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Edge computing-enabled green multisource fusion indoor positioning algorithm based on adaptive particle filter

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Abstract

Edge computing enables portable devices to provide smart applications, and the indoor positioning technique offers accurate location-based indoor navigation and personalized smart services. To achieve the high positioning accuracy, an indoor positioning algorithm based on particle filter requires a large number of sample particles to approximate the probability density function, which leads to the additional computational cost and high fusion delay. Focusing on real-time and accurate positioning, an edge computing-enabled green multi-source fusion indoor positioning algorithm called APFP is proposed based on adaptive particle filter in this paper. APFP considers both pedestrian dead reckoning (PDR) signals in mobile terminals and the received signal strength indication (RSSI) of Bluetooth, and effectively merges the error-free accumulation of trilateral positioning and the accurate short-range positioning of PDR, which enables mobile terminals adaptively perform particle filter to reduce the computing time and power consumption while ensuring positioning accuracy simultaneously. Detailed experimental results show that, compared with the traditional particle filter algorithm and the map-constrained algorithm, the proposed APFP reduces fusion computing cost by 59.89% and 54.37%, respectively.

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Data availability

The data used to support the findings of this study are available from the corresponding author upon reasonable request.

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Funding

This work was supported in part by the National Natural Science Foundation of China (No. 61772562), the Knowledge Innovation Program of Wuhan-Basic Research (No. 2662022YJ012), the Talent Foundation of Huazhong Agricultural University (No. 11042110018), and the Fundamental Research Funds for the Central Universities, South-Central MinZu University (No. CZZ21003).

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Authors and Affiliations

  1. College of Computer Science, South-Central University for Nationalities, Wuhan, 430074, China

    Mengyao Li, Qianao Ding & Jun Wang

  2. College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China

    Rongbo Zhu

  3. School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan, 430073, China

    Shaohua Wan

  4. College of Engineering, Qatar University, Doha, Qatar

    Maode Ma

Authors
  1. Mengyao Li
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  2. Rongbo Zhu
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  3. Qianao Ding
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  4. Jun Wang
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  5. Shaohua Wan
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  6. Maode Ma
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Contributions

Conceptualization, ML, QD and RZ; formal analysis, RZ and JW; investigation, ML and QD; methodology, RZ, SW and MM; supervision, RZ; writing-original-draft preparation, ML, QD and JW; writing-review and editing, RZ, SW and MM. All authors have read and agreed to the submitted version of the manuscript.

Corresponding author

Correspondence to Rongbo Zhu.

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Li, M., Zhu, R., Ding, Q. et al. Edge computing-enabled green multisource fusion indoor positioning algorithm based on adaptive particle filter. Cluster Comput 26, 667–684 (2023). https://doi.org/10.1007/s10586-022-03682-4

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