Vehicular Edge Computing Servers Research Articles

Multi-User Computation Offloading and Resource Allocation Algorithm in a Vehicular Edge Network.

In Vehicular Edge Computing Network (VECN) scenarios, the mobility of vehicles causes the uncertainty of channel state information, which makes it difficult to guarantee the Quality of Service (QoS) in the process of computation offloading and the resource allocation of a Vehicular Edge Computing Server (VECS). A multi-user computation offloading and resource allocation optimization model and a computation offloading and resource allocation algorithm based on the Deep Deterministic Policy Gradient (DDPG) are proposed to address this problem. Firstly, the problem is modeled as a Mixed Integer Nonlinear Programming (MINLP) problem according to the optimization objective of minimizing the total system delay. Then, in response to the large state space and the coexistence of discrete and continuous variables in the action space, a reinforcement learning algorithm based on DDPG is proposed. Finally, the proposed method is used to solve the problem and compared with the other three benchmark schemes. Compared with the baseline algorithms, the proposed scheme can effectively select the task offloading mode and reasonably allocate VECS computing resources, ensure the QoS of task execution, and have a certain stability and scalability. Simulation results show that the total completion time of the proposed scheme can be reduced by 24-29% compared with the existing state-of-the-art techniques.

Federated learning-based trajectory prediction for dynamic resource allocation in moving small cell networks

With the evolution of fifth generation (5G) of mobile communication, vehicular edge computing (VEC) and moving small cell (MSC) network are gaining attention because of their capability to provide improved quality-of-service (QoS) to vehicular users. The ultimate goal of VEC-enabled MSC network is to diminish the vehicular penetration effect and path loss resulting in improved network performance for vehicular users. In this paper, we explore distributed resource block (RB) allocation for fronthaul links of MSCs with probabilistic mobility in VEC-enabled MSC network. The proposed work exploits the computational power of road side units (RSUs) deployed with VEC servers present along the road sides to allocate the resources to MSCs in a distributed manner by exchanging limited information, with the objective of maximizing the data rate achieved by MSC network. Moreover, we propose federated learning (FL)-based position prediction of MSCs to predict the trajectory of MSCs in advance for efficient prediction of resource allocation in MSC network. Simulations results are presented to compare the position prediction dependent distributed and centralized time interval dependent interference graph-based resource allocation to MSCs in terms of RB utilization and average achievable data rate of MSC network. For comparison, we further investigated centralized as well as distributed threshold time dependent interference graph-based allocation of resources to MSC network.

BARGAIN-MATCH: A Game Theoretical Approach for Resource Allocation and Task Offloading in Vehicular Edge Computing Networks

Vehicular edge computing (VEC) is emerging as a promising architecture of vehicular networks (VNs) by deploying the cloud computing resources at the edge of the VNs. However, efficient resource management and task offloading in the VEC network is challenging. In this work, we first present a hierarchical framework that coordinates the heterogeneity among tasks and servers to improve the resource utilization for servers and service satisfaction for vehicles. Moreover, we formulate a joint resource allocation and task offloading problem (JRATOP), aiming to jointly optimize the intra-VEC server resource allocation and inter-VEC server load-balanced offloading by stimulating the horizontal and vertical collaboration among vehicles, VEC servers, and cloud server. Since the formulated JRATOP is NP-hard, we propose a cooperative resource allocation and task offloading algorithm named BARGAIN-MATCH, which consists of a bargaining-based incentive approach for intra-server resource allocation and a matching method-based horizontal-vertical collaboration approach for inter-server task offloading. Besides, BARGAIN-MATCH is proved to be stable, weak Pareto optimal, and polynomial complex. Simulation results demonstrate that the proposed approach achieves superior system utility and efficiency compared to the other methods, especially when the system workload is heavy.

