Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> C. Edge nodes <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> BIB001 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> C. Edge nodes <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> BIB002 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> C. Edge nodes <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> BIB003 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> C. Edge nodes <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> C. Edge nodes <s> Mobile computation offloading is an emerging technology to migrate resource-intensive computations from resource-limited mobile devices (MDs) to resource-rich devices (such as a cloud server) via wireless access. Accessing to remote cloud server usually introduces a long delay to first deliver parameters to the server and then retrieve the results back. For applications that are time sensitive, offloading to nearby cloudlets is preferred. However, the link duration between an MD and a single cloudlet can be very limited in a vehicular network. As a result, offloading actions taken by an MD may fail due to link breakage caused by mobility. Meanwhile, some vehicles, such as buses, always follow relatively fixed routes, and their locations can be predicted much easier than other vehicles. By taking advantage of this fact, we propose a bus-based cloudlet cooperation strategy, where the bus-based cloudlets act as computation service providers for the MDs in vehicles, and an application generated by an MD includes a series of tasks that have dependency among each other. The proposed bus-based cloudlet cooperation strategy (BCCS) finds the optimal set of tasks to be offloaded to each cloudlet. Experimental results show that the proposed BCCS strategy can reduce both the energy consumption of the MDs and completion time of the applications. <s> BIB005 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> C. Edge nodes <s> Mobile computation offloading (MCO) is an emerging technology to offload the resource-intensive computations from smart mobile devices (SMDs) to nearby resource-rich devices (i.e., cloudlets) via wireless access. However, the link duration between a SMD and a single cloudlet can be very limited in a vehicular network. As a result, offloading actions taken by a SMD may fail due to link breakage caused by mobility. Meanwhile, some vehicles, such as buses, always follow relatively fixed routes, and their locations can be predicted much easier than other vehicles. By taking advantage of this fact, we propose a semi-Markov decision process (SMDP)-based cloudlet cooperation strategy, where the bus-based cloudlets act as computation service providers for the SMDs in vehicles, and an application generated by a SMD includes a series of tasks that have dependency among each other. In this paper, we adopt a semi-Markov decision process (SMDP) framework to formulate the bus-based cooperation computing problem as a delay-constrained shortest path problem on a state transition graph. The value iteration algorithm (VIA) is used to find the efficient solution to the bus-based cooperation computing problem. Experimental results show that the proposed SMDP-based cloudlet cooperation strategy can improve the performance of computation on the SMD in the cost (i.e., energy consumption and application completion time) and the offloading rate. <s> BIB006 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> C. Edge nodes <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> BIB007 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> C. Edge nodes <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> C. Edge nodes <s> The smart vehicles construct Vehicle of Internet which can execute various intelligent services. Although the computation capability of the vehicle is limited, multi-type of edge computing nodes provide heterogeneous resources for vehicular services.When offloading the complicated service to the vehicular edge computing node, the decision should consider numerous factors.The offloading decision work mostly formulate the decision to a resource scheduling problem with single or multiple objective function and some constraints, and explore customized heuristics algorithms. However, offloading multiple data dependency tasks in a service is a difficult decision, as an optimal solution must understand the resource requirement, the access network, the user mobility, and importantly the data dependency. Inspired by recent advances in machine learning, we propose a knowledge driven (KD) service offloading decision framework for Vehicle of Internet, which provides the optimal policy directly from the environment. We formulate the offloading decision of multi-task in a service as a long-term planning problem, and explores the recent deep reinforcement learning to obtain the optimal solution. It considers the future data dependency of the following tasks when making decision for a current task from the learned offloading knowledge. Moreover, the framework supports the pre-training at the powerful edge computing node and continually online learning when the vehicular service is executed, so that it can adapt the environment changes and learns policy that are sensible in hindsight. The simulation results show that KD service offloading decision converges quickly, adapts to different conditions, and outperforms the greedy offloading decision algorithm. <s> BIB009 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> C. Edge nodes <s> Low-Iatency communication is crucial to satisfy the strict requirements on latency and reliability in 5G communications. In this paper, we firstly consider a contract-based vehicular fog computing resource allocation framework to minimize the intolerable delay caused by the numerous tasks on the base station during peak time. In the vehicular fog computing framework, the users tend to select nearby vehicles to process their heavy tasks to minimize delay, which relies on the participation of vehicles. Thus, it is critical to design an effective incentive mechanism to encourage vehicles to participate in resource allocation. Next, the simulation results demonstrate that the contract-based resource allocation can achieve better performance. <s> BIB010 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> C. Edge nodes <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> BIB011 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> C. Edge nodes <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> BIB012 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> C. Edge nodes <s> Mobile edge computing (MEC) is a new paradigm to improve the quality of vehicular services by providing computation offloading close to vehicular terminals (VTs).However, due to the computation limitation of the MEC servers, how to optimally utilize the limited computation resources of MEC servers while maintaining a high quality of experience (QoE) of VTs becomes a challenge. To address the problem, we investigate a novel computation offloading scheme based on the MEC offloading framework in vehicular networks. Firstly, the utility of VTs for offloading their computation tasks is presented, where the utility is jointly determined by the execution time, computation resources and the energy for completing the computation tasks. Next, with the theoretical analysis of the utility, the QoE of each VT can be guaranteed. Then, combined with the pricing scheme of the MEC servers, we propose an efficient distributed computation offloading algorithm to make the optimal offloading decisions for VTs, where the utility of the MEC servers is maximized and the QoE of the VTs is enhanced. In addition, simulation results demonstrate that the proposal can lead to a higher utility for MEC servers than the conventional schemes. <s> BIB013 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> C. Edge nodes <s> Technological evolutions in the automobile industry, especially the development of connected and autonomous vehicles, have granted vehicles more computing, storage, and sensing resources. The necessity of efficient utilization of these resources leads to the vision of vehicular cloud computing (VCC), which can offload the computing tasks from the edge or remote cloud to enhance the overall efficiency. In this paper, we study the problem of computation offloading through the vehicular cloud (VC), where computing missions from edge cloud can be offloaded and executed cooperatively by vehicles in VC. Specifically, computing missions are further divided into computing tasks with interdependency and executed in different vehicles in the VC to minimize the overall response time. To characterize the instability of computing resources resulting from the high vehicular mobility, a mobility model focusing on vehicular dwell time is utilized. Considering the heterogeneity of vehicular computing capabilities and the interdependency of computing tasks, we formulate an optimization problem for task scheduling, which is NP-hard. For low complexity, a modified genetic algorithm based scheduling scheme is designed where integer coding is used rather than binary coding, and relatives are defined and employed to avoid infeasible solutions. In addition, a task load based stability analysis of the VCC system is presented for the cases where some vehicles within the VC are offline. Numerical results demonstrate that the proposed scheme can significantly improve the utilization of computing resources while guaranteeing low latency and system stability. <s> BIB014 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> C. Edge nodes <s> Mobile computation offloading enables resources-constrained mobile devices to offload their computation intensive tasks to other available computing resources for local energy savings. In this paper, we study the offloading decision and task scheduling issues when multiple serving vehicles (SVs) can be utilized in vehicular network. The overall energy consumption of users with Dynamic Voltage Scaling (DVS) technology is minimized subject to the delay constraint of each task. For the ideal cases, the assignment and scheduling in an offline style are firstly formulated as a mixed-integer nonlinear programming (MINLP) problem, and the optimal solution is derived based on dynamic programming. After that, two online strategies, Energy Consumption Minimization (ECM) based low-complexity assignment, and Resource Reservation (RR) assignment are also proposed. Simulation results demonstrate the improvements in energy saving when the proposed strategies incorporated with the DVS technology are adopted. <s> BIB015 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> C. Edge nodes <s> Vehicular cloud computing (VCC) is proposed to effectively utilize and share the computing and storage resources on vehicles. However, due to the mobility of vehicles, the network topology, the wireless channel states and the available computing resources vary rapidly and are difficult to predict. In this work, we develop