Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> B. VEC layers <s> As vehicle applications, mobile devices and the Internet of Things are growing fast, and developing an efficient architecture to deal with the big data in the Internet of Vehicles (IoV) has been an important concern for the future smart city. To overcome the inherent defect of centralized data processing in cloud computing, fog computing has been proposed by offloading computation tasks to local fog servers (LFSs). By considering factors like latency, mobility, localization, and scalability, this article proposes a regional cooperative fog-computing-based intelligent vehicular network (CFC-IoV) architecture for dealing with big IoV data in the smart city. Possible services for IoV applications are discussed, including mobility control, multi-source data acquisition, distributed computation and storage, and multi-path data transmission. A hierarchical model with intra-fog and inter-fog resource management is presented, and energy efficiency and packet dropping rates of LFSs in CFC-IoV are optimized. <s> BIB001 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> B. VEC layers <s> Vehicular networks enable an efficient communication with the aim of improving data dissemination among vehicles. However, a growing number of vehicles expect to conduct data dissemination through road side facilities which cause an increment of network load. To address the issue, this letter applies fog computing technologies for enhancing vehicular network that is planned as a layered network architecture. Moreover, two dynamic scheduling algorithms are proposed on the fog computing scheme for the data scheduling in vehicular networks. These algorithms can dynamically adapt a changeable network environment and benefit in efficiency. For performance analysis, a compositional formal method, named performance evaluation process algebra, is applied to model scheduling algorithm in a fog-based vehicular network. <s> BIB002 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> B. VEC layers <s> Vehicular edge computing (VEC) is introduced to extend computing capacity to vehicular network edge recently. With the advent of VEC, service providers directly host services in close proximity of mobile vehicles for great improvements. As a result, a new networking paradigm, vehicular edge networks is emerged along with the development of VEC. However, it is necessary to address security issues for facilitating VEC well. In this paper, we focus on reputation management to ensure security protection and improve network efficiency in the implementation of VEC. A distributed reputation management system ( DREAMS ) is proposed, wherein VEC servers are adopted to execute local reputation management tasks for vehicles. This system has remarkable features for improving overall performance: 1) distributed reputation maintenance; 2) trusted reputation manifestation; 3) accurate reputation update; and 4) available reputation usage. In particular, we utilize multi-weighted subjective logic for accurate reputation update in DREAMS. To enrich reputation usage in DREAMS, service providers optimize resource allocation in computation offloading by considering reputation of vehicles. Numerical results indicate that DREAMS has great advantages in optimizing misbehavior detection and improving the recognition rate of misbehaving vehicles. Meanwhile, we demonstrate the effectiveness of our reputation-based resource allocation algorithm. <s> BIB003 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> B. VEC layers <s> Connected vehicles provide advanced transformations and attractive business opportunities in the automotive industry. Presently, IEEE 802.11p and evolving 5G are the mainstream radio access technologies in the vehicular industry, but neither of them can meet all requirements of vehicle communication. In order to provide low-latency and high-reliability communication, an SDN-enabled network architecture assisted by MEC, which integrates different types of access technologies, is proposed. MEC technology with its on-premises feature can decrease data transmission time and enhance quality of user experience in latency-sensitive applications. Therefore, MEC plays as important a role in the proposed architecture as SDN technology. The proposed architecture was validated by a practical use case, and the obtained results have shown that it meets application- specific requirements and maintains good scalability and responsiveness. <s> BIB004 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> B. VEC layers <s> With advances in the information and communication technology (ICT), connected vehicles are one of the key enablers to unleash intelligent transportation systems (ITS). On the other hand, the envisioned massive number of connected vehicles raises the need for powerful communication and computation capabilities. As an emerging technique, fog computing is expected to be integrated with existing communication infrastructures, giving rise to a concept of fog-enhanced radio access networks (FeRANs). Such architecture brings computation capabilities closer to vehicular users, thereby reducing communication latency to access services, while making users capable of sharing local environment information for advanced vehicular services. In the FeRANs service migration, where the service is migrated from a source fog node to a target fog node following the vehicle's moving trace, it is