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Learning-Based Localization In Wireless Sensor Networks

Posted on:2010-11-28Degree:DoctorType:Dissertation
Country:ChinaCandidate:C Q WangFull Text:PDF
GTID:1118360302483068Subject:Control Science and Engineering
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Recent advances in wireless communication, integrated circuit, sensor technology and MEMS have made it possible to deploy large scale wireless sensor networks (WSNs) by using low-cost and low-power sensor nodes. In emerging WSNs applications, it is necessary to accurately position the nodes where their reported data are geographically meaningful. In WSNs, how to design a fast converging and low-cost method to accurately estimate the locations of the nodes, is always an active area.The dissertation targets at the localization problem in WSNs and focuses on decreasing the number of anchors and alleviating the influence of the measurement error. It presents indepth study in the localization problem by using kernel-based and manifold learning methods. The main contributions of the dissertation are as follows:1. We survey the localization problem, and analyze the existing localization methods in terms of computing complexity, number of anchors and localization accuracy. Furthermore, we also summarize the drawbacks and disadvantages of the existing localization methods, and point out the unsolved problems.2. We consider the location estimation problem using signal strength in WSNs. In order to alleviate the influence of measurement errors between nodes, by analyzing the neighborhood topological structure, we formulate the localization problem as a nonlinear graph embedding problem, and propose a KLPP-based localization method. The main idea is to measure the similarities between nodes by a Gaussian kernel function and optimally preserving the neighborhood similarities in the localization procedure. The simulation results show that the KLPP-based localization method is not significantly sensitive to the number of anchors, i.e., when the number of anchors is small, the localization accuracy is still high. Compared with the related localization methods, e.g., KPCA-based localization method, MDS-MAP, our KLPP-based localization method is less sensitive to the measurement error.3. To reduce the complexity of KLPP-based localization method, we formulate the nonlinear graph embedding problem as regularized kernel least-squares regression problem, and propose a KSR-based localization method. Both theoretical analysis and simulation results show that KSR-based localization method optimally preserves the advantages of the KLPP-based localization method. Besides, the former achieves lower computing complexity with a little decrease of the localization accuracy than the latter.4. When the measurements are signal strengths or pair-wise distances, in order to reduce the influence of the number of anchors, we propose a semi-supervised Laplacian regularized least squares (S~2LapRLS) method for the localization problem. The main idea is that when nodes are close in their location space, their localization feature vectors will be similar. We first propose a solution to choose an appropriate kernel function based upon alignment criterion. Then, we construct a mapping between the measurement space and the location space under semi-supervised framework. The location of non-anchors can be estimated based upon the mapping. Compared with the related methods, e.g., RKLS-based localization method, KMR-based localization method, the simulation results show that S~2LapRLS method can obtain a higher localization accuracy. By relatively reducing the maximum communication range of nodes, the localization accuracy can be further improved.5. When the measurements are pair-wise distances, in the purpose of reducing parameter influence of the Isomap and the transformation matrix dependence on the anchors, we propose improved Isomap-based localization approach(IIsomap), which is composed of three factors: Isomap parameter selection based upon the locations of the anchors, relative location estimation with Isomap, and absolute location estimation by using the least squares (LS) method and the manifold regression (MR) method. Simulations show that MR can achieve relatively higher localization accuracy, but at the expense of spending more time than LS. For any topological sensor network, when the number of anchors is small and the measurements are error-prone, IIsomap method achieves higher localization accuracy than the Euclidean-based localization methods. Especially, the average location error increases with the increase of the communication range, Thus, IIsomap method makes it possible to simultaneously reduce the transmitting power and improve the localization accuracy.In this dissertation, we introduce the kernel-based approach and the manifold learning method into the localization problem, which bring new ideas into this area, and propose four location estimation methods. In the end of this dissertation, we point out several aspects waiting for improvement in our localization methods, and introduce our future work.
Keywords/Search Tags:Wireless sensor networks, Node localization, Semi-supervised localization, Kernel function, Manifold learning, Neighborhood topological structure, Geodesic distance
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