANET-cloud computing model is devised in [19], where thecloud computing resources are used to enhance the quality ofservice (QoS). Depending on the presented cloud system in[20], vehicles can find their requested resources from mobilemobile services.

Despite the advantages of MCC, considering that the cloudis far from users, high transmission latency is a consequence.Moreover, the explosive growth of mobile data will furtherimpose huge stress on the load of back-haul networks. If allthe data are sent to the cloud for processing, the bandwidthconsumption and competition are extremely significant. Mo-

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bile Edge Computing (MEC) interchangeably regarded as fogcomputing (FC), is envisioned as a promising paradigm toaddress such issues [21], [22], [23], [24]. In MEC, the cloudservice is pushed to the network edge, i.e., the computationand storage resources are moved to proximity of users, bywhich latency can be greatly reduced and energy can besaved to a large extent. MEC literature has also seen severalsurvey papers [25], [26], [27], [28]. The work in [25] givesa comprehensive overview of existing research developmentsin MEC along with advantages, architectures and applications.Meanwhile, the issues and existing solutions of security andprivacy are also elaborated. The authors in [26] introducethe work on computing and communication in MEC witha focus on joint-radio-and-computational resource allocation.The enabling technologies in MEC, such as Virtual Machine(VM), Software Defined Networking (SDN), Network Func-tion Virtualization (NFV) are discussed in [28].

Vehicular Edge Computing (VEC) has a great potentialto enhance traffic safety and improve travel comport byintegrating MEC into vehicular networks. VEC literature hasseen several work mainly on computation offloading [29], [30],content caching [31], [32], security and privacy [33] and [34].In view of prior work, it can be seen that there has been nowork to survey the existing research in VEC. In this paper, wemake an attempt to present a comprehensive review of state-of-the-art research on VEC. The contributions of this paperare summarized as follows:

• A comprehensive overview of VEC is provided, wherethe introduction, architecture, advantages, challenges andapplication scenarios of VEC are elaborated in detail.

• The existing research models in VEC, which are relatedto computation offloading, content caching, data manage-ment, flexible network management as well as securityand privacy are summarized, respectively. Besides, a com-prehensive literature review for each model is presented.

• The open issues are identified and future research direc-tions are discussed.

The organization of this paper is illustrated in Fig. 2, whichis introduced as follows. We provide an overview of VEC inSection II. We present several VEC research topics in Section

III. In Section IV-VIII, we summarize the existing elaboratedmodels in VEC, respectively. The open issues and futureresearch directions are introduced in Section IX, followed bythe conclusion in Section X. The used definitions of acronymsin this paper is summarized in Table I for easy reading.

II. VEHICULAR EDGE COMPUTING: AN OVERVIEW

VEC is the integration of emerging MEC with traditionalvehicular networks. VEC aims to move communication, com-puting and caching resources to close proximity of vehicularusers. Thus, VEC has the potential of playing a key role inaddressing the exponentially increasing needs of edge deviceson low delay and high bandwidth. Different from traditionalMEC, the prominent features of VEC are fast mobility of vehi-cles which leads to the frequent and more dynamic topologychanges and the complicated communication characteristicsdue to the rapidly varying channel environment over time.

In VEC, vehicles own certain communication, computationand storage resources; RSUs which often act as edge serversare placed close to vehicles for gathering, processing andstoring data timely. Due to the constrained capacity, vehiclescan offload their computation-intensive and latency-sensitivetasks to the edge servers, which can considerably reduce theresponse time and efficiently alleviate the heavy burden onbackhaul networks. In a VEC framework, the content requestercan directly obtain the needed content from caching nodeswithout accessing the core network, thus reducing end-to-endlatency and improving efficiency of network bandwidth usage.Due to the limited storage space, each caching node cannotcache all the contents. Thus, the following key issues arise:how to determine what to cache, how to determine where tocache and how to determine cache policy.

It is anticipated that by combing emerging new technolo-gies, such as SDN [35], blockchain [36], Artificial Intelligence(AI) [37], [38], [39], [40], [41], VEC enable services effi-ciently and effectively.

Next, we introduce architectures, advantages, challenges andapplication scenarios of VEC, respectively.


Architecture

II. Overview

III. Research topics

V. Caching

IV. Task offloading

VI. Data Management

VII. Flexible network

Management

IX.Open issues

and future work

VIII. Security and Privacy

Fig. 2. Architecture of the content organization

…RSU1

VEC server

RSU2

VEC server

RSUM

VEC server

L1 L2

…LM

:V2R links:V2V links

Cloud

: Storage : Computation

:Backhaul links

User layer

MEC layer

Cloud layer

Fig. 3. Architecture of vehicular edge computing

A. Vehicular Edge Computing:Architecture

The architecture of VEC is presented in Fig. 3. Typically,this architecture is composed of three layers, i.e., vehicularterminals as the user layer, RSUs as the MEC layer and cloudservers as the cloud layer.

1) Vehicular Terminals: In VEC, vehicular terminals aremainly represented by vehicles. Rather than ordinary mobilenodes, vehicles have the following prominent features: 1) sens-ing: vehicles can sense the environment from both inside andoutside and are able to collect various information using theequipped vehicular devices, including cameras, radars, GlobalPositioning System (GPS), etc.; 2) communicate: vehicles canexchange and share information with other vehicles or RSUsusing V2V and V2R communication manners; 3) computing:in addition to transferring parts of computation tasks to theedge servers or the cloud for processing, vehicles can executeparts of the tasks locally by themselves; 4) storage: the idlestorage space of vehicles can be used to cache popular contentsfor data sharing.

2) Edge Servers: RSUs often act as edge servers in VEC,which are distributed along the road in a city. They have richcommunication, computation and storage resources comparedto vehicles. RSUs are responsible for receiving the informationsent from vehicles, processing these collected information, andeven uploading these information to the cloud. By computation

offloading and caching technologies, RSUs are beneficial forhandling strict performance requirements. Besides, they canalso provide diverse services for vehicles, such as videostreaming, traffic control, path navigation.

3) Cloud Servers: Cloud services are deployed in a remotecloud. They can get the uploaded information from edgeservers. Compared with edge servers, cloud services have richcapacities in terms of computation and storage, and cover amuch broader area. By getting the uploaded information frommobile nodes and edge servers, they can have a global view ofthe covered area. The cloud paradigm can provide global levelmanagement and centralized control, which helps in makingoptimal decisions.

B. Vehicular Edge Computing:Key Enablers

In this subsection, several enabling technologies for VECare introduced, including cloud technology, SDN [42], NFVand smart vehicles [21], [25], [26].

1) Cloud Technology [15]: Cloud has powerful computa-tion capacity and storage resources. Generally speaking, itis deployed far away from the users, which will incur thelong latency. By moving the functionality of the cloud to thenetwork edge, VEC is capable of sustaining diverse applicationservices.


TABLE ISUMMARY OF ACRONYMS

Acronym DefinitionITS Intelligent Transportation SystemVANET Vehicular Ad Hoc NetworkMANET Mobile Ad Hoc NetworkRSU Road-Side UnitsV2V Vehicle-to-VehicleV2R Vehicle-to-RSUMCC Mobile Cloud ComputingQoS Quality of ServiceMEC Mobile Edge ComputingFC Fog ComputingVM Virtual MachineSDN Software Defined NetworkingNFV Network Function VirtualizationVEC Vehicular Edge ComputingAR Augmented RealityAI Artificial intelligenceGPS Global Positioning SystemHD High DefinitionV2I Vehicle-to-InfrastructureAV Autonomous VehicleSBS Small Base StationE2E End-to-EndPVs Parked VehiclesQoE Quality of ExperienceUAVs Unmanned Aerial VehiclesSRSU Solar-powered RSUsP2P Peer-to-PeerBSs Base StationsLUR Load Utilization RatioQCR Query to Connectivity RatioMLP Multi-Layer PerceptionCNN Conventional Neural NetworkVDTNs Vehicular Delay-tolerant Networks5G-SDVNs 5G-enabled Software-Defined Vehicular NetworksRF Radio FrequencyLOS Line of SightVFS Vehicular Fog ServiceCL-A-SC CertificateLess Aggregate SignCryptionPOI Point of Interest

2) Software Defined Networking [35]: SDN is regarded asan innovation network architecture, the root feature of whichis to decouple the control plane and the data plane. By thismethod, it can simply the network management.