necessary for users to access service as close as possible in order to maintain the service continuity and satisfy stringent latency requirements of real-time services. Fog servers, however, need to have sufficient computational resources available to support such migration. Indeed, a fog node typically has limited resources and hence can easily become overloaded when a large number of user requests arrive, e.g., during peak traffic, resulting in degraded performance. This paper addresses resource management in FeRANs with a focus on management strategies at each individual fog node to improve quality of service (QoS), particularly for real-time vehicular services. To this end, the paper proposes two resource management schemes, namely fog resource reservation and fog resource reallocation. In both schemes, real-time vehicular services are prioritized over other services so that their respective vehicular users can access the services with only one hop. Simulation results show that the proposed schemes can effectively improve one-hop access probability for real-time vehicular services implying low delay performance, even when the fog resource is under heavy load. <s> BIB005 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> B. VEC layers <s> Automated driving is coming with enormous potential for safer, more convenient, and more efficient transportation systems. Besides onboard sensing, autonomous vehicles can also access various cloud services such as high definition maps and dynamic path planning through cellular networks to precisely understand the real-time driving environments. However, these automated driving services, which have large content volume, are time-varying, location-dependent, and delay-constrained. Therefore, cellular networks will face the challenge of meeting this extreme performance demand. To cope with the challenge, by leveraging the emerging mobile edge computing technique, in this article, we first propose a two-level edge computing architecture for automated driving services in order to make full use of the intelligence at the wireless edge (i.e., base stations and autonomous vehicles) for coordinated content delivery. We then investigate the research challenges of wireless edge caching and vehicular content sharing. Finally, we propose potential solutions to these challenges and evaluate them using real and synthetic traces. Simulation results demonstrate that the proposed solutions can significantly reduce the backhaul and wireless bottlenecks of cellular networks while ensuring the quality of automated driving services. <s> BIB006 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> B. VEC layers <s> As a typical application of the technology of Internet of Things (IoT), Internet of Vehicle (IoV) is facing the explosive computation demands and restrict delay constrains. Vehicular networks with mobile edge computing (MEC) is a promising approach to address this problem. In this paper, we focus on the problem of reducing the completion time of Virtual Reality (VR) applications for IoV. To this end, we propose a cooperative approach for parallel computing and transmission for VR. In our proposed scheme, a VR task is divided into two sub-tasks firstly. Then one of the two is offloaded to the vehicle via wireless transmission so that the two sub-tasks can be processed at the MEC server and the vehicle separately and simultaneously. We formulate the scheme as a nonlinear optimization problem to jointly determine computation offloading proportion, communication resource and computation resource allocation. Due to the NP-hard property of this problem, a joint offloading proportion and resource allocation optimization (JOPRAO) algorithm is designed to obtain the optimal solution. Simulation results demonstrate that latency of VR task completion time can be decreased significantly by offloading the task and resource allocation strategy reasonably. <s> BIB007 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> B. VEC layers <s> Vehicular edge computing is essential to support future emerging multimedia-rich and delay-sensitive applications in vehicular networks. However, the massive deployment of edge computing infrastructures induces new problems including energy consumption and carbon pollution. This motivates us to develop BEGIN (Big data enabled EnerGy-efficient vehIcular edge computiNg), a programmable, scalable, and flexible framework for integrating big data analytics with vehicular edge computing. In this article, we first present a comprehensive literature review. Then the overall design principle of BEGIN is described with an emphasis on computing domain and data domain convergence. In the next section, we classify big data in BEGIN into four categories and then describe their features and potential values. Four typical application scenarios in BEGIN including node deployment, resource adaptation and workload allocation, energy management, and proactive caching and pushing, are provided to illustrate how to achieve energy-efficient vehicular edge computing by using big data. A case study is presented to demonstrate the feasibility of BEGIN and the superiority of big data in energy efficiency improvement. Finally, we conclude this work and outline future research open issues. <s> BIB008 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> B. VEC layers <s> In vehicular edge computing (VEC), resource-intensive tasks are offloaded to computing nodes at the network edge. Owing