Aug 2026· 2026 IEEE/CIC International Conference on Communications in China (ICCC)· pp. 57-62· 0 citations· 16 references
Abstract
Cooperative computing in unmanned aerial vehicle (UAV) ad hoc networks can improve the overall computing service capability. Unfortunately, open air-to-air wireless links make task offloading vulnerable to eavesdropping, making covert and low-latency offloading a dual challenge. Task offloading performance is subject to multi-layer factors, and isolated singlelayer optimization is inherently limited. This paper investigates the cross-layer optimization of covert task offloading in UAV ad hoc networks. An algorithm for jointly optimizing the offloading decision, offloading size, route selection, bandwidth allocation, and beamforming vectors is proposed to minimize task completion delay. This algorithm is based on a double-chromosome genetic algorithm (DCGA) with an alternating optimization (AO) method during fitness evaluation. Simulation results demonstrate that the proposed algorithm effectively reduces task completion delay while guaranteeing communication covertness, achieving a maximum reduction of 19.87% compared to the baselines.
A rating-aware graded security offloading framework for UAV-assisted MEC networks that maintains a higher offloading ratio and lower system cost than the fixed-security and no-rating baselines in the considered hover-based UAV-MEC scenario.
Han Zhang, Jian-Bin Xue· Journal of King Saud Univers...· 0 citations
Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) utilizes low-altitude resources to provide computation services for ground users (GUs). However, the wireless channels connecting UAVs and GUs are often weak, and the priority of heterogeneous tasks is usually ignored, leading to unsatisfactory quality...
Liang Zhao, Si-Nong Zhang, Huan Zhou et al.· ACM Transactions on Internet...· 0 citations
Recent advances in mobile edge computing (MEC) have transformed unmanned aerial vehicles (UAVs) into intelligent platforms capable of distributed sensing, computing, and communication. However, coordinating multiple UAVs presents significant challenges in task assignment, path planning, and resource allocation. To addr...
Jian-Hua Tang, Ya-Hao Yang, Wei-Jie Hong et al.· IEEE Transactions on Network...· 0 citations
UAV (unmanned aerial vehicle) and IRS (intelligent reflecting surface) assisted mobile edge computing (MEC) faces low quality of service (QoS) due to the single functionality of UAV and IRS. In response, we propose a simultaneous wireless information and power transfer (SWIPT)-MEC network aided by multi-functional UAV...
Si-Nong Zhang, Liang Zhao, Xing-Wang Li et al.· IEEE Wireless Communications...· 1 citation
The low-altitude economy is accelerating the deployment of unmanned aerial vehicles (UAVs) as flexible sensing, communication, and edge-computing platforms. In UAV-assisted Internet of Things (IoT) mobile edge computing (MEC), conventional greedy offloading can become unreliable because per-task decisions ignore shared...
Xiao-Chen Zhang, Tian-Xiang Shen, Wen-Xi Mo et al.· 2026 2nd International Confe...· 0 citations
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