Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> F. Mobility Model <s> Vehicular ad hoc networks (VANETs) have emerged as a serious and promising candidate for providing ubiquitous communications both in urban and highway scenarios. Consequently, nowadays it is widely believed that VANETs will be able to support both safety and non-safety applications. For both classes of applications, since a zero-infrastructure is the typical premise assumed, it is crucial to understand the dynamics of network connectivity when one operates without relying on any telecommunications infrastructure. Using the key metrics of interest (such as link duration, connection duration, and re-healing time) we provide a comprehensive framework for network connectivity of urban VANETs. Our study, in addition to extensive simulations based on a new Cellular Automata Model for mobility, also provides a comprehensive analytical framework. This analytical framework leads to closed form results which facilitate physical insight into the impact of key system parameters on network connectivity. The predictions of our analytical framework also shed light on which type of safety and non-safety applications can be supported by urban VANETs. <s> BIB001 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> F. Mobility Model <s> SUMO is an open source traffic simulation package including the simulation application itself as well as supporting tools, mainly for network import and demand modeling. SUMO helps to investigate a large variety of research topics, mainly in the context of traffic management and vehicular communications. We describe the current state of the package, its major applications, both by research topic and by example, as well as future developments and extensions. Keywords-microscopic traffic simulation; traffic management; open source; software <s> BIB002 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> F. Mobility Model <s> Different research communities varying from telecommunication to traffic engineering are working on problems related to vehicular traffic congestion, intelligent transportation systems, and mobility patterns using information collected from a variety of sensors. To test the solutions, the first step is to use a vehicular traffic simulator with an appropriate scenario in order to reproduce realistic mobility patterns. Many mobility simulators are available, and the choice is usually done based on the size and type of simulation required, but a common problem is to find a realistic traffic scenario. In order to evaluate and compare new communication protocols for vehicular networks, it is necessary to use a wireless network simulator in combination with a vehicular traffic simulator. This additional step introduces further requirements for the scenario. The aim of this work is to provide a scenario able to meet all the common requirements in terms of size, realism and duration, in order to have a common basis for the evaluations. In the interest of building a realistic scenario, we decided to start from a real city with a standard topology common in mid-size European cities, and real information concerning traffic demands and mobility patterns. In this paper we show the process used to build the Luxembourg SUMO Traffic (LuST) Scenario, and present a summary of its characteristics together with an overview of its possible use cases. <s> BIB003 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> F. Mobility Model <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> BIB004 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> F. Mobility Model <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> BIB005 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> F. Mobility Model <s> Mobile computation offloading (MCO) is an emerging technology to offload the resource-intensive computations from smart mobile devices (SMDs) to nearby resource-rich devices (i.e., cloudlets) via wireless access. However, the link duration between a SMD and a single cloudlet can be very limited in a vehicular network. As a result, offloading actions taken by a SMD may fail due to link breakage caused by mobility. Meanwhile, some vehicles, such as buses, always follow relatively fixed routes, and their locations can be predicted much easier than other vehicles. By taking advantage of this fact, we propose a semi-Markov decision process (SMDP)-based cloudlet cooperation strategy, where the bus-based cloudlets act as computation service providers for the SMDs in vehicles, and an application generated by a SMD includes a series of tasks that have dependency among each other. In this paper, we adopt a semi-Markov decision process (SMDP) framework to formulate the bus-based cooperation computing problem as a delay-constrained shortest path problem on a state transition graph. The value iteration algorithm (VIA) is used to find the efficient solution to the bus-based cooperation computing problem. Experimental results show that the proposed SMDP-based cloudlet cooperation strategy can improve the performance of computation on the SMD in the cost (i.e., energy consumption and application completion time) and the offloading rate. <s> BIB006 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> F. Mobility Model <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> BIB007 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> F. Mobility Model <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> BIB008 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> F. Mobility Model <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> F. Mobility Model <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> BIB010 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> F. Mobility Model <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> BIB011 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> F. Mobility Model <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> BIB012 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> F. Mobility Model <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> BIB013 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> F. Mobility Model <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> BIB014 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> F. Mobility Model <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> BIB015 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> F. Mobility Model <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> BIB016 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> F. Mobility Model <s> Abstract The information-centric networking (ICN) paradigm has gained increasing