Optimization of node deployment in underwater internet of things using novel adaptive long short-term memory-based egret swarm optimization algorithm

Abstract

Optimizing node deployment in the underwater Internet of Things (UIoT) poses significant challenges due to the complex and dynamic nature of underwater environments. This research introduces the adaptive long short-term memory-based egret swarm optimization algorithm (ALSTM-ESOA), a novel approach designed to enhance network coverage and performance efficiently. Unlike traditional methods, ALSTM-ESOA incorporates cognitive learning capabilities from long short-term memory (LSTM) and dynamic adaptation strategies inspired by the hunting behaviors of egrets. The algorithm's effectiveness was tested through extensive simulations in MATLAB, demonstrating notable improvements over existing models: network throughput increased by up to 55.56%, deployment time decreased by 88.89%, and energy efficiency improved significantly. These enhancements are critical for robust, real-time data collection and monitoring in underwater settings, providing substantial benefits for marine research and resource management. The findings suggest that ALSTM-ESOA significantly outperforms conventional algorithms, offering a promising new tool for the advancement of UIoT applications. After being implemented in MATLAB, the suggested ALSTM-ESOA model for the node deployment optimization in UIoT is examined. The proposed ALSTM-ESOA in terms of network throughput is 55.56%, 38.89%, 36.11%, and 11.11% better than CNN, LSTM, ARO-RTP, and IGOR-TSA, respectively. Similarly, the proposed ALSTM-ESOA with respect to deployment time is 88.89%, 81.82%, 75%, and 50% better than CNN, LSTM, ARO-RTP, and IGOR-TSA, respectively. For the purpose of exploring marine resources, monitoring underwater environments, and conducting marine scientific investigation, the research's findings are extremely valuable.