Machine Learning in Wireless Sensor Networks: A survey

Abstract

Wireless Sensor Network (WSN) is a network which consists of multiple sensors which are small in size, autonomous, low power and low cost. Main objective of WSN is to measure or sense change in environmental or physical conditions overtime. WSN are often used in applications where remote data collection is required such as military applications, health monitoring, smart homes, environment monitoring etc. Often WSNs is use to monitor dynamic change in environment parameters which occur over period time. Many practical solutions which enhance the lifespan of the network and maximize resource utilization can be achieved using Machine Learning algorithms. In this paper, we present and literature review of Machine Learning methods that were used to address in general problems in WSN. The advantages and disadvantages of each algorithm are evaluated.