Aug 2026· Cluster Computing· Vol 29· 0 citations· 166 references
Computer Science
TL;DR
This paper surveys the U-MEC landscape through the lens of method–scenario mapping, and compares optimization approaches across six categories—convex optimization, heuristic algorithms, game theory, deep reinforcement learning, multi-agent reinforcement learning, and federated learning—in terms of applicability and technical characteristics.
The results confirm that the integration of artificial intelligence, energy-aware routing, and UAV trajectory optimization provides an effective and scalable solution for next-generation UAV-assisted IoT systems and establishes a robust foundation for intelligent 6G-enabled wireless sensor networks.
Mojtaba Nasehi· Internet of Things and Cloud...· 0 citations
Timely vehicular sensing is important for traffic monitoring, cooperative driving, and road-safety management. High mobility, time-varying wireless conditions, and limited edge resources nevertheless make information freshness difficult to maintain. This paper studies age of information (AoI) minimization in a three-la...
Xue-Yuan Wang, Si-Yu Bai, Yu Zhang et al.· Italian National Conference...· 0 citations
A comprehensive overview of lightweight AI techniques for UAV-mounted RIS systems, including Reinforcement Learning (RL), meta-learning, meta-learning, Federated Learning (FL), Multi-Armed Bandits (MAB), and energy-aware optimization are provided.
Sherief Hashima, Kohei Hatano, Eiji Takimoto et al.· 0 citations
Unmanned aerial vehicle (UAV)-assisted wireless-powered communication networks (WPCNs) have emerged as a promising solution for energy-constrained Industrial Internet of Things systems, where ground sensor nodes are often deployed in harsh and hard-to-reach environments. However, efficient UAV-assisted data collection...
Si-Liang Gong, Kai-Yang Qu, Qi-Sen Wang et al.· Electronics· 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, Jian-Hang Tang et al.· Journal of King Saud Univers...· 0 citations
Driven by the vision of a thriving low-altitude economy and aiming to provide on-demand services for diverse entities, this paper investigates an integrated sensing and communication (ISAC)-enabled low-altitude wireless network (LAWN). Benefiting from flexible mobility and cost-effective cooperative deployment, multipl...
Cheng Ma, Ze-Wei Jing, Qinghai Yang et al.· IEEE Transactions on Wireles...· 1 citation
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