Machine Learning Technique for Wireless Sensor Networks

Wireless Sensor Networks comprise of various low-cost, low-energy sensor nodes that perform the data gathering task. In a network, data or packets are transferred from source to destination via sink node or other coordinating nodes. It can be outlined as a network of devices that communicate information collected from the sensor field. The information flow takes place with the help of wireless links. Sensors are normally qualified by limited interaction abilities because of power and bandwidth constraints. In this paper, the main focus is on network issues and their solution. We consider Machine Learning techniques implemented in this network to solve some network problems. Machine Learning is the process where we train the model or machine based on training data, the model is programmed in such a way so that it “learns” from the information that it holds. This paper contains details of publications spanning a period of 2015–2020 for Machine Learning techniques that describe the challenging issues of Wireless Sensor Network.