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A Scalable and Hybrid Location Estimation Algorithm for Long-Range RFID Systems

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Abstract

Localization of assets is a critical component of smart building applications. This problem involves a lot of challenges and gets even more challenging when a consistently high level of accuracy has to be ensured while employing long-range RFID readers. In general, these antennas are placed so that there is some level of overlap of their ranges of influence, in order to support asset tracking with high precision. However, it is desirable to reduce the level of overlap when employing multiple readers for large-scale asset tracking, due to the high cost of these RFID readers and antennas. In such scenarios, it is important to maintain accuracy while increasing the coverage area of the readers, and for this a robust localization algorithm is necessary. This paper describes a novel hybrid indoor localization algorithm that combines the transmission power level control and the signal strength information in an intelligent manner to locate assets accurately. The algorithm does not require much calibration and is easily scalable. Additionally, it allows for both coarse and fine-grained location estimation depending on application requirements. Our experiments in real indoor environments, and with different degrees of overlap between long-range RFID antennas show that our algorithm provides higher accuracy in comparison to other algorithms, and is also consistent.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India

    K. Dharani Tejaswini & Vidhya Balasubramanian

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  1. K. Dharani Tejaswini
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  2. Vidhya Balasubramanian
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Correspondence to Vidhya Balasubramanian.

Additional information

This work has been funded in part by DST (India) grant DyNo. C/4902/IFD/2016-2017.

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Dharani Tejaswini, K., Balasubramanian, V. A Scalable and Hybrid Location Estimation Algorithm for Long-Range RFID Systems. Int J Wireless Inf Networks 25, 186–199 (2018). https://doi.org/10.1007/s10776-018-0394-3

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  • DOI: https://doi.org/10.1007/s10776-018-0394-3

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