Wireless Sensor Networks (WSNs) form a new paradigm of computing that allows the physical world to be measured at an unprecedented resolution; and the importance of the technology has been increasingly recognised. However, WSNs are still facing critical challenges, including low data quality and high energy consumption. In this thesis, formal statistical models are employed to address these two practical problems. With the formalism that is properly designed, sound statistical inferences can be made to guide local sensor nodes to make reasonable and timely decisions at local level in the face of uncertainties. To improve data reliability, we introduce formal Bayesian statistical method to form two on-line in-network fault detectors. The two detection techniques are well integrated with existing data collection protocols. Experimental results demonstrate the techniques have good detection accuracy but limited computational and communication overhead. To improve energy efficiency, we propose a novel data collection framework that features both energy conservation and data fault filtering by exploiting Hidden Markov Models (HMMs). Another data collection framework, a Dynamic Linear Model (DLM) based solution, featuring both adaptive sampling and efficient data collection is also proposed. Experimental results show the two solutions effectively suppress unnecessary packet transmission while satisfying users’ precision requirement. To prove the feasibility, we show all the proposed solutions are lightweight by either real world implementation or formal complexity analysis.
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