Federated learning for resource allocation in vehicular edge computing-enabled moving small cell networks

Moving networks comprising of moving small cells (MoSCs) is an emerging technology that provide ubiquitous connectivity to the cellular users in vehicular environment. MoSCs are the small cells deployed on the top of vehicles (city buses, trains, trams etc.) to support the vehicular users with improved quality-of-service (QoS). However, the deployment of small cells in vehicular environment demands for an efficient resource allocation mechanism. This is due to high mobility of MoSCs resulting in dynamic interference between MoSCs, high computational cost, privacy issues, latency issues, and high data transmission requirement. To overcome these issues, gated recurrent unit (GRU)-based federated learning (FL) model is proposed for resource allocation in moving networks. In our proposed work, we investigate resource allocation in vehicular edge computing (VEC)-enabled MoSC network with MoSCs deployed on trams travelling with deterministic mobility. In the proposed MoSC network, road side units (RSUs) equipped with VEC servers use their computational power to train the resource allocation model in a distributed manner, in which each RSU exploits the training data of its associated MoSCs to generate a shared model. The proposed GRU-based FL model enables RSUs to cooperatively train a global model that can predict resource block (RB) allocation to MoSCs without transmitting the historical data to the central server. We have conducted extensive system level simulations to determine key performance comparison between FL and centralized learning-based resource allocation in MoSC network.

Task Offloading and Resource Allocation in Vehicular Networks: A Lyapunov-Based Deep Reinforcement Learning Approach

Vehicular Edge Computing (VEC) has gained popularity due to its ability to enhance vehicular networks. VEC servers located at Roadside Units (RSUs) allow low-power vehicles to offload computation-intensive and delay-sensitive applications, making it a promising solution. However, optimal resource allocation between edge servers is a complex issue due to vehicle mobility and dynamic data traffic. To address this issue, we propose a Lyapunov-based Multi-Agent Deep Deterministic Policy Gradient (L-MADDPG) method that jointly optimizes computing task distribution and radio resource allocation to minimize energy consumption and delay requirements. We evaluate the trade-offs between the performance of the optimization algorithm, queuing model, and energy consumption. We first examine delay, queue and energy models for task execution at the vehicle or RSU, followed by the L-MADDPG algorithm for jointly optimizing task offloading and resource allocation problems to reduce energy consumption without compromising performance. Our simulation results show that our algorithm can reduce energy consumption while maintaining system performance compared to existing algorithms.

A Markov Decision Process Solution for Energy-Saving Network Selection and Computation Offloading in Vehicular Networks

Vehicular Edge Computing (VEC) enables the integration of edge computing facilities in vehicular networks (VNs), allowing data-intensive and latency-critical applications and services to end-users. Though VEC brings several benefits in terms of reduced task computation time, energy consumption, backhaul link congestion, and data security risks, VEC servers are often resource-constrained. Therefore, the selection of proper edge nodes and the amount of data to be offloaded becomes important for having VEC process benefits. However, with the involvement of high mobility vehicles and dynamically changing vehicular environments, proper VEC node selection and data offloading can be challenging. In this work, we consider a joint network selection and computation offloading problem over a VEC environment for minimizing the overall latency and energy consumption during vehicular task processing, considering both user and infrastructure side energy-saving mechanisms. We have modeled the problem as a sequential decision-making problem and incorporated it in a Markov Decision Process (MDP). Numerous vehicular scenarios are considered based upon the users' positions, the states of the surrounding environment, and the available resources for creating a better environment model for the MDP analysis. We use a value iteration algorithm for finding an optimal policy of the MDPs over an uncertain vehicular environment. Simulation results show that the proposed approaches improve the network performance in terms of latency and consumed energy.

Blockchain-Based Cooperative Computation Offloading and Secure Handover in Vehicular Edge Computing Networks

Facing the requirements of intelligent vehicles for massive data processing, vehicular edge computing (VEC) utilizes computing resources deployed on the roadside infrastructure to provide proximity computing services for vehicles and forms a novel computing paradigm. Thus, vehicles can reduce the burden of local computing and improve computing efficiency by offloading tasks to roadside computing servers or neighboring resource-idle vehicles for execution via cooperative computation offloading (CO). However, dynamic communication channel states and data handover among multiple VEC servers caused by vehicle mobility pose challenges for CO decision-making and data security. This paper applies blockchain to the cooperative CO of VEC and thus proposes a cooperative CO and secure handover framework with a consensus mechanism to guarantee the efficiency of cooperative CO and secure handover. In this framework, models for vehicle mobility and cooperative CO handover are constructed, and a consensus mechanism is proposed. This mechanism ensures the synchronization and immutability of offloaded data in the CO handover. A cooperative CO decision optimization is also formulated considering secure handover with blockchain technology to optimize the latency of vehicular computing tasks. To solve this complex problem, this optimization is transformed into a Markov decision process and a cooperative CO decision algorithm with multiagent deep reinforcement learning is designed, thus achieving the optimal solution. Extensive simulations verified the performance and effectiveness of the proposed method.