a learning-based task offloading framework using the multi-armed bandit (MAB) theory, which enables vehicles to learn the potential task offloading performance of its neighboring vehicles with excessive computing resources, namely service vehicles (SeVs), and minimizes the average offloading delay. We propose an adaptive volatile upper confidence bound (AVUCB) algorithm and augment it with load-awareness and occurrence-awareness, by redesigning the utility function of the classic MAB algorithms. The proposed AVUCB algorithm can effectively adapt to the dynamic vehicular environment, balance the tradeoff between exploration and exploitation in the learning process, and converge fast to the optimal SeV with theoretical performance guarantee. Simulations under both synthetic scenario and a realistic highway scenario are carried out, showing that the proposed algorithm achieves close-to-optimal delay performance. <s> BIB016 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> C. Edge nodes <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> BIB017 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> C. Edge nodes <s> Vehicular fog computing (VFC) is a promising approach to provide ultra-low-latency service to vehicles and end users by extending the fog computing to conventional vehicular networks. Parked vehicle assistance (PVA), as a critical technique in VFC, can be integrated with smart parking in order to exploit its full potentials. In this paper, we propose a VFC system by combining both PVA and smart parking. A single- round multi-item parking reservation auction is proposed to guide the on-the-move vehicles to the available parking places with less effort and meanwhile exploit the fog capability of parked vehicles to assist the delay-sensitive computing services. The proposed allocation rule maximizes the aggregate utility of the smart vehicles and the proposed payment rule guarantees incentive compatible, individual rational and budget balance. The simulation results confirmed the win-win performance enhancement to fog node controller (FNC), vehicles, and parking places from the proposed design. <s> BIB018 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> C. Edge nodes <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> BIB019 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> C. Edge nodes <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> BIB020 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> C. Edge nodes <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> BIB021 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> C. Edge nodes <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> BIB022 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> C. Edge nodes <s> This paper studies computation offloading for mobile users in legacy vehicles that are not equipped with powerful computing devices. While running a time-sensitive and computation-intensive application on a mobile device (MD) of these users, the MD may offload part or all the applications to a nearby smart vehicle that can serve as a cloudlet. This provides a possibility to complete the application in time while saving the battery energy of the MD. However, high mobility of the vehicles causes short-lived communication links between the vehicles. As a result, the MD may have to offload tasks to a new cloudlet each time it loses the connection to the current cloudlet. In this paper, we propose a vehicle-based cloudlet relaying (VCR) scheme for mobile computation offloading. The objective is to effectively utilize the computation resources available in surrounding smart vehicles of the MD in the highly dynamic network environment, where each vehicle-based cloudlet may only be available to help the MD execute a small portion of the application. Instead of offloading tasks directly from the MD to individual cloudlets, which may consume high power and a long time, especially when the link just becomes available or is about to disconnect, we propose computation relaying that allows the current executing cloudlet to offload tasks to the next executing cloudlet on behalf of the MD. A set of conditions are established to enable the inter-cloudlet offloading so that the computation relaying can be achieved between the vehicle cloudlets, while the results can be returned to the MD. In addition, the computation relaying is always backed up by direct offloading between the MD and individual cloudlets. An algorithm is proposed to find the time to switch to a new cloudlet and select the best cloudlet as the next execution site or return the execution back to the MD, in order to minimize the energy consumption of the MD while satisfying the delay constraint of the application. Extensive simulation results are shown to demonstrate the effectiveness of the proposed VCR scheme. <s> BIB023 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> C. Edge nodes <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> BIB024 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> C. Edge nodes <s> Mobile Edge Computing (MEC) is a promising solution to improve vehicular services through offloading computation to cloud servers in close proximity to mobile vehicles. However, the self-interested nature together with the high mobility characteristic of the vehicles make the design of the computation offloading scheme a significant challenge. In this paper, we propose a new Vehicular Edge Computing (VEC) framework to model the computation offloading process of the mobile vehicles running