Multi-queue Lyapunov-guided deep reinforcement learning scheme for mobile edge computing offloading

Purpose In time-critical natural disaster scenarios, unmanned aerial vehicles (UAVs) are crucial for search and rescue. While mobile edge computing (MEC) enables real-time data processing for these UAVs, it introduces a significant challenge: balancing low-delay data analysis to locate survivors against the UAVs’ limited battery life. This paper aims to propose a solution to minimize task processing delay in dynamic rescue environments while conserving UAV energy. Design/methodology/approach To overcome this challenge, this study proposes the multi-queue Lyapunov-guided deep reinforcement learning (MQ-LyDRL) method to minimize task processing delay by jointly optimizing task offloading and resource allocation. This method innovatively integrates Lyapunov optimization with DRL. Specifically, by constructing Lyapunov functions based on queue stability and energy constraints, MQ-LyDRL decomposes the complex multistage stochastic optimization problem into a deterministic, per-time-slot subproblem. An adaptive DRL framework is then employed to solve this subproblem, enabling it to learn the optimal policy for real-time decision-making without requiring prior knowledge of the environment’s dynamics. Findings Extensive simulations demonstrate that MQ-LyDRL significantly outperforms existing methods. It maintains operational stability in fluctuating conditions and reduces average delay by at least 9.21% while adhering to an energy budget. This reduction translates to faster data-to-decision cycles, accelerating life-saving interventions by extending the operational time of UAVs. Originality/value This work’s primary value is providing a blueprint for intelligent and efficient edge computing systems in high-stakes scenarios. By combining stability theory with adaptive artificial intelligence (AI), this study offers a practical framework applicable to critical missions where performance and reliability are nonnegotiable.