Algorithms for Fault Detection and Diagnosis in Wireless Sensor Networks Using Deep Learning and Machine Learning - An Overview

The integration of artificial intelligence (AI) with wireless sensor networks (WSN) has created several possibilities, making it a highly discussed topic in the technological realm and holds promise for optimizing data gathering, analysis, and decision-making across diverse domains. The amalgamation of Artificial Intelligence (AI) and Wireless Sensor Networks (WSN) possesses immense potential for diverse sectors and domains. Incorporating Artificial Intelligence (AI) with Wireless Sensor Networks (WSN) can revolutionize many sectors, enhancing their efficiency, productivity, and sustainability. WSNs are made up of numerous low-power and affordable sensor nodes placed in surroundings for a range of military and commercial uses. WSNs are susceptible to various types of faults, including interaction errors, equipment errors, defects in software, link errors, node defects, energy discharge, and physical damage. Due to inexpensive chips, sensor nodes can become unreliable or defective. When faulty nodes lose the ability to interact with the remaining nodes in the wireless system. Even though they may still be operational, they can start transmitting incorrect data to the other nodes, ultimately impacting the operational efficiency of WSNs. To ensure successful networking, sensor nodes need reliable fault identification and diagnosis techniques. The study on fault diagnosis will prioritize data quality, enhance response time, and prolong the network lifetime. This paper explores different fault identification and diagnosis methods and algorithms across various industries over the past five years. This paper assesses the effectiveness of current artificial intelligence-driven fault identification algorithms and suggests potential areas for future research in artificial intelligence.