Age-of-Task-Aware AAV-Based Mobile Edge Computing Techniques in Emergency Rescue Applications

Abstract

In the case of extreme natural disasters like typhoons, earthquakes, and forest fires, the terrestrial communication infrastructure often suffers from severe damage, which seriously undermines the effectiveness of emergency response efforts, leading to critical challenges, such as the timely assessment of disasters, the quick emergency response strategy development, and the rapid implementation of reconnaissance and search-and-rescue operations. To address these challenge issues, autonomous aerial vehicles (AAVs)-based mobile edge computing (MEC) techniques had attracted research attention to effectively support emergency communication, disaster assessment, and rescue strategy decisions. In order to characterize the time-critical requirements of many emergency rescue applications, the concept of “Age of Task” (AoT) was introduced in this article as a metric for assessing the timeliness of task, and the minimization of the weighted AoT across all the terrestrial user equipments (UEs) was formulated. By leveraging the Lyapunov optimization analysis framework, the problem of minimizing the time-averaged weighted AoT was transformed into a series of real-time subproblems that involve task offloading scheduling decision, computational resource allocation, UE transmit power control, and AAV flight trajectory planning, all of which enable an AoT-aware AAV-based MEC network for emergency rescue applications. To highlight the effectiveness, four benchmark schemes were included for comparison to show the advantages of the AoT-aware adaptive AAV-based MEC algorithm (AAAUMA) in terms of the realized task freshness performance, lower energy consumption by the AAV, and smaller data buffer backlog sizes at all ground source nodes.