Energy-Efficient Space-Air-Ground-Ocean-Integrated Network Based on Intelligent Autonomous Underwater Glider

Zhengjian Li, Jiabao Wen, Jiachen Yang, Jingyi He, Tianlei Ni, Yang Li

Abstract: Internet of Things (IoT) has extended its coverage to various spatial domains and has established interconnection to serve widespread applications of a larger spatial scale. Such IoT is called the space–air–ground–ocean-integrated network (SAGOI-Net), which consists of multiple battery-powered heterogeneous devices. Hence, energy efficiency is the key point of SAGOI-Net to be stably operated for a long time without manual maintenance. This article proposes a novel scheme of energy-efficient autonomous and decentralized SAGOI-Net establishment using an intelligent autonomous underwater glider (AUG) to serve marine applications. The proposed SAGOI-Net is energy efficient because the energy consumption is minimized by: 1) employing nonpropeller-driven AUG; 2) navigating AUG under water without acoustic sensor or extra energy-consuming vision sensors; and 3) equipping the self-navigation (SN) system based on lightweight neural network model to save the energy consumption of onboard computing resource. Moreover, assuming the AUG navigation problem as time-series regression, the proposed scheme designs SAGOI-Net to be autonomous and decentralized with the aid of lightweight long short-term memory (LSTM) network-based SN (SN-LSTM) system of AUG. The lightweight SN-LSTM model is trained end-to-end on dynamically modeled AUG motion information along with numerically modeled ocean environment data to quantitatively analyze the impact of the ocean environment on AUG. The simulation results demonstrate a superior performance of the AUG SN along with energy efficiency of the proposed SAGOI-Net.