An energy efficient dead reckoning localization for mobile Underwater Acoustic Sensor Networks

Abstract

A wide range of marine applications require accurate positioning information of sensor nodes in Underwater Acoustic Sensor Networks (UASNs). In large-scale UASNs, locating the accurate position of the Mobile Sensor Node (MSN) is difficult without Global Positioning System (GPS) signal. Though the prediction of active MSN location is possible by the received information, predicting the location of a dead MSN without consuming sensor node energy is a challenging task for many decades, which has not gained any practical solution. In literature, the underwater sensor nodes are localized by sending and receiving the beacons from the source to a Base Station (BS) by consuming sensors energy. In this article, an Energy-free Heuristic Neural Network (HNN) Localization approach is proposed using Deep Learning (DL) technique to locate the dead MSN in a large-scale UASN. The proposed HNN leverages the Long- and Short-Term Memory (LSTM) technique to train huge data sets, and introduces Particle Swarm Optimization (PSO) with Repeated Iterative Technique (RFT) to reduce prediction errors during dead reckoning localization. The HNN localization achieves minimum localization error and high accuracy compared to the existing DL approaches. The localization is carried out at the Bay of Bengal, the Indian Ocean, and the Arctic Ocean. The simulation results of HNN model provide a good localization accuracy of 99% at the Bay of Bengal, 98% accuracy at the Indian Ocean, and 97% accuracy at the Arctic Ocean respectively. This model also achieves less time synchronization and low communication overhead without consuming the energy of MSN.