Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> B. Hybrid Optimisation Approaches <s> Vehicular edge computing (VEC) is introduced to extend computing capacity to vehicular network edge recently. With the advent of VEC, service providers directly host services in close proximity of mobile vehicles for great improvements. As a result, a new networking paradigm, vehicular edge networks is emerged along with the development of VEC. However, it is necessary to address security issues for facilitating VEC well. In this paper, we focus on reputation management to ensure security protection and improve network efficiency in the implementation of VEC. A distributed reputation management system ( DREAMS ) is proposed, wherein VEC servers are adopted to execute local reputation management tasks for vehicles. This system has remarkable features for improving overall performance: 1) distributed reputation maintenance; 2) trusted reputation manifestation; 3) accurate reputation update; and 4) available reputation usage. In particular, we utilize multi-weighted subjective logic for accurate reputation update in DREAMS. To enrich reputation usage in DREAMS, service providers optimize resource allocation in computation offloading by considering reputation of vehicles. Numerical results indicate that DREAMS has great advantages in optimizing misbehavior detection and improving the recognition rate of misbehaving vehicles. Meanwhile, we demonstrate the effectiveness of our reputation-based resource allocation algorithm. <s> BIB001 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> B. Hybrid Optimisation Approaches <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> BIB002 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> B. Hybrid Optimisation Approaches <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> BIB003 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> B. Hybrid Optimisation Approaches <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> BIB004 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> B. Hybrid Optimisation Approaches <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> BIB005 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> B. Hybrid Optimisation Approaches <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> BIB006 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> B. Hybrid Optimisation Approaches <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> BIB007 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> B. Hybrid 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> BIB008 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> B. Hybrid Optimisation Approaches <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> BIB009 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> B. Hybrid Optimisation Approaches <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> BIB010 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> B. Hybrid Optimisation Approaches <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> BIB011 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> B. Hybrid Optimisation Approaches <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> BIB012 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> B. Hybrid Optimisation Approaches <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> BIB013 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> B. Hybrid Optimisation Approaches <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> BIB014 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> B. Hybrid 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> BIB015 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> B. Hybrid Optimisation Approaches <s> The proliferation of smart vehicular terminals (VTs) and their resource hungry applications impose serious challenges to the processing capabilities of VTs and the delivery of vehicular services. Mobile Edge Computing (MEC) offers a promising paradigm to solve this problem by offloading VT applications to proximal MEC servers, while TV white space (TVWS) bands can be used to supplement the bandwidth for computation offloading. In this paper, we consider a cognitive vehicular network that uses the TVWS band, and formulate a dual-side optimization problem, to minimize the cost of VTs and that of the MEC server at the same time. Specifically, the dual-side cost minimization is achieved by jointly optimizing the offloading decision and local CPU frequency on the VT side, and the radio resource allocation and server provisioning on the server side, while guaranteeing network stability. Based on Lyapunov optimization, we design an algorithm called DDORV to tackle the joint optimization problem, where only current system states, such as channel states and traffic arrivals, are needed. The closed-form solution to the VT-side problem is obtained easily by derivation and comparing two values. For MEC server side optimization, we first obtain server provisioning independently, and then devise a continuous relaxation and Lagrangian dual decomposition based iterative algorithm for joint radio resource and power allocation. Simulation results demonstrate that DDORV converges fast, can balance the cost-delay tradeoff flexibly, and can obtain more performance gains in cost reduction as compared with existing schemes. <s> BIB016 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> B. Hybrid Optimisation Approaches <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> BIB017
Hybrid optimisation schemes are classified for the optimisation techniques proposing a combination of several mechanisms or parameters, such as joint offloading ratio and resource allocation, joint QoS and energy efficiency, and joint security with QoS. In contrast to the single optimisation, joint optimisation is often designed based on the summation of number of paramaters. 1) Hybrid QoS: The authors in BIB011 , BIB002 proposed a joint optimisation for selection, computation and offloading algorithm (JSCO) to maximise QoS-based system utility. The system model consists of multiple VEC servers and multiple vehicles as illustrated in Figure 6 . The problem is solved using relaxation and rounding method and Lagrange multipliers. The proposed JSCO demonstrated the fair allocation of VEC servers to the requesting vehicles besides optimum computation resource and offloading ratio. Similarly, BIB003 presented joint offloading proportion and resource allocation optimisation (JOPRAO) for all vehicles to minimise the task completion time. The key difference of both works is the offloading ratio whereby BIB011 introduced the amount of data offloads to the VEC server and computes the remaining locally, while BIB003 offloaded the sub-task between a vehicle edge node and MEC server. The offloading proportion for vehicle increases with the computation capability of the vehicle (i.e., MHz) and is enabled to achieve lower delay BIB003 , but is not specifically discussed in BIB011 . Another work BIB012 formulated a network utility maximization consisted of two-level resource allocation, system bandwidth slicing, and resources partitioning for AVs. The work is then extended to three aggregate network-wide utility maximization problems (refer to Table XII) focusing on transmit power control and solved by Linear programming relaxation and first-order Taylor series approximation and an alternate concave search algorithm BIB013 . However, both works BIB012 , BIB013 do not consider AVs as computing edge nodes. Unlike others, BIB014 solved the offloading problem by joining two different QoS parameters, which were latency and quality task allocation using Linear Programming based Optimization BIB011 , BIB002 and Binary Particle Swarm Optimization prior to dynamic task allocation approach BIB004 . The study might far more convincing if the authors had attempted the multi vehicular cloudlets. 