Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> Communication protocols <s> Cloud-based vehicular networks are a promising paradigm to improve vehicular services through distributing computation tasks between remote clouds and local vehicular terminals. To further reduce the latency and the transmission cost of the computation off-loading, we propose a cloud-based mobileedge computing (MEC) off-loading framework in vehicular networks. In this framework, we study the effectiveness of the computation transfer strategies with vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication modes. Considering the time consumption of the computation task execution and the mobility of the vehicles, we present an efficient predictive combination-mode relegation scheme, where the tasks are adaptively off-loaded to the MEC servers through direct uploading or predictive relay transmissions. Illustrative results indicate that our proposed scheme greatly reduces the cost of computation and improves task transmission efficiency. <s> BIB001 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> Communication protocols <s> Heterogeneous vehicular networks (HetVNETs), which applies the heterogenous access technologies (e.g., cellular and WiFi) complementarily to provide seamless and ubiquitous connections to vehicles, have emerged as a promising and more practical paradigm to enable vehicular service applications on the road. However, with different access technologies presenting different costs in terms of download latency and bandwidth cost, how to optimize the connection along the vehicle's trip towards the lowest cost represents fundamental challenges. This paper investigates the issue by proposing an optimal access control scheme for vehicles in HetVNETs. In specific, with different access links, we first model the cost of each vehicle to download its content by jointly considering the conventional vehicle to vehicle (V2V) communication and the available access links. A coalition formation game is then introduced to formulate the cooperation among vehicles based on different interests (content cached in vehicles) and requests (content needs to download). After forming the coalition, vehicles in the same coalition can download their requested content by selecting the optimal access link to achieve the minimum cost. Simulation results demonstrate that the proposed game approach can lead to the optimal strategy for vehicles and reduce the cost. <s> BIB002 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> Communication protocols <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> Communication protocols <s> Distributed reputation systems can be used to foster cooperation between nodes in decentralized and self-managed systems due to the nonexistence of a central entity. In this paper, a Robust and Distributed Reputation System for Delay-Tolerant Networks (REPSYS) is proposed. REPSYS is robust because despite taking into account first- and second-hand information, it is resilient against false accusations and praise, and distributed, as the decision to interact with another node depends entirely on each node. Simulation results show that the system is capable, while evaluating each node's participation in the network, to detect on the fly nodes that do not accept messages from other nodes and that disseminate false information even while colluding with others, and while evaluating how honest is each node in the reputation system, to classify correctly nodes in most cases. <s> BIB004 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> Communication protocols <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> Communication protocols <s> Recently, parked vehicles have been shown to be useful to deliver content in vehicular ad hoc networks, where the parked vehicles can form social communities to share and exchange content with other moving vehicles and road side units (RSUs). However, as it takes resource such as bandwidth and power for parked vehicles and RSUs to deliver content, the incentive scheme with the optimal pricing strategy needs to be studied. Furthermore, because multiple places including RSUs and parked vehicles can deliver content to moving vehicles, the optimal algorithm to determine where to obtain the requested content should also be discussed. Therefore, in this paper, we first propose a framework of content delivery with parked vehicles, where moving vehicles can obtain content from both the RSU and parked vehicles according to the competition and cooperation among them. Then, based on a Stackelberg game, we develop a pricing model where each of the three players, including moving vehicles, RSU, and parked vehicles, can obtain their maximum utilities. Next, a gradient based iteration algorithm is presented to obtain the Stackelberg equilibrium. Finally, the simulation results prove that the proposal can outperform other conventional methods and that each player in the game can obtain its optimal strategy during the content delivery. <s> BIB006 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> Communication protocols <s> Mobile-edge computation offloading (MECO) off-loads intensive mobile computation to clouds located at the edges of cellular networks. Thereby, MECO is envisioned as a promising technique for prolonging the battery lives and enhancing the computation capacities of mobiles. In this paper, we study resource allocation for a multiuser MECO system based on time-division multiple access (TDMA) and orthogonal frequency-division multiple access (OFDMA). First, for the TDMA MECO system with infinite or finite cloud computation capacity, the optimal resource allocation is formulated as a convex optimization problem for minimizing the weighted sum mobile energy consumption under the constraint on computation latency. The optimal policy is proved to have a threshold-based structure with respect to a derived offloading priority function , which yields priorities for users according to their channel gains and local computing energy consumption. As a result, users with priorities above and below a given threshold perform complete and minimum offloading, respectively. Moreover, for the cloud with finite capacity, a sub-optimal resource-allocation algorithm is proposed to reduce