Drift-Plus-Penalty-Based Joint Optimization of Computational Resource Scheduling, Power Control, and UAV Flight Decisions in UAV-Enabled Mobile Edge Computing

Multi-UAV Energy Consumption Minimization for Multilayer Aerial Wireless-Powered MEC: An Online Stochastic Optimization Approach

Efficient dynamic cooperative deployment and task scheduling in multi-UAV-assisted MEC for dense dynamic environments.

With the rapid development of the Internet of Things (IoT) and mobile computing, edge computing has emerged as a promising paradigm for providing low-latency and energy-efficient services. However, in some extremely computation-intensive scenarios, conventional terrestrial edge computing may fail due to the insufficient computing capability of ground base stations. Fortunately, multi-UAV-assisted edge computing offers a promising solution to this challenge. Nevertheless, existing methods often struggle to provide efficient horizontal cooperative deployment for multiple UAVs with low computational overhead. To address this issue, this paper considers user randomness and inter-UAV collaboration, and proposes a low-complexity yet highly adaptive approach for cooperative deployment and task-scheduling optimization in multi-UAV-assisted edge computing systems. Specifically, we formulate the problem as a stochastic optimization problem that minimizes the energy consumption of ground users while ensuring UAV battery endurance and overall system performance. We then propose a dynamic cooperative deployment and task scheduling (DCDTS) algorithm that integrates K-means clustering with the Lyapunov optimization framework. Through Lyapunov optimization, the original dynamic optimization problem is transformed into a deterministic problem and further decomposed into multiple subproblems that can be solved in parallel. K-means is exploited to enable cooperative UAV deployment and user offloading decisions, while non-convex optimization and nonlinear programming are employed to solve the task-scheduling and resource-allocation subproblem. Extensive parameter analysis and comparative experiments demonstrate that the proposed dynamic cooperative deployment algorithm can effectively reduce user energy consumption while maintaining UAV energy constraints and system performance.

Toward Low-Delay and Energy-Efficient UAV-Assisted MEC Systems Through Intelligent Resource Allocation

A Prioritized Adaptive Weighting based on Deep Deterministic Policy Gradient (PAW-DDPG) as an enhanced Deep Deterministic Policy Gradient (DDPG) algorithm to minimize both processing delay and energy consumption by jointly optimizing user scheduling, partial-task offloading, and UAV trajectory is proposed.

Coverage-aware offloading in multi-UAV-aided terrestrial MEC networks using quantum-inspired particle swarm optimization

An MECN that integrates UAVs as the aerial layer and TESs as the terrestrial layer is introduced, and a quantum-inspired particle swarm optimization-based offloading strategy (QIPSO-TOS) is proposed to facilitate coverage-aware task offloading.

Joint Task Offloading and Resource Allocation with Data Caching in UAV-Aided Mobile Edge Computing Networks for Latency-Sensitive Applications

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.

Causality-Aware Scheduling and Mobility Control in Multi-UAV Cooperative MEC via Graph-Enhanced Dual-Timescale Learning

Multi-uncrewed aerial vehicle (UAV) cooperative mobile edge computing (MEC) systems present significant challenges owing to task causal dependencies, dynamic channel variations, and multi-dimensional resource coupling. In this study, a multi-UAV cooperative MEC system with task causality constraints and time-varying wireless channels is considered, and the joint optimization of task offloading, task migration, dynamic UAV clustering, and continuous UAV trajectory planning is investigated. The objective is to minimize the long-term weighted sum of system latency and energy consumption while ensuring task queue stability. Lyapunov optimization is first introduced to transform the formulated stochastic mixed-integer problem into a deterministic per-slot optimization. Afterward, a dual-timescale graph-enhanced multi-agent proximal policy optimization (DT-HGMAPPO) framework is proposed to coordinate long-timescale UAV clustering and trajectory planning with short-timescale task offloading and migration. Specifically, this framework decouples the problem by utilizing dual-layer weighted hypergraph matching (DL-WHM) for joint clustering and association, a dynamic priority scoring (DPS) mechanism for intra-cluster load balancing, and a graph-enhanced MAPPO algorithm for trajectory optimization. Simulation results reveal that the proposed DT-HGMAPPO algorithm outperforms conventional multi-agent deep reinforcement learning baselines in terms of both convergence speed and policy stability. It achieves a final reward that is at least 18.0% greater than that of other multi-agent algorithms. Moreover, the proposed framework reduces total system cost by 33.3% compared with MASAC and 18.9% compared with MATD3, thereby achieving a superior delay-energy trade-off while ensuring queue stability.