Machine Learning in Wireless Sensor Networks: A Retrospective

Abstract

Wireless Sensor Networks consist of spatially dispersed autonomous sensor nodes which collect data from the environment and forward to the other gateway for processing. These network controls the dynamic environment that changes frequently with time. This effectual behavior is created or initialized by outward parameters such as temperature, sound, light, events. To adjust with such situations these networks follow Machine Learning techniques. In this paper, a review on the Machine Learning techniques that can be applied on these networks is presented. These networks are the most trending technologies for some real applications because of its features such as low-cost, tiny and mobility. Further, a relative guide to the network designers is suggested for developing appropriate Machine Learning solutions for requisite application.