• Heta Desai

Wireless sensor networks (WSNs) are used to monitor environments that often undergo rapid and unpredictable changes. These variations can stem from external influences or be introduced intentionally by system designers. To effectively respond to such dynamic conditions, WSNs frequently employ machine learning techniques, which reduce the need for frequent manual reconfiguration. Machine learning also contributes to efficient resource usage and helps extend the operational life of these networks. This paper presents a comprehensive review of machine learning approaches applied to WSNs from 2002 to 2013. It examines how various algorithms have been used to tackle key challenges in WSNs and evaluates their strengths and limitations in relation to each problem. Additionally, a comparative overview is provided to help WSN developers select appropriate machine learning methods tailored to their specific application needs.

Wireless sensor networks, machine learning, data mining, security, localization, clustering, data aggregation, event detection, query processing, data integrity, fault detection, medium access control, compressive sensing.

"Applying Machine Learning Techniques to Wireless Sensor Networks: Methods and Use Cases", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.3, Issue 5, page no.339-362, May-2016, Available :http://www.jetir.org/papers/JETIR1701D04.pdf

"Applying Machine Learning Techniques to Wireless Sensor Networks: Methods and Use Cases", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.3, Issue 5, page no. pp339-362, May-2016, Available at : http://www.jetir.org/papers/JETIR1701D04.pdf