Jun 2026· 2026 IEEE Cloud Summit· pp. 244-247· 0 citations· 5 references
Abstract
With advancements in connected vehicle technology, sophisticated computing equipment is installed to assist resource-intensive applications for better and faster processing. However, due to the high demand for computation, local resources are insufficient, and therefore, tasks are offloaded to nearby network edges to meet task deadlines. A similar approach is adopted for vehicle-to-vehicle task offloading, where underutilized vehicles are used to meet the computation demands of heavily loaded vehicles. Due to dynamic changes in topology caused by vehicle speed and direction, many tasks fail to deliver results after remote computation. In this work, we explore energy consumption in the latter approach, where tasks are executed but fail to deliver results. Furthermore, we propose a multi-layer, energy-enabled task offloading strategy that relies on degree, closeness, and betweenness centrality as the initial selection mechanism, where the second tier selection relies on features such as service time and potential path diversion time. The results show a 56% to 45.17% energy loss reduction in the proposed approach with varying vehicle arrival rates.
A 5G-based task offloading scheme is proposed, aiming to manage computational resources in a mobility-aware manner by considering vehicle mobility, and adapts task export decisions to changing network conditions, thereby improving system robustness and efficiency.
Muhammet Tay, Arafat Şentürk· Pamukkale Üniversitesi Mühen...· 0 citations
A framework based on GTGO to jointly offload, schedule and allocate resources to different tasks and augment it with an integrated explainable AI (XAI) module is presented, indicating that the suggested framework is an effective, efficient, and transparent resource management solution in intelligent vehicular edge computing systems.
Aditi Moudgil, S. Rani, Fazlullah Khan· PLoS ONE· 0 citations
This paper proposes a hierarchical computation framework that flexibly supports task execution across local vehicles, neighboring vehicles, RSUs, and cloud resources, and designs an efficient task migration and resource scheduling strategy that improves overall system performance under dynamic network conditions.
Liqun Yang· Journal of Grid Computing· 0 citations
A task scheduling method using the Deep Q-Network to determine the computation node for the computation task and a dynamic congestion-aware mechanism to determine a low-cost routing path is proposed, which gradually obtains an effective task scheduling scheme through multiple rounds of alternating iterations.
The Internet of Electric Vehicles (IoEV) has emerged as a key component of future networks. However, some computation‐intensive vehicular applications cannot be executed locally owing to IoEV infrastructure limitations and computing resource bottlenecks. In this study, we propose a task offloading and resource management scheme based on Parked Electric Vehicle (PEV)‐assisted distributed edge intelligence (DEI), making use of underutilized PEV resources to handle offloaded tasks. In the proposed scheme, distributional reinforcement learning, the normalized average bargaining solution (NABS), and V2G charging scheduling are jointly combined to dynamically control availability prediction, resource sharing, and scheduling. The proposed scheme maximizes hybrid optimization benefits through PEV and edge server cooperation. Simulation results confirm performance improvements of 10%, 10%, and 15% in normalized service payoff, system throughput, and task failure rate, respectively, compared with existing benchmark protocols. Open issues and research directions for PEV‐DEI systems are also discussed.
The rapid growth of Internet of Vehicles (IoV) applications has imposed strict requirements on low-latency and energy-efficient computing services. This letter investigates a multi-Uncrewed Aerial Vehicle (UAV)-assisted IoV system, where multiple Mobile Edge Computing (MEC)-enabled UAVs (MUs) collaboratively provide computing services for vehicular terminals (VTs). To improve service capability, we propose an energy-efficient task offloading and load balancing scheme that jointly considers vehicle mobility, task offloading and migration, and computing resource allocation to formulate an optimization problem. To solve this problem, a collective learning (CL)-enabled multi-agent reinforcement learning (CL-MARL) algorithm is proposed, where each agent learns optimal policies through centralized training and collective cooperative learning. Simulation results demonstrate that the proposed scheme outperforms benchmark strategies in terms of energy efficiency, task completion rate, and load balancing.
Yongbin Wang, Peng Lin, Yan Liu et al.· IEEE Wireless Communications...· 0 citations