Aug 2026· Cluster Computing· Vol 29· 0 citations· 138 references
Computer Science
TL;DR
This survey aims to provide a unified perspective for understanding DTO and provide methodological guidance for designing next-generation VEC systems and proposes a synthesized five-dimensional dependency taxonomy specifically designed for VEC.
This survey classifies hybrid algorithms into six basic categories and two extended categories, characterize environmental dynamism along three dimensions—channel dynamics, topology dynamics, and task dynamics—and define four operating regimes from static to highly dynamic.
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...
Insaf Rzig, W. Jaafar, Safwan Alfattani· International Conference on...· 0 citations
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
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
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.
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.