3) Network Function Virtualization [43]: NFV aims toprovide a new approach to design, deploy as well as managenetwork services. Its key is to decouple network functionsfrom the physical devices where they run. It can significantlyreduce the operating costs and capital costs as well make thedeployment of new services more flexible and efficient.

4) Smart Vehicles: With the development of vehicularequipments and information technologies, vehicles will be-come much smart in the future. At that moment, smart vehiclesnot only communicate with each other, but also have ownample resources in terms of computation and storage. Usingthese resources, vehicles can process tasks locally, alleviatingthe network burden and reducing the delay.

C. Vehicular Edge Computing: Advantages

The key advantages of VEC are as follows.

1) Response Time: The response time consists of the deliv-ery time of data offloaded to edge servers and back plus thetime for processing in the servers. When compared with thecloud, the edge servers in VEC are closer to vehicular users.Thus, the took execution time is considerably less, which isspecially beneficial for delay-sensitive applications, such assafety applications.

2) Energy Efficiency: The increasing prevalence of smartvehicles drives an explosive growth of various vehicular appli-cations, ranging from driving, communication, and computingto storage in the future. These applications will bring hugeenergy consumption. Assisted by VEC, electric vehicles withlimited energy can still provide enough support for theseapplications.

3) Bandwidth: With the popularization of smart vehicles,the amount of generated data from them will grow explosively.In addition, the content requests will also become diverse.Because of the long distance from users, cloud computing can-not guarantee the bandwidth requirements for processing suchenormous amount of data computation and content deliveryusing the centralized management. By moving the computationand storage resources of the cloud to the network edge, VECcan efficiently alleviate the huge bandwidth stress to back-haulnetworks.

4) Storage: Different from the cloud, the data can be storedin edge servers which are in proximity of vehicular users inVEC. The caching technology enables to access the stored datain time for vehicular users, and reduces the storage burden onthe remote cloud.

5) Proximity Services: Because edge servers are closer tovehicular users in VEC, various proximity services can beprovided to the users. In this way, the user experience canbe better guaranteed while maintaining efficiently the trafficmanagement. For example, when receiving the location andsensing information uploaded from vehicles, edge servers canhelp in processing the data to build a high definition (HD)map and then send the HD-map to vehicles.

6) Context Information: Edge servers in VEC can obtainthe real time information related to the behavior and locationof vehicle, traffic environments, network conditions, etc. Withthese information, various applications can be improved. Oneexample is that these real time information can be used todeliver contents to vehicular users based on their interests.

D. Vehicular Edge Computing:Challenges

The main VEC challenges are as follows.1) High Mobility: Due to the high mobility of vehicles, the

network topology in vehicular environments is highly dynamicchanging [44]. Under this condition, links can be easilydisconnected, which will deteriorate communication quality.Besides, vehicles may switch among multiple edge servers,leading to handovers. The frequent handovers contribute tothe delay and inversely impact service continuity, degradingthe user experience.

2) Harsh Channel Environment: In vehicular networks,especially urban vehicular environments, there exist many ob-stacles, e.g., trees, buildings, which may hinder the success of


Face recognition

Traffic control

Road safety Gaming

Autonomous driving

Video streamingAugmented Reality

Data aggregation and mining

VEC

Fig. 4. Application scenarios of vehicular edge computing

data transmission [45]. Meanwhile, it is difficult to characterisethe time-varying channels.

3) Resource Management: Compared to cloud computing,the resources of VEC in terms to computation and storage arelimited. Thus, how to manage these resources is vital. Givendynamic resource demands, diverse application characteristics,complicated traffic environments, the optimization of resourceallocation is a challenging task.

4) Task Migration: Due to the constrained capacity, it isneeded for vehicular users to offload computation-intensiveand delay-sensitive tasks to edge servers. Considering thedynamic channel environment and frequent changing topology,optimizing task migration decision is crucial.

5) Security and Privacy: In VEC, due to the dynamictopology changes, vehicles may not fully trust each other.Meanwhile, when the same physical edge servers are allowedto be accessed by different vehicular users, there are the issuesof security and privacy without a strong protection mechanism.

E. Vehicular Edge Computing:Application Scenarios

The emergence of VEC enables diverse types of applicationsas illustrated in Fig. 4. In the following, several typicalscenarios where VEC can be applicable are introduced.

1) Road Safety: Receiving directly the data forwarded fromvehicles and sensors deployed along the road, edge servers inclose proximity of vehicles can real-time analyze and processthese data. Once finding risk data, edge servers are responsiblefor notifying surrounding vehicles to avoid possible danger bytaking reasonable measures, such as braking, lane changingand turning round.

2) Entertainment: With the advent of smart vehicles,drivers can be liberated from complex driving operations andspend abundant time on entertianment, e.g., surfing the inter-net, playing gaming, watching video [46]. These applicationswill benefit from rich computation and storage resources in

VEC. For example, by caching popular contents cooperativelyamong edge servers and vehicles, vehicular users can directlyfetch the requested videos without resorting the remote cloud,thereby decreasing the delay and improving the experience ofusers.

3) Traffic Control: In VEC, edge servers cover a communi-cation region. Each vehicle within this region send its currentstatus (location, speed) and collected information (weatherconditions, road conditions) to the associated server. Afterreceiving the information uploaded from vehicles, the serverhas a knowledge of local traffic conditions, then it can controltraffic flow. By this method, traffic congestion [47] can beavoided.

4) Path Navigation: Real-time navigation system is ofquite importance to provide an optimal route for drivers. Theimplementation of real-time navigation involves data sens-ing, collection and processing. VEC is capable of providingsignificant computation and storage resources for real-timenavigation system.

5) Ultra-low Latency Service: VEC allows service pro-visioning with ultra-low latency and high reliability, e.g.,autonomous driving [48]. In autonomous driving, it is crucialfor vehicles to understand surrounding environments accu-rately and timely and performs more efficient and effectiveoperations. VEC makes it possible for autonomous driving toobtain the support of powerful computation resource for taskexecution.

6) Computation-intensive Service: The computation of-floading of VEC can be leveraged to service computation-intensive applications. Two typical examples among theseapplications are Augmented Reality (AR) and face recognition.Due to the limited resource, vehicular users cannot satisfy theircomputation requirements. These applications can be migratedto edge servers empowered by powerful computation resourcein VEC for implementation.


7) Data Aggregation and Data Mining: The prevalence ofsmart vehicles drives the exponential growth of data. Thesedata can be generated by sensors or received from othervehicles [49]. If these data are be deeply exploited in VEC,more learned knowledge can be used to facilitate the dataefficiency as well as improve the network performance.

III. RESEARCH TOPICS IN VEHICULAR EDGE COMPUTING

There have been many efforts devoted on the research ofVEC, mainly including the following topics :

1) Task offloading: In addition to be processed locally, taskscan be offloading to other edge devices for processing.

2) Caching: The surplus storage resources of edge devicescan be exploited for content caching.

3) Data management: Data management involves data col-lecting, processing and dissemination, etc.

