Electrical & Computer Engineering, Department of


Date of this Version



Published in Y. Shi et al. (eds.), Lecture Notes In Computer Science, Vol. 4489; Proceedings of the 7th International Conference on Computational Science, Part III: ICCS 2007, Beijing, China (2007), pp. 859–866 Copyright © 2007 Springer-Verlag Berlin Heidelberg. Used by permission.


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.