attention as a solution for boosting content delivery in vehicular network applications. The content-naming oriented search and in-network data caching procedures of ICN can improve content delivery by avoiding the need to determine and maintain end-to-end routing paths in vehicular networks, which is challenging due to the mobility of vehicles, and intermittent and short-lived connections between them. However, the use of the ICN paradigm in vehicular networks aggravates the broadcast storm problem, and constantly suffers from breaks in the reverse path used for content discovery and delivery. In this paper, we propose the location-based and information-centric (LoICen) architecture to improve the content request procedure and reduce the broadcast storm problem in intelligent vehicular networks. In the LoICen architecture, vehicles opportunistically obtain the location information of the vehicles that might have desired content in their cache. This location information is used whenever possible to improve content search and discovery by directing Interest packets to the area where the content may be located, mitigating the broadcast storm problem by selecting only the most suitable neighboring vehicle at each hop to continue forwarding the Interest packet, and improving content delivery by selecting more stable paths. Simulation results show that the LoICen architecture outperformed related work in terms of content delivery ratio, while reducing the delay for the content delivery and the number of transmissions of Interest packets for content search. <s> BIB017 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> F. Mobility Model <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> BIB018 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> F. Mobility Model <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> BIB019 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> F. Mobility Model <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> BIB020 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> F. Mobility Model <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> BIB021 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> F. Mobility Model <s> Vehicular fog computing (VFC) has emerged as a promising solution to relieve the overload on the base station and reduce the processing delay during the peak time. The computation tasks can be offloaded from the base station to vehicular fog nodes by leveraging the under-utilized computation resources of nearby vehicles. However, the wide-area deployment of VFC still confronts several critical challenges, such as the lack of efficient incentive and task assignment mechanisms. In this paper, we address the above challenges and provide a solution to minimize the network delay from a contract-matching integration perspective. First, we propose an efficient incentive mechanism based on contract theoretical modeling. The contract is tailored for the unique characteristic of each vehicle type to maximize the expected utility of the base station. Next, we transform the task assignment problem into a two-sided matching problem between vehicles and user equipment. The formulated problem is solved by a pricing-based stable matching algorithm, which iteratively carries out the “propose” and “price-rising” procedures to derive a stable matching based on the dynamically updated preference lists. Finally, numerical results demonstrate that significant performance improvement can be achieved by the proposed scheme. <s> BIB022 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> F. Mobility Model <s> Due to the dynamically changing topology of Internet of Vehicles (IoV), it is a challenging issue to achieve efficient data dissemination in IoV. This paper considers strongly connected IoV with a number of heterogenous vehicular nodes to disseminate information and studies distributed replication-based data dissemination algorithms to improve the performance of data dissemination. Accordingly, two data replication algorithms, a deterministic algorithm and a distributed randomised algorithm, are proposed. In the proposed algorithms, the number of message copies spread in the network is limited and the network will be balanced after a series of average operations among the nodes. The number of communication stages needed for network balance shows the complexity of network convergence as well as network convergence speed. It is proved that the network can achieve a balanced status after a finite number of communication stages. Meanwhile, the upper and lower bounds of the time complexity are derived when the distributed randomised algorithm is applied. Detailed mathematical results show that the network can be balanced quickly in complete graph; thus highly efficient data dissemination can be guaranteed in dense IoV. Simulation results present that the proposed randomised algorithm outperforms the present schemes in terms of transmissions and dissemination delay. <s> BIB023 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> F. Mobility Model <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> BIB024 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> F. Mobility Model <s> In future vehicular networks, to satisfy the ever-increasing capacity requirements, ultrahigh-speed directional millimeter-wave (mmWave) communications will be used as vehicle-to-vehicle (V2V) links to disseminate large-volume contents. However, the conventional IP-based routing protocol is inefficient for content disseminations in high-mobility and dynamic vehicular environments. Furthermore, the vehicle association also has a significant effect on the content dissemination performance. The content segment diversity, defined as the difference of desired content segments between content requesters and content repliers, is a key consideration during vehicle associations. The relative velocity between vehicles, which highly influences the link stability, should also been taken into account. The beam management, such as the beamwidth control, determines the link quality and, therefore, the final content dissemination rate. Based on the above observations, to improve the content dissemination performance, we propose an information-centric network (ICN)-based mmWave vehicular framework together with a decentralized vehicle association algorithm to realize low-latency content disseminations. In the framework, by using the ICN protocol, contents are cached and retrieved at the edge of the network, thereby reducing the content retrieval latency. To enhance the content dissemination rate, the vehicle association algorithm, which jointly