Jul 2026· 2026 8th International Conference on Electronics and Communication, Network and Computer Technology (ECNCT)· pp. 339-342· 0 citations· 15 references
Abstract
Multi-UAV mobile edge computing can provide flexible computing services for ground users, but it introduces coupled decisions in user association, channel allocation, cooperative offloading, and power control. This paper studies collaborative task offloading in a multi-UAV-assisted MEC system. A multi-stage service process is modeled, including user-to-UAV uploading, local computation, UAV-to-UAV cooperative transmission, cooperative computation, and result return. A comprehensive performance objective is formulated by jointly considering task success rate, delay cost, and energy consumption. To solve the resulting mixed discrete-continuous sequential decision problem, we propose GNN-MAHPPO, a multi-agent hybrid-action PPO algorithm enhanced by dual-layer heterogeneous graph attention. The proposed method jointly optimizes user association, channel allocation, cooperative offloading ratios, and transmit powers. Simulation results show that the proposed method achieves a higher task success rate and lower completion delay and energy consumption than the representative baselines, demonstrating its effectiveness in coordinating access, cooperation, and resource allocation.
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.
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
This paper proposes a heterogeneous multi-agent proximal policy optimization (MAPPO)-based framework where both user devices and UAVs act as heterogeneous agents and utilizes a centralized training and decentralized execution (CTDE) paradigm to enable collaborative strategies between computing requesters and providers.
Ming Cheng, Canlin Zhu, Jiang-Hang Tang et al.· Journal of King Saud Univers...· 0 citations
An adaptive Beta-policy and delayed-update multi-agent soft actor-critic method, abbreviated as ABDMASAC, which uses a Beta policy to model bounded actions and achieves a better overall trade-off than the selected MASAC-backbone and on-policy MARL baselines under the considered simulation settings.
Zheng Yao, Jie Liu, Changjun Deng et al.· Computers, Materials & C...· 0 citations
Simulation results confirm that the proposed JORC framework substantially reduces latency, energy consumption, and overall system cost, while increasing the successful task completion ratio compared to existing baseline approaches.
Tanmay Baidya, S. Moh· Italian National Conference...· 0 citations
This paper investigates the joint optimization of UAV deployment and computation task offloading in a hybrid UAV-enhanced mobile edge computing (MEC) network comprising high-performance computing UAVs (H-UAVs) and relay UAVs (R-UAVs). In this network, all UAVs serve as access nodes (ANs) and computing nodes (CNs) for mobile terrestrial devices (MTDs), while R-UAVs additionally function as relay ANs that can forward offloaded tasks from their covered MTDs to adjacent H-UAVs. To tackle the combinatorial complexity, we propose a hierarchical framework integrating two core innovations: a three-sided matching game with hybrid pReferences for task offloading among MTDs, ANs, and CNs, and a state-based potential game that embeds the offloading state into UAV deployment optimization. The inner-layer matching game achieves stable matching via individually rational hybrid preferences, while the outer-layer potential game converges to a stable-state equilibrium (SSE) through marginal-contribution-based payoffs. We prove the existence of the SSE and develop a joint distributed iterative algorithm to attain it. Numerical results demonstrate the superiority of the proposed approach over baseline methods.
Ducheng Wu, Wenping Song, Miao Li et al.· IEEE Open Journal of the Com...· 0 citations