A UAV-enabled mobile edge computing paradigm for dependent tasks based on a computing power pool

• Proposes a UAV-enabled MEC paradigm based on a computing power pool that exploits task repeatability and dependency to optimize resource sharing. • Joint optimization of offloading strategy, task scheduling, and resource allocation balances UAV energy consumption in a computationally intensive environment. • A two-stage alternate optimization algorithm combining SCA and improved GA effectively solves the NP-hard resource management problem. • Simulation results demonstrate average reductions of 18.41% in time consumption and 21.68% in energy consumption, improving UAV working efficiency.