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Indoor Geofencing Based on Sensorless Motion Sensing and Fingerprint Self-Updating

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Abstract

Indoor location is definitively a key feature with immense value especial for geofencing. The received signal strength (RSS) fingerprinting based methodology is widely adopted to determine his/her proximity to that particular region. Its dynamic nature and maintain overhead remain a primary challenge. In this paper, we propose a hybrid electronic geofence approach that combines self-updating RSS fingerprints based localization and Channel State Information (CSI) motion detection. Multidimensional matching and filtering principle achieves fingerprints self-updating and improves the localization accuracy. CSI-based speed estimation reduces localization frequency and overhead. Our extensive real-world experiment results show that the proposed indoor geofencing method works well for more than 30 days without manual Wi-Fi fingerprints updating.

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Notes

  1. Timing synchronization function (TSF) is specified in IEEE 802.11 wireless local area network (WLAN) standard which is based on a 1-MHz clock and “tick” in microseconds [49].

  2. In IEEE 802.11 wireless local area networking standards, basic service sets (BSS) are units of devices operating with the same medium access characteristics.

  3. In order to obtain more CSI samples, we set transmitting rate to be 100 packet/s (more may result buffer overflows).

  4. Suppose the first and the last values are the min and max ones.

  5. We ignore the noise since independent repeated measurements can effectively reduce random noise in train phase.

  6. The sample size restriction is an empirical value. Meanwhile, it can be deduced by large deviation theory detailed in [12].

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Acknowledgements

We thank the valuable comments from our reviewers and editors. This work was supported by National Key R&D Program of China 2017YFB1003000, NSFC Grant No.61751211, 61572-396, 61772413, 61672424, National Science and Technology Major Project of the Ministry of Science and Technology of China JZ-20150910, and ShaanXi Provincial Natural Science Foundation (No.2017JM6109).

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Authors and Affiliations

  1. Xi’an Jiaotong University, Xi’an, China

    Kun Zhao, Wei Xi, Zhi Wang, Hui He, Xiaobin Zhang & Jizhong Zhao

  2. Xidian University, Xi’an, China

    Zhiping Jiang

  3. Arizona State University, Tempe, AZ, USA

    Tao Li

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  1. Kun Zhao
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Correspondence to Kun Zhao.

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Zhao, K., Xi, W., Jiang, Z. et al. Indoor Geofencing Based on Sensorless Motion Sensing and Fingerprint Self-Updating. Mobile Netw Appl 26, 851–869 (2021). https://doi.org/10.1007/s11036-019-01329-0

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