Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.658905
Title: Wireless sensor network control through statistical methods
Author: Fang, Lei
ISNI:       0000 0004 5357 0584
Awarding Body: University of St Andrews
Current Institution: University of St Andrews
Date of Award: 2015
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Abstract:
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.
Supervisor: Dobson, Simon; Balasuhramaniam, Dharini Sponsor: Scottish Informatics and Computer Science Alliance
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.658905  DOI: Not available
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