Machine learning in wireless sensor networks: Algorithms, strategies, and applications

Mohammad Abu Alsheikh, Shaowei Lin, Dusit Niyato, Hwee Pink Tan

Research output: Contribution to journalArticlepeer-review

707 Citations (Scopus)


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.

Original languageEnglish
Article number6805162
Pages (from-to)1996-2018
Number of pages23
JournalIEEE Communications Surveys and Tutorials
Issue number4
Publication statusPublished - 24 Apr 2014


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