1 research outputs found
Wireless sensor network control through statistical methods
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 the 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 technique has 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