Optimizing UAV Task Processing in Disaster Response with Lyapunov-Based Edge Computing

This paper introduces a novel cooperative task assignment model tailored for unmanned aerial vehicles (UAV s) leveraging edge computing to enhance data collection and processing efficiencies during disaster scenarios. The objective is to optimize end-to-end delay and energy consumption across UAV trajectories, critical factors in emergency response operations. We present a Lyapunov-based model that strategically addresses these optimization challenges. A pivotal contribution of this work is the development of a Lyapunov Optimization-based task processing for queue-based offloading algorithm that efficiently manages task computations by dynamically balancing between local processing and edge offloading based on real-time assessments of system states and resource constraints. Simulations val-idate the effectiveness of the proposed algorithm, demonstrating notable improvements over existing methodologies. Specifically, the Lyapunov-Based Optimisation (LBO) algorithm reduces the overall operational cost by up to 35 % and energy consumption by 40% compared to benchmark schemes. These enhancements are crucial for deploying UAV s in disaster management, providing a robust framework that ensures rapid, reliable, and energy-efficient data handling capabilities.