Fault Detection and Tolerance in Wireless Sensor Networks: a Study on Reliable Data Transmission Using Machine Learning Algorithms

Abstract

Abstract This research addresses the challenge of enhancing fault detection and tolerance in wireless sensor networks (WSNs) to ensure reliable data transmission in adverse conditions. Through simulation, experimentation, and modeling, the study develops techniques and algorithms for improving WSN fault resilience. Key evaluation criteria include Detection Accuracy, Response Time, Energy Efficiency, and Scalability. Redundancy-based methods, such as node and path redundancy, are explored as effective fault tolerance techniques. Results demonstrate lower response times, improved detection accuracy, energy efficiency, and scalability. The findings contribute to WSN technology by enhancing data accuracy, network resilience, and energy conservation, though challenges and limitations persist.