4) Flexible network management: Several new technolo-gies, e.g., SDN, can be integrated into VEC to simplynetwork management.

5) Security and privacy: The characteristics of vehicularnetworks bring new challenges on security and privacyin VEC compared to MEC.

In the following, we will introduce the research topics statedabove in detail from Section IV to Section VIII, respectively.

IV. TASK OFFLOADING IN VEHICULAR EDGE COMPUTING

With the rapid deployment of vehicular networks, moreand more vehicles become smart. The proliferation of smartvehicles will arise a significant increase of various typesof application services. Many of these application serviceshave stringent requirement in terms of computation and de-lay. Despite abundant resources, the cloud is unfeasible tosupport delay-sensitive application services because of thelong distance from vehicular users. Besides, massive dataprocessing generated from these application services will bringhuge pressure on the network bandwidth. By contrast, VECis envisioned as a promising solution. As shown in Fig. 5,in addition to being executed locally in VEC, user tasks canbe transferred to the network edge empowered by the cloudwith respect to computation and storage resources, greatlyshortening the delay and alleviating the network load.

RSU

VEC server

: Computation

V2R offloading V2V offloading

local computing

Fig. 5. Example of task offloading

A lot of research has been done to investigate task offloadingin VEC. In this section, some existing work on task offloadingin VEC are introduced by classification, as shown in Fig. 6.

A summary of literatures on task offloading is illustrated inTable II.

Task offloading

Server-based offloading

Cooperative offloading

Mobility awareness

Incentive strategy

Energy efficiency

AI-enabled offloading

Fig. 6. Classification of Offloading models

A. Server-based Offloading

In VEC, edge servers (e.g., RUSs) play an important rolein facilitating the network functionality of edge computingevolving from the cloud computing. Edge servers are commonplaces selected by vehicular users for task offloading, forthe reason that they can process the tasks effectively withoutseeking help from the remote cloud. Besides communicatingwith other devices and acting as gateways to access theinternet, RUSs in vehicular networks can be enhanced andupgraded by rich computing and storage resources for serviceprovisioning. The work in [50] investigates the problem ofmultiple-user computation offloading on an edge server withthe aim to reduce communication overhead. The computationoffloading of each vehicle refers to channel selection for up-loading its task to the edge server. Considering the coupling ofoffloading decision from vehicles, game theory is used to makeoptimal offloading decisions and suitable channel selection forvehicles. When determining the offloading strategy, the authorsof [51] take into account the profits of both vehicle and edgeserver simultaneously. To the end, a dual-side optimization isformulated to minimize their costs. At the side of vehicles,the offloading decision and local CPU frequency are jointlyoptimized; while at the side of edge server, the radio resourceallocation and service provisioning are considered at the sametime. Different from the studies in [50] and [51] with a focusof single edge server, multi-server scenario is considered in[52], where a high reliability and low latency Vehicle-to-Infrastructure (V2I) communication architecture is proposedby jointly optimizing Autonomous Vehicle (AV)-to-Small BaseStation (SBS) association and wireless resource management.Using matching theory, the provided optimization problemaccounts for not only End-to-End (E2E) delay demands ofAVs but also scarce wireless bandwidth resources and limitedcomputational resources of SBSs.


TABLE IISUMMARY OF LITERATURES ON TASK OFFLOADING

Theme Reference Metric Key Points

Server-based Offloading

[50] Delay Propose a multi-user computation offloading scheme[51] Cost Present a dual-side optimization problem to minimize the costs of

vehicles and corresponding edge server simultaneously[52] Reliability Propose a scheme to jointly make the optimization of AV-to-SBS

association and bandwidth allocation

Cooperative Offloading

[53] Cost The resources of PVs are exploited for task executation[54] Utility An incentive scheme based on contact theory is designed to encourage

PVs to share their resources[29] Delay present a collaborative task offloading scheme[55] Delay A learning-based task replication scheme is designed with combina-

torial multi-armed theory[56] Utility The designed network system can improve the QoE of users while

satisfying the QoS of users[57] Utility Propose a task allocation optimization problem assisted by static and

mobile fog nodes

Mobility Awareness [58] Cost The tasks can be adaptively offloaded to edge servers through twomethods:directly offloading and predictive transmission

[59] Utility The offloading scheme accounts for load balancing

Incentive Strategy [60] Utility Contract theory is utilized to determine optimal offloading strategies[61] Utility Stackelberg game is leveraged to devise a multi-level offloading

algorithm

Energy Efficiecy[62] Energy The energy consumption optimization problem is formulated as a joint

workload offloading and power control problem[63] Utility UAV is introduced to improve computation performance due to the

low cost and flexible deployment[64] QoS Optimize the QoS of users with the assistance of solar-powered RSUs

AI-enabled Offloading [65] Revenue Networking, caching and computation are jointly considered to im-prove the network performance

[66] Revenue Present a resource allocation strategy with the consideration of highmobility of vehicles and strict delay requirement

B. Cooperative Offloading

With the ever-increasing application demands, more com-munication, computation and storage resources are requiredurgently. Considering the scarcity of computation and band-width resources of edge servers as well as the high costof deploying these edge servers, it is necessary to utilizeall available underutilized resources from existing networkentities. By aggregating abundant and unused resources ofvehicles, the QoS of applications can be greatly enhanced [67].Because of rich and idle resources, Parked Vehicles (PVs) havethe potential of providing a powerful platform for service pro-visioning. The resources of PVs are fully exploited in [53] tocoordinate with edge servers for running application services.A response scheduling algorithm based on Stackelberg gameis formulated to implement resource allocation among PVswith the objective of reducing the overall cost of users. Theother investigation of exploring the resources of PVs has beendone in [54], where an incentive scheme is designed to providerewards for PVs when their resources are shared.

On the other hand, some vehicles with unused resourcescan be leveraged to assist other vehicles in computationoffloading. Vehicles are treated as rich resources in [29].A collaborative task offloading strategy is presented [29]to execute tasks in a cooperative manner with the goal ofachieving low computation and communication delay. Thework in [55] targets the exploitation of ample computationresources on vehicles as well as the improvement of QoSby the proposed learning-based task replication scheme. Inthis scheme, vehicles are scheduled to process the same task

replication at the same time. In [56], resources from differentdevices are cooperatively scheduled to improve the Quality ofExperience (QoE) of users and enhance the QoS of serviceproviders. A novel task allocation solution proposed in [57]takes into consideration the mobility of vehicles to strive fora balance between latency and quality, where vehicles areclassified into two types: the one generating data and the oneacting as fog node.

C. Mobility Awareness

In VEC, edge servers are empowered by rich computationand storage resources in proximity of vehicular users. How-ever, suffering the limited coverage range of edge servers(e.g., RSUs) and the high mobility of vehicles, there is apossibility that the task from one vehicle cannot be finishedsuccessfully within the original edge server. That is to say,multiple edge servers may be passed through before the taskis accomplished. In order to deal with this issue, mobility-based task migration among different edge servers representsone attractive solution, which has gained increasing attention[58] [59]. When a vehicle is approaching a new server, its taskcan be migrated to this server, or the task can be computed inthe original server and then the obtained result is forwarded tothe vehicle via the new server. As shown in Fig. 7, vehicle Ahas a task for implementation. Thus, it uploads the task to theassociated edge server RSU1. Before the result is calculated,vehicle A has entered into the coverage range of RSUm. Inthis situation, RSUm can obtain the result from RSU1, andthen deliver the result to vehicle A. By considering the re-


quirements of computation tasks and the mobility of vehicles,a predictive-model transmission strategy is introduced in [58]for task offloading, improving the transmission efficiency andsatisfying the required delay. Besides, an optimal offloadingscheme based on prediction is designed for serving diversetypes of computation tasks.

