| Summary: | In this thesis, we focus on the localization of sparse wireless sensor networks (WSN) through exploiting two types of new information, namely the deployment and negative constraint information. For this research, we have proposed and developed a new distributed localization algorithm termed as the likelihood localization algorithm (LLA). Different from other algorithms, the LLA takes advantage of the deployment information through a deployment agent (DA). After a sensor node has been deployed, the system uses both the deployment and the inter-node radio information to improve the estimates of its’ position through a maximum likelihood estimation (MLE) scheme. LLA has been implemented and evaluated using a low-cost microcontroller. Simulation and experimental results show that it outperforms the conventional approaches in terms of localization accuracy in both sparse and dense WSNs. Furthermore, an enhanced likelihood localization algorithm (ELLA) that hybridizes the MLE and an extended Kalman filter is also proposed to achieve better localization accuracy.
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