2nd International ICST Conference on Communications and Networking in China

Research Article

Distributed Weighted Least Squares Scaling with Soft-Constraint for Node Localization in Wireless Sensor Networks

  • @INPROCEEDINGS{10.1109/CHINACOM.2007.4469505,
        author={Fang Zhao and Yan Ma and Quan Lin and Haiyong Luo and Wu Yuan},
        title={Distributed Weighted Least Squares Scaling with Soft-Constraint for Node Localization in Wireless Sensor Networks},
        proceedings={2nd International ICST Conference on Communications and Networking in China},
        publisher={IEEE},
        proceedings_a={CHINACOM},
        year={2008},
        month={3},
        keywords={localization  soft constraint  weighted least square scaling  wireless sensor networks},
        doi={10.1109/CHINACOM.2007.4469505}
    }
    
  • Fang Zhao
    Yan Ma
    Quan Lin
    Haiyong Luo
    Wu Yuan
    Year: 2008
    Distributed Weighted Least Squares Scaling with Soft-Constraint for Node Localization in Wireless Sensor Networks
    CHINACOM
    IEEE
    DOI: 10.1109/CHINACOM.2007.4469505
Fang Zhao1,*, Yan Ma1, Quan Lin1, Haiyong Luo2,*, Wu Yuan2
  • 1: Beijing University of Posts and Telecommunications Beijing, China
  • 2: Institute of Computing Technology Chinese Academy of Sciences Beijing, China
*Contact email: zhaofang@email.buptsse.cn, yhluo@ict.ac.cn

Abstract

This paper presents a robust distributed localization approach which employs weighted least squares scaling with soft constrains. It incorporates the a priori deployment constraints, i.e., minimum and maximum node separation among 2-hop nodes, into localization as soft constraints and penalizes pairs of 2-hop nodes whose assigned coordinates violate the minimum and maximum constraints. Combined with applying consistency checking and statistical filtering of ranging measurements, we improve the location estimates compared with classical least squares scaling. For received signal strength based range measurements, extensive simulation results confirm that this localization scheme outperforms classical least squares scaling and is resilient against large ranging errors and sparse range measurements, which are common in wireless sensor network deployments.