Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications release_2xawt5rugrg7rd2oaike2cjtwa

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

Released as a article .

2015  

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.
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Type  article
Stage   accepted
Date   2015-03-19
Version   v2
Language   en ?
arXiv  1405.4463v2
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