1st International ICST Conference on Mobile Wireless Middleware, Operating Systems and Applications

Research Article

Localization Using Neural Networks in Wireless Sensor Networks

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  • @INPROCEEDINGS{10.4108/ICST.MOBILWARE2008.2901,
        author={Ali Shareef and Yifeng Zhu and Mohamad Musavi},
        title={Localization Using Neural Networks in Wireless Sensor Networks},
        proceedings={1st International ICST Conference on Mobile Wireless Middleware, Operating Systems and Applications},
        publisher={ICST},
        proceedings_a={MOBILWARE},
        year={2010},
        month={5},
        keywords={},
        doi={10.4108/ICST.MOBILWARE2008.2901}
    }
    
  • Ali Shareef
    Yifeng Zhu
    Mohamad Musavi
    Year: 2010
    Localization Using Neural Networks in Wireless Sensor Networks
    MOBILWARE
    ICST
    DOI: 10.4108/ICST.MOBILWARE2008.2901
Ali Shareef1,*, Yifeng Zhu1,*, Mohamad Musavi1,*
  • 1: Department of Electrical and Computer Engineering, University of Maine
*Contact email: ashareef@eece.maine.edu, zhu@eece.maine.edu, musavi@eece.maine.edu

Abstract

Noisy distance measurements are a pervasive problem in localization in wireless sensor networks. Neural networks are not commonly used in localization, however, our experiments in this paper indicate neural networks are a viable option for solving localization problems. In this paper we qualitatively compare the performance of three different families of neural networks: Multi-Layer Perceptron (MLP), Radial Basis Function (RBF), and Recurrent Neural Networks (RNN). The performance of these networks will also be compared with two variants of the Kalman Filter which are traditionally used for localization. The resource requirements in term of computational and memory resources will also be compared. In this paper, we show that the RBF neural network has the best accuracy in localizing, however it also has the worst computational and memory resource requirements. The MLP neural network, on the other hand, has the best computational and memory resource requirements.