Task scheduling of computation-intensive graph jobs in UAV-assisted hybrid vehicular networks

As a promising new form in mobile edge computing network, Vehicular Edge Computing (VEC) can further improve the Quality of Service (QoS) of users. However, when edge servers are damaged or overloaded, the requirements of users are difficult met by VEC servers owing to incomplete connectivity and time-varying channel conditions in dynamic and resource-limited vehicular networks. In this paper, we introduce Unmanned Aerial Vehicle (UAV) to enhance the connectivity and total resources in the vehicular networks. Added to that, we focus on the problem of computation-intensive graph jobs scheduling, where the information interactions among the traditional vehicles, autonomous vehicles and UAV are integrated. And the above scheduling problem is modeled as a binary nonlinear programming problem aiming at minimizing average completion delay of jobs. Then, we develop an effective multiple jobs scheduling scheme by considering the scheduling order of multiple jobs, the remaining processing delay of servers, and the available resources of vehicular networks, which consists of two stages: (1) the multiple jobs priority scheduling algorithm for optimizing the jobs scheduling order; (2) the task offloading decision algorithm for optimizing average completion delay of jobs. Finally, the effectiveness of proposed scheme is proved by extensive simulation experiments. Compared with the other schemes, the proposed scheme can effectively reduce average completion delay of jobs.

Deep Reinforcement Learning for Shared Offloading Strategy in Vehicle Edge Computing

Vehicular edge computing (VEC) effectively reduces the computing load of vehicles by offloading computing tasks from vehicle terminals to edge servers. However, offloading of tasks increase in quantity the transmission time and energy of the network. In order to reduce the computing load of edge servers and improve the system response, a shared offloading strategy based on deep reinforcement learning is proposed for the complex computing environment of Internet of Vehicles (IoVs). The shared offloading strategy exploits the commonality of vehicles task requests, similar computing tasks coming from different vehicles can share the computing results of former task submitted. The shared offloading strategy can be adapted to the complex scenarios of the IoVs. Each vehicle can share the offloading conditions of the VEC servers, and then adaptively select three computing modes: local execution, task offloading, and shared offloading. In this article, the network state and offloading strategy space are the input of the deep reinforcement learning (DRL). Through the DRL, each task unit selects the offloading strategy with the optimal energy consumption at each time period in the dynamic IoVs transmission and computing environment. Compared with the existing proposals and DRL-based algorithms, it can effectively reduce the delay and energy consumption required for tasks offloading.

Efficient Caching in Vehicular Edge Computing Based on Edge-Cloud Collaboration

In vehicular edge computing (VEC), the execution of offloading task needs not only the task data uploaded by the requesting vehicle, but also the additional data to support the task to be executed successfully, and how to efficiently cache and access these supporting data becomes the key issue for task offloading in VEC. In this paper, we study the efficient caching mechanism to minimize the acquisition delay of the supporting data. Firstly, with the software defined network (SDN) based VEC framework, we analyze the acquisition ways of the supporting data and the caching collaboration between VEC servers. Then, according to the density of the requesting vehicles, we divide the VEC coverage into dense and ordinary areas. With the consideration of the similarity of the requested data and the distance between edge servers, the edge servers are clustered into multiple groups based on K-mean++ algorithm. Finally, each server's storage space is divided into three partitions, and the most beneficial data for itself, its group and the whole system are respectively stored in these partitions. Based on service area dividing, server grouping and storage space partitioning, we propose an efficient edge-cloud collaborative caching strategy, which can reduce the delay of data migration while task execution. Simulation results show that, compared with other schemes, the proposed caching strategy has better performance in terms of average data migration delay and application QoS.