to high mobility and distributed nature, optimal task offloading in vehicular environments is still a challenging problem. In this paper, we first introduce a software-defined vehicular edge computing (SD-VEC) architecture where a controller not only guides the vehicles’ task offloading strategy but also determines the edge cloud resource allocation strategy. To obtain the optimal strategies, we formulate a problem on the edge cloud selection and resource allocation to maximize the probability that a task is successfully completed within a pre-specified time limit. Since the formulated problem is a well-known NP-hard problem, we devise a mobility-aware greedy algorithm (MGA) that determines the amount of edge cloud resources allocated to each vehicle. Trace-driven simulation results demonstrate that MGA provides near-optimal performance and improves the successful task execution probability compared with conventional algorithms. <s> BIB009 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> B. VEC layers <s> Vehicular networks have attracted much attention from both industry and academia. Due to the high-speed and complex network topology of vehicles, vehicular networks have become extremely challenging. The development of mobile communication technologies, such as SDN and FC, provides a great platform for vehicular networks. SDN separates the software plane and data plane, which enables efficient management and centralized control of vehicular networks. FC is an extension of cloud computing. By pushing significant storage, control, management, and communication mechanisms onto the network edge or user equipment, FC alleviates the pressure of the core network. Accordingly, we consider a novel SDFC-VeNET architecture in this article. Based on the SDFC-VeNET architecture, mobility management and resource allocation are discussed. Simulation results show the superiority of the presented SDFC-VeNET architecture, as well as the associated handover scheme and resource allocation mechanism. Furthermore, the existing challenges and open issues are discussed. <s> BIB010 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> B. VEC layers <s> Over the last few years, we have witnessed an exponential increase in the computing and storage capabilities of smart devices that has led to the popularity of an emerging technology called edge computing. Compared to the traditional cloud-computing- based infrastructure, computing and storage facilities are available near end users in edge computing. Moreover, with the widespread popularity of unmanned aerial vehicles (UAVs), huge amounts of information will be shared between edge devices and UAVs in the coming years. In this scenario, traffic surveillance using UAVs and edge computing devices is expected to become an integral part of the next generation intelligent transportation systems. However, surveillance in ITS requires uninterrupted data sharing, cooperative decision making, and stabilized network formation. Edge computing supports data processing and analysis closer to the deployed machines (i.e., the sources of the data). Instead of simply storing data and missing the opportunity to capitalize on it, edge devices can analyze data to gain insights before acting on them. Transferring data from the vehicle to the edge for real-time analysis can be facilitated by the use of UAVs, which can act as intermediate aerial nodes between the vehicles and edge nodes. However, as the communication between UAVs and edge devices is generally done using an open channel, there is a high risk of information leakage in this environment. Keeping our focus on all these issues, in this article, we propose a data-driven transportation optimization model where cyber-threat detection in smart vehicles is done using a probabilistic data structure (PDS)- based approach. A triple Bloom filter PDS- based scheduling technique for load balancing is initially used to host the real-time data coming from different vehicles, and then to distribute/collect the data to/from edges in a manner that minimizes the computational effort. The results obtained show that the proposed system requires comparatively less computational time and storage for load sharing, authentication, encryption, and decryption of data in the considered edge-computing-based smart transportation framework. <s> BIB011 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> B. VEC layers <s> Vehicular fog computing, which extends the mobile cloud paradigm, is usually composed of stable infrastructures, a large volume of vehicles, portable devices, and robust networks. As a service-providing platform, it is significant to quickly obtain the required service with the aim to correctly save the energy of the corresponding nodes and effectively improve the network survivability. However, the limited capacity of components makes such a situation more complicated. This paper aims to reduce serving time by allocating the available bandwidth to four kinds of services. A utility model is built according to the above-mentioned serving methods and is solved through a two-step approach. For the first step, all the sub-optimal solutions are provided based on a Lagrangian algorithm. For the second step, an optimal solution selection process is presented and analyzed. A numerical simulation is executed to illustrate the allocation results and the optimal utility model while optimizing the survivability. <s> BIB012 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> B. VEC