on a bidirectional road. Based on this framework, we adopt a contract theoretic approach to design optimal offloading strategies for the VEC service provider, which maximize the revenue of the provider while enhancing the utilities of the vehicles. To further improve the utilization of the computing resources of the VEC servers, we incorporate task priority distinction as well as additional resource providing into the design of the offloading scheme, and propose an efficient VEC server selection and computing resource allocation algorithm. Numerical results indicate that our proposed schemes greatly enhance the revenue of the VEC provider, and concurrently improve the utilization of cloud computing resources. <s> BIB025 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> C. Edge nodes <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> BIB026 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> C. Edge nodes <s> Vehicular networks are facing the challenges to support ubiquitous connections and high quality of service for numerous vehicles. To address these issues, mobile edge computing (MEC) is explored as a promising technology in vehicular networks by employing computing resources at the edge of vehicular wireless access networks. In this paper, we study the efficient task offloading schemes in vehicular edge computing networks. The vehicles perform the offloading time selection, communication, and computing resource allocations optimally, the mobility of vehicles and the maximum latency of tasks are considered. To minimize the system costs, including the costs of the required communication and computing resources, we first analyze the offloading schemes in the independent MEC servers scenario. The offloading tasks are processed by the MEC servers deployed at the access point (AP) independently. A mobility-aware task offloading scheme is proposed. Then, in the cooperative MEC servers scenario, the MEC servers can further offload the collected overloading tasks to the adjacent servers at the next AP on the vehicles' moving direction. A location-based offloading scheme is proposed. In both scenarios, the tradeoffs between the task completed latency and the required communication and computation resources are mainly considered. Numerical results show that our proposed schemes can reduce the system costs efficiently, while the latency constraints are satisfied. <s> BIB027 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> C. Edge nodes <s> The proliferation of smart vehicular terminals (VTs) and their resource hungry applications impose serious challenges to the processing capabilities of VTs and the delivery of vehicular services. Mobile Edge Computing (MEC) offers a promising paradigm to solve this problem by offloading VT applications to proximal MEC servers, while TV white space (TVWS) bands can be used to supplement the bandwidth for computation offloading. In this paper, we consider a cognitive vehicular network that uses the TVWS band, and formulate a dual-side optimization problem, to minimize the cost of VTs and that of the MEC server at the same time. Specifically, the dual-side cost minimization is achieved by jointly optimizing the offloading decision and local CPU frequency on the VT side, and the radio resource allocation and server provisioning on the server side, while guaranteeing network stability. Based on Lyapunov optimization, we design an algorithm called DDORV to tackle the joint optimization problem, where only current system states, such as channel states and traffic arrivals, are needed. The closed-form solution to the VT-side problem is obtained easily by derivation and comparing two values. For MEC server side optimization, we first obtain server provisioning independently, and then devise a continuous relaxation and Lagrangian dual decomposition based iterative algorithm for joint radio resource and power allocation. Simulation results demonstrate that DDORV converges fast, can balance the cost-delay tradeoff flexibly, and can obtain more performance gains in cost reduction as compared with existing schemes. <s> BIB028 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> C. Edge nodes <s> The vehicular edge computing (VEC) system integrates the computing resources of vehicles, and provides computing services for other vehicles and pedestrians with task offloading. However, the vehicular task offloading environment is dynamic and uncertain, with fast varying network topologies, wireless channel states and computing workloads. These uncertainties bring extra challenges to task offloading. In this work, we consider the task offloading among vehicles, and propose a solution that enables vehicles to learn the offloading delay performance of their neighboring vehicles while offloading computation tasks. We design an adaptive learning-based task offloading (ALTO) algorithm based on the multi-armed bandit (MAB) theory, in order to minimize the average offloading delay. ALTO works in a distributed manner without requiring frequent state exchange, and is augmented with input-awareness and occurrence-awareness to adapt to the dynamic environment. The proposed algorithm is proved to have a sublinear learning regret. Extensive simulations are carried out under both synthetic scenario and realistic highway scenario, and results illustrate that the proposed algorithm achieves low delay performance, and decreases the average delay up to 30% compared with the existing upper confidence bound based learning algorithm. <s> BIB029 