Cluster-based adaptive power control protocol using Hidden Markov Model for Wireless Sensor Networks

This paper presents strategies for an efficient and dynamic transmission power control technique, in order to reduce packet drop and hence energy consumption of power-hungry sensor nodes operated in highly non-linear channel conditions of Wireless Sensor Networks. Besides, we also focus to prolong n...

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Bibliographic Details
Published inInternational journal of electronics Vol. 104; no. 6; pp. 968 - 981
Main Authors Vinutha, C. B., Nalini, N., Nagaraja, M.
Format Journal Article
LanguageEnglish
Published Abingdon Taylor & Francis 03.06.2017
Taylor & Francis LLC
Subjects
Channels
clustering
Clusters
communication protocol
Control systems
MAC protocol
Markov analysis
Markov Model
Markov models
Mathematical models
Networks
Packet transmission
Power control
Remote sensors
Sensors
Wireless networks
Wireless Sensor Networks
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Summary:This paper presents strategies for an efficient and dynamic transmission power control technique, in order to reduce packet drop and hence energy consumption of power-hungry sensor nodes operated in highly non-linear channel conditions of Wireless Sensor Networks. Besides, we also focus to prolong network lifetime and scalability by designing cluster-based network structure. Specifically we consider weight-based clustering approach wherein, minimum significant node is chosen as Cluster Head (CH) which is computed stemmed from the factors distance, remaining residual battery power and received signal strength (RSS). Further, transmission power control schemes to fit into dynamic channel conditions are meticulously implemented using Hidden Markov Model (HMM) where probability transition matrix is formulated based on the observed RSS measurements. Typically, CH estimates initial transmission power of its cluster members (CMs) from RSS using HMM and broadcast this value to its CMs for initialising their power value. Further, if CH finds that there are variations in link quality and RSS of the CMs, it again re-computes and optimises the transmission power level of the nodes using HMM to avoid packet loss due noise interference. We have demonstrated our simulation results to prove that our technique efficiently controls the power levels of sensing nodes to save significant quantity of energy for different sized network.
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ISSN:0020-7217
1362-3060
DOI:10.1080/00207217.2017.1279228