TL;DR
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.
A Lyapunov-based joint optimization framework for UAV-enabled MEC systems achieves a balanced tradeoff between delay, energy consumption, and UAV flight activity, supporting energy-efficient and delay-aware UAV-MEC operation.
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.
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.
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.
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.