Uncertainty-aware Task Offloading for Multi-vehicle Perception Fusion over Vehicular Edge Computing

Multi-vehicle perception fusion is an emerging advanced vehicular application providing Vehicle Users (VUs) with comprehensive driving assistance. This involves each VU's individual vehicular perception tasks and additional fusion tasks at the end. However, performing these perception fusion applications on some VUs may not be feasible due to the applications' high computing complexity. Vehicular Edge Computing (VEC) server, which is collocated with a Road-side Unit, together with VUs' Vehicular Local Computing (VLC) units can be used to support these perception fusion applications through task offloading. Achieving this edge-collaborated perception fusion with task offloading involves multiple challenges: (i) frequently varying uplink channel conditions, (ii) sequential and parallel task dependencies, and (iii) uncertain task composition induced by unknown object detection outputs in the dynamic driving environment. In this paper, a real-time mechanism is proposed to minimize the end-to-end delay of an edge-collaborated multi-vehicle perception fusion application by addressing these challenges. Based on the real-time channel conditions, VEC and VLC server capacities, and number of detected objects, this algorithm jointly determines the bandwidth allocation, task partitioning, offloading, and execution scheduling for all the involved tasks of the multi-vehicle perception fusion application. Numerical results show that the proposed approach can significantly reduce the end-to-end fusion application latency compared to existing techniques.

Idle-parked vehicles assisted collaborative resource allocation in VEC based on Stackelberg game

With the advent of autonomous vehicles, how to meet the computing demands of vehicles with computing-intensive and latency-sensitive tasks becomes a challenge. Vehicular Edge Computing (VEC) is an advanced computing paradigm that improves the Quality of Service (QoS) of vehicles by offloading tasks to VEC servers. However, the computing resources of the VEC server are limited and thus cannot meet the offloading needs of the vehicle. This paper proposes to use idle-parked vehicles to assist the VEC server to offload computing tasks, which increases the resource capacity and expands the communication range. Firstly, we propose an idle-parked vehicle-assisted VEC model, which incentivizes idle-parked vehicles to assist VEC server computing tasks by obtaining rewards from VEC servers. Secondly, this paper proposes a more flexible dynamic offloading scheme based on comprehensive consideration of the selection strategy and pricing strategy. Then, based on the Stackelberg game, the interaction between the VEC server and the idle-parked vehicle is analyzed, and utilize backward induction to prove that there is a unique Nash equilibrium in this game. Finally, an improved joint selection decision and pricing decision algorithm based on the Branch and Bound (JSPBB) algorithm is proposed, which achieves the optimal pricing under the condition of maximizing the utility of idle-parked vehicles and each VEC server under this pricing optimal offloading strategy to maximize system utility. The simulation results can show that the proposed algorithm can solve the problem effectively and has good performance so that the system can obtain higher benefits.

Federated Deep Reinforcement Learning Based Task Offloading with Power Control in Vehicular Edge Computing.

Vehicular edge computing (VEC) is a promising technology for supporting computation-intensive vehicular applications with low latency at the network edges. Vehicles offload their tasks to VEC servers (VECSs) to improve the quality of service (QoS) of the applications. However, the high density of vehicles and VECSs and the mobility of vehicles increase channel interference and deteriorate the channel condition, resulting in increased power consumption and latency. Therefore, we proposed a task offloading method with the power control considering dynamic channel interference and conditions in a vehicular environment. The objective is to maximize the throughput of a VEC system under the power constraints of a vehicle. We leverage deep reinforcement learning (DRL) to achieve superior performance in complex environments and high-dimensional inputs. However, most conventional methods adopted the multi-agent DRL approach that makes decisions using only local information, which can result in poor performance, while single-agent DRL approaches require excessive data exchanges because data needs to be concentrated in an agent. To address these challenges, we adopt a federated deep reinforcement learning (FL) method that combines centralized and distributed approaches to the deep deterministic policy gradient (DDPG) framework. The experimental results demonstrated the effectiveness and performance of the proposed method in terms of the throughput and queueing delay of vehicles in dynamic vehicular networks.