the computation complexity for computing the threshold. Next, we consider the OFDMA MECO system, for which the optimal resource allocation is formulated as a mixed-integer problem. To solve this challenging problem and characterize its policy structure, a low-complexity sub-optimal algorithm is proposed by transforming the OFDMA problem to its TDMA counterpart. The corresponding resource allocation is derived by defining an average offloading priority function and shown to have close-to-optimal performance in simulation. <s> BIB007 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> Communication protocols <s> With the emergence of in-vehicle applications, providing the required computational capabilities is becoming a crucial problem. This paper proposes a framework named autonomous vehicular edge (AVE) for edge computing on the road, with the aim of increasing the computational capabilities of vehicles in a decentralized manner. By managing the idle computational resources on vehicles and using them efficiently, the proposed AVE framework can provide computation services in dynamic vehicular environments without requiring particular infrastructures to be deployed. Specifically, this paper introduces a workflow to support the autonomous organization of vehicular edges. Efficient job caching is proposed to better schedule jobs based on the information collected on neighboring vehicles, including GPS information. A scheduling algorithm based on ant colony optimization is designed to solve this job assignment problem. Extensive simulations are conducted, and the simulation results demonstrate the superiority of this approach over competing schemes in typical urban and highway scenarios. <s> BIB008 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> Communication protocols <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> BIB009 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> Communication protocols <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> BIB010 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> Communication protocols <s> Recent advances in networking, caching and computing have significant impacts on the developments of vehicular networks. Nevertheless, these important enabling technologies have traditionally been studied separately in the existing works on vehicular networks. In this paper, we propose an integrated framework that can enable dynamic orchestration of networking, caching and computing resources to improve the performance of next generation vehicular networks. We formulate the resource allocation strategy in this framework as a joint optimization problem. The complexity of the system is very high when we jointly consider these three technologies. Therefore, we propose a novel deep reinforcement learning approach in this paper. Simulation results are presented to show the effectiveness of the proposed scheme. <s> BIB011 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> Communication protocols <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> BIB012 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> Communication protocols <s> The emergence of computation intensive on-vehicle applications poses a significant challenge to provide the required computation capacity and maintain high performance. Vehicular Edge Computing (VEC) is a new computing paradigm with a high potential to improve vehicular services by offloading computation-intensive tasks to the VEC servers. Nevertheless, as the computation resource of each VEC server is limited, offloading may not be efficient if all vehicles select the same VEC server to offload their tasks. To address this problem, in this paper, we propose offloading with resource allocation. We incorporate the communication and computation to derive the task processing delay. We formulate the problem as a system utility maximization problem, and then develop a low-complexity algorithm to jointly optimize offloading decision and resource allocation. Numerical results demonstrate the superior performance of our Joint Optimization of Selection and Computation (JOSC) algorithm compared to state of the art solutions. <s> BIB013 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> Communication protocols <s> Emerging vehicular applications, such as real-time situational awareness and cooperative lane change, demand for sufficient computing resources at the edge to conduct time-critical and data-intensive tasks. This paper proposes Fog Following Me (Folo), a novel solution for latency and quality balanced task allocation in vehicular fog computing. Folo is designed to support the mobility of vehicles, including ones generating tasks and the others serving as fog nodes. We formulate the process of task allocation across stationary and mobile fog nodes into a joint optimization problem, with constraints on service latency, quality loss, and fog capacity. As it is a NP-hard problem, we linearize it and solve it using Mixed Integer Linear Programming. To evaluate the effectiveness of Folo, we simulate the mobility of fog nodes at different times of day based on real-world taxi traces, and implement two representative tasks, including video streaming and real-time object recognition. Compared with naive and random fog node selection, the latency and quality balanced task allocation provided by Folo achieves higher performance. More specifically, Folo shortens the average service latency by up to 41\% while reducing the quality loss by up to 60\%. <s> BIB014 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> Communication protocols <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> BIB015 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> Communication protocols <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> BIB016 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> Communication protocols <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> BIB017 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> Communication protocols <s> Mobile edge computing (MEC) has emerged as a promising paradigm to realize user requirements with low-latency applications. The deep integration of multi-access technologies and MEC can significantly enhance the access capacity between heterogeneous devices and MEC