Energy-Efficient Task Split and Resource Allocation in LEO-Satellite-Assisted IoT Network
Qingtian Wang, Siyu Chen, Changlin Yang, Wen Qi, Jiaying Zong, Xinjiang Xia, Dong Wang
Abstract
The Internet of Things (IoT) system provides sensing and computing services via terrestrial networks. However, the restricted coverage of terrestrial networks, such as base stations, limits the ubiquitous IoT services. Low-Earth orbit (LEO) satellites are able to provide network coverage for terrestrial IoT devices in unconnected scenarios, e.g., maritime. IoT devices in such scenarios usually have restricted onboard computation and power resources. In this article, we present an LEO-assisted IoT network (L-IoT) architecture where a device splits its task and offloads a portion of its task to the LEO to process within the coverage time. We formulate a task split problem with communication and computation resource allocation (SCC) to minimize the L-IoT energy consumption. We proposed an alternating optimization for split ratio and resource allocation (AOSR) algorithm. In particular, we use the outputs of Karush-Kuhn–Tucker (KKT) for resource allocation as part of the reward that feeds twin-delayed deep deterministic policy gradient. Lastly, the results of numerical simulations show that the proposed AOSR approach reduces 12.7% energy consumption compared to soft actor-critic (SAC) and 15% to deep deterministic policy gradient (DDPG).