Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> A. Single Optimisation Approaches <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> BIB001 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> A. Single Optimisation Approaches <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> BIB002 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> A. Single Optimisation Approaches <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> BIB003 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> A. Single Optimisation Approaches <s> With the rapid advances in vehicular technologies and the ever-increasing demands on mobile multimedia services, vehicular networks play a crucial role in intelligent transport systems by providing resilient connection among vehicles and users. Meanwhile, a huge number of parked vehicles may have abundant and underutilized resources in forms of computation, communication and storage. In this paper, we propose a vehicular edge computing (VEC) caching scheme in which content providers (CPs) collaboratively cache popular contents in the storage of parked vehicles located in multiple parking lots. The proposed VEC caching scheme extends the data center capability from the core to the edge of the networks. As a result, the duplicate transmissions from remote servers can be removed and the total transmission latency can be significantly reduced. In order to minimize the average latency to mobile users, we present a content placement algorithm based on an iterative ascending price auction. Numerical results show that the proposed caching scheme achieves a performance gain up to 24% in terms of average latency, compared to the widely-used scheme with most popularity caching. <s> BIB004 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> A. Single Optimisation Approaches <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> BIB005 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> A. Single Optimisation Approaches <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> BIB006 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> A. Single Optimisation Approaches <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> BIB007 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> A. Single Optimisation Approaches <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> BIB008 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> A. Single Optimisation Approaches <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> BIB009 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> A. Single Optimisation Approaches <s> Mobile Edge Computing (MEC) offers a new paradigm to improve vehicular services and augment the capabilities of vehicles. In this paper, to reduce the latency of the computation offloading of vehicles, we study multiple vehicles computation offloading problem in vehicular edge networks. We formulate the problem as a multi-user computation offloading game problem, prove the existence of Nash equilibrium (NE) of the game and propose a distributed computation offloading algorithm to compute the equilibrium. We analyze the price of anarchy of the game algorithm and evaluate the performance of the game algorithm using extensive simulations. Numerical results show that the proposed algorithm can greatly reduce the computation overhead of vehicles. <s> BIB010 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> A. Single Optimisation Approaches <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> BIB011 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> A. Single Optimisation Approaches <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> BIB012 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> A. Single Optimisation Approaches <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> BIB013 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> A. Single Optimisation Approaches <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> BIB014 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> A. Single Optimisation Approaches <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> BIB015 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> A. Single Optimisation Approaches <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> BIB016 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> A. Single Optimisation Approaches <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> BIB017 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> A. Single Optimisation Approaches <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> BIB018 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> A. Single Optimisation Approaches <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> BIB019 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> A. Single Optimisation Approaches <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> BIB020 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> A. Single Optimisation Approaches <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> BIB021 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> A. Single Optimisation Approaches <s> Vehicular edge computing has emerged as a promising technology to accommodate the tremendous demand for data storage and computational resources in vehicular networks. By processing the massive workload tasks in the proximity of vehicles, the quality of service can be guaranteed. However, how to determine the task offloading strategy under various constraints of resource and delay is still an open issue. In this paper, we study the task offloading problem from a matching perspective and aim to optimize the total network delay. The task offloading delay model is derived based on three different velocity models, i.e., a constant velocity model, vehicle-following model, and traveling-time statistical model. Next, we propose a pricing-based one-to-one matching algorithm and pricing-based one-to-many matching algorithms for the task offloading. The proposed algorithm is validated based on three different simulation scenarios, i.e., straight road, the urban road with the traffic light, and crooked road, which are extracted from the realistic road topologies in Beijing and Guangdong, China. The simulation results confirm that significant delay decreasing can be achieved by the proposed algorithm. <s> BIB022 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> A. Single Optimisation Approaches <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> BIB023 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> A. Single Optimisation Approaches <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> BIB024 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> A. Single Optimisation Approaches <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> BIB025 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> A. Single Optimisation Approaches <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> A. Single Optimisation Approaches <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> BIB027 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> A. Single Optimisation Approaches <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> BIB028
