2014/01/01 by Mohammad Abu Alsheikh, Shaowei Lin, Dusit Niyato +1
Computer Science · Engineering · Environmental Science · #Air Quality Monitoring and Forecasting #Energy Efficient Wireless Sensor Networks #IoT-based Smart Home Systems #Key (lock) #Online machine learning #Resource (disambiguation) #Resource constraints #Wireless #Wireless sensor network #cs.LG #cs.NI
paper · pdf · doi:10.1109/comst.2014.2320099
published as IEEE Communications Surveys & Tutorials, vol. 16, no. 4, pp. 1996-2018, Fourthquarter 2014 · Accepted for publication in IEEE Communications Surveys and Tutorials
openalex publication_date 2014/01/01 · arxiv created 2015/03/19 · openalex created_date 2016/06/24 · arxiv updated 2016/08/16 · openalex updated_date 2026/08/05
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.