layers <s> In the era of smart cities, all vehicle systems will be connected to enhance the comfort of driving, relieve traffic congestion, and enjoy in-vehicle multimedia entertainment. The vision of all vehicles connected poses a crucial challenge for an individual vehicle system to efficiently support these applications. Network virtualization is a very promising enabling solution, by allowing multiple isolated and heterogeneous virtual networks (VNs), to satisfy the different quality of service (QoS) requirements. Smart identifier network (SINET) may provide VNs through effective resources allocation and control based on its model of three layers and two domains. In this paper, we provide resource allocation and mapping of the vehicular networks through elastic network virtualization based on SINET. The appropriate vehicles are selected and generated as a function group for special service, by which the difference of heterogeneous vehicular resources is hided. Vehicular nodes are autonomously organized that each of them can evaluate others’ resource availability in a topology-aware way with information by leveraging the learning technology and make its own decision to realize the whole mapping process through a phasing virtual network embedding (PVNE) algorithm. The results demonstrate that our proposed mechanism has better performance in long-term acceptance ratio and average revenue than existing state-of-the-art solutions. <s> BIB013 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> B. VEC layers <s> With the explosion in the number of connected devices and Internet of Things (IoT) services in smart city, the challenges to meet the demands from both data traffic delivery and information processing are increasingly prominent. Meanwhile, the connected vehicle networks have become an essential part in smart city, bringing massive data traffic as well as significant communication, caching, and computing resources. As the two typical services types in smart city, delay-tolerant and delay-sensitive traffic requires very different quality of service (QoS)/quality of experience (QoE), and could be delivered through the routes with different features to meet their QoS/QoE requirements with the lowest costs. In this paper, we propose a novel vehicle network architecture in the smart city scenario, mitigating the network congestion with the joint optimization of networking, caching, and computing resources. Cloud computing at the data centers as well as mobile edge computing at the evolved node Bs and on-board units are taken as the paradigms to provide caching and computing resources. The programmable control principle originated from the software-defined networking paradigm has been introduced into this architecture to facilitate the system optimization and resource integration. With the careful modeling of the services, the vehicle mobility, and the system state, a joint resource management scheme is proposed and formulated as a partially observable Markov decision process to minimize the system cost, which consists of both network overhead and execution time of computing tasks. Extensive simulation results with different system parameters reveal that the proposed scheme could significantly improve the system performance compared to the existing schemes. <s> BIB014 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> B. VEC layers <s> By leveraging the 5G enabled vehicular ad hoc network (5G-VANET), it is widely recognized that connected vehicles have the potentials to improve road safety, transportation intelligence and provide in-vehicle entertainment experience. However, many enabling applications in 5G-VANET rely on the efficient content sharing among mobile vehicles, which is a very challenging issue due to the extremely large data volume, rapid topology change, and unbalanced traffic. In this paper, we investigate content prefetching and distribution in 5G-VANET. We first introduce an edge computing based hierarchical architecture for efficient distribution of large-volume vehicular data. We then propose a multi-place multi-factor prefetching scheme to meet the rapid topology change and unbalanced traffic. The content requests of vehicles can be served by neighbors, which can improve the sharing efficiency and alleviate the burden of networks. Furthermore, we use a graph theory based approach to solve the content distribution by transforming it into a maximum weighted independent set problem. Finally, the proposed scheme is evaluated with a greedy transmission strategy to demonstrate its efficiency. <s> BIB015 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> B. VEC layers <s> Vehicular Edge Computing (VEC) has been studied as an important application of mobile edge computing in vehicular networks. Usually, the generalization of VEC involves large-scale deployment of dedicated servers, which will cause tremendous economic expense. We also observe that the Parked Vehicles (PVs) in addition to mobile vehicles have rich and underutilized resources for task execution in vehicular networks. Thus, we consider scheduling PVs as available edge computing nodes to execute tasks, and this leads to a new computing paradigm, called by parked vehicle edge computing (PVEC). In this paper, we investigate PVEC and explore opportunistic resources from PVs to run distributed mobile applications. PVs coordinate with VEC servers for collective task execution. First, a system architecture with