Energy-Aware Task Offloading and Resource Allocation in Space-Aerial-Integrated MEC System

Abstract

Space-aerial-assisted multi-access edge computing (SA-MEC) has recently been a promising solution to offer the ubiquitous communication and computing services to the resource-constrained internet of things (IoT) devices. Particularly, low earth orbit satellites (LEOSats) and unmanned aerial vehicles (UAVs) having the computing resources onboard assist those devices to compute their generated tasks with the minimum delay under the energy budget. However, due to the existence of inter-cell interference among the devices, they may consume more energy or incur longer delay to offload the tasks to the UAVs. Therefore, the optimal task offloading and resource (channels) allocation should be determined without dissipating much energy and overloading the UAVs. In this work, BCD-based task offloading and resource allocation scheme is proposed to minimize the total task completion latency of the devices by considering the scarce communication resources and energy limitation of devices and UAVs.