…RSU1

VEC server

RSU2

VEC server

RSUM

VEC server

:V2R links:Vehicle mobility: Storage : Computation

:Wired connection

AA

B

BC

C

Fig. 7. Example of mobility awareness

If all the vehicles offload their tasks to the same edge server,it is likely to be overloaded, which can degrade the networkperformance. The authors of [59] investigate resource alloca-tion by jointly considering load balancing and offloading in amulti-user multi-server vehicular environment. This proposedsolution not only allows vehicles to select their preferable edgeservers based on their requirements but also accounts for themobility of vehicles.

D. Incentive Strategy

Because of the self-interested characteristic, existing entitiesin the network are unwilling to contribute their availableresources freely to others. Thus, a satisfied incentive schemeis the necessity of encouraging them to participate in resourcesharing, i.e., they should be rewarded once sharing the re-sources. As described above, the work of [54] exploits theresources of PVs by giving them necessary compensation.

Contact theory has been widely applied in resource man-agement owing to its benefit in making two rational entitiesof a trading to reach consensus. Thus, a contact theoreticalapproach is employed in [60] to provide the optimal offloadingstrategy for an edge server by maximizing the revenue ofthe edge server while improving the utilities of vehicles.An efficient resource management algorithm is provided tooptimize the resource utilization of edge servers by consid-ering the distinction of task priorities as well as additionalresources. Stacklberg game is used in [61] to design an optimalmultilevel offloading scheme to make the utilities of vehiclesand edge servers maximum. A backup server in vicinity isutilized to supplement insufficient resources of edge servers,due to the fact that if edge servers are located in dense roads,their constrained capacities may negative impact the QoS ofvehicular users.

E. Energy Efficiency

Constrained by the limited battery energy of vehicularusers ( e.g., electrical vehicles, smart phones and wearable

devices), the battery endurance time will be sharply reducedwhen they process tasks locally. Under this condition, VECallows vehicular users to enlarge the battery life by offloadingtheir energy-hungry tasks on the network edge with sufficientenergy supply. The work in [62] devises an energy-efficientVEC architecture to enable service provisioning for energy-constrained users. Based on two built models with respectto energy consumption and delay, the problem of minimizingenergy consumption is formulated as a joint workload offload-ing and power control problem. Unmanned Aerial Vehicles(UAVs) assisted by MEC are introduced in [63] to enhancecomputation capacity. Because of the advantages of low costand flexible deployment, UAVs can deal with the limitedresources of vehicles as well as the considerable cost and hugeburden of RSUs. The interesting idea of [64] is the introductionof Solar-powered RSUs (SRSU) to VEC for providing vehiclesmore energy-efficient communication. The investigation aimsto solve the gap between solar power generation and SRSUenergy consumption for improving the QoS.

F. AI-enabled Offloading

There have been several work to apply AI into task of-floading in VEC. For example, machine learning is widelyused [68]. In [65], a resource allocation strategy by jointtaking networking, caching and computing into account isformulated as an optimization problem. However, the jointconsideration of these three factors increase the complexityof the problem. Thus, a deep reinforcement learning methodis introduced to solve the optimization problem. In [66], thejoint communication, computation cache problem is studied.The resource scheduling is designed by taking into account themobility of vehicles and the hard delay requirement. The deeplearning algorithm is utilized to solve the resource allocationproblem. In [69], by utilizing deep reinforcement learning, theauthors deal with joint edge computing and caching resourceallocation problem. The authors of [70] design a deep Q-learning approach for making optimal offloading decisionsby taking into account the selection of target server and thedetermination of data transmission strategy simultaneously.

V. CACHING IN VEHICULAR EDGE COMPUTING

With the proliferation of smart vehicles, vehicular networksare undergoing substantial growth in data traffic due to thethe boom in various data-consuming applications. This bringsgreat challenges on backhual networks and latency. Edgecaching is regarded as an attractive scheme to deal withthese challenges [71]. Being supplement to the cloud, theedge network can be enriched with rich storage resources. Asshown in Fig. 8, edge servers and vehicles with ample storageresources can be utilized to cache popular contents. By cachingcontents in proximity of users, the contents can be accessedlocally without resorting to the remote cloud, enabling thereduction of redundancy data traffic, the alleviation of scarcenetwork bandwidth and the improvement of user experience.


…RSU1

VEC server

RSU2

VEC server

RSUM

VEC server

:Data caching(V2R):Data fetching(V2R) : Storage

provider Interest

data data data

:Data delivery (V2V)

Fig. 8. Example of caching

Generally, there are three primary questions that need to beanswered for content caching:

1) Caching place:In vehicular edge network, there aretwo main edge devices, say, edge servers (e.g., RSUs)and vehicular users (e.g., vehicles), which are commonplaces to cache contents;

2) Caching contents:Because of the limited storage capacityof edge devices and the extreme amount of contentsavailable over the entire network, it is not practical tocache all the contents for edge devices. In this situation,it is better to selectively cache the contents. By com-parison, popularity-based cache placement represents amainstream solution, where content popularity is anindicator of contents being requested by users;

3) Caching policy:Caching policy depends on the moti-vation behind content caching, e.g., offloading traffic,reducing delay, improving QoE and minimizing energyconsumption.

In the following, the existing work related to edge caching isdescribed by classification in detail. A summary of literatureson caching is illustrated in Table III.

A. Caching at RSUs

The explosion of emerging applications in vehicular net-works, e.g., in-vehicle infotainment, poses tremendous chal-lenges on the network capacity. Being important connecteddevices, RSUs are seen as the significant infrastructures tobe deployed for enhancing the network capacity [88], [89].Though moving popular contents in close proximity to vehic-ular users, RSUs are capable of offering benefits in reducingdelay and alleviating network burden.

Different from BSs, the storage capacity and communicationrange are limited, which will impact the caching decision.There is a massive amount of contents in vehicular networks,e.g., videos and audio files. It is crucial to make full use of thelimited resource of RSUs and effectively decide the cachedcontents for improving the QoE of users. In [72], multiplecontent providers intend to cache their contents themselves tofacilitate data transmission. This leads to the competition fromcontent providers for sharing limited resources of edge servers,thereby arising the issue of optimizing storage utilization ofRSUs. A multi-object auction-based algorithm is used to solvethe competition.

In vehicular networks, different content requestors may havedifferent files. Thus, there are conflicts among them. How toplace files in RSUs is extremely of importance. The study in[73] focuses on distribution of popular contents among RSUsby minimizing the average time needed for content requestorsobtaining their requested contents. The relationships betweenthe average content downloading time with several parameters,including the number of RSUs, caching space and vehiclespeed, are analyzed. In [74], A dynamic content cachingscheme is designed to optimize caching through the requestsof vehicles and the cooperation among RSUs. The featuresof content requests depend on content access pattern, vehiclemobility as well as density, by which a vehicle can decidewhether and where to fetch the requested content. On the otherhand, it is highlighted in [90] that it will lead to an inefficientutilization of storage resources when all the RSUs are used tocache contents. Thus, a subset of RSUs are selected in [90]to store data which are available for vehicles. An interestingpoint is that coding techniques allow the entire multimediadata being split into a series of small coded pieces. Vehiclescan recover the entire data via any set of linearly independentcodes pieces. The method can help in content transmission indynamic vehicular networks.

High movement of vehicles in VEC gives rise to the varyingnetwork topology with time. The caused unstable connectivitymakes it difficult to maintain efficient and continuous datatransmission among nodes, negatively impacting the userexperience [91]. For example, it is likely that vehicles cannotfetch the entire requested content in the coverage range of aRSU, and there is a possibility of passing though multipleRSUs for vehicles before obtaining the requested content.Thus, the mobility of vehicles is a prime concern whendesigning the caching scheme. A probabilistic caching strategyis designed in [75] with consideration of the prediction ofvehicular trajectories and the content serve time at edge nodes,by which a mobility-aware caching scheme is proposed, aimedto optimally caching contents at edge nodes. The storage ofcontent chunks on edge nodes in [76] is dependent on thehistorical statistics of achievable data rates and the standingtime of vehicles within the coverage area of edge nodes.

