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ARTS: Adaptive rule triggers on sensors for energy conservation in applications using coarse-granularity data
conference contribution
posted on 2008-01-01, 00:00 authored by S K Chong, M M Gaber, Seng LokeSeng Loke, S KrishnaswamyCommunicating extensive in-network data generated by resource-constrained wireless sensor nodes is an energy consuming process. To minimise the amount of data exchanged in sensor networks, several researchers have proposed novel and efficient protocols to perform data aggregations, clustering or regression on sensor nodes. Most of these approaches focus on optimising conventional mining techniques to work on resource-constrained sensor nodes. However, the application of association rules for sensor networks is an area of study that has not been investigated. This is due to the high computational cost of obtaining meaningful rules. Thus, in this paper, we propose Adaptive Rule Triggers on Sensors ARTS, to extract highly correlated rules from sensor data and apply them. The learnt rules are used to extend sensor lifetime by controlling sensor operations using triggers. Our approach is optimised to run on non-critical sensing applications/data-aggregation applications that can tolerate a coarse-granularity for sensed data. For this category of applications, our approach can derive meaningful rules efficiently to further conserve energy of wireless sensors. In this paper, these energy savings are evidenced in our experiments that adapt ARTS to a state-of-the-art clustering protocol. © 2008 IEEE.
History
Event
Embedded Software and Systems. Conference (2008 Sichuan, China)Pagination
314 - 321Publisher
IEEELocation
Sichuan, ChinaPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2008-07-29End date
2008-07-31ISBN-13
9780769532875Language
engPublication classification
E1.1 Full written paper - refereedTitle of proceedings
ICESS 2008 : Proceedings of The International Conference on Embedded Software and Systems 2008Usage metrics
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