primary network entities is proposed for enabling PVEC. We also elaborately design an interactive protocol to support mutual communications among them with security guarantee. Moreover, we measure the availability of opportunistic resources and formulate a resource scheduling optimization problem by using Stackelberg game approach. A subgradient-based iterative algorithm is presented to determine workload allocation among PVs and minimize the overall cost of users. Numerical results indicate that compared with existing schemes, PVEC serves more vehicles and reduces service fee for users. We also demonstrate that the Stackelberg game approach is effective and efficient. <s> BIB016 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> B. VEC layers <s> Vehicular network aims at providing intelligent transportation and ubiquitous network access. Edge computing is able to reduce the consumption of core network bandwidth and serving latency by processing the generated data at the network edge, and social network is able to provide precise services by analyzing user’s personal behaviors. In this paper, we propose a new network system referred to as vehicular social edge computing (VSEC) that inherits the advantages of both edge computing and social network. VSEC is capable of improving the drivers’ quality of experience while enhancing the service providers’ quality of service. In order to further improve the performance of VSEC, the network utility is modeled and maximized by optimally managing the available network resources via two steps. First, the total processing time is minimized to achieve the optimal payment of the user to each edge device for each kind of the required resource. Second, a utility model is proposed, and the available resources are optimally allocated based on the results from the first step. The two optimization problems are solved by the Lagrangian theory, and the closed-form expressions are obtained. Numerical simulations show different capacities in different scenarios, which may provide some useful insights for VSEC design. <s> BIB017 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> B. VEC layers <s> By analogy with Internet of things, Internet of vehicles (IoV) that enables ubiquitous information exchange and content sharing among vehicles with little or no human intervention is a key enabler for the intelligent transportation industry. In this paper, we study how to combine both the physical and social layer information for realizing rapid content dissemination in device-to-device vehicle-to-vehicle (D2D-V2V)-based IoV networks. In the physical layer, headway distance of vehicles is modeled as a Wiener process, and the connection probability of D2D-V2V links is estimated by employing the Kolmogorov equation. In the social layer, the social relationship tightness that represents content selection similarities is obtained by Bayesian nonparametric learning based on real-world social big data, which are collected from the largest Chinese microblogging service Sina Weibo and the largest Chinese video-sharing site Youku. Then, a price-rising-based iterative matching algorithm is proposed to solve the formulated joint peer discovery, power control, and channel selection problem under various quality-of-service requirements. Finally, numerical results demonstrate the effectiveness and superiority of the proposed algorithm from the perspectives of weighted sum rate and matching satisfaction gains. <s> BIB018 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> B. VEC layers <s> Social Internet of Vehicles (SIoV) is a new paradigm that enables social relationships among vehicles by integrating vehicle-to-everything communications and social networking properties into the vehicular environment. Through the provision of diverse socially-inspired applications and services, the emergence of SIoV helps to improve the road experience, traffic efficiency, road safety, travel comfort, and entertainment along the roads. However, the computation performance for those applications have been seriously affected by resource-limited on-board units as well as deployment costs and workloads of roadside units. Under such context, an unmanned aerial vehicle (UAV)-assisted mobile edge computing environment over SIoV with a three-layer integrated architecture is adopted in this paper. Within this architecture, we explore the energy-aware dynamic resource allocation problem by taking into account partial computation offloading, social content caching, and radio resource scheduling. Particularly, we develop an optimization framework for total utility maximization by jointly optimizing the transmit power of vehicle and the UAV trajectory. To resolve this problem, an energy-aware dynamic power optimization problem is formulated under the constraint of the evolution law of energy consumption state for each vehicle. By considering two cases, i.e., cooperation and noncooperation among vehicles, we obtain the optimal dynamic power allocation of the vehicle with a fixed UAV trajectory via dynamic programming method. In addition, under the condition of fixed power, a search algorithm is introduced to derive the optimized UAV trajectory based on acceptable ground-UAV distance metric and the optimal offloaded data size of the vehicle. Simulation results are presented to demonstrate the effectiveness of the proposed framework over alternative benchmark schemes. <s> BIB019 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> B. VEC