Jul 2026· International Conference on Edge Computing [Services Society]· pp. 122-127· 0 citations· 11 references
Abstract
With the rapid development of intelligent vehicles and the Internet of Things (IoT), Internet of Vehicles (IoV) technology is gradually being applied in more streets. However, traditional cloud computing is unable to meet the higher requirements of the IoV system. Mobile edge computing (MEC), which is suitable for mobile networks, provides a better paradigm for intelligent transportation systems (ITS). This paper proposes a joint task offloading method based on MEC (JTOM) in the IoV environment. Firstly, it obtains the sequence of transferable routes for onboard tasks in the system. Then, it determines the offloading location of the tasks based on the task requirements and computing resources. Finally, it optimizes multiple objectives using the non-dominated sorting genetic algorithm II (NSGA-II) with the latency and energy consumption as the optimization goals. In the end, experiments are conducted to prove the feasibility of JTOM.
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 comp...
Aditi Moudgil, S. Rani, Fazlullah Khan· PLoS ONE· 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 work proposes a meta-heuristic approach that integrates the honey bee Food Foraging process with Genetics Algorithm (FFGA) for vehicular task offloading, and demonstrates that the proposed FFGA system outperforms other existing schemes, including the hybrid vehicular edge cloud (HVC), particle swarm optimization (...
Mohamed Kamel Benbraika, O. Kraa· Electrotehnică, electronică,...· 0 citations
This work aims to design an efficient framework by incorporating a novel hybrid metaheuristic algorithm that combines Draco Lizard Optimization (DLO) and Sand Cat Optimization (SCO) for optimal task offloading and resource allocation for IoT applications.
Vehicular fog computing (VFC) enhances compute-intensive task processing by exploiting idle vehicle resources. However, existing offloading mechanisms may fail due to dynamic factors, such as vehicle mobility, unstable links, and service overload. This paper proposes an offloading-failure-aware (OFA) task offloading sc...
Yi-Hao Wu, Yanli Qi, Yiqing Zhou et al.· IEEE Transactions on Network...· 0 citations
To address the urgent practical challenge of service migration for connected autonomous vehicles (CAVs) in mobile edge computing (MEC), this study aims to maximize the task completion rate, particularly for safety-critical operations. Existing approaches often overlook the completion status of tasks with different prio...
