Energy-Efficient Mobility Prediction Routing Protocol for Freely Floating Underwater Acoustic Sensor Networks

Abstract

Recently, there has been an increasing interest in monitoring and exploring underwater environments for scientific applications such as oceanographic data collection, marine surveillance, and pollution detection. Underwater acoustic sensor networks (UASNs) have been proposed as the enabling technology to observe, map, and explore the ocean. The unique characteristics of underwater aquatic environments such as low bandwidth, long propagation delays, and high energy consumption make the data forwarding process very difficult. Moreover, the mobility of the underwater sensors is considered an additional constraint for the success of the data forwarding process. That being said, most of the data forwarding protocols do not realistically consider the dynamic topology of underwater environment as sensor nodes move with the water currents, which is a natural phenomenon. In this research, we propose a mobility prediction optimal data forwarding (MPODF) protocol for UASNs based on mobility prediction. Indeed, by considering a realistic, physically inspired mobility model, our protocol succeeds to forward every generated data packet through one single best path without the need to exchange notification messages, thanks to the mobility prediction module. Simulation results show that our protocol achieves a high packet delivery ratio, high energy efficiency, and reduced end-to-end delay.

Optimal path selection and secured data transmission in underwater acoustic sensor networks: LSTM-based energy prediction

“…In 2021, Alqahtani GJ et al [ 30 ] Proposed a mobility prediction optimal data forwarding (MPODF) protocol for UASNs, leveraging mobility prediction techniques. The protocol incorporates a realistic and physically inspired mobility model to predict sensor node movements accurately.…”

“…Wang et al [ 29 ] designed the Edge prediction-based adaptive data transmission algorithm (EP-ADTA) with an end-edge-cloud architecture, aiming to develop a comprehensive data transmission algorithm considering application requirements. Alqahtani GJ et al [ 30 ] introduced the Mobility prediction optimal data forwarding (MPODF) protocol for UASNs, incorporating a realistic and physically inspired mobility model, achieving high packet delivery ratio, energy efficiency, and reduced end-to-end delay. Anitha et al [ 31 ] proposed an intelligent selection method for modulation schemes in UWA communication systems using a hybrid learning model, achieving a high accuracy rate and improved Bit Error Rate (BER) performance.…”

Chimp Optimization Algorithm Contribution Support To Increases Energy Efficiency In UWASN

“…Buffalo algorithm value of propagation delay is more compared to chimp and same similarly to mean delay. Chimp algorithm produces better output compared to Buffalo etc[21]. Several nodes use a transmission from anchor nodes to sink nodes.…”