layers <s> Data offloading plays an important role for the mobile data explosion problem that occurs in cellular networks. This paper proposed an idea and control scheme for offloading vehicular communication traffic in the cellular network to vehicle to vehicle (V2V) paths that can exist in vehicular ad hoc networks (VANETs). A software-defined network (SDN) inside the mobile edge computing (MEC) architecture, which is abbreviated as the SDNi-MEC server, is devised in this paper to tackle the complicated issues of VANET V2V offloading. Using the proposed SDNi-MEC architecture, each vehicle reports its contextual information to the context database of the SDNi-MEC server, and the SDN controller of the SDNi-MEC server calculates whether there is a V2V path between the two vehicles that are currently communicating with each other through the cellular network. This proposed method: 1) uses each vehicle’s context; 2) adopts a centralized management strategy for calculation and notification; and 3) tries to establish a VANET routing path for paired vehicles that are currently communicating with each other using a cellular network. The performance analysis for the proposed offloading control scheme based on the SDNi-MEC server architecture shows that it has better throughput in both the cellular networking link and the V2V paths when the vehicle’s density is in the middle. <s> BIB020 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> B. VEC layers <s> Several novel and promising wireless vehicular-network applications have been developed recently. Examples include trip planning, media sharing, and Internet access. However, sufficient network resources [e.g., equipped vehicles, base stations (BSs), roadside units (RSUs), and other infrastructure] to support these applications are not yet available or are deployed at a slow pace. Moreover, these applications require considerable efforts in information gathering and data processing to allow quick decision making and fast feedback to all users. This imposes significant challenges on the development of an efficient wireless vehicularcommunication platform. In this article, we propose a new hierarchical software-defined-network (SDN)-based wireless vehicular fog architecture called a hierarchical SDN for vehicular fog (HSVF) architecture. The HSVF is based on a hybrid SDN control plane that reinforces centralized and distributed management. The proposed SDN control plane includes a trajectory prediction module to mitigate the drawbacks of the frequent handover problem between the RSUs and vehicles. The proposed architecture is then evaluated using a relevant case study that addresses the scheduling of electric vehicle (EV) energy demands. <s> BIB021 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> B. VEC layers <s> Vehicular fog computing (VFC) has emerged as a promising solution to relieve the overload on the base station and reduce the processing delay during the peak time. The computation tasks can be offloaded from the base station to vehicular fog nodes by leveraging the under-utilized computation resources of nearby vehicles. However, the wide-area deployment of VFC still confronts several critical challenges, such as the lack of efficient incentive and task assignment mechanisms. In this paper, we address the above challenges and provide a solution to minimize the network delay from a contract-matching integration perspective. First, we propose an efficient incentive mechanism based on contract theoretical modeling. The contract is tailored for the unique characteristic of each vehicle type to maximize the expected utility of the base station. Next, we transform the task assignment problem into a two-sided matching problem between vehicles and user equipment. The formulated problem is solved by a pricing-based stable matching algorithm, which iteratively carries out the “propose” and “price-rising” procedures to derive a stable matching based on the dynamically updated preference lists. Finally, numerical results demonstrate that significant performance improvement can be achieved by the proposed scheme. <s> BIB022 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> B. VEC layers <s> Enabling HD-map-assisted cooperative driving among CAVs to improve navigation safety faces technical challenges due to increased communication traffic volume for data dissemination and an increased number of computing/storing tasks on CAVs. In this article, a new architecture that combines MEC and SDN is proposed to address these challenges. With MEC, the interworking of multiple wireless access technologies can be realized to exploit the diversity gain over a wide range of radio spectrum, and at the same time, computing/storing tasks of a CAV are collaboratively processed by servers and other CAVs. By enabling NFV in MEC, different functions can be programmed on the server to support diversified AV applications, thus enhancing the server's flexibility. Moreover, by using SDN concepts in MEC, a unified control plane interface and global information can be provided, and by subsequently using this information, intelligent traffic steering and efficient resource management can be achieved. A case study is presented to demonstrate the effectiveness of the proposed architecture. <s> BIB023 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> B. VEC layers <s> In this paper, we propose Chimera , a novel hybrid edge computing framework, integrated with the emerging