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> C. Edge nodes <s> Fog computing extends the facility of cloud computing from the center to edge networks. Although fog computing has the advantages of location awareness and low latency, the rising requirements of ubiquitous connectivity and ultra-low latency challenge real-time traffic management for smart cities. As an integration of fog computing and vehicular networks, vehicular fog computing (VFC) is promising to achieve real-time and location-aware network responses. Since the concept and use case of VFC are in the initial phase, this article first constructs a three-layer VFC model to enable distributed traffic management in order to minimize the response time of citywide events collected and reported by vehicles. Furthermore, the VFC-enabled offloading scheme is formulated as an optimization problem by leveraging moving and parked vehicles as fog nodes. A real-world taxi-trajectory-based performance analysis validates our model. Finally, some research challenges and open issues toward VFC-enabled traffic management are summarized and highlighted. <s> BIB030 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> C. Edge nodes <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> BIB031 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> C. Edge nodes <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> BIB032 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> C. Edge nodes <s> With the emerging vehicular applications, such as real-time situational awareness and cooperative lane change, there exist huge demands for sufficient computing resources at the edge to conduct time-critical and data-intensive tasks. This paper proposes Folo, a novel solution for latency and quality optimized task allocation in vehicular fog computing (VFC). Folo is designed to support the mobility of vehicles, including vehicles that generate tasks and the others that serve as fog nodes. Considering constraints on service latency, quality loss, and fog capacity, the process of task allocation across stationary and mobile fog nodes is formulated into a joint optimization problem. This task allocation in VFC is known as a nondeterministic polynomial-time hard problem. In this paper, we present the task allocation to fog nodes as a bi-objective minimization problem, where a tradeoff is maintained between the service latency and quality loss. Specifically, we propose an event-triggered dynamic task allocation framework using linear programming-based optimization and binary particle swarm optimization. To assess the effectiveness of Folo, we simulated the mobility of fog nodes at different times of a day based on real-world taxi traces and implemented two representative tasks, including video streaming and real-time object recognition. Simulation results show that the task allocation provided by Folo can be adjusted according to actual requirements of the service latency and quality, and achieves higher performance compared with naive and random fog node selection. To be more specific, Folo shortens the average service latency by up to 27% while reducing the quality loss by up to 56%. <s> BIB033 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> C. Edge nodes <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> BIB034 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> C. Edge nodes <s> Vehicular fog computing (VFC) is a promising approach to provide ultra-low-latency service to vehicles and end users by extending fog computing to the conventional vehicular networks. Parked vehicle assistance (PVA), as a critical technique in VFC, can be integrated with smart parking in order to exploit its full potentials. In this paper, we propose a smart VFC system, by combining both PVA and smart parking. A VFC-aware parking reservation auction is proposed to guide the on-the-move vehicles to the available parking places with less effort and meanwhile exploit the fog capability of parked vehicles to assist the delay-sensitive computing services by monetary rewards to compensate for their service cost. The proposed allocation rule maximizes the aggregate utility of the smart vehicles and the proposed payment rule guarantees incentive compatibility, individual rationality, and budget balance. We further provide an observation stage with dynamic offload pricing update to improve the offload efficiency and the profit of the fog system. The simulation results confirm the win–win performance enhancement to the fog node controller, the smart vehicles, and the parking places from the proposed design. <s> BIB035 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> C. Edge nodes <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> BIB036