platforms. However, the traditional MEC network architecture cannot be directly applied to the Internet of Vehicles (IoV) due to high speed mobility and inherent characteristics. Furthermore, given a large number of resource-rich vehicles on the road, it is a new opportunity to execute task offloading and data processing onto smart vehicles. To facilitate good merging of the MEC technology in IoV, this article first introduces a vehicular edge multi-access network that treats vehicles as edge computation resources to construct the cooperative and distributed computing architecture. For immersive applications, co-located vehicles have the inherent properties of collecting considerable identical and similar computation tasks. We propose a collaborative task offloading and output transmission mechanism to guarantee low latency as well as the application- level performance. Finally, we take 3D reconstruction as an exemplary scenario to provide insights on the design of the network framework. Numerical results demonstrate that the proposed scheme is able to reduce the perception reaction time while ensuring the application-level driving experiences. <s> BIB018 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> Communication protocols <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> BIB019 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> Communication protocols <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> BIB020 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> Communication protocols <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> BIB021 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> Communication protocols <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> BIB022 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> Communication protocols <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> BIB023 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> Communication protocols <s> In vehicular networks, Mobile Edge Computing (MEC) is applied to meet the offloading demand from vehicles. However, the mobility of vehicles may increase the offloading delay and even reduce the success rate of offloading, because vehicles may access another road side unit (RSU) before finishing offloading. Therefore, an offloading algorithm with low time complexity is required to make the offloading decision quickly. In this paper, we put forward an efficient offloading algorithm based on Support Vector Machine (SVMO) to satisfy the fast offloading demand in vehicular networks. The algorithm can segment a huge task into several sub-tasks through a weight allocation method according to available resources of MEC servers. Then each sub-task is decided whether it should be offloaded or executed locally based on SVMs. As the vehicle moves through several MEC servers, sub-tasks are allocated to them by order if they are offloaded. Each server ensures the sub-task can be processed and returned in time. Our proposed algorithm generate training data through Decision Tree. The simulation results show that the SVMO algorithm has a high decision accuracy, converges faster than other algorithms and has a small response time. <s> BIB024 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> Communication protocols <s> In this paper, an energy-efficient vehicular edge computing (VEC) framework is proposed for in-vehicle user equipments (UEs) with limited battery capacity. Firstly, the energy consumption minimization problem is formulated as a joint workload offloading and power control problem, with the explicit consideration of energy consumption and delay models. Queuing theory is applied to derive the stochastic traffic models at UEs and VEC nodes. Then, the original NP-hard problem is transformed to a convex global consensus problem, which can be decomposed into several parallel subproblems and solved subsequently. Next, an alternating direction method of multipliers (ADMM)-based energy-efficient resource allocation algorithm is developed, whose outer loop representing iterations of nonlinear fractional programming, while inner loop representing iterations of primal and dual variable updates. Finally, the relationships between energy consumption and key parameters such as workload offloading portion and transmission power are validated through numerical results. <s> BIB025 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> Communication protocols <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> BIB026 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> Communication protocols <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> BIB027 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> Communication protocols <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> BIB028 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> Communication protocols <s> This paper shows the viability of Solar-powered Road Side Units (SRSU), consisting of small cell base stations and Mobile Edge Computing (MEC) servers, and powered solely by solar panels with battery, to provide connected vehicles with a low- latency, easy-to-deploy and energy-efficient communication and edge computing infrastructure. However, SRSU may entail a high risk of power deficiency, leading to severe Quality of Service (QoS) loss due to spatial and temporal fluctuation of solar power generation. Meanwhile, the data traffic demand also varies with space and time. The mismatch between solar power generation and SRSU power consumption makes optimal use of solar power challenging. In this paper, we model the above problem with three sub-problems, the SRSU power consumption minimization problem, the temporal energy balancing problem and spatial energy balancing problem. Three algorithms are proposed to solve the above sub-problems, and they together provide a complete joint battery charging and user association control algorithm to minimize the QoS loss under delay constraint of the computing tasks. Results with a simulated urban environment using actual solar irradiance and vehicular traffic data demonstrates that the proposed solution reduces the QoS loss significantly compared to greedy approaches. <s> BIB029 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> Communication protocols <s> This paper studies the