The deployment of traditional RSUs relies on the sup-port of power lines and wired backhuals. This is extremelychallenging and costly in areas where power systems areunder-developed and infrastructures are sparse. In order totackle this issue, RSUs installed with solar panels or windturbines are alternative. Such Green RSUs have the abilityof harvesting renewable energy as power source. This typeof cache-enabled green RSUs is utilized in [77], which canfacilitate flexible deployment, energy-saving operation as wellas low delay services. Assisted by these RSUs, a cost-effectivenetwork planning is designed to reduce the network cost whileachieving desired data rate.

B. Caching at Vehicles

Enabled by rich storage resources, smart vehicles can beemployed as special infrastructures to make the full utilizationof these resources owned by them. Cooperative caching allows


TABLE IIISUMMARY OF LITERATURES ON CACHING

Theme Reference Metric Key Points

Caching at RSUs

[72] Dissemination efficiency Introduce a multi-object auction-based solution to deal with thecompetition among multiple content providers

[73] Delay Present three algorithms to allocate contents on RSUs[74] Hit ratio Propose a dynamic content caching scheme based on cross-entropy[75] Probability of download-

ing contentsDesign a probabilistic caching scheme with the consideration ofvehicles’ trajectories and content service time by edge nodes

[76] Probability of download-ing contents

The probabilistic caching scheme accounts for the mobility of vehicles

[77] Network deployment costand QoS

Propose a cost-effective network planning assisted by cache-enabledgreen RSUs

Caching at vehicles

[78] Delay The storage resources of PVs in multiple parking lots are exploited tocache contents from multiple content providers

[79] Traffic load Design a P2P cooperative caching scheme[80] Network cost Propose a cooperative caching scheme by considering the global status

of clusters as well as the constraints of vehicles with respect to cachingcapacity

[81] Efficiency of content re-trieval

Propose a popularity-aware content caching and retrieving mechinasim

[82] Retrieval outage and delay Propose an in-vehicle caching scheme which has the advantage offlexibly accommodating multi-file popularity according to the designeddynamic distributed storage relay scheme

[83] Delay and caching utiliza-tion

Caching decisions of vehicles depend on the demands of users,the importance of vehicles as well as the characteristics of relativemovement

[84] Retrieval outage, delayand caching utilization

A cooperative caching scheme is designed based on the mobilityprediction of nodes

Multi-layer caching [85] Delay and QoE The caching policy is designed by considering both of vehicular layerand RSU layer

[86] Delay Vehicles are exploited as caching nodes for collaborative contentcaching with BSs

AI-enabled caching

[87] delay MLP is utilized to predict the probability of contents being requested,and CNN is employed to predict the ages and genders of passengers

[31] delay Present a Q-learning based caching scheme by integrating a deeplearning network

vehicles to achieve desirable hit ratio and reduced delay. Inurban environments, storage resources can be greatly improvedwhen vehicles share their unused storage space in a collaborateway. A typical example is PVs. Benefiting from the largenumber and long dwell time, PVs are ideal choice for storageprovisioning. In [78], the storage of PVs in multiple parkinglots are used to cache popular contents provided by contentproviders in a cooperative manner.

Although the existing fixed infrastructures, e.g., RSUs, canprovide connectivity to vehicles for content access, sufferingfrom the high mobility and sparse distribution of vehicles, itis not an ideal option to only cache contents in the existinginfrastructures deployed in specified places. In this situation,a cooperative caching scheme is useful for data dissemination[79]. In [79], traffic information is shared in a Peer-to-Peer(P2P) manner among vehicles. The previous cached data isupdated by newly arrived data using a probabilistic manner.

The caching at vehicles also faces several challenges withrespect to limited storage capacity, popularity calculationand vehicle movement. The cooperative caching algorithmdesigned in [80] considers the global caching status of clustersand the limited caching capability of vehicles. In [81], thecalculation of popularity is based on the reference frequencyof content and chunk. Based on the popularity of contents,vehicles can switch the retrieval models between V2V andV2R. The designed caching scheme will be triggered once

the interest increasing ratio of a requested content is too big,for the purpose of alleviating sudden huge requests. The workof [82] provides a dynamic distributed data storage schemeby explicitly accounting for the mobility of vehicles, the keycore of which is to maintain the survival of storage data ina designated region of interest by means of forwarding thedata from the leaving vehicles to the arriving vehicles. Basedon the novel data storage scheme, the designed in-vehiclecaching strategy can support multi-file popularity flexibly. In[83], a distributed probabilistic caching strategy is designed,where each node makes caching decisions by consideringthe demands of vehicles, the importance of vehicles and therelative movement of vehicles. The authors in [84] focus onthe selection of caching nodes. In this work, based on thehistorical trajectories of vehicles, the probability of vehicles tovisit different hot spot regions can be predicted. The vehiclewhich stays longer in a hot spot region is preferable to becaching node.

C. Multi-layer Caching

In VEC, in order to improve resource utilization, anydevices with storage space are potential caching nodes. Inother words, contents are allowed to be cached in at differentlayers, including BSs, RSUs and vehicles if they have storageresources. Caching contents at different layers is beneficialfor increasing the availability of contents being successfully


accessed by users on move [92]. Thus, an advanced cachingpolicy should take full advantage of different layers to achieveoptimal performance. In [85], the caching placement is in-vestigated to take into account both of vehicular layer andRSU layer, aimed to minimize the delay while satisfying theQoE of users. The work [86] investigates a mobility-awarecaching framework for vehicular uses, where vehicles areused to provide content sharing with Base Stations (BSs).Frequent disconnections caused by high mobility of vehiclesmake it difficult to provide and maintain the QoS of videostreaming applications. Thus, the proposed caching schemein [92] considers both of the mobility of nodes and the linkdisconnection. In this proposed scheme, two metrics of LoadUtilization Ratio (LUR) and Query to Connectivity Ratio(QCR) are designed to maintain the QoS.

D. AI-enabled Caching

In order to decide what to cache, the content popularity istaken into account by a lot of work. However, the mobility ofvehicles will lead to the varying membership of vehicular usersassociated with individual edge nodes. Besides, the user inter-ests in contents are changing with time in different contexts,e.g., location, network topology, as well as personal status.This makes content popularity unknown before performingcaching operation [68]. The authors of [87] try to solve thisissue by employing deep learning. In [87], the probabilityprediction of requested content is made by using a Multi-LayerPerception (MLP) algorithm. Meanwhile, Conventional NeuralNetwork (CNN) is utilized to predict the interest of passenger.By comparing CNN output with MLP output, the contentsneeded to be downloaded from edge servers for caching canbe identified.

In order to improve caching services, proactive caching iswidely used. However, due to the limited capacity of RSUs,the design of an optimal caching strategy to balance the QoSof users and the caching cost of RSUs is challenging. A Q-learning based caching scheme by combining a deep learningnetwork is devised in [31] to solve this issue. In this scheme,the mobility of each vehicle is predicted using a long short-term memory network. Based on the predictions, an optimalproactive caching scheme is developed.

