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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.

References

  1. Jun, Y., Zhijin, W.: A location algorithm based on Stiefel manifold particle filtering. Clust. Comput. 22, 4877–4882 (2019)

    Article  Google Scholar 

  2. Shang, W., Chen, J., Bi, H., Sui, Y., Chen, Y., Yu, H.: Impacts of COVID-19 pandemic on user behaviors and environmental benefits of bike sharing: a big-data analysis. Appl. Energy 285, 116429 (2021)

    Article  Google Scholar 

  3. Liu, Q., Qiu, J., Chen, Y.: Research and development of indoor positioning, China. Communications 13(Supplement 2), 67–79 (2016)

    Google Scholar 

  4. Alvarez, Y., Las Heras, F.: ZigBee-based sensor network for indoor location and tracking applications. IEEE Lat. Am. Trans. 14(7), 3208–3214 (2016)

    Article  Google Scholar 

  5. Zafari, F., Gkelias, A., Leung, K.K.: A survey of indoor localization systems and technologies. IEEE Commun. Surv. Tutor. 21(3), 2568–2599 (2019)

    Article  Google Scholar 

  6. Pivato, P., Palopoli, L., Petri, D.: Accuracy of RSS-based centroid localization algorithms in an indoor environment. IEEE Trans. Instrum. Meas. 60(10), 3451–3460 (2011)

    Article  Google Scholar 

  7. Faragher, R., Harle, R.: Location fingerprinting with bluetooth low energy beacons. IEEE J. Sel. Areas Commun. 33(11), 2418–2428 (2015)

    Article  Google Scholar 

  8. Pei, L., Liu, D., Zou, D., Choy, R.L., Chen, Y., He, Z.: Optimal heading estimation based multidimensional particle filter for pedestrian indoor positioning. IEEE Access 6, 49705–49720 (2018)

    Article  Google Scholar 

  9. Adler, S., Schmitt, S., Wolter, K., Kyas, M.: A survey of experimental evaluation in indoor localization research. In: 2015 international conference on indoor positioning and indoor navigation (IPIN), Banff, AB, pp. 1–10. (2015)

  10. Hu, Z., Huang, G., Hu, Y., Yang, Z.: WI-VI fingerprint: WiFi and vision integrated fingerprint for smartphone-based indoor self-localization. In: 2017 IEEE international conference on image processing (ICIP), Beijing, pp. 4402–4406. (2017)

  11. Huang, C., Lee, L., Ho, C.C., Wu, L., Lai, Z.: Real-time RFID indoor positioning system based on Kalman-filter drift removal and Heron-bilateration location estimation. IEEE Trans. Instrum. Meas. 64(3), 728–739 (2015)

    Article  Google Scholar 

  12. Grami, T., Sghaier Tlili, A.: Indoor mobile robot localization based on a particle filter approach. In: 2019 19th international conference on sciences and techniques of automatic control and computer engineering (STA), Sousse, Tunisia, pp. 47–52. (2019)

  13. Yang, G., Zhao, L., Dai, Y., Xu, Y.: A KFL-TOA UWB indoor positioning method for complex environment. In: 2017 Chinese Automation Congress (CAC), Jinan, pp. 3010–3014. (2017)

  14. Ens, A., Reindl, L.M., Bordoy, J., Wendeberg, J., Schindelhauer, C.: Unsynchronized ultrasound system for TDOA localization. In: 2014 international conference on indoor positioning and indoor navigation (IPIN), Busan, pp. 601–610. (2014)

  15. Garrote, L., Barros, T., Pereira, R., Nunes, U.J.: Absolute indoor positioning-aided laser-based particle filter localization with a refinement stage. In: 45th annual conference of the IEEE industrial electronics society, Lisbon, Portugal, pp. 597–603. (2019)

  16. Xue, H., Ma, L., Tan, X.: A fast visual map building method using video stream for visual-based indoor localization. In: 2016 international wireless communications and mobile computing conference (IWCMC), Paphos, pp. 650–654. (2016)

  17. Zhang, W., Liu, G., Tian, G.: A coarse to fine indoor visual localization method using environmental semantic information. IEEE Access 7, 21963–21970 (2019)

    Article  Google Scholar 

  18. Shi, S., Sigg, S., Chen, L., Ji, Y.: Accurate location tracking from CSI-based passive device-free probabilistic fingerprinting. IEEE Trans. Veh. Technol. 67(6), 5217–5230 (2018)

    Article  Google Scholar 

  19. Chawathe, S.S.: Indoor localization using bluetooth-LE beacons. In: 2018 9th IEEE annual ubiquitous computing, electronics & mobile communication conference (UEMCON), New York City, NY, pp. 262–268. USA. (2018)

  20. Obreja, S.G., Vulpe, A.: Evaluation of an indoor localization solution based on bluetooth low energy beacons, In: 2020 13th international conference on communications (COMM), Bucharest, Romania, pp. 227–231.