joint communication, caching and computing design problem for achieving the operational excellence and the cost efficiency of the vehicular networks. Moreover, the resource allocation policy is designed by considering the vehicle's mobility and the hard service deadline constraint. These critical challenges have often been either neglected or addressed inadequately in the existing work on the vehicular networks because of their high complexity. We develop a deep reinforcement learning with the multi-timescale framework to tackle these grand challenges in this paper. Furthermore, we propose the mobility-aware reward estimation for the large timescale model to mitigate the complexity due to the large action space. Numerical results are presented to illustrate the theoretical findings developed in the paper and to quantify the performance gains attained. <s> BIB030 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> Communication protocols <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> BIB031 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> Communication protocols <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> BIB032 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> Communication protocols <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> BIB033 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> Communication protocols <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> BIB034 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> Communication protocols <s> As autonomous and connected vehicles are becoming a reality, mobile-edge computing (MEC) off-loading provides a promising paradigm to trade off between the long latency of clouding computing and the high cost of upgrading the on-board computers of vehicles. However, due to the randomness of task arrivals, vehicles always have a tendency to choose MEC server for offloading in a selfish way, which is not satisfactory for the social good of the whole system and even results in a failure possibility of some tasks due to the overflow of MEC servers. This paper elaborates the modeling of task arrival process and the influence of various offloading modes on computation cost. Interestingly, by formulating task arrivals as a compound process of vehicle arrivals and task generations, we found that the task arrival model for MEC servers does not belong to the standard Poisson distribution, which contradicts the popular assumption in most existing studies. Considering the load distribution and the prediction of cost, we propose a load-aware MEC offloading method, in which each vehicle makes MEC server selection based on the predicted cost with the updated knowledge on load distribution of MEC servers. Analysis and simulation show that the proposed scheme can achieve up to 65% reduction of total cost with almost 100% task success ratio. <s> BIB035 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> Communication protocols <s> The emergence of computation intensive and delay sensitive on-vehicle applications makes it quite a challenge for vehicles to be able to provide the required level of computation capacity, and thus the performance. Vehicular edge computing (VEC) is a new computing paradigm with a great potential to enhance vehicular performance by offloading applications from the resource-constrained vehicles to lightweight and ubiquitous VEC servers. Nevertheless, offloading schemes, where all vehicles offload their tasks to the same VEC server, can limit the performance gain due to overload. To address this problem, in this paper, we propose integrating load balancing with offloading, and study resource allocation for a multiuser multiserver VEC system. First, we formulate the joint load balancing and offloading problem as a mixed integer nonlinear programming problem to maximize system utility. Particularly, we take IEEE 802.11p protocol into consideration for modeling the system utility. Then, we decouple the problem as two subproblems and develop a low-complexity algorithm to jointly make VEC server selection, and optimize offloading ratio and computation resource. Numerical results illustrate that the proposed algorithm exhibits fast convergence and demonstrates the superior performance of our joint optimal VEC server selection and offloading algorithm compared to the benchmark solutions. <s> BIB036 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> Communication protocols <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> BIB037 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> Communication protocols <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> BIB038 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> Communication protocols <s> The heterogeneous vehicular networks (HetVNETs), which apply the heterogeneous access technologies ( e.g. , cellular networks and WiFi) complementarily to provide seamless and ubiquitous connections to vehicles, have emerged as a promising and practical paradigm to enable vehicular service applications on the road. However, with different costs in terms of latency time and price, how to optimize the connection along the vehicle’s trip toward the lowest cost represents fundamental challenges. This paper investigates the issue by proposing an optimal access control scheme for vehicles in HetVNETs. In specific, with different access networks, we first model the cost of each vehicle to download the requested content by jointly considering the vehicle’s requirements of the requested content and the features of the available access networks, including conventional vehicle to vehicle communication and the heterogeneous access technologies. A coalition formation game is then introduced to formulate the cooperation among vehicles based on their different interests (contents cached in vehicles) and requests (contents to be downloaded). After forming the coalitions, vehicles in the same coalition can download their requested contents cooperatively by selecting the optimal access network to achieve the minimum costs. The simulation results demonstrate that the proposed game approach can lead to the optimal strategy for the vehicle. <s> BIB039 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> Communication protocols <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> BIB040 