Environmental fields widely exist around us in our daily life and some of them are so important that cannot be ignored. For instance, temperature distribution, environment contamination and nuclear leaking can all be categorised as envirornmental fields. Some of the fields are invisible, some are dynamic changing and some are harmful to human. Therefore, deploying a mobile wireless sensor network (WSN) will be a better solution than manually sampling and estimating an environmental field. Bayesian framework is an elegant mathematical model that interprets the recognition procedures of human being and is widely used for iterative learning processes. Based on two regression methods in this platform, a complete field estimation solution for mobile WSNs is proposed. First, two distributed platforms are provided based on support vector regression (SVR), and centroidal Voronoi tessellation (CVT) is employed to optimise the sensor deployment. Second, to overcome the defects existed in the solution of SVR-CVT, Gaussian process regression (GPR) is being investigated due to its additional estimation accuracy information. To further improve the performance of this GPR based solution. A data selection strategy for GPR and a hybrid criterion for CVT are investigated.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:635987 |
| Date | January 2014 |
| Creators | Lu, Bowen |
| Publisher | University of Essex |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
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