VI. DATA MANAGEMENT IN VEHICULAR EDGECOMPUTING

The significant increase of various types of application ser-vices leads to the exponential growth of data. From one hand,the installed devices in vehicles, such as GPS, camera andlarder, can generate a multitude of dada; from the other hand,data (e.g., video streaming) can be shared among vehicles viawireless communication manner. This brings huge challengeson response time and bandwidth of backhaual networks fordata processing. In this situation, it is a major concern toefficiently manage data, including data collecting, analyzing,processing and dissemination. To the end, there have beenplenty of work done for data management. In the following,some of them are introduced by classification. A summary ofliteratures on data management is illustrated in Table IV.

A. Data Collecting and Processing

A large amount of data are generated every day. Eachvehicle is a data producer as well as a consumer in vehicularnetworks. These data should be collected and processed well.If these data are be deeply exploited, more learned knowledgecan be used to facilitate the data efficiency and improve thenetwork performance.

A filter-based framework is provided in [93] by integratingfog computing with vehicular sensing. This framework makesfull use of pull and push schemes to collect the requested datain vehicular networks in an adaptive and efficient manner. Theauthors of [94] introduce the other data collection approach.The concept of vehicular micro clouds as virtual edge serversis established to optimize data processing and aggregationbefore delivering data to a data center. The problem of unnec-essary data transmission is solved in [95] by the introducedfog-based two-level threshold approach. From one hand, thepresented scheme can adaptively adjust the threshold to unloada suitable volume of data for decision making; from theother hand, it can prevent unnecessary data transmission. Therealtime big data analytics in vehicular networks is investigatedin [96], by combining intelligent computing and real time bigdata analytics.

B. Data Sharing

1) Data Dissemination: In vehicular networks, delay-sensitive applications account for a large proportion. Theseapplications have stringent delay requirement. An efficient datatransmission scheme is crucial to facilitate the reduction ofdelay and the improvement of reliability [110]. For example,safety message needs to be forwarded quickly and precisely toenhance road safety and avoid potential danger. With the aidof MEC, a publish/subscribe message dissemination scheme isdeveloped in [97]. This designed scheme is composed of threeimportant roles. The first one is message broker which supportsmessage services, the second one is message hander aimed toexplore potential data value, and the last one is message hubintegrating different parts of the system. In this scheme, MECis used to provide diverse application services, especially thosewith critical latency requirement. The article in [98] providesa data analytics framework driven by the faced challengeswhen offering context-aware services in vehicular networks,which enables delay-critical applications by enhancing the foglayer of traditional vehicular network architecture. A multicastdata dissemination protocol is proposed in [99] to reduce theenergy consumption with the constraints of bandwidth anddeadline. Classification algorithm is designed to make the clas-sification of received multicast requests according to differentapplication types, while scheduling algorithm is introduced tomanage multicast requests on a basis of priorities. Moreover,a partitioning scheme is employed to reduce the overhead andtime complexity of the proposed algorithm.

2) Content Delivery: The explosive growth of vehiclesgives rise of high amount of content requests. The use ofcontent delivery can alleviate the huge network burden aswell as reduce the delay. If the requested contents are storedin some nodes, by depending on the content delivery among


TABLE IVSUMMARY OF LITERATURES ON DATA MANAGEMENT

Theme Reference Objective Key Points

Data Collecting and Processing

[93] Request Answering Propose a filter-based request answering framework[94] Data collection Vehicular virtual cloud is introduced as virtual edge server[95] Data monitoring and gath-

eringA fog-based two-level threshold scheme is designed for preventing thetransmissions of unnecessary data

[96] Data analytics Present a real-time big data analytics framework

Data Dissemination[97] Message dissemination Design a message dissemination scheme assisted by MEC[98] Context-aware service Propose a context-aware data-driven intelligent framework[99] Multicast routing Devise an energy-efficient multicast routing scheme with SDN and fog

computing

Content Delivery[100] Content delivery Study the tradeoff problem of communication, computing and cache

for proactive caching[101] realtime streaming The streaming contents are allocated in advance from computing

service providers[102] Content distribution Investigate the prefetching and distribution of contents

Incentive Scheme [103] Content dissemination Because of the selfishness and capacity of vehicles, a relay selectionscheme is provided to select vehicles to relay the contents

[104] Caching and dissemina-tion

Propose a robust and distributed incentive mechanism for contentcaching and dissemination in a collaborative method

Performance Optimization

[105] Bandwidth optimization A vehicle flow model is introduced into the optimization framework[106] Congestion avoidance Present a fog-based congestion avoidance strategy[107] Data scheduling Design two dynamic scheduling algorithms to schedule data[108] Data processing Propose a cooperative fog computing mechanism for big data process-

ing[109] Energy-efficient Integrate big data analytical into VEC

nodes, there is no need to access the remote cloud for contentacquiring, shortening the delivery time. The work of [100] pro-vides a theoretical architecture to enable content delivery bybalancing computation, caching and communication resources.A concrete distributed scheme for reliably delivering realtimecontents is presented in [101]. The core of this scheme liesin that the streaming contents are allocated in advance fromcloud and fog to guarantee the QoS of realtime streaming.By predicting the possible locations, the amount of requiredcontents as well the needed tokens, mobile nodes can reservecontents for streaming, ensuring the reliability of streamingcontent. Besides, this scheme is capable of effectively adaptingto the changing behaviors of mobile nodes as well as thecondition regarding computation resources. Distinguished with[101], both of content prefetching and distribution are studiedin [102]. A hierarchial architecture based on edge computingis introduced for efficiently facilitate the distribution of large-volume data. In order to deal with the challenges of dynamictopology change and unbalanced traffic, a multi-place andmulti-factor prefecting mechanism is proposed. The problemof content distribution is solved by using a game theoryapproach.

C. Incentive Scheme

Nodes with rich contents have no obligation to share theirresources in vehicular networks. The selfishness of nodes isnot considered in these work above. In order to encouragedata sharing, the necessary incentive scheme needs to bedesigned well [111]. This issue is investigated in [103],where a content delivery solution based on edge computingis provided. In this scheme, contents need to be transmittedto an edge computing device. The edge computing devicemakes the selection of available vehicles to deliver the contentswith the joint consideration of selfishness and capacity of

vehicles. The selected vehicles are responsible for relayingthe contents to the vehicles which have interest in the contentson the way to the destinations. The work in [104] provides anincentive approach for collaborative caching and disseminationin vehicular delay-tolerant networks (VDTNs). This approachhas two distinct benefits: robust and distributed. It performsrobust because of the resilience against false accusation andpraise, and is distributed based on the fact that the decisionmade to interact with another node should reply on each node.

D. Performance Optimization

The performance metrics, including delay, energy, band-width, reliability, etc., are main indicators of the networkperformance, which provide the guidance of optimizing thenetwork performance. In [105], with the objective of band-width optimization, a centralized optimization framework isintroduced to deliver data and provide services to vehicles.The framework takes into consideration the edge resourceconstraints and integrates the vehicle flow model simultane-ously. A fog-based congestion avoidance scheme is proposedin [106], aimed to alleviating the congestion and reducingthe delay. In this scheme, a fog server is employed to con-duct services in vehicular networks. In order to alleviate thepressure of the network load brought by the increasing datadisseminations, fog computing is utilized in [107]. The widedeployment of edge computing infrastructures will lead tothe issues of energy consumption and air pollution, whichmotivates the design of a programmable, scalable and flexiblearchitecture in [108] by the integration of big data analytics inVEC. The authors of [109] present a regional cooperative fogcomputing to provide diverse services. A localized coordinatoris utilized to support interoperability and cooperative operationamong local fog servers. A hierarchial resource managementmodel is developed to optimize the network performance in the


fog computing network. Two scheduling schemes on a basisof response time and queue length are presented to scheduledata for adapting the changing network and enhancing theefficiency of data dissemination, respectively.

