Wireless sensor networks (WSNs) 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 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.
Wireless sensor networks, machine learning, data mining, security, localization, clustering, data aggregation, event detection, query processing, data integrity, fault detection, medium access control, compressive sensing.
Computer Sciences | Software Engineering | Theory and Algorithms
Software and Cyber-Physical Systems
IEEE Communications Surveys and Tutorials
Institute of Electrical and Electronics Engineers (IEEE)
ALSHEIKH, Mohammad Abu; LIN, Shaowei; NIYATO, Dusit; and Hwee-Pink TAN.
Machine learning in wireless sensor networks: Algorithms, strategies, and applications. (2014). IEEE Communications Surveys and Tutorials. 16, (4), 1996-2018. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2963
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