2) Hybrid Energy: BIB005 developed the total utility maximisation by jointly combined computation bits and caching bits while optimising the transmit power of each vehicle and the trajectory of UAV adaptive to the instantaneous time and environment. To solve the power optimization problem, the energyaware dynamic power optimization problem was presented in the non-cooperation and cooperation cases under a fixed UAV trajectory. The former case was where the vehicles competed for the resources among each other, whereas the latter case was all the vehicles cooperatively share their common interest by forming a grand coalition. Simulation results demonstrated that the cooperation case with optimised trajectory achieved the best performance. However, the proposed optimisation scheme has high complexity for a real implementation. Meanwhile, BIB006 combined the energy with QoS in their proposed algorithm called QoS Loss Minimization algorithm. It comprises static RSU estimated power minimisation, Temporal Energy Balancing Algorithm and Spatial Energy Balancing Algorithm under a constraint of the delay workload. The proposed algorithm significantly reduced the QoS loss owing to power deficiency of SRSU. The researchers in BIB015 devised a joint control algorithm of workload assignment and VM pool resource allocation by minimising the energy consumption of vehicles for task processing. The proposed algorithm also reconciled the application latency with the incentive of vehicles in long-term. The online task scheduling algorithm, TaskSche, designed an efficient task workload assignment policy based on graph transformation and a knapsack-based VM pool resource allocation policy as the core components. To minimise the energy consumption for the offloading, the authors in BIB007 formulated a joint workload offloading and power control. The problem is solved by using an alternating direction method of multipliers (ADMM) based energy efficient resource allocation algorithm. For certain transmission power, the increase of the workload offloading portion decreased the energy consumption, and the transmission power had negative impact on the energy performance. However, the aforementioned approaches BIB006 , BIB015 , BIB007 suffer from a serious weakness in terms of vehicle mobility. 3) Hybrid Prices: The deep reinforcement learning approach with the multi-timescale framework is developed to solve the joint optimal resource allocation in minimising the cost of communication, caching, and computing for VEC BIB008 . Due to the NP-hard problem, the mobility-aware reward estimation is proposed, and yet the critical metric, such as latency is not considered for simplicity. Likewise, [23] explored a reverse auction mechanism based on Vickrey-Clarke-Groves for the cost of V2V computation offloading in which the requesting vehicles and the service provider acted as buyers and seller, respectively. Due to the fact that VCG is an optimal solution and an NP-hard problem, a suboptimal solution called the unilateral-matching-based mechanism is proposed and evaluated. The performance of sub-optimal was close to that of optimal, and the proposed sub-optimal served more vehicles with the increasing number of sellers, and the computation-intensive applications can be processed approximately by 75% faster than local. However, the study considers the offloading to the seller first rather than the local vehicle that can lead to high payment. Besides that, BIB009 minimised the system cost, i.e., network overhead and execution time of computing task, and formulated an optimal policy called Partially Observable Markov Decision Process to select the network access, computing node and also caching node. Simulation results demonstrated that the proposed framework reduced the system cost significantly across different number of data sizes offloaded. The benefit of the proposed framework is that various edge nodes are considered from local vehicles to the cloud computing server, but the revenue to service provider is not highlighted. Meanwhile, BIB016 formulated two optimisation problems for Vehicle Terminals (VTs) and Mobile Radio Side Units (MR-SUs) by minimising the average cost of VTs and the MRSUs in a MEC enabled IEEE 802.22-cognitive vehicular networks. The problems are solved based on Lyapunov optimisation theory with a low-complexity online algorithm called Dualside Dynamic Joint Task Offloading and Resource Allocation Algorithm in Vehicular Networks (DDORV), but vehicle application and mobility were not specified. The simulation results demonstrated that the DDORV achieved the highest profit for MRSU apart from the lowest cost of VTs with a trade-off the queue backlog in VTs. Investigating a different optimisation problem, BIB017 minimised the total computation overhead, which was the summation of weighted-sum of task completion time and monetary cost for using cloud resources of the MEC provider. The original problem is decomposed into two-subproblem, resource allocation (RA) problem with fixed task offloading and node selection (NS) problem with the optimal-value function prior to the RA. Although the cost of both computational resources of vehicle and core cloud is considered, the bottleneck of the work is to investigate on the mobility effect. 4) Hybrid Security: The reputation value of a vehicle is computed to exhibit the trustworthiness of vehicles before the offloading takes place BIB001 . The computation is based on previous experience with the targeted vehicles, recommendations from the neighbouring vehicles and also from a central authority. A bargaining game is formulated for the service provider to consider the vehicle reputation value in the bargaining power and also the required task allocation, which is known as reputation-assisted optimisation. A vehicle with high reputation is given high priority to determine its required resources resulted in high user satisfaction. Despite that, the exchange of reputation messages between vehicles and VEC server might lead to a high overhead in the networks. Another similar work BIB010 demonstrated data security and load balancing for the proposed 5G-enabled Intelligent Transport System (ITS) framework. It integrated the unmanned aerial vehicles (UAVs), dispatcher, edge nodes, and aggregator to maximize the processing capabilities, minimize delay, and maximize the security of the ITS. With the Bloom-filter-based security protocol used in the proposed framework, the delay can be reduced by increasing the number of edge nodes and transmitting UAV. One concern with the emergence of UAVs as edge nodes is on the battery lifetime for handling the data.