The edge nodes are known for the nodes that promptly compute, store and transmit data located at distributed edge networks. In general, the vehicles, which are over utilised or faced a slow computation, offload (i.e., upload) their data to nearby edge nodes. On one hand, there is a case of contents like movies caching and delivery from the edge nodes instead of directly fetching from the centre cloud. Table V presents the types of edge nodes employed in the ComOf and CachDel for VEC. The edge nodes are commonly characterised into two types which are: 1) Stationary Edge Nodes: SENs are the computing nodes that co-located at a cellular BS BIB007 , RSUs BIB008 , wireless access router BIB009 or any other stationary infrastructure. SENs are often connected to an MEC server BIB001 , VEC server BIB025 , BIB010 , BIB026 , BIB011 , BIB012 , BIB027 , BIB013 , BIB028 , BIB014 , BIB002 , BIB015 , BIB016 , BIB029 • Edge cloud • Vehicles Vehicular Edge Computing Networks (VECN) BIB027 , Fog-enhanced Radio Access Networks (FeRANs) BIB002 low delay and overhead insufficient resources for high density areas BIB007 , BIB017 ,  , BIB030 , BIB031 • Vehicles, access [ BIB007 for managing data computation, storage and distribution in edge networks. Despite that, a pool of edge nodes can also share the computational resources and communication resources of a single MEC/VEC server BIB032 . This sharing may reduce the cost of VEC implementation rather than the former. Nevertheless, the comparison of latency performance between the two remains a question. In supporting cooperative and interoperability between fog servers, a localised coordinator server is introduced BIB003 .In addition to that, the SEN with the MEC server has higher computing platform, higher power consumption and more expensive rather than the Vehicular Edge Nodes (VENs). Since the network operator provides the SEN, the cost and revenue of computational resources become great attention in proposing the optimum offloading and catching mechanisms. 2) Vehicular Edge Nodes: VENs are smart vehicles equipped with communication modules and OBUs with com-puting and low storage capabilities as the concept of Vehicle as a Resource (VaaR) BIB033 . As shown in Table V , the types of VENs considered in the offloading and caching works are cars, UAVs BIB017 or buses BIB034 . The mobility of fog nodes explicitly expands new opportunities, such as on-demand computing where the moving vehicles may offer ubiquitously their available computational resources, particularly at the area without any SEN, like in rural areas. Leveraging Parked Vehicles (PV) BIB035 at the parking area, for examples in the airport or shopping mall, as a primary VEN for computing and caching is also a promising solution for VEC. Also, parked vehicles integrated with fog node controller as a data centre can expand the storage capacity for improving the performance of delivery services in vehicular networks BIB018 , BIB035 . However, the key limitation is that a cluster of parked cars cannot be fully used as supplemental resources when their power are turned off. Although VEN has a small storage capacity compared with • abstract and slice physical infrastructure resources into distinct virtual vehicular networks • administers the complex control and management functionalities BIB019 • facilitates flexible and dynamic network management by separating control plane and data plane • collects all the data plane information periodically • facilitates optimal task offloading BIB036 • splits and handles control plane and data plane. • wireless access interworking • abstracts and reallocates diverse radio spectrum resources to BSs BIB007 • provide control functions, including radio resource management, mobility management, communication management, traffic management • receives real-time vehicle information, such as speed, traffic density, channel state information (CSI) and queue state information (QSI) BIB020 • collects information from vehicles, RSUs, and BSs and servers within the fog cell (information-gathering module). • manages different wireless networks (wireless vehicles network manager module). • generates control directives and forwards them to all devices in the fog cell (forward and control instructions module) • manages the links status communications (management links status module ). BIB021 • stores contextual information of vehicles • computes the V2V path • controlls the switching for V2V offloading BIB004 • updates current storage requested, location and link • acquires and predicts system • executes algorithm and determine optimal decision state • sends flow control information and decision policies state to all edge nodes • manage resources centrally and dynamically • adjust transmit power of wireless router • execute spectrum slicing at edge nodes BIB003 • divide into several independent VMs • control and adjust size of a task assigned to each VM and also its processing rate BIB036 • task balancing for computation and storage between MEC servers BIB022 • provides network virtualisation mapping based on function-group of vehicles (i.e. multimedia entertainment, mobile business, and locationbased services) BIB020 • creates and configures different VMs according to quantity of data offloaded SEN, the vehicles are likely to offer a cheaper computational cost. Another significant issue is that not all VENs including the PV are keen to offer their computational resources to other vehicles. To solve this, a good reputation VEN is necessary to receive a token or reward based on the acquired utility. Another important aspect, the term of cloudlet has been used to represent the coverage and connection of VENs and SENs, such as a bus-based cloudlet BIB005 , BIB006 , a vehiclebased cloudlet BIB023 , roadside cloudlet  , cloudlet layer BIB030 . Similar to the cloud, a cloudlet constitutes a group of servers that are nearby to the requesting user resulting in low computation power and short communication latency  . If edge nodes encounter insufficient computational resources, a central cloud computing platform BIB024 , BIB017 is the final alternative for computation and storage that causes high computational cost, high communication overhead and a long delay. Next, we discuss the communication technologies used between edge nodes and requested vehicles.