A Bayesian Multilevel Modeling Approach for Data Query in Wireless Sensor Networks

UMMS Affiliation

Department of Quantitative Health Sciences

Publication Date


Document Type



Bayes Theorem; Models, Statistical; Computer Communication Networks


Bioinformatics | Biostatistics | Epidemiology | Health Services Research


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 real-time 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.

DOI of Published Version



Wang, H., Fang, H., Espy, K. A., Peng, D, Sharif, H. (2007). A Bayesian Multilevel Modeling Approach for Data Query in Wireless Sensor Networks. Lecture Notes in Computer Science (LNCS),Y. Shi et al. (Eds.): Part III, LNCS vol. 4489, pp. 859–866. DOI: 10.1007/978-3-540-72588-6_137

Journal/Book/Conference Title

Lecture Notes in Computer Science