Hardware-Based Model of Node Clustering Using Q-Learning for Wireless Sensor Networks

Recent researches on the development and deployment of large-scale wireless sensor networks focus on the reduction of power consumption to increase network lifetime. This entails the development of more efficient hardware and the integration of the multiple components into a single platform; possibl...

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Bibliographic Details
Published in:TENCON ... IEEE Region Ten Conference pp. 1673 - 1678
Main Authors: Delos Santos Manarang, Gienel Francheska, Rodriguez Mina, Rusty John Lloyd, Reyes Salvador, Mikko Chino, Gusad De Leon, Maria Theresa, Jagunap Densing, Chris Vincent, Rosales, Marc Driz, Ballesil Alvarez, Anastacia
Format: Conference Proceeding
Language:English
Published: IEEE 01.10.2018
Subjects:
Clustering algorithms
Data aggregation
Hardware
Measurement
Power demand
Semiconductor device modeling
Wireless sensor networks
ISSN:2159-3450
Online Access:Get full text
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Summary:Recent researches on the development and deployment of large-scale wireless sensor networks focus on the reduction of power consumption to increase network lifetime. This entails the development of more efficient hardware and the integration of the multiple components into a single platform; possibly, a system-on-a-chip sensor node platform. This paper focuses on the application of Q-learning to node clustering as an effective technique in reducing power consumption. A hardware implementation of CLIQUE, a Q-learning-based clustering algorithm that enhances the operation and resiliency of the network and improves the energy expenditure of the sensor nodes, is presented. The power consumption of the hardware is recorded to be around 6.4 mW. By incorporating this value into OMNeT++, it was demonstrated that CLIQUE improves energy expenditure by 6%-19% and extends network lifetime by 5%-22%, when compared to LEACH, a traditional clustering algorithm.
ISSN:2159-3450
DOI:10.1109/TENCON.2018.8650214