ABSTRACT
Machine learning (ML) algorithms play a crucial role in wireless sensor networks as they help in simplifying huge amounts of data generated and gathered at the central nodes. Moreover, the nodes are deployed in a dynamic environment. This changing environment is caused by external factors, like environmental conditions, which influence the design of the system. Due to the dynamic nature of wireless sensor networks, the ML approach is appropriate to monitor and control the sensor node activities, ranging from data collection, node mobility, routing, security, etc. ML algorithms have been successfully implemented for WSNs to improve event detection functions, such as forest and residential building fires. Detecting forest fires using machine learning in WSNs has been found to be much cheaper when compared to traditional satellite-based solutions. Moreover, in most of the scalable WSN systems, the use of global positioning systems (GPS) in every node is financially infeasible.
