Search

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the rapid advancement of Internet of vehicles and autonomous driving technology, there is a growing need for increased computing power in vehicle operations. However, the strict latency requirements of vehicle tasks may pose challenges to communication and computing resources within the vehicle edge computing network. This paper introduces a two-stage joint optimization to address challenges, focusing on minimizing vehicle task latency and optimizing resource allocation. In addition, the task completion rate is considered an important indicator to ensure safety and reliability in practical application scenarios. Next, we propose a global adaptive offloading and resource allocation optimization model named GOAL. The GOAL model dynamically adjusts the weight coefficients of the reward function to optimize the model, integrating the actor-critic algorithm to effectively adapt to uncertain environments. Through experimental comparisons of various weight coefficients for task arrival rates and reward functions, we were able to determine the optimal hyperparameters for our proposed model. The simulation results show that the GOAL model outperforms the benchmark methods by over 30% in reward value. It also performs better in terms of task delay and energy consumption. Additionally, the GOAL model has a higher task completion rate compared to the benchmark methods, and it exhibits strong search capabilities and faster convergence speed. [ABSTRACT FROM AUTHOR]

In vehicular edge computing (VEC) networks, the rapid expansion of intelligent transportation and the corresponding enormous numbers of tasks bring stringent requirements on timely task offloading. However, many tasks typically appear within a short period rather than arriving simultaneously, which makes it difficult to realize effective and efficient resource scheduling. In addition, some key information about tasks could be learned due to the regular data collection and uploading processes of sensors, which may contribute to developing effective offloading strategies. Thus, in this paper, we propose a model that considers the deterministic demand of multiple tasks. It is possible to generate effective resource reservations or early preparation decisions in offloading strategies if some feature information of the deterministic demand can be obtained in advance. We formulate our scenario as a 0‐1 programming problem to minimize the average delay of tasks and transform it into a convex form. Finally, we proposed an efficient optimal offloading algorithm that uses the interior point method. Simulation results demonstrate that the proposed algorithm has great advantages in optimizing offloading utility. [ABSTRACT FROM AUTHOR]

Cloudlet-based vehicular networks are a promising paradigm to enhance computation services through a distributed computation method, where the vehicle edge computing (VEC) cloudlet are deployed in the vicinity of the vehicle. In order to further improve the computing efficiency and reduce the task processing delay, we present a parallel task scheduling strategy based on the multi-agent deep reinforcement learning (DRL) approach for delay-optimal VEC in vehicular networks, where multiple computation tasks select the target threads in a VEC server to execute the computing tasks. We model the target thread decision of computation tasks as a multi-agent reinforcement learning problem, which is further solved by using a task scheduling algorithm based on multi-agent DRL that is implemented in a distributed manner. The computation tasks, with each selection acting on the target thread acting as an agent, collectively interact with the VEC environment and receive observations with respect to a common reward and learn to reduce the task processing delay by updating the multi-agent deep Q network (MADQN) using the obtained experiences. The experimental results show that the proposed DRL-based scheduling algorithm can achieve significant performance improvement, reducing the task processing delay by 40% and increasing the processing probability of success for computation tasks by more than 30% compared with the traditional task scheduling algorithms. [ABSTRACT FROM AUTHOR]

The emergence of vehicular edge computing (VEC) has introduced a new computational paradigm for high-quality processing of computing services in Internet of Vehicles (IoV) scenarios. However, due to the limited computational resources of the VEC server, it is not sufficient to adequately meet the demand for highly concurrent computational services in high-density vehicular communication networks. To address this issue, we consider an idle mobile vehicles assisted vehicular edge computing framework and propose a hybrid Stackelberg-Match cooperative task offloading and resource pricing algorithm (SMOP). The algorithm considers the mobility of vehicles and the duration of channels, coordinating the computational resources of fixed VEC servers and idle mobile vehicles within the vehicular network (VN). This enhances offloading efficiency and maximizes participant benefits. Specifically, the Stackelberg game is used to derive differentiated pricing schemes for idle mobile vehicles and VEC servers for different vehicular tasks, and the stable matching method is employed to determine task offloading strategies. Finally, we conduct experiments on a real Chengdu traffic dataset. The results demonstrate that the proposed solution effectively reduces offloading costs and exhibits strong robustness in handling latency-sensitive and data-intensive service requests. [ABSTRACT FROM AUTHOR]

The rapid growth and technological progressions in the vehicle edge computing and networks (VECON) enhanced existing vehicular administrations through information sharing and information investigation, which further incremented traffic security difficulties. This carries the need to verify vehicle networks with gigantic information stockpiling substances. Recognizing the vehicles that communicate fashioned messages and ensuring the protection of every vehicle is an essential assignment. Blockchain can be utilized as an effective solution to provide security during vehicle edge computing. Every transaction and data is recorded in the blockchain, which expands the number of blocks after some time. Another test in blockchain methods is utilizing a consensus mechanism, which can be effectively undermined by the attackers. Artificial intelligence (AI) trained by machine learning (ML) algorithms as an amazing paradigm can be incorporated with blockchain to settle these issues. The data storing methods of blockchain can likewise be enhanced with the assistance of ML algorithms. In this paper, a two-tier authenticated consortium blockchain (TTA-CB) protocol is proposed for secure information partaking in Vehicle Edge Computing and Networks (VECONs). Utilizing a one-time password (OTP) based notoriety estimation calculation, the misbehaving vehicles are recognized. The use of Particle Swarm Optimization (PSO) understands the ideal data provider selection issue utilizing notoriety esteems. Exploratory outcomes demonstrate the importance of the proposed strategy, and the correlation results demonstrate that the proposed technique is unrivaled and secure. [ABSTRACT FROM AUTHOR]

Mobile Edge Computing (MEC) is a promising technology to extend the diverse services to the edge of Internet of Things (IoT) system. However, the static edge server deployment may cause “service hole” in IoT networks in which the location and service requests of the User Equipments (UEs) may be dynamically changing. In this paper, we firstly explore a vehicle edge computing network architecture in which the vehicles can act as the mobile edge servers to provide computation services for nearby UEs. Then, we propose as vehicle-assisted offloading scheme for UEs while considering the delay of the computation task. Accordingly, an optimization problem is formulated to maximize the long-term utility of the vehicle edge computing network. Considering the stochastic vehicle traffic, dynamic computation requests and time-varying communication conditions, the problem is further formulated as a semi-Markov process and two reinforcement learning methods: $Q$ -learning based method and deep reinforcement learning (DRL) method, are proposed to obtain the optimal policies of computation offloading and resource allocation. Finally, we analyze the effectiveness of the proposed scheme in the vehicular edge computing network by giving numerical results. [ABSTRACT FROM AUTHOR]