considers the content segment diversity, the relative velocity between vehicles, and the transceiver’s beamwidths, is operated in every vehicle. Considering the blindness of directional mmWave links, a common control channel operating at low frequency bands with omni-directional coverage is used to share the information related to vehicle associations. The simulation results demonstrate that the proposed algorithm can improve the content dissemination efficiency and reduce the content retrieval latency. <s> BIB025 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> F. Mobility Model <s> Sharing perceptual data with other vehicles enhances the traffic safety of autonomous vehicles because it helps vehicles locate other vehicles and pedestrians in their blind spots. Such safety applications require high throughput and short delay, which cannot be achieved by conventional microwave vehicular communication systems. Therefore, millimeter-wave (mmWave) communications are considered to be a key technology for sharing perceptual data because of their wide bandwidth. One of the challenges of data sharing in mmWave communications is broadcasting because narrow-beam directional antennas are used to obtain high gain. Because many vehicles should share their perceptual data to others within a short time frame in order to enlarge the areas that can be perceived based on shared perceptual data, an efficient scheduling for concurrent transmission that improves spatial reuse is required for perceptual data sharing. This paper proposes a data sharing algorithm that employs a graph-based concurrent transmission scheduling. The proposed algorithm realizes concurrent transmission to improve spatial reuse by designing a rule that is utilized to determine if the two pairs of transmitters and receivers interfere with each other by considering the radio propagation characteristics of narrow-beam antennas. A prioritization method that considers the geographical information in perceptual data is also designed to enlarge perceivable areas in situations where data sharing time is limited and not all data can be shared. Simulation results demonstrate that the proposed algorithm doubles the area of the cooperatively perceivable region compared with a conventional algorithm that does not consider mmWave communications because the proposed algorithm achieves high-throughput transmission by improving spatial reuse. The prioritization also enlarges the perceivable region by a maximum of 20%. <s> BIB026
The mobility model in VEC characterises the movements of vehicles with regards to their locations, velocity and direction over a period of time. In the mobility model also the researchers incorporated the distribution and movement of the vehicles at a specific area or region in the real world. The model is essential for demonstrating the performance of VEC nearer to the reality. In VEC, significant works used Simulation of Urban MObility (SUMO)  , BIB004 , BIB013 , BIB014 , BIB015 , BIB008 , BIB016 , BIB017 , followed by specific vehicle speed BIB005 ,  , BIB018 , BIB019 , BIB020 , BIB021 , vehicle acceleration BIB005 , BIB006 , BIB022 , BIB014 and other models BIB009 , BIB010 , BIB011 , BIB012 , BIB009 . They are detailed as follows: 1) SUMO: SUMO BIB002 is an open source of multi-modal traffic simulation for vehicles, public transport, and pedestrians. It is a microscopic simulation where the vehicles can be modeled explicitly on the actual lanes or highways. Generally, SUMO can transform the real map often from Open Street Map (OSM) into a simplified road topology and integrated into the SUMO simulator with a specific configuration of vehicle speed or actual vehicle movements from trace file  . The Luxembourg SUMO Traffic scenario (LuST) BIB003 is employed in BIB004 , BIB013 ,  to emulate real traffic of Luxembourg divided into two regions, namely the highways and urban. Highways consist of high speed vehicles, and urban has a long inter-vehicle distance resulting in a low density of vehicles. On the other hand, the real-world map of three different roads in China is imported to SUMO for simulations BIB014 . Three different velocity models, which are a constant velocity model, vehicle-following model, and traveling-time statistical model, are generated. The traffic simulator SUMO is integrated with a network simulator OMNeT++3 that enables to use the real maps from OSM for G6 Highway in Beijing BIB015 . Other works also used SUMO to simulate a particular area in France with the vehicle speed of 30km/h and 80 km/h BIB008 , Al-Ain City in the United Arab Emirates with the maximum speed of 100 km/h BIB016 . Furthermore, content distribution works also use SUMO to generate the movement trajectories of vehicles in the area of Ottawa, Canada BIB017 and San Franciso BIB023 , which are obtained from OSM. In contrast, SUMO is used to simulate the mobility of the vehicles without the actual road topology, i.e., three-lanes, at the speed of 60 km/h and 80 km/h BIB024 . 2) Vehicle Speed Range: Referring to  , BIB001 , the speed of the vehicles used is Gaussian distributed and is varied from 30 km/h to 60 km/h within an urban area of 400 km 2 with 400 distributed BS-fogs BIB007 . The range of speed between 70 km/h and 150 km/h is used in most of the works BIB018 , BIB019 , BIB020 , BIB021 , as shown in Table IX . In addition to that, the proposed DREAMS assumed the speed ranges 50 to 150 km/h and acceleration ranges 0.5 to 1.5 m/s 2 BIB005 . Other speed ranges of the vehicles are also explored in VEC, such as 10m/s and 20 m/s BIB006 , 2 to 20 m/s BIB022 , and 0 to 27.7m/s BIB014 . On the other hand, the data caching works investigated the average mobility of vehicle about 100 km/h BIB025 and the low mobility between 40 km/h and 60 km/h BIB026 . 3) Miscellaneous: Another exciting work in  used the trajectory data of all green taxis and limousines trip in New York City (NYC) [104] for simulations. Similarly, vehicle trajectory prediction based on GPS and GIS big data is also examined in BIB010 . The mobility of vehicles is modeled by discrete random jumps characterised by the contact time or sojourn time and the transmission frequency BIB011 . It is assumed that the vehicle was connecting to the same vehicle edge node and RSU within the contact time. Evaluating the impact of vehicles mobility on the resources, thereby a stochastic geometry is applied to model the random vehicular networks, and the locations of nodes are generated by a Poisson point process (PPP) BIB012 . Another work used the Manhattan Grid as a vehicle mobility model in the proposed software-defined vehicular networks BIB009 .