Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

TL;DR

This paper reviews machine learning techniques applied to wireless sensor networks from 2002 to 2013, highlighting their advantages, disadvantages, and guiding designers in selecting suitable solutions for various challenges.

Contribution

It provides an extensive literature review and comparative analysis of machine learning methods used in WSNs, aiding in the development of tailored solutions.

Findings

Evaluates pros and cons of various algorithms

Provides a comparative guide for WSN designers

Highlights machine learning's role in resource optimization

Abstract

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.