edge cloud radio access network, to augment network-wide vehicle resources for future large-scale vehicular crowdsensing applications, by leveraging a multitude of cooperative vehicles and the virtual machine (VM) pool in the edge cloud via the control of the application manager deployed in the edge cloud. We present a comprehensive framework model and formulate a novel multivehicle and multitask offloading problem, aiming at minimizing the energy consumption of network-wide recruited vehicles serving heterogeneous crowdsensing applications, and meanwhile reconciling both application deadline and vehicle incentive. We invoke Lyapunov optimization framework to design TaskSche , an online task scheduling algorithm, which only utilizes the current system information. As the core components of the algorithm, we propose a task workload assignment policy based on graph transformation and a knapsack-based VM pool resource allocation policy. Rigorous theoretical analyses and extensive trace-driven simulations indicate that our framework achieves superior performance (e.g., 20%–68% energy saving without overstepping application deadlines for network-wide vehicles compared with vehicle local processing) and scales well for a large number of vehicles and applications. <s> BIB024 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> B. VEC layers <s> The drastically increasing volume and the growing trend on the types of data have brought in the possibility of realizing advanced applications such as enhanced driving safety, and have enriched existing vehicular services through data sharing among vehicles and data analysis. Due to limited resources with vehicles, vehicular edge computing and networks (VECONs) i.e., the integration of mobile edge computing and vehicular networks, can provide powerful computing and massive storage resources. However, road side units that primarily presume the role of vehicular edge computing servers cannot be fully trusted, which may lead to serious security and privacy challenges for such integrated platforms despite their promising potential and benefits. We exploit consortium blockchain and smart contract technologies to achieve secure data storage and sharing in vehicular edge networks. These technologies efficiently prevent data sharing without authorization. In addition, we propose a reputation-based data sharing scheme to ensure high-quality data sharing among vehicles. A three-weight subjective logic model is utilized for precisely managing reputation of the vehicles. Numerical results based on a real dataset show that our schemes achieve reasonable efficiency and high-level of security for data sharing in VECONs. <s> BIB025 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> B. VEC layers <s> Mobile edge computing (MEC) has been recently proposed to bring computing capabilities closer to mobile endpoints, with the aim of providing low latency and real-time access to network information via applications and services. Several attempts have been made to integrate MEC in intelligent transportation systems (ITS), including new architectures, communication frameworks, deployment strategies and applications. In this paper, we explore existing architecture proposals for integrating MEC in vehicular environments, which would allow the evolution of the next generation ITS in smart cities. Moreover, we classify the desired applications into four major categories. We rely on a MEC architecture with three layers to propose a data dissemination protocol, which can be utilized by traffic safety and travel convenience applications in vehicular networks. Furthermore, we provide a simulation-based prototype to evaluate the performance of our protocol. Simulation results show that our proposed protocol can significantly improve the performance of data dissemination in terms of data delivery, communication overhead and delay. In addition, we highlight challenges and open issues to integrate MEC in vehicular networking environments for further research. <s> BIB026 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> B. VEC layers <s> The resource limitation of multi-access edge computing (MEC) is one of the major issues in order to provide low-latency high-reliability computing services for Internet of Things (IoT) devices. Moreover, with the steep rise of task requests from IoT devices, the requirement of computation tasks needs dynamic scalability while using the potential of offloading tasks to mobile volunteer nodes (MVNs). We, therefore, propose a scalable vehicle-assisted MEC (SVMEC) paradigm, which cannot only relieve the resource limitation of MEC but also enhance the scalability of computing services for IoT devices and reduce the cost of using computing resources. In the SVMEC paradigm, a MEC provider can execute its users’ tasks by choosing one of three ways: (i) Do itself on local MEC, (ii) offload to the remote cloud, and (iii) offload to the MVNs. We formulate the problem of joint node selection and resource allocation as a Mixed Integer Nonlinear Programming (MINLP) problem, whose major objective is to minimize the total computation overhead in terms of the weighted-sum of task completion time and monetary cost for using computing resources. In order to solve it, we adopt alternative optimization techniques by decomposing the original problem into two sub-problems: Resource allocation sub-problem and node selection sub-problem. Simulation results demonstrate that our proposed scheme