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Overview of Fault Diagnosis in Wireless Sensor Network
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Author(s): Dattatray G. Takale (BRACT's Vishwakarma Institute of Information Technology, India), Parikshit N. Mahalle (BRACT's Vishwakarma Institute of Technology, Pune, India)and Bipin Sule (Vishwakarma Institute of Technology, India)
Copyright: 2025
Pages: 14
Source title:
Machine Learning for Environmental Monitoring in Wireless Sensor Networks
Source Author(s)/Editor(s): Parikshit N. Mahalle (Department of Artificial Intelligence and Data Science, Vishwakarma Institute of Technology, Pune, India), Dattatray G. Takale (Vishwakarma Institute of Information Technology, India), Sachin Sakhare (Vishwakarma Institute of Information Technology, India)and Ganesh B. Regulwar (Vardhaman College of Engineering, India)
DOI: 10.4018/979-8-3693-3940-4.ch011
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Abstract
Fault diagnosis in WSNs)is a critical aspect of ensuring network reliability and performance. Fault diagnosis in WSNs involves the identification, localization, and resolution of these faults to maintain network functionality and data integrity. Several approaches are commonly used for fault diagnosis in WSNs. These include centralized methods, where data is collected and analysed at a central node, distributed algorithms that leverage collaborative processing among sensor nodes, and hybrid techniques that combine elements of both centralized and distributed approaches. Additionally, advancements in machine learning, artificial intelligence, and data fusion techniques have enhanced fault diagnosis capabilities in WSNs, enabling proactive detection and predictive maintenance. Challenges in fault diagnosis include energy efficiency, scalability, and adaptability to dynamic network conditions. Efficient diagnosis algorithms are required to minimize energy consumption while improving diagnosis latency, detection accuracy, and reducing false alarm rates.
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