  21. Zafari, F.: iBeacon based proximity and indoor localization system, Master’s thesis, Dept. Comput. Inf. Technol., Purdue Univ., West Lafayette, IN, USA. (2016)

  22. Xun, W., Sun, L., Han, C., Lin, Z., Guo, J.: Depthwise separable convolution based passive indoor localization using CSI fingerprint. In: 2020 IEEE wireless communications and networking conference (WCNC), Seoul, Korea (South), pp. 1–6. (2020)

  23. Labinghisa, B., Lee, D.M.: Drift-free indoor pedestrian dead reckoning using empirical mode decomposition. In: 2019 25th asia-pacific conference on communications (APCC), Ho Chi Minh City Vietnam, pp. 262–266. (2019)

  24. Altinpinar, O.V., Yalçin, M.E.: Design of a pedestrian dead-reckoning system and comparison of methods on the system. In: 2018 26th signal processing and communications applications conference (SIU), Izmir, pp. 1–4 (2018)

  25. Fang-Min, Li., Tao, Z., Kai, L., Guo, L., Xiao-Lin, Ma.: An indoor positioning method based on range measuring and location fingerprinting. Chin. J. Comput. 42(2), 109–120 (2019)

    Google Scholar 

  26. Cai, C., Ma, X., Hu, M., Yang, Y., Li, Z., Liu, J.: SAP: A novel stationary peers assisted indoor positioning system. IEEE Access 6, 76475–76489 (2018)

    Article  Google Scholar 

  27. Shu, Y., Bo, C., Shen, G., Zhao, C., Li, L., Zhao, F.: Magicol: indoor localization using pervasive magnetic field and opportunistic WiFi sensing. IEEE J. Sel. Areas Commun. 33(7), 1443–1457 (2015)

    Article  Google Scholar 

  28. Pipelidis, G., Tsiamitros, N., Gentner, C., Ahmed, D.B., Prehofer, C.: A novel lightweight particle filter for indoor localization. In: 2019 international conference on indoor positioning and indoor navigation (IPIN), Pisa, Italy, pp. 1–8. (2019)

  29. Liu, Y., Li, S., Sun, Q., Chang, C., He, G., Kang, X.: An UWB/PDR fusion algorithm based on improved square root unscented Kalman filter". In: 2019 Chinese control conference (CCC), Guangzhou, China, pp. 4124–4129. (2019)

  30. El-Absi, M., Alhaj Abbas, A., Abuelhaija, A., Zheng, F., Solbach, K., Kaiser, T.: High-accuracy indoor localization based on chipless RFID systems at THz band. IEEE Access 6, 54355–54368 (2018)

    Article  Google Scholar 

  31. Hasani, M., Talvitie, J., Sydänheimo, L., Lohan, E., Ukkonen, L.: Hybrid WLAN-RFID indoor localization solution utilizing textile tag. IEEE Antennas Wirel. Propag. Lett. 14, 1358–1361 (2015)

    Article  Google Scholar 

  32. Lai, J., et al.: TagSort: accurate relative localization exploring RFID phase spectrum matching for Internet of things. IEEE Internet Things J. 7(1), 389–399 (2020)

    Article  Google Scholar 

  33. Zhang, M., Shen, W., Yao, Z., Zhu, J.: Multiple information fusion indoor location algorithm based on WiFi and improved PDR. In: 2016 35th Chinese control conference (CCC), Chengdu, pp. 5086–5092. (2016)

  34. Patel, M., Girgensohn, A., Biehl, J.: Fusing map information with a probabilistic sensor model for indoor localization using RF beacons. In: 2018 international conference on indoor positioning and indoor navigation (IPIN), Nantes, pp. 1–8. (2018)

  35. Liu, W., Li, J., Deng, Z., Fu, X., Cheng, Q.: A calibrated-RSSI/PDR/Map integrated system based on a novel particle filter for indoor navigation. In: 2019 international conference on indoor positioning and indoor navigation (IPIN), Pisa, Italy, pp. 1–8. (2019)

  36. Radoglou-Grammatikis, P., Robolos, K., Sarigiannidis, P., Argyriou, V., Lagkas, T., Sarigiannidis, A., Goudos, S.K., Wan, S.: Modelling, detecting and mitigating threats against industrial healthcare systems: a combined SDN and reinforcement learning approach. IEEE Trans. Ind. Inf. 18(3), 2041–2052 (2022)

    Article  Google Scholar 

  37. Sivasakthiselvan, S., Nagarajan, V.: A new localization technique for node positioning in wireless sensor networks. Clust. Comput. 22, 4027–4034 (2019)

    Article  Google Scholar 

  38. Wan, S., Ding, S., Chen, C.: Edge computing enabled video segmentation for real-time traffic monitoring in internet of vehicles. Pattern Recogn. 121, 108146 (2022)

    Article  Google Scholar 

Download references

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

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  1. Mengyao Li
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  2. Rongbo Zhu
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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.

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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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