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

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 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 paper proposes a novel scheme of energy efficient autonomous and decentralized SAGOI-Net establishment using 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 non-propeller-driven AUG; 2) Navigating AUG under water without acoustic sensor or extra energy-consuming vision sensors; 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 self-navigation (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 quantitively analyze the impact of the ocean environment on AUG. The simulation results demonstrate superior performance of the AUG self-navigation along with energy efficiency of the proposed SAGOI-Net.