DocumentCode :
2438352
Title :
CLIQUE: Role-Free Clustering with Q-Learning for Wireless Sensor Networks
Author :
Förster, Anna ; Murphy, Amy L.
Author_Institution :
Fac. of Inf., Univ. della Svizzera Italiana, Lugano, Switzerland
fYear :
2009
fDate :
22-26 June 2009
Firstpage :
441
Lastpage :
449
Abstract :
Clustering and aggregation inherently increase wireless sensor network (WSN) lifetime by collecting information within a cluster at a cluster head, reducing the amount of data through computation, then forwarding it. Traditional approaches, however, both spend extensive communication energy to identify the cluster heads and are inflexible to network dynamics such as those arising from sink mobility, node failure, or dwindling battery reserves. This paper presents CLIQUE, an approach for data clustering that saves cluster head selection energy by using machine learning to enable nodes to independently decide whether or not to act as a cluster head on a per-packet basis. We refer to this lack of actual cluster head assignment as being role-free, and demonstrate through simulations that, when combined with learning dynamic network properties such as battery reserves, up to 25% less energy is consumed in comparison to a traditional, random cluster head selection approach.
Keywords :
learning (artificial intelligence); telecommunication computing; telecommunication network reliability; wireless sensor networks; CLIQUE approach; Q-learning; WSN lifetime; cluster heads identification; data aggregation; machine learning; role-free clustering; wireless sensor network; Base stations; Batteries; Clustering algorithms; Distributed computing; Informatics; Machine learning; Magnetic heads; Routing; Spread spectrum communication; Wireless sensor networks; clustering; energy-efficient; q-learning; reinforcement learning; role-free; wireless sensor networks;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Distributed Computing Systems, 2009. ICDCS '09. 29th IEEE International Conference on
Conference_Location :
Montreal, QC
ISSN :
1063-6927
Print_ISBN :
978-0-7695-3659-0
Electronic_ISBN :
1063-6927
Type :
conf
DOI :
10.1109/ICDCS.2009.43
Filename :
5158454
Link To Document :
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