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Grouping multi-duolateration localization using partial space information for indoor wireless sensor networks

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4 Author(s)
Hojae Lee ; Dept. of Electr. & Electron. Eng., Yonsei Univ., Seoul, South Korea ; Sanghoon Lee ; Yeonsoo Kim ; Hakjin Chong

Recently, sensor network technologies are being applied to more places such as intelligent home, offices and universities. Localization algorithms are required for indoor wireless sensor network (WSN) applications because the identification of real positions can upgrade the importance of sensing information. In this paper, a sensor network, where small-sized, low-cost and low-rate sensors are uniformly deployed in an indoor WSN environment, is taken into account. We introduce efficient algorithms, termed multi-duolateration localization (MDL) and grouping multi-duolateration localization (GMDL), which can improve the accuracy of location identification by employing jumper setting. The MDL algorithm can estimate 2-dimensional coordinates with high accuracy by the acquisition of edge information from the setting. In addition, the GMDL algorithm can be applied to the estimation of 3-dimensional coordinates by the acquisition of edge and surface information from the setting. The proposed algorithms perform localization more accurately than trilateration and faster than multidimensional scaling (MDS). The results from a MATLAB simulation show the outperformance of the proposed algorithms and demonstrate the possibility of 3-dimensional localization.

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Consumer Electronics, IEEE Transactions on  (Volume:55 ,  Issue: 4 )