Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
This is an incremental review paper that synthesizes existing knowledge to help WSN designers select appropriate machine learning solutions for application-specific challenges.
The paper presents a literature review of machine learning methods used in wireless sensor networks from 2002 to 2013, evaluating their advantages and disadvantages to provide a comparative guide for designers.
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.