Aug 2026· PLoS ONE· Vol 21, pp. e0354977 - e0354977· 0 citations· 33 references
Medicine
TL;DR
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.
Abstract
Due to the fast development of intelligent transportation systems and connected vehicles, efficient computation offloading and resource management in vehicular edge computing (VEC) environments have become crucial issues. Low latency, optimality in resource usage, and clarity in decision-making is an open research issue. This paper presents 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. The proposed method enhances system welfare by approximately 15–25 percent and decreases the average task delay by 10–20 percent compared to the baseline approaches as the number of task vehicles increases. The GTGO algorithm converges rapidly and it will stabilize after 30–50 iterations hence guaranteeing computational efficiency. Also, the XAI module is a way of quantitatively understanding the contribution of decision variables to the interpretation of the results, without affecting optimization performance. These findings indicate that the suggested framework is an effective, efficient, and transparent resource management solution in intelligent vehicular edge computing systems.
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
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.
To enable efficient and rational task offloading within the UAV swarm, a matching game‐based task offloading algorithm is proposed, and its stability and convergence are theoretically proven.
Ting Lyu, Yong Heng, Hao Zhang et al.· Concurrency and Computation· 0 citations
A task-driven offloading algorithm based on Balanced Multi-Agent Deep Deterministic Policy Gradient (BMADDPG) that reduces average task processing latency by approximately 22.67% and decreases total system cost by at least 18.32% under high-load scenarios.
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
Integrated Terrestrial–Non-Terrestrial Networks (ITNTN), which combine terrestrial base stations (BSs), High-Altitude Platform Stations (HAPS), and Low-Earth Orbit (LEO) satellites, are key enablers of 6G communication and edge computing (EC) services. However, energy-limited BSs, particularly HAPS and satellites, pose significant sustainability challenges under continuous operation. To address this issue, we propose an on-demand EC server activation framework integrated with intelligent task offloading across ITNTN. A joint optimization problem is formulated to maximize task offloading success while satisfying energy and quality-of-service requirements. To solve it, we propose an online Q-learning policy that adaptively manages task offloading and EC server activation without prior knowledge of traffic dynamics. Simulation results show that the proposed method achieves superior task offloading success and energy efficiency compared to online heuristic and offline metaheuristic baselines. These findings highlight the importance of energyaware On-Off EC control for sustainable ITNTN systems.
Insaf Rzig, W. Jaafar, Safwan Alfattani· International Conference on...· 0 citations