Jun 2026· Cluster Computing· Vol 29· 0 citations· 30 references
Computer Science
TL;DR
It is demonstrated that the proposed IA-SAC based task offloading algorithm effectively reduces the total task processing delay and improves resource utilization.
Vehicular edge computing (VEC), a key enabler for the Internet of Things (IoT) in intelligent transportation, addresses onboard processing constraints through collaborative task offloading among vehicles, facilitating latency-sensitive applications such as autonomous driving. However, developing efficient offloading st...
Momiao Zhou, Yimin Zhou, Yanshi Sun et al.· IEEE Internet of Things Jour...· 0 citations
6G vehicular services, including cooperative perception, augmented reality navigation, and high-definition map updating, need computation support close to moving vehicles. Vehicular Edge Computing (VEC) is a natural solution, but the offloading decision becomes difficult when wireless channel conditions, vehicle densit...
Zi-Heng Gu· 2026 8th International Confe...· 0 citations
A constraint-aware multi-agent edge collaborative offloading algorithm (CARE-CTDE) that achieves better scheduling performance, resource utilization, and constraint satisfaction than baseline methods in dynamic heterogeneous MEC scenarios, demonstrating its effectiveness and robustness for constrained edge computing sy...
Yuxuan Yang, Hexing Wang, Yang Zhou· Mathematics· 0 citations
Results show that coordinated MEC-side control of AI model selection and computation, under a shared deadline-aware objective, provides a robust latency–energy trade-off for MEC-assisted vehicular networks.
Inseok Song, Seung-Woo Kang, Seyha Ros et al.· Italian National Conference...· 0 citations
This paper considers priority-aware partial computation offloading in an uplink mobile edge computing (MEC) network. Devices assigned to different groups occupy orthogonal subbands, whereas devices within each group use power-domain non-orthogonal multiple access (NOMA) with successive interference cancellation. Task-i...
Jamil K. J. Bataineh, Ahlam Jawarneh, K. Hayajneh et al.· Italian National Conference...· 0 citations
This paper addresses the joint task offloading and resource allocation problem in multi-user MEC systems and proposes a decentralized control framework based on Multi-Agent Reinforcement Learning (MARL), which achieves lower total system cost and faster convergence than the full-local, full-offload, and heuristic basel...
Youssef Oukissou, Mohamed Amine Meddaoui, Ayoub Belaidi et al.· International journal of Com...· 0 citations
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