This paper introduces an original method for sensors localization in WSNs. Based on radio-location fingerprinting and machine learning, the method consists of defining a model whose inputs and outputs are, respectively, the received signal strength indicators and the sensors locations. To define this model, several kernel-based machine-learning techniques are investigated, such as the ridge regression, support vector regression, and vector-output regularized least squares. The performance of the method is illustrated using both simulated and real data.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Kernel-based machine learning using radio-fingerprints for localization in wsns


    Contributors:


    Publication date :

    2015-04-01


    Size :

    948260 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Decentralized localization using radio-fingerprints and accelerometer in WSNs

    Lv, Xiaowei / Mourad-Chehade, Farah / Snoussi, Hichem | IEEE | 2015


    Online Pricing Crowdsensed Fingerprints for Accurate Indoor Localization

    Tian, Xiaohua / Zhang, Wencan / Wang, Jingchao et al. | IEEE | 2017


    Micro-UAV Detection and Classification from RF Fingerprints Using Machine Learning Techniques

    Ezuma, Martins / Erden, Fatih / Anjinappa, Chethan Kumar et al. | IEEE | 2019



    JLR: Joint localization and routing for energy balance and obstacle resistence in WSNs

    Li-Ling Hung, / Chih-Yung Chang, / Tzu-Lin Wang, et al. | IEEE | 2011