Machine learning algorithms for wireless sensor networks: A survey
Information FusionPublished 22 September 2018
D. Praveen Kumar, Tarachand Amgoth, Chandra Sekhara Rao Annavarapu
Citations668
SJR quartileQ1
SJR score4.13
SNIP4.60
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TL;DR
This survey presents various ML-based algorithms for WSNs with their advantages, drawbacks, and parameters effecting the network lifetime, covering the period from 2014–March 2018.
Abstract
Wireless sensor network (WSN) is one of the most promising technologies for some real-time applications because of its size, cost-effective and easily deployable nature. Due to some external or internal factors, WSN may change dynamically and therefore it requires depreciating dispensable redesign of the network. The traditional WSN approaches have been explicitly programmed which make the networks hard to respond dynamically. To overcome such scenarios, machine learning (ML) techniques can be applied to react accordingly. ML is the process of self-learning from the experiences and acts without human intervention or re-program. The survey of the ML techniques for WSNs is presented in [1], covering period of 2002–2013. In this survey, we present various ML-based algorithms for WSNs with their advantages, drawbacks, and parameters effecting the network lifetime, covering the period from 2014–March 2018. In addition, we also discuss ML algorithms for synchronization, congestion control, mobile sink scheduling and energy harvesting. Finally, we present a statistical analysis of the survey, the reasons for selection of a particular ML techniques to address an issue in WSNs followed by some discussion on the open issues.
Keywords
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IEEE AccessRouting Protocols Based on Ant Colony Optimization in Wireless Sensor Networks: A Survey
82 Citations2017Xuxun Liu
This survey aims to provide useful guidance for system designers on how to evaluate and select appropriate routing schemes for specific applications and to put forward some open issues concerning the design of WSNs.
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