Publication Type

Journal Article

Version

acceptedVersion

Publication Date

4-2014

Abstract

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.

Keywords

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.

Discipline

Computer Sciences | Software Engineering | Theory and Algorithms

Research Areas

Software and Cyber-Physical Systems

Publication

IEEE Communications Surveys and Tutorials

Volume

16

Issue

4

First Page

1996

Last Page

2018

ISSN

1553-877X

Identifier

10.1109/COMST.2014.2320099

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Additional URL

http://doi.org/10.1109/COMST.2014.2320099

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