COPP-DDPG: Computation Offloading with Privacy Preservation in a Vehicular Edge Network

Vehicular edge computing (VEC) is emerging as a prospective technology in the era of 5G and beyond to support delay-sensitive and computation-intensive vehicular applications. However, designing an efficient approach for joint computation offloading and resource allocation is challenging due to the limited resources of VEC servers, the highly dynamic vehicular networks (VNs), different priorities of vehicular applications, and the threat of privacy disclosure. In this work, we propose a cooperative optimization for privacy-preserving and priority-aware offloading and resource allocation in VEC network (VECN) based on deep reinforcement learning (DRL). Firstly, we employed a privacy-preserving framework where the certificate authority (CA) is integrated into the VEC architecture. Furthermore, we formulated the dynamic optimization problem as a Markov decision process (MDP) by constructing a weighted cost function that integrates the priority of stochastic arrival tasks, privacy-preserving of offloading, and dynamic interaction between the edge servers and intelligent connected vehicles (ICVs). To solve this problem, a cooperative optimization for privacy and priority based on deep deterministic policy gradient (COPP-DDPG) is proposed by learning the optimal actions to minimize the weighted cost function. The simulation results show that COPP-DDPG has good convergence and outperforms the other four comparison algorithms in many aspects.

Latency Minimization of Reverse Offloading in Vehicular Edge Computing

Cooperative Vehicle-Infrastructure System (CVIS) can provide innovative services for traffic management and enable trips to be safer, more coordinated, and smarter. In the CVIS, the vehicles upload crowd sensing data to the Vehicular Edge Computing (VEC) server for quick data fusion and informed decision-making. However, with the ever-increasing number of vehicles, the VEC server cannot undertake massive computation-intensive tasks due to the limited edge computing capabilities. In this paper, we propose a reverse offloading framework that can fully utilize the vehicular computation resource to relieve the burden of the VEC server and further reduce the system latency. Under the proposed offloading framework, the binary reverse offloading (BRO) and partial reverse offloading (PRO) strategies are designed for two types of tasks, i.e., non-partitioned tasks and partitioned tasks. We formulate the system latency minimization problem by optimizing reverse offloading decisions, and the communication and computation resources allocation. Due to the non-convex and existing variables coupling, the original problem is transformed into the equivalent weighted-sum optimization problem. Based on the alternative optimization, we decouple the weighted-sum optimization problems into the two subproblems, and the closed-form expressions of transmission power and computation frequency of vehicles and RSU are derived. Low complexity greedy based efficient searching (GES) algorithm and joint alternative optimization based bi-section searching (JAOBSS) algorithm are proposed for BRO and PRO strategies, respectively. The algorithm complexity and performance bounds are analyzed. Simulation results show that the proposed GES algorithm can achieve optimal performance with low complexity. Besides, the proposed GES and JAOBSS algorithms can significantly improve the performance compared with other baseline schemes by 6.14% and 13.46% at least.

MIDP: An MDP-based intelligent big data processing scheme for vehicular edge computing

The number of Vehicle Equipment (VE) connected to the Internet is increasing, and these VEs generate tasks that contain large amounts of data. Processing these tasks requires a lot of computing resources. Therefore, it is a promising issue that offloading compute-intensive tasks from resource-limited vehicles to Vehicular Edge Computing (VEC) servers, which involves big data transmission, processing and computation. In a network, multiple providers provide VEC servers. When a vehicle generates a task, our goal is to make an intelligent decision on whether and when to offload this task to VEC servers to minimize the task completion time and total big data processing time. When each vehicle passes VEC servers, the vehicle can decide to offload its task to the VEC server in the current communication range, or continue to drive until it reaches the next server's communication range. This issue can be considered as an asset selling problem. It is a challenging issue to make a smart decision for the vehicle with a location view because the vehicle is not sure when the next VEC server will be available and how much about the available computing capacity of the next VEC server. Firstly, this paper formulates the problem as a Markov Decision Process (MDP), defines and analyzes the state set, action set, reward model, and state transition probability distribution. Then it uses Asynchronous Advantage Actor-Critic (A3C) algorithm to solve this MDP problem, builds the various elements of the A3C algorithm, uses Actor (the strategy function) to generate two actions of the vehicle: offloading and moving without offloading. Thirdly, it uses Critic (the value function) to evaluate Actor's behavior, and guide Actor's actions in subsequent stages. The Actor starts from the initial state in the state space until it enters the termination state, forming a complete decision-making process. It minimizes the completion time of task offloading through learning thereby reducing the delay of big data processing. Compared to the Immediately Offload (IO) scheme and Expect Offload (EO) scheme, the MIDP scheme proposed in this paper reduces the average task offloading delay to 29.93% and 29.99%, close to the EO scheme in terms of task completion rate and up to 66.6% improvement compared to the IO scheme.

