AN OVERVIEW OF MACHINE LEARNING ALGORITHMS FOR WIRELESS SENSOR NETWORKS

Abstract

Wireless sensor networks (WSNs) are particularly desirable for real-time applications because of their small size, low cost, and simplicity of installation. Nevertheless, WSNs may need to be modified or redesigned due to a variety of internal or external circumstances, which is difficult for conventional, explicitly planned WSN systems to manage. Machine learning (ML) approaches can be used to solve this problem. ML makes it possible for networks to learn from their experiences and adapt without requiring reprogramming or human intervention.A prior investigation [1] examined machine learning methods for WSNs between 2002 and 2013. We review ML-based algorithms for WSNs from 2014 to March 2018 in this revised study, stressing their advantages, drawbacks, and effects on network lifetime. We also discuss machine learning techniques for energy harvesting, congestion control, mobile sink scheduling, and synchronization. The survey discusses why certain ML approaches are selected for particular WSN difficulties and offers a statistical analysis of the data obtained. We also talk about some outstanding issues in the sector. Keywords: Wireless sensor networks, Machine learning, Energy efficiency, Network lifetime, Data aggregation