Using association rules for energy conservation in wireless sensor networks

Chong, Suan Khai, Krishnaswamy, Shonali, Loke, Seng Wai and Gaber, Mohamed Medhat 2008, Using association rules for energy conservation in wireless sensor networks, in SAC 2008 : Proceedings of the ACM Symposium on Applied Computing, ACM, New York, N.Y., pp. 971-975, doi: 10.1145/1363686.1363911.

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Title Using association rules for energy conservation in wireless sensor networks
Author(s) Chong, Suan Khai
Krishnaswamy, Shonali
Loke, Seng WaiORCID iD for Loke, Seng Wai orcid.org/0000-0001-9568-5230
Gaber, Mohamed Medhat
Conference name Applied Computing. Symposium (2008 : 23rd : Fortaleza, Brazil)
Conference location Fortaleza, Brazil
Conference dates 16-20 Mar 2008
Title of proceedings SAC 2008 : Proceedings of the ACM Symposium on Applied Computing
Editor(s) Wainwright, R
Haddad, HM
Publication date 2008
Start page 971
End page 975
Total pages 5
Publisher ACM
Place of publication New York, N.Y.
Keyword(s) Science & Technology
Technology
Computer Science, Interdisciplinary Applications
Computer Science
Summary Frequent radio transmissions among sensors, or from sensors to the basestation, have always been a major energy drain. One of the approaches to reduce the data transmitted to the basestation is to shift the bulk of data processing to networked sensor nodes; for instance, sensors to send only data aggregates to reduce the over all amount of data exchanged. Sensor nodes. however, are quite limited in terms of their energy and processing power, and as such. traditional centralised data mining algorithms are infeasible to be directly implemented on sensors. In this paper, we modify APRIORI to find strong rules from sensor readings in a sensor network and using these rules, autonomously conbol sensor network operations or supplement sensor operations with a rule knowledge base. For example, triggers activated from the rules could be used to sleep sensors or reduce data transmissions to conserve sensor energy Our work here includes a detailed implementation of a lightweight rule learning algorithm for a resource-constrainted sensor network, with simulation results for a group node setup running the algorithm. Copyright 2008 ACM.
ISBN 9781595937537
Language eng
DOI 10.1145/1363686.1363911
Indigenous content off
HERDC Research category E1.1 Full written paper - refereed
Persistent URL http://hdl.handle.net/10536/DRO/DU:30125667

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