VII. FLEXIBLE NETWORK MANAGEMENT IN VEHICULAREDGE COMPUTING

It is mentioned in [112] that the key of next generationvehicular networks lies in the design of heterogeneous net-works using advanced technologies, such as FC and SDN.SDN has the feature of enabling flexible and dynamic networkmanagement by separating control plane and data plane. Whenreceiving the commands from the controller deployed in thecontrol plane, the data plane conducts the corresponding op-erations. SDN benefits the reduction of network managementcosts and is able to get a global knowledge of networksvia the collected information from the data plane. The in-tegration of VEC with SDN can provide more efficient andreliable centralized control management. The recent researchregarding software-defined vehicular networks is summarizedin [113], where many edge-up software-defined networkingdecisions are proposed with a special focus of delay controlfor providing various services in vehicular networks. However,because of the diversity of fog applications, the design ofan efficient vehicular network architecture is still an openproblem. The authors in [114] make an attempt to studythe design principles for fog-enabled vehicular SDN, mainlyemphasizing the perspectives of systems, networking as wellas services. Next, some efforts regarding the architecturedesign with the combination of SDN and VEC in the literatureare introduced.

A vehicular architecture based on SDN and FC is developedin [112] for satisfying the requirements of users in terms oflow delay and high reliability. In this architecture, the mobilitymanagement is analyzed with a focus of handover managementmechanism. Then, a hybrid access handover strategy forimproving the handover performance and a resource allocationscheme for optimizing the delay are proposed, respectively.

A SDN-enabled heterogeneous vehicular network architec-ture assisted by MEC is proposed in [115]. This architecturecan ensure required data ratios and good reliability in ve-hicular communication, and achieve desired scalability andresponsiveness as well. The other network architecture bycombining SDN with MEC is introduced in [116] for 5G-enabled Software-Defined Vehicular Networks (5G-SDVNs).In this architecture, SDN brings the benefit of improvingnetwork management and MEC is used to enhance the networkcontrol. This architecture can achieve significant performanceimprovements in terms of network management, resourceutilization and network development.

The designed vehicular network architecture in [117] aimsto alleviate the traffic congestion by jointly optimizingnetworking, storage and computation resources. The pro-grammable control principle of SDN is introduced to thearchitecture to improve the network optimization and resourcemanagement.

The presented architecture of 5G vehicular networks in[118] combines SDN, cloud computing and fog computing.

In this architecture, vehicles serve as fog infrastructures, bywhich the network performance can be enhanced.

In the architecture introduced in [119], the controller isresponsible for determining the task offloading strategy ofvehicles and resource allocation strategy of edge cloud. It iscrucial to select the edge cloud and make the resource alloca-tion to maximize the probability of a task being successfullyfinished with a given period.

The work in [120] tries to optimize the utilization offog computing resource, with the aim to fulfill the delayrequirement of tasks by assigning each task with a best fogserver with the assistance of SDN.

VIII. SECURITY AND PRIVACY IN VEHICULAR EDGECOMPUTING

With the advent of VEC, security and privacy are mainconcerns, which will impact the wide deployment and fastdevelopment of VEC. In VEC, the high mobility of vehiclesmakes it challenging to solve trustworthy problem amongnodes. Because of the fact that a large number of differenttypes of devices coexist in vehicular networks, the gener-ated heterogeneity makes traditional trust and authenticationschemes inefficient. Despite of the merit of moving the cloudto close proximity of users, edge servers are the potential targetdue to the public deployment without any physical isolationand also have the possibility of turning into a maliciousbecause of the curious for user information. However, theresearch on security and privacy in VEC is still at initialstage with a lot of problems unsolved. On the other hand, thisprovides many attractive research opportunities for researchesfrom academia and industry. Actually, there have been sig-nificant efforts devoted to this field with some introduced asfollows.

A. Security

1) Trust and Authentication: Trust is of vital importancein security, the basic idea behind which is to know theidentity of an entity with whom there is an interaction.Authentication management represents one possible approachto provide the guarantee of trust [27]. The explosive growth ofvehicles increases the number of misbehavior vehicles. Thesemisbehavior vehicles can disrupt the VEC and reduce thenetwork efficiency. Reputation management is seen as onekey metric to access trustworthiness of vehicles [121], [122].By rewarding cooperative vehicles but punishing mishehaviorvehicles, reputation management is of significant benefit inguiding user behaviors, preventing possible attacks as well asimproving overall network performance. In [123], reputationmanagement is investigated to provide secure protection andpromote network efficiency. In the proposed system, edgeservers are responsible for carrying out local reputation man-agement tasks. Reputation values of vehicles can also providethe guidance of optimizing resource allocation. Different from[123], to deal with untrustworthy nodes, negative messagesare used to indicate negative attributes of such nodes in[124]. A scheme is provided to efficiently distribute thesenegative messages in vehicular networks, where RUSs act


as the functionality of fog. Besides malicious attackers andfaulty nodes which bring huge security thereat, the widelyexisting obstacles, e.g., buildings, trees and big trucks, willobstruct the vision and communication Line of Sight (LOS) ofdrivers, creating negative impact on the integrity and reliabilityof data as well as availability of localization service. Thisissue motivates the proposal of a fuzzy trust model on a basisof experience and plausibility in [125], where many securitychecks are carried out to guarantee the correctness of receivedinformation from authorized vehicles and fog nodes serve toassess the accuracy of event location.

2) Confidentiality and Integrity: In VEC, being the sup-plement of a remote cloud, RSUs with powerful computa-tion capacity can undertake the tasks from vehicular usersto improve the user experience. However, because they areregularly deployed at public places, there is the risk of beingeavesdropped. After locating a RUS by using traffic statistics,the adversary can launch an attack on the RSU and interrupton-gonging services. In order to avoid this issue, the workin [126] introduces one proactive defend solution dependingon generating dummy traffic delivery. The generated dummypackets by vehicles can be used to provide protection for RSUsby misleading the traffic statistics.

The data offloaded to RSUs may be sensitive and evenprivate information, such as location, purchase history andhealthcare record. Thus, in order to avoid information leakage,some necessary measures should be taken. The study in[127] targets the confronted eavesdropping attack in case thatvehicles offload their tasks to the network edge via RadioFrequency (RF) channels. The resource management is inves-tigated for secrecy provisioning based on a tool of physicallayer security. The authors of [128] focus on the security ofdata delivery. Vehicular Fog Service (VFS) is provided byexploiting the computation and storage resources of PVs. Amechanism composed of a vehicular fog construction approachand a VFS access approach is used to guarantee the reliabilityand security of VFS. In [129], data collected by vehicles canbe sent to RSUs in a privacy-preserved manner. The suggestedcertificateless aggregate signcryption mechanism (CL-A-SC)can support certificateless cryptograph and signcryption at thesame time. Being the extension of CL-A-SC, the proposedanonymous aggregation protocol helps in obtaining good se-curity properties.

Although cryptography-based schemes have the benefit indealing with security and privacy, resource-constrained vehi-cles cannot carry out them due to the intensive computationrequirement. In [130], the complicated encryption and decryp-tion tasks are migrated to fog servers and cloud server withthe preservation of confidentiality and privacy. Besides, thedesigned interactive protocol is capable of providing verifiablerecording of fog servers’ reports while preserving users’anonymity and unlinkability.

B. Privacy

Real-time navigation system is beneficial for finding anoptimal route by the collected realtime traffic information.Before implementing this system, security and privacy should

be addressed well. To the end, a secure and privacy-preservingnavigation scheme is proposed in [131].

