TY - GEN
T1 - A bayesian multilevel modeling approach for data query in wireless sensor networks
AU - Wang, Honggang
AU - Fang, Hua
AU - Espy, Kimberly Andrew
AU - Peng, Dongming
AU - Sharif, Hamid
PY - 2007
Y1 - 2007
N2 - In power-limited Wireless Sensor Network (WSN), it is important to reduce the communication load in order to achieve energy savings. This paper applies a novel statistic method to estimate the parameters based on the realtime data measured by local sensors. Instead of transmitting large real-time data, we proposed to transmit the small amount of dynamic parameters by exploiting both temporal and spatial correlation within and between sensor clusters. The temporal correlation is built on the level-1 Bayesian model at each sensor to predict local readings. Each local sensor transmits their local parameters learned from historical measurement data to their cluster heads which account for the spatial correlation and summarize the regional parameters based on level-2 Bayesian model. Finally, the cluster heads transmit the regional parameters to the sink node. By utilizing this statistical method, the sink node can predict the sensor measurements within a specified period without directly communicating with local sensors. We show that this approach can dramatically reduce the amount of communication load in data query applications and achieve significant energy savings.
AB - In power-limited Wireless Sensor Network (WSN), it is important to reduce the communication load in order to achieve energy savings. This paper applies a novel statistic method to estimate the parameters based on the realtime data measured by local sensors. Instead of transmitting large real-time data, we proposed to transmit the small amount of dynamic parameters by exploiting both temporal and spatial correlation within and between sensor clusters. The temporal correlation is built on the level-1 Bayesian model at each sensor to predict local readings. Each local sensor transmits their local parameters learned from historical measurement data to their cluster heads which account for the spatial correlation and summarize the regional parameters based on level-2 Bayesian model. Finally, the cluster heads transmit the regional parameters to the sink node. By utilizing this statistical method, the sink node can predict the sensor measurements within a specified period without directly communicating with local sensors. We show that this approach can dramatically reduce the amount of communication load in data query applications and achieve significant energy savings.
KW - Bayesian multilevel modeling
KW - Wireless sensor network
UR - http://www.scopus.com/inward/record.url?scp=38149138143&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=38149138143&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-72588-6_137
DO - 10.1007/978-3-540-72588-6_137
M3 - Conference contribution
AN - SCOPUS:38149138143
SN - 9783540725879
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 859
EP - 866
BT - Computational Science - ICCS 2007 - 7th International Conference, Proceedings
PB - Springer Verlag
T2 - 7th International Conference on Computational Science, ICCS 2007
Y2 - 27 May 2007 through 30 May 2007
ER -