outperforms the existing schemes in terms of the total computation overhead. <s> BIB027 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> B. VEC layers <s> In this paper, a dynamic spectrum management framework is proposed to improve spectrum resource utilization in a multi-access edge computing (MEC) in autonomous vehicular network (AVNET). To support the increasing communication data traffic and guarantee quality-of-service (QoS), spectrum slicing, spectrum allocating, and transmit power controlling are jointly considered. Accordingly, three non-convex network utility maximization problems are formulated to slice spectrum among base stations (BSs), allocate spectrum among autonomous vehicles (AVs) associated with a BS, and control transmit powers of BSs, respectively. Through linear programming relaxation and first-order Taylor series approximation, these problems are transformed into tractable forms and then are jointly solved through an alternate concave search (ACS) algorithm. As a result, the optimal spectrum slicing ratios among BSs, optimal BS-vehicle association patterns, optimal fractions of spectrum resources allocated to AVs, and optimal transmit powers of BSs are obtained. Based on our simulation, a high aggregate network utility is achieved by the proposed spectrum management scheme compared with two existing schemes. <s> BIB028
In general, the VEC architecture may consist of different number of tiers, which are two, three or four. Table II tabulates some examples of VEC architectures in terms of layers, name of the VEC systems, their advantages and disadvantages. Mostly ComOf works focused on the two primary layers, namely the vehicle and edge layers BIB022 , BIB006 , BIB007 , which can be distinguished in terms of types of edge nodes used, as discussed in the next subsection. However, some works introduced different types of two tiers, such as Big data enabled EnerGy-efficient vehIcular edge computing (BEGIN) BIB008 proposed a computing domain and a data domain whilst Software-Defined Vehicular Edge Computing (SD-VEC) BIB009 proposed a data plane and a control plane in their VEC architectures. The two layers can also be in the form of types of computing, cloud and edge computing layers BIB001 , BIB023 . The third layer of the VEC architecture involves with the cloud, such as core or central cloud BIB010 , BIB011 ,  , cloud computing BIB012 , BIB002 , cloud-enabled control layer [55], regional cloud  , public cloud BIB024 , BIB025 , cloud server layer BIB026 , remote cloud BIB027 . The key advantage of the third layer is to provide an additional computational resources in the case of insufficient resources at edge nodes. Unlike others, the proposed VEC layers in BIB013 constitutes of three unique layers as seen in Table II. Subsequently, the application layer BIB014 , local authority BIB003 , city-wide controller BIB015 or service provider BIB016 is the fourth and also the last layer expanded in the VEC architecture. The entities in such layer deal with the Quality of Service (QoS) BIB014 , resource allocation BIB015 or reward policy BIB003 , BIB016 . However, the bottleneck is the high overhead as the number of control messages, for instance, the amount of storage offered and computation time, may presence in the system. Regardless number of tiers in the VEC, interestingly for the scenario of vehicles social communication a layer related to the social edge computing BIB017 , BIB018 , BIB019 is introduced. The social relationships of vehicles are created based on their social interest and ties BIB019 , preferences of content selection BIB018 , or temporary storage of current contents or movies BIB017 . Apart from that, the enabler technology, such as big data BIB008 , blockchain BIB025 , Software-Defined Networks (SDN) BIB009 , BIB020 , BIB004 , BIB021 and Network Function Virtualisation (NFV) BIB013 , BIB028 are also incorporated in the VEC system. The SDN and NFV bring new insights as their benefits can be in the Table III and Table IV , respectively. Typically, SDN is used to separate the control plane and data plane in serving VEC BIB009 , BIB023 and even to separate the control plane for cloud and fog servers BIB021 . Since SDN stored the contextual information of vehicles BIB020 , BIB004 , such as vehicle identification, location, speed, link, and contents in a database that resulted to an optimal offloading decision including vehicle-to-vehicle (V2V) paths and handover BIB020 . With the information and optimal decision, the performance of a VEC system can then be predicted BIB004 . In addition to that, the resource abstraction and slicing BIB008 as well as wireless access interworking BIB023 are handled by SDN. Therefore, SDN has a potential to improve the agility, reliability, scalability, and latency performance in VEC BIB004 . On the other hand, NFV primarily concentrates on the creation and configuration of virtual resources whereby the task is variably segmented dependent on the available virtual machine (VM) BIB001 , BIB013 . Despite that, the spectrum slicing, load balancing and power control can also be administered by NFV BIB023 , BIB028 . Therefore, NFV offers scalable virtual resources for scheduling different tasks in a timely manner. The connected edge server including the SDN/NFV controller offers important capabilities for identifying and selecting edge nodes, allocating and migrating tasks BIB005 and giving rewards to the AV that undertakes the residual workloads  .