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A Novel Multihop Range-Free Localization Based on Kernel Learning Approach for the Internet of Things

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Abstract

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.

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Acknowledgments

The paper is sponsored by the NSF of China (61272379), Prospective and Innovative Project of Jiangsu Province (BY2012201); China Postdoctoral Science Foundation (2015M571633), Jiangsu Postdoctoral Science Foundation (1401016B), Natural Science Foundation of the Jiangsu Higher Education Institutions of China (15KJB520009), Project of Modern Educational Technology of Jiangsu Province (2015-R-42440), Jiangsu Province Undergraduate Training Programs for Innovation and Entrepreneurship (201513573005Z) and Doctoral Scientific Research Startup Foundation of Jinling Institute of Technology (JIT-B-201411).

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Authors and Affiliations

  1. School of Intelligence Science and Control Engineering, Jinling Institute of Technology, Nanjing, 211169, China

    Xiaoyong Yan, Zhong Yang & Ronghui Zhu

  2. School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, China

    Xiaoyong Yan & Aiguo Song

  3. Remote Measurement and Control Key Lab of Jiangsu Province, Nanjing, 210096, China

    Xiaoyong Yan & Aiguo Song

  4. School of Automation, Southeast University, Nanjing, 210096, China

    Wankou Yang

  5. School of Computer Engineering, Jinling Institute of Technology, Nanjing, 211169, China

    Yu Liu

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  1. Xiaoyong Yan
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  2. Zhong Yang
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  3. Aiguo Song
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  4. Wankou Yang
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  5. Yu Liu
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Correspondence to Xiaoyong Yan.

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Yan, X., Yang, Z., Song, A. et al. A Novel Multihop Range-Free Localization Based on Kernel Learning Approach for the Internet of Things. Wireless Pers Commun 87, 269–292 (2016). https://doi.org/10.1007/s11277-015-3042-6

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  • DOI: https://doi.org/10.1007/s11277-015-3042-6

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