A single optimisation (SO) is defined for the computation offloading technique that maximises or minimises a single 1) QoS Improvement: Significant works formulated the optimisation problem for task offloading with regards to the minimisation of latency BIB006 , BIB007 , BIB021 , BIB022 , BIB008 , followed by message response time BIB009 , BIB023 , message overhead BIB010 , and the maximisation of routing and path lifetime BIB011 . A two-stage of collaborative task offloading that comprises the vehicular function partition and task assignment stages is proposed BIB006 . Based on task similarity and task computing, the former classified the vehicles into two kinds of subcloudlets respectively, which were task offloading subcloudlet and task computing subcloudlet. Then, the later stage used the graph theory and maximum weight independent set for a two-sided matching process between the requesting vehicles and edge nodes by minimising the service latency and balancing the load of heterogeneous edge nodes. However, the results demonstrated high latency when there is a small number of resource-rich vehicles in the networks. Meanwhile, BIB007 suggested an adaptive volatile upper confidence bound algorithm with load-awareness and occurrence-awareness where the utility function with regards to the classic multi-armed bandit is designed for V2V. The work is then extended by presenting an adaptive learning-based task offloading algorithm in minimising the average offloading delay BIB021 . The algorithm improved the average delay between 10% and 13% compared with the former algorithm, but the scenario is only a single requesting vehicle. Another delay optimisation problem presented a pricing-based one-to-one matching and one-tomany matching algorithms for task offloading primarily at an RSU subject to minimise the total network delay BIB022 . The advantage is that the algorithm is validated based on three different road conditions, which are straight road, urban road with a traffic light, and winding road extracted from the realistic road topologies of Beijing and Guangdong, China. Interestingly, the lowest average delay is achieved when the offloading presents on the straight road. On the other hand, BIB023 minimised the response time for each time slot using some steps based on brand-and-bound and Edmonds-Karp algorithms. The message response time is the summation of the response time of cloudlet, the response time of parked vehicles and response time of moving vehicles as well as the delay caused by incoming messages BIB023 . The key advantage of the work is that the offloading is performed at both moving and parked vehicles. The findings demonstrated that the average response time achieved less than 1s with the increase of message arrival rates in Shanghai and considerably dropped when the total number of parked vehiclebased edge nodes raises. Similarly, BIB009 devised the task scheduling optimisation problem by minimising the average response time of computation based on a modified genetic algorithm where integer coding was used. One major drawback of both works BIB023 , BIB009 is that the mobility analysis is not conducted. Whereas, BIB010 devised the computation offloading problem subject to minimise the computation overhead based on game theory, i.e., a strategic game, and was able to achieve Nash Equilibrium. The offloading problem also can be solved using total utility jobs completed at instantaneous time, and an Autonomous Vehicular Edge framework BIB001 is proposed that comprises of four main phases, namely, job caching, discovery, ant-colony optimisation-based scheduling and data transmission. The limitation of the work is that a high computation required. Comparing with others, the computation offloading problem can be solved also based on the longest lifetime of V2V routing path and a recovery of a broken V2V path using LifeTime-based Network State Routing and LifeTimebased Path Recovery algorithms, respectively BIB011 . The results demonstrated that the average lifetime of V2V paths increased with low mobility vehicles due to stable connections. The works in BIB010 , BIB011 might be more persuasive if the authors considered the QoS and mobility. 2) Energy Efficiency: BIB012 , BIB002 , BIB003 , BIB013 explored the offloading optimisation problem from the same ground to minimise the sum of energy consumption for local task execution and offloading task from the mobile device (MD). Despite that, the works are distinctive in terms of the type of edge nodes used, which are a bus-based cloudlet BIB003 , BIB002 and a vehicle-based cloudlet BIB012 . A vehicle-based cloudlet relaying scheme BIB012 allows the inter-cloudlet offloading or back to the MD if there is no cloudlet available. Due to the offloading rate performance increases with the number of buses in the cloudlet BIB002 , BIB003 as well as with the number of cloudlets BIB012 , the completion time is found improved for both cases. The key advantage of BIB012 , BIB002 , BIB003 is that multi-cloudlets and high mobility are considered and yet the computation of application completion time is not properly discussed. Another technique called Energy Consumption Minimisation (ECM) and Resource Reservation (RR) assignment for UE is proposed based on dynamic programming BIB013 . Simulation results demonstrated that the RR assignment could achieve a near-optimal performance with proper parameter settings, and the tasks were offloaded to multiple serving vehicles via multiradio access that beneficial in energy saving. 3) Prices Optimisation: The monetary involved with the price of a unit size of computing resource BIB024 , BIB004 , BIB014 , the price of VEC offloading time BIB024 , the price of energy BIB015 , BIB002 , the price of transmission bandwidth or link BIB014 , the incentive or reward paid to VEN BIB016 , BIB025 , BIB026 and the revenue generated by service provider BIB024 , BIB015 , BIB014 . In general, monetary-based optimisation problems significantly adopted game theory, such as Stackelberg game BIB017 , BIB015 , a contract theoretic BIB024 , BIB018 , BIB026 , and auction BIB019 . Only few works presented the offloading optimisation problems based on machine learning, i.e., deep reinforcement learning BIB020 , BIB016 . In an optimal Predictive Combination-mode Offloading mechanism BIB005 , the vehicle offloaded the task file to the ahead MEC server via number of vehicle hops as illustrated in Figure  5 . The strength of the proposed scheme is that the transmission mode is adaptive with vehicle speeds; however, the vehicle hops may cause some delay. The results demonstrated that the mobile-edge computing servers reduced the cost of computational resources and transmission besides fast response time compared with direct vehicle-to-infrastructure (V2I), i.e., RSU with multihops backhaul RSU relay. Considering the minimisation of the predicted cost of the load and also the load distribution among MEC servers, a loadaware MEC offloading method called as Load-Aware Mode is proposed BIB027 . It is seen that the proposed scheme can reduce up to 65% of total cost and achieve approximately 100% of the task success ratio. A mobility-aware and a locationbased task offloading are introduced in BIB028 based on Newton-Raphson methods by minimising the completed system cost includes the communication and computation costs of the required vehicle while satisfying the task latency. Numerical results showed that the proposed schemes can reduce the system costs approximately 50% from other techniques, and the latency constraint was satisfied. However, both works BIB027 , BIB028 overlook the types of vehicle applications that can be investigated. Exploring a machine learning technique, specifically deep reinforcement learning BIB020 can solve the offloading problem by minimising the expected cumulative discounted reward (i.e., task execution delay) for multi-task services. The advantage of the work is that dynamic VEC environment is studied, but the results are not explicitly discussed.