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Yan X.,Nanjing Southeast University | Yan X.,Remote Measurement and Control Key Laboratory of Jiangsu Province | Yan X.,Jinling Institute of Technology | Song A.,Nanjing Southeast University | And 2 more authors.
International Journal of Distributed Sensor Networks | Year: 2014

In accordance with the problem that the traditional trilateral or multilateral estimation localization method is highly dependent on the proportion of beacon nodes and the measurement accuracy, an algorithm based on kernel sparse preserve projection (KSPP) is proposed in this dissertation. The Gaussian kernel function is used to evaluate the similarity between nodes, and the location of the unknown nodes will be commonly decided by all the nodes within communication radius through selection of sparse preserve projection self-adaptation and maintaining of the topological structure between adjacent nodes. Therefore, the algorithm can effectively solve the nonlinear problem while ranging, and it becomes less affected by the measuring error and beacon nodes quantity. © 2014 Xiaoyong Yan et al. Source


Yan X.,Jinling Institute of Technology | Yan X.,Nanjing Southeast University | Yan X.,Remote Measurement and Control Key Laboratory of Jiangsu Province | Yang Z.,Jinling Institute of Technology | And 5 more authors.
Wireless Personal Communications | Year: 2015

In internet of things (IoT) study, to determine that the location of an event is the key issue, realize that a target location in IoT is one of the research hotspots by using the multihop range-free method. Multihop range-free could obtain relatively reasonable location estimation in the isotropic network, however, during the practical application, it tends to be affected by various anisotropic factors such as the electromagnetic interference, barriers and network attack, which can significantly reduce its performance. In accordance with these problems faced by multihop range-free, this paper proposes a novel IoT localization method: location estimation-kernel partial least squares (LE-KPLS). First of all, this method uses kernel function to define the connectivity information (hop-counts) between nodes, then, the maximum covariance is used to guide and build the inter-node localization model, and then, this model and the hop-counts between the unknown nodes and beacons are used to estimate the coordinate of the unknown nodes. Compared to the existing methods, the LE-KPLS has a high localization precision, great stability and strong generalization performance, without having a high requirement of the number of beacons, and it can well adapt to numerous complicated environments. © 2015 Springer Science+Business Media New York Source


Yan X.,Nanjing Southeast University | Yan X.,Remote Measurement and Control Key Laboratory of Jiangsu Province | Yan X.,Jinling Institute of Technology | Song A.,Nanjing Southeast University | And 3 more authors.
Computers and Electrical Engineering | Year: 2015

Multihop range-free localization methods could obtain relatively reasonable location estimation in the isotropic network; however, during the practical application, it is often affected by various anisotropic factors such as the radio irregularity and barriers, which can significantly reduce its performance. In this paper, we propose a new approach for multihop localization in wireless sensor network based on nonlinear mapping and learning algorithm. The proposed method is simple, efficient, higher accuracy and no need to set complex parameter in that only hop-counts information and position information of the beacons are used for the localization. The proposed approach is composed of two steps: firstly, this algorithm uses kernel function to define the connectivity information (hop-counts) between nodes, then, learning method is used to guide and build the inter-node localization model; secondly, the hop-counts between the unknown nodes and beacons are used to estimate the coordinate of unknown nodes. We evaluate our algorithm under various anisotropic network and real environment, and analyze its performance. We also compare our approach with several available advanced approaches, and demonstrate the superior performance of our proposed algorithm in terms of location estimation adaptability. © 2015 Elsevier Ltd. Source

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