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.