International Journal of Science, Engineering and TechnologyAuthors: Rishikesh Kumar, Professor Sujeet Gautam, Professor S Vishwakarma
Abstract: In underwater acoustic sensor networks (UWASN), ensuring energy-efficient data communication remains a significant challenge due to the harsh and unpredictable underwater environment. Acoustic signal transmission is often affected by high ambient noise, extremely long propagation delays, increased bit error rates, limited available bandwidth, and various forms of interference. As a result, one of the primary objectives of UWASN research is to prolong the operational lifetime of the network by optimizing energy usage. The process of reliably transmitting data from a source node to a destination node in UWASN is inherently complex and continues to be a critical area of investigation. To address this issue, the Acoustic Wireless Node Energy Optimization (AWNEO) model is proposed as an efficient network clustering strategy aimed at minimizing communication-related energy losses. The model leverages a group-based concept inspired by the Teacher Learning Algorithm, which enhances overall operational efficiency by facilitating effective knowledge sharing and optimization among nodes. In the proposed approach, selected nodes act as cluster heads and are responsible for forwarding aggregated data packets to the base station, thereby reducing redundant transmissions and conserving energy. Experimental evaluation demonstrates that the AWNEO model outperforms existing acoustic network optimization algorithms across multiple performance parameters, including energy consumption, network stability, and data transmission efficiency.