A cluster-based cooperative computation offloading scheme for C-V2X networks

The explosive growth of computation-intensive and delay-sensitive applications in vehicular edge network demands high computation resources and energy. With the cellular-vehicle to everything (C-V2X) technology, these applications can be transferred to another node for processing. However, offloading all these tasks to the mobile edge computing (MEC) server becomes a bottleneck. A cluster-based cooperative task offloading scheme for C-V2X networks is proposed. The proposed scheme explores the collaboration between the MEC servers and vehicles with idle computation resources known as the vehicular edge computing (VEC) servers. Vehicles can utilize the direct sidelink vehicle to vehicle (V2V) communication for task offloading to the VEC server and the cellular vehicle to network (V2N) communication for task offloading to the MEC server. In the proposed scheme, vehicles are grouped to form clusters in order to enhance link reliability between nodes for efficient task offloading. We then formulate an optimization problem to minimize energy consumption and task offloading latency, which is NP-hard. Accordingly, an improved matching algorithm is proposed to solve this problem by optimizing offloading decisions and resource allocation. According to our results, the proposed scheme reduces offloading latency and energy consumption compared to other existing schemes.

A Deep Reinforcement Learning-Based Resource Management Game in Vehicular Edge Computing

Vehicular Edge Computing (VEC) is a promising paradigm that leverages the vehicles to offload computation tasks to the nearby VEC server with the aim of supporting the low latency vehicular application scenarios. Incentivizing VEC servers to participate in computation offloading activities and make full use of computation resources is of great importance to the success of intelligent transportation services. In this paper, we formulate the competitive interactions between the VEC servers and vehicles as a two-stage Stackelberg game with the VEC servers as the leader players and the vehicles as the followers. After obtaining the full information of vehicles, the VEC server calculates the unit price of computation resource. Given the unit prices announced by VEC server, the vehicles determine the amount of computation resource to purchase from VEC server. In the scenario that vehicles do not want to share their computation demands, a deep reinforcement learning based resource management scheme is proposed to maximize the profits of vehicles and VEC server. The extensive experimental results have demonstrated the effectiveness of our proposed resource management scheme based on Stackelberg game and deep reinforcement learning.

Timing-Oriented Task Offloading Algorithms for Internet-of-Vehicles

The emergence of computation intensive automotive applications poses significant challenges on computation capacity of automotive electronic systems, thus vehicular edge computing (VEC) is introduced as a new computing paradigm into the internet of vehicle (IoV) to improve its data processing capability. However, as the computation capacity is limited in VEC servers, efficient task offloading algorithms need to be proposed. This paper first proposes a multi-task offloading model and gives the related task response time analysis method, and then, both a mixed-integer linear programming (MILP)-based algorithm and a simulated annealing-based heuristic algorithm are proposed to minimize the task response time. By comparing with a baseline algorithm, the MILP-based offloading algorithm can reduce the average task response time by 91.45%, and the heuristic offloading algorithm can reduce the average task response time by 70%.

Adaptive Computation Partitioning and Offloading in Real-Time Sustainable Vehicular Edge Computing

In this paper, we explore the feasibility of solar-powered road-side unit (SRSU)-assisted vehicular edge computing (VEC) system, where SRSU is equipped with small cell base station (SBS) and VEC server, both of which are powered solely by solar energy. However, the limited capacity of solar energy, VEC server's computing, and SBS's bandwidth resources may prohibit vehicle users (VUs) from offloading their vehicular applications to VEC server for better service quality. We address this challenge by dynamically determining vehicular task partitioning and offloading, VEC server's system configuration, and vehicular application level adjustment decisions. We aim at minimizing the end-to-end delay of vehicular applications while maximizing their application level performance (e.g., accuracy). We also implement an object detection vehicular application on an edge computing platform and measure the corresponding energy consumption, computation delay, and detection accuracy performance to establish empirical models for the SRSU-assisted VEC system. We then propose a dynamic programming-based heuristic algorithm which jointly makes the task partitioning and offloading, as well as system and application-level adaption decisions in real-time. We build a simulation framework with the above empirical models to evaluate the proposed algorithm. The simulation results show that our proposed approach can significantly reduce the end-to-end delay while maximizing the detection accuracy compared to existing techniques.