Optimizing Wireless Sensor Networks Using Machine Learning
Sandhya Armoogum, Doorgesh Sookarah · 2024
Sensor networks are becoming increasingly common with the advent of IoT. Such networks include industrial sensor networks, body sensor networks, smart home sensor networks, smart cities sensor networks, etc. As wireless sensors are easily deployed and configured, wireless sensor networks (WSNs) are more flexible and thus quite popular, especially in harsh and inaccessible environments. Such WSNs can be enhanced by means of intelligent clustering, routing, and localization. Machine learning can greatly contribute to optimizing the performance of such WSN systems. Machine learning techniques have been used to improve the security, routing, coverage and connectivity, localization, congestion control, data aggregation, quality of service, as well as energy harvesting for improving the lifetime of WSNs. Machine learning is also being adopted in WSN applications. This chapter presents a review of the research work done related to the adoption of machine learning techniques for optimizing WSNs. Furthermore, this chapter discusses the challenges related to the application of machine learning algorithms in the area of WSNs.