Title: A systematic probabilistic approach to energy-efficient and robust data collections in wireless sensor networks
Authors: Wei Zhao, Yao Liang
Addresses: Department of Computer and Information Science, Indiana University–Purdue University Indianapolis, Indianapolis, IN 46202, USA. ' Department of Computer and Information Science, Indiana University–Purdue University Indianapolis, Indianapolis, IN 46202, USA
Abstract: We propose a systematic approach, based on probabilistic graphical model, to infer missing observations in Wireless Sensor Networks (WSNs) for sustaining environmental monitoring. This enables us to effectively address two critical challenges in WSNs: (a) energy-efficient data gathering through planned energy-saving sleep cycles and (b) sensor-node failure tolerance in harsh environments. In our approach, we model the spatial correlations in a sensor network as a pairwise Markov Random Field (MRF). Our MRF model is constructed from historical data using Iterative Proportional Fitting (IPF). Then Loopy Belief Propagation (LBP) is employed to estimate the missing data at the data sink. We demonstrate our approach using real-world sensed data on 32 × 32 grids. Empirical results show our approach can achieve the high rates of estimation accuracy (e.g. 65–90% for soil moisture data), even when the unobserved nodes consist of more than 50% of the total sensing nodes.
Keywords: graphical modelling; automatic learning; information inference; wireless sensor networks; WSNs; environmental monitoring; data collection; spatial correlations; data quality; wireless networks; energy efficiency; energy saving; sensor nodes; failure tolerance; harsh environments; soil moisture.
International Journal of Sensor Networks, 2010 Vol.7 No.3, pp.162 - 175
Published online: 08 May 2010 *Full-text access for editors Access for subscribers Purchase this article Comment on this article