Road condition is regarded as one key metric of reflectingthe quality of roads. Thus, it is crucial to monitor road condi-tions for enhancing road safety. By using the sensing capacityof mobile devices without the deployment of additional sensornodes, the sensory data can be collected to improve thetransportation system, forming a new sending paradigm namedas mobile crowd sensing. However, safety and privacy issuesemerge during the process of data processing. In [132], a fog-based privacy-preserving mechanism is designed to ensure thesecurity of vehicular crowd sensing networks. A certificatelessaggregate signcryption algorithm is proposed in [133]. Basedon the algorithm, a data transmission scheme is presentedfor monitoring road conditions to guarantee the requirementsin terms of information confidential, integrity, authenticity,privacy and anonymity.

Location privacy has been becoming more and more im-portant in vehicular networks due to a large amount of datasharing, especially with the emergence of location sharingapplications, e.g., Google Maps. A vehicle can be easilytracked by its adversary based on its communication andmovement behaviors. Vehicles need to send their drivinginformation to neighbors for safe driving and expose their lo-cation information because of the utilization of location-basedservices. An adversary is able of locating the vehicle’ positiononce obtaining these information. The work in [134] makesan attempt to address the user privacy issue stemming fromroute sharing by introducing a privacy-preserved route-sharingscheme. In order to deal with location privacy, the real identityof vehicles can be hidden using pseudonym technology. Theauthors of [135] provide a privacy-preserved pseudonym toprotect location privacy. Pseudonym fogs consisting of a groupof fog infrastructures are responsible for pseudonym manage-ment of vehicles passing by. Based on the context informationprovided by local authorities deployed in pseudonym fogs,vehicles can conduct context-aware pseudonym changing. Inorder to avoid the problem of a completely anonymized vehicleturning into a malicious, conditional privacy is needed toidentify and revoke misbehaving vehicles. In [136], conditionalprivacy is provided to allow vehicles to be anonymous untilthey are identified and revoked when they turn into malicious.The proposed privacy preservation protocol permits vehiclesto use pseudonyms for privacy protection. For the sake ofanonymous communication, vehicles get the pseudonyms byinteracting with RSUs. During this process, only vehiclesknow their pseudonyms. In addition to pseudonym technology,a differentially location privacy-preserving scheme is designedin [137] with the aim to guarantee location privacy. When avehicle sends the request of location-based service, it needsto send its location to the edge server, then the edge serverexecutes the privacy-preserving scheme to query the Pointof Interest (POI) and filter the useless results. This designedscheme can make vehicles to get useful information based onthe submitted location with no need to reveal their privacylocation. In [138], a query-based location and communicationscheme is provided to using a fog server with fog anonymizer.The provided protocol can preserve location privacy well.


C. Blockchain-enabled Protection Scheme

Benefiting from the characteristics of blockchain includingdecentralization, anonymity and trust, many researchers havedevoted their efforts in integrating blockchain into VEC forsecurity and privacy protection [139], [140].

In order to ensure secure data storage and sharing inVEC, two technologies of consortium blockchain and smartcontact are fully exploited in [139], which can efficientlyavoid unauthorized data sharing. Then, a data sharing strategybased on reputation is provided to achieve high-quality datasharing, where a three-weight subjective model is used tomake precise reputation management among vehicles. Theproposed system of enhancing security protection in [34] isalso related to reputation management. In this system, localreputation management tasks are conducted by the servers. Toenable the improvement of network performance, this systemincludes the following outstanding characteristics: distributedreputation management, trusted reputation manifestation, ac-curate reputation update as well as available reputation usage.The optimization of resource allocation is made by serviceproviders in computation offloading to enhance reputation us-age with the consideration of reputation of vehicles. The workin [141] develops a secure P2P data sharing strategy, wherethe improved delegated proof-of-stake consensus scheme isemployed as a hard security solution. This delegated proof-of-stake consensus scheme is enhanced by a two-stage softsecurity approach: miner selection and block verification. Theformer is used to select miners through reputation-based vot-ing, where the reputation of each miner candidate is calculatedby a multi-weight subjective logic scheme; while the latteraims to incentive the standby miners with high reputation totake part in block verification.

Assisted by this presented blockchain-based decentralizedtrust management system in [142], vehicles can assess thereceived messages from neighbors. With the result, the vehicleis able to create a rating for each source vehicle generatingthe message. Based on the ratings sent by vehicles, RSUs cancalculate the trust value offsets of involved vehicles. Usingblockchain technique, all the RUSs work with cooperation tobuild a reliable and consistence database.

In this provided privacy-preserving carpooling scheme in[143], users are authenticated in a conditional anonymousmanner. Private proximity test is used to realize one-to-manymatching, which is extended to establish a unique key betweena passenger and a driver. With a range query technique, get-off location matching can be achieved. A private blockchainis built in the carplloing system for the storage of carplloingrecords.

IX. OPEN RESEARCH ISSUES AND FUTURE WORK

The VEC has significant potential in improving the currenttransportation systems by integrating MEC into vehicularnetworks. However, the research on VEC is still in early stage.Many issues remain to be solved well. In this section, weidentify some open research issues which can be extended tofuture research directions.

A. QoS

There are a variety of applications in vehicular networks,which are mainly classified into safety applications and non-safety applications. The QoS demands of different types ofapplications are expected to be different. Safety applications(e.g., collision avoidance and traffic control) have strict delayrequirement, which should be addressed as soon as possi-ble, while several non-safety applications (e.g., multimediadownloading) can tolerant some delay. Thus, providing aflexible scheduling scheme to guarantee the QoS of differentapplications based on their priorities is a matter of concern inVEC.

B. Scalability

A massive amount of applications are increasing rapidly.This gives arise of large requirements for low delay, highreliability, rich computational capacity and storage space.It is crucial to schedule resources optimally and conductconnectivity management efficiently for executing heteroge-nous task types. In addition, different from traditional cloud,vehicular users in VEC may be uneven distributed in vehicularnetworks. The densities of vehicles are varying with timein different areas. The designed system should adapt to thevarying network conditions.

C. Economic Profit

The core of implementing the VEC lies in resource sharing.The resource owners are willing to share their resources ifthey can be rewarded properly. In this situation, the pricingmechanism becomes extremely vital. This involves how toquantify the values of different resources to balance theprofits of both resource consumers and resource providers.For resource providers, it should be considered to how todivide the retained profits among the related entities includingthe mobile service provider, edge service provider and cloudservice provider.

D. Security and Privacy

Pushing the tasks with incentive computation and strictdelay to the edge of networks is main application in VEC. Thetasks offloaded to edge servers usually includes sensitive andprivate data. In order to avoid the information leakage, dataconfidentiality should be ensured. In addition, with the aim toprevent the modification of data which are being forwardedor have been stored, it is extremely important to guaranteethe integrity requirement. Meanwhile, a verifiable computingscheme for vehicular users is needed to demonstrate thecorrectness of obtained computation results from edge servers.

X. CONCLUSION

By the seamless merging of vehicular networks and MEC,VEC extends the function of cloud to the edge of networks,bringing rich resources in close proximity to vehicles. VEC isseen as an attractive network paradigm to fulfill the increasingrequirements in terms of computation and storage resources.Different from traditional MEC, VEC is faced with several


new challenges because of the intrinsic features of vehicularnetworks, including fast mobility of vehicles and harsh channelenvironments. Although many efforts have been done in VEC,there still lacks a survey on the research of VEC, which is themotivation of this paper.

In this paper, we present a comprehensive survey on theexisting work in VEC. To this end, we first elaborate anoverview of VEC consisting of the introduction, architecture,key enablers, advantages, challenges and application scenarios.Then, different VEC research topics are introduced. Afterthat, we give a careful literature review on recent research byclassification including task offloading, caching, data sharing,flexible network management as well as security and privacy.Finally, we identify several open issues and discuss futureresearch directions. The research on VEC is still on infancywith a lot of questions to be solved. It is expected that moreefforts can be devoted into the new research field of VEC.

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