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> Communication protocols <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> BIB041 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> Communication protocols <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> BIB042 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> Communication protocols <s> The emergence of computation-intensive vehicle applications poses a significant challenge to the limited computation capacity of on-board equipments. Mobile edge computing has been recognized as a ... <s> BIB043 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> Communication protocols <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> BIB044 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> Communication protocols <s> The growth of smart vehicles and computation-intensive applications poses new challenges in providing reliable and efficient vehicular services. Offloading such applications from vehicles to mobile edge cloud servers has been considered as a remedy, although resource limitations and coverage constraints of the cloud service may still result in unsatisfactory performance. Recent studies have shown that exploiting the unused resources of nearby vehicles for application execution can augment the computational capabilities of application owners while alleviating heavy on-board workloads. However, encouraging vehicles to share resources or execute applications for others remains a sensitive issue due to user selfishness. To address this issue, we establish a novel computation offloading marketplace in vehicular networks where a Vickrey–Clarke–Groves based reverse auction mechanism utilizing integer linear programming (ILP) problem is formulated while satisfying the desirable economical properties of truthfulness and individual rationality. As ILP has high computation complexity which brings difficulties in implementation under larger and fast changing network topologies, we further develop an efficient unilateral-matching-based mechanism, which offers satisfactory suboptimal solutions with polynomial computational complexity, truthfulness and individual rationality properties as well as matching stability. Simulation results show that, as compared with baseline methods, the proposed unilateral-matching-based mechanism can greatly improve the system efficiency of vehicular networks in all traffic scenarios. <s> BIB045 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> Communication protocols <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> BIB046 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> Communication protocols <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> BIB047
Related Works DSRC BIB012 , BIB034 , BIB001 , BIB035 , BIB036 , BIB013 , BIB037 , BIB014 , BIB038 , BIB002 , BIB039 , [ BIB015 ,  , BIB016 OFDMA BIB040 , BIB041 LTE/4G BIB017 , BIB037 , BIB014 , BIB003 , BIB042 , BIB043 Wireless LAN/WIFI/IEEE 802.11p BIB017 , BIB038 , BIB003 , BIB042 , BIB018 , BIB044 WiMAX  BIB019 , BIB004 , BIB036 , BIB045 , BIB020 , BIB005 , BIB006 , BIB002 , [ BIB007 , BIB021 , BIB022 , BIB008 , BIB009 , BIB023 , BIB003 , BIB046 , BIB024 , BIB025 , BIB035 Real content BIB026 Real-time object recognition, Face recognition BIB037 , BIB047 & [64] Safety-sensitive packets or messages BIB038 , BIB042 , BIB010 , BIB044 Socially-aware applications BIB027 , [65] Video BIB015 , BIB018 , BIB028 , BIB011 , BIB029 a G/M/c model because the vehicular tasks follow a more general process rather than a Poisson distribution, which is evident in the results BIB035 . The works in BIB030 , BIB031 , BIB026 , BIB032 , BIB027 differ from others in terms of how they represent the vehicle applications. The authors in BIB030 assumed coded packets to be cached at vehicles for the resource allocation optimisation problem. The researchers in BIB031 dealt with a scheduling of electric vehicle energy demands via user datagram protocol-based calendars for both charging and discharging requests whereas BIB026 used time series analysis to model the content demands patterns subject to actual content request logs, which the model is called seasonal auto-regressive integrated moving average. On the other hand, BIB027 , BIB032 explored the fog computing under the assumption of social-based vehicular services such as Waze, Mooveit, SocialDrive, CaravanTrack, and GeoVanet, in Social Internet of vehicles (SOIV)  . Thus far, only little efforts BIB042 , BIB038 , BIB010 , BIB033 , BIB047 investigated various traffic in the offloading and catching works. While BIB042 , BIB038 considered delay-sensitive (i.e., safetyrelated packets) and delay-tolerant traffic (i.e. downloading HD map), BIB010 focused on HPAs (i.e.safety-related vehicular services) and LPAs. Besides that, BIB033 examined the elastic and inelastic groups of services. The elastic group is typically tolerant of latency and bandwidth that explicitly divides into two: traditional elastic services (e.g., data transmission) and interactive elastic services (e.g., online chatting). In contrast, the inelastic group that is defined for delay-sensitive services can also be divided into two: hard real-time services (e.g., Voice-over-Internet Protocol) and soft real-time services (e.g., Video over Demand or living streaming). All these four kinds of services are considerably explored by BIB033 . On the contrary, BIB047 explored a hundred of vehicles concurrently run three types of applications, namely audio transcoder, face recognition and parking lot detection with the same deadline of 10-time slots, but different processing density level (i.e., 400, 2500 and 100000). Answering what edge nodes appropriately served the vehicle applications, the vehicle must initially prioritise its applications, and itself serves the CPAs so that the latency is guaranteed unless insufficient computation arises. Meanwhile, HPAs may be offloaded to nearby VEN and SEN. The low-priority applications are likely to be computed by all edge nodes and even the core cloud platform. Note that offloading HPAs and LPAs to other edge nodes may only take place when the vehicle undergoes insufficient computational resources. Next, the mobility models considered in the VEC offloading and caching works are discussed.