A Perspective on Impact of Machine Learning in WSNs: Applications and Research Challenges

In the area of new technology Wireless Sensor continues to make rapid progress and work. Communication mainly occurs in the Wireless Sensor Networks (WSNs) with the aid of spatially dispersed, autonomous tiny sensor systems called nodes designed for tracking and sensing related surrounding data. WSNs have unique features, such as node density and in non-infrastructure remotely deployment, non-rechargeable energy, on-demanding computing, and limited storage, which pose many challenges in the implementation and application of WSNs. However, researchers have to face several technical difficulties due to the multidisciplinary dimension of this area. In this chapter, an overview of the wireless sensor network, the types of sensor devices, and its role in a real-time environment and discuss research challenges and applications. Importance of machine learning approaches to solve several WSNs issues. The ultimate purpose of most WSN research is to provide optimum life and secure connectivity to the network.