Routing Protocol Optimization of WSNs for Underwater Acoustic and Medical Applications

Abstract

This thesis delves into the hierarchical organization of sensors within newlinewireless sensor networks, proposing a comprehensive approach to newlineaggregate data and relay it directly to remote base stations. While the newlinecurrent Fuzzy formulation has been utilized in developing FUZZY KNN and newlineFUZZY KNN PCA, it proves insufficient for creating a hybrid fuzzy machine newlinelearning algorithm. The research introduces a Probabilistic KNN and newlineexplores fuzzy base-logic theory parameter analyses for KNN, employing a newlinesupervised machine learning approach for aggregated data classification. newlineThe combined use of two fine and weight Fuzzy KNNs achieves 100% newlineaccuracy, outperforming alternatives with a 96% difference in Fuzzy s PCA newlineat a reduced time of 10.34 seconds. Shifting focus to underwater acoustic newlinesensor networks, the study addresses energy limitations due to battery newlineissues and extends fuzzy logic mechanisms to enhance network lifetime, newlineparticularly in the context of acoustic waves from rock fractures. The newlineproposed cooperative opportunistic routine protocol, designed for newlinegeophysical exploration, finds application in remote health monitoring in newlinerural or underserved areas. This protocol facilitates efficient medical data newlinetransmission from wearable devices or sensors, overcoming communication newlinechallenges in remote locations. The optimization of routing protocols is newlineidentified as a key contributor to enhanced energy efficiency, improved data newlineaccuracy, and increased reliability, thereby supporting advancements in newlinetelemedicine, patient care, and public health i newline