Skip to main content

Advertisement

SpringerLink
Log in

Intelligent scanning period dilation based Wi-Fi fingerprinting for energy efficient indoor positioning in IoT applications

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Internet of Things (IoT) is steadily revolutionizing people’s lives, and accurate location sensing is crucial in achieving this. Global positioning system (GPS) is being widely used outdoors, but its accuracy decreases in indoor environments due to signal attenuation and multipath effect. Simultaneously, Wi-Fi fingerprint-based techniques that use signal strengths from Wi-Fi access points in a building have become more popular for performing indoor positioning. However, location-based services also result in smartphone’s battery life consumption because of frequent access point scanning. There are very few studies that focus on the energy conservation of localization systems, despite the fact that it is a significant factor in real-world applications. This paper proposes an intelligent scanning period dilation (ISPD) technique that uses a semi-centralized architecture and schedules Wi-Fi scans by allocating dynamic time intervals for each user. Experimental results show that the proposal saves 7.56% energy while reducing the location accuracy only by 1.35%.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Furfari F, Crivello A, Baronti P, Barsocchi P, Girolami M, Palumbo F, Quezada-Gaibor D, Mendoza GM, Silva J-S (2021) Discovering location based services: a unified approach for heterogeneous indoor localization systems. Internet Things 13:10034. https://doi.org/10.1016/j.iot.2020.100334

    Article  Google Scholar 

  2. Tiglao NM, Alipio M, Cruz RD, Bokhari F, Rauf S, Khan SA (2021) Smartphone-based indoor localization techniques: state-of-the-art and classification. Meas 179:109349. https://doi.org/10.1016/j.measurement.2021.109349

    Article  Google Scholar 

  3. Tiku S, Pasricha S, Notaros B, Han Qi (2020) A hidden Markov model based smartphone heterogeneity resilient portable indoor localization framework. J Syst Architect 108:101806. https://doi.org/10.1016/j.sysarc.2020.101806

    Article  Google Scholar 

  4. Roy P, Chowdhury C (2022) A survey on ubiquitous WiFi-based indoor localization system for smartphone users from implementation perspectives. CCF Trans Pervasive Comp Interact 4:1–21. https://doi.org/10.1007/s42486-022-00089-3

    Article  Google Scholar 

  5. Luo RC, Hsiao T-J (2019) Indoor localization system based on hybrid Wi-Fi/BLE and hierarchical topological fingerprinting approach. IEEE Trans Veh Technol 68:10791–10806. https://doi.org/10.1109/TVT.2019.2938893

    Article  Google Scholar 

  6. Einavipour S, Javidan R (2021) An intelligent IoT-based positioning system for theme parks. J Supercomput 77:9879–9904. https://doi.org/10.1007/s11227-021-03669-9

    Article  Google Scholar 

  7. Yoo W, Kim H, Shin M (2020) Stations-oriented indoor localization (SOIL): A BIM-Based occupancy schedule modeling system. Build Environ 168:106520. https://doi.org/10.1016/j.buildenv.2019.106520

    Article  Google Scholar 

  8. Varma PS, Anand V (2021) Indoor localization for IoT applications: review, challenges and manual site survey approach. In: IEEE Bombay Section Signature Conference (IBSSC-2021) pp. 1–6. https://doi.org/10.1109/IBSSC53889.2021.9673236

  9. Lie MMK, Kusuma GP (2021) A fingerprint-based coarse-to-fine algorithm for indoor positioning system using bluetooth low energy. Neural Comput Appl 33:2735–2751. https://doi.org/10.1007/s00521-020-05159-0

    Article  Google Scholar 

  10. Lee Y-C, Myung H (2019) Indoor localization method based on sequential motion tracking using topological path map. IEEE Access 7:46187–46197. https://doi.org/10.1109/ACCESS.2019.2909309

    Article  Google Scholar 

  11. Liu X, Zhan Y, Cen J (2018) An energy-efficient crowd-sourcing-based indoor automatic localization system. IEEE Sens J 18:6009–6022. https://doi.org/10.1109/JSEN.2018.2842239

    Article  Google Scholar 

  12. Barsocchi P, Calabrò A, Crivello A, Daoudagh S, Furfari F, Girolami M, Marchetti E (2021) COVID-19 & privacy: enhancing of indoor localization architectures towards effective social distancing. Array 9:100051. https://doi.org/10.1016/j.array.2020.100051

    Article  Google Scholar 

  13. Pinto B, Barreto R, Souto E, Oliveira H (2021) Robust RSSI-based indoor positioning system using K-means clustering and Bayesian estimation. IEEE Sens J 21:24462–24470. https://doi.org/10.1109/JSEN.2021.3113837

    Article  Google Scholar 

  14. Moreno V, Zamora MA, Skarmeta AF (2016) A low-cost indoor localization system for energy sustainability in smart buildings. IEEE Sens J 16:3246–3262. https://doi.org/10.1109/JSEN.2016.2524501

    Article  Google Scholar 

  15. Nguyen CL, Georgiou O, Gradoni G, Di Renzo M (2021) Wireless fingerprinting localization in smart environments using reconfigurable intelligent surfaces. IEEE Access 9:135526–135541. https://doi.org/10.1109/ACCESS.2021.3115596

    Article  Google Scholar 

  16. Thaljaoui A, El Khediri S (2019) Adopting dilution of precision for indoor localization. Wirel Pers Commun. https://doi.org/10.1007/s11277-019-06896-9

    Article  Google Scholar 

  17. Ravi A, Misra A (2021) Practical server-side WiFi-based indoor localization: addressing cardinality & outlier challenges for improved occupancy estimation. Ad Hoc Netw 115:102443. https://doi.org/10.1016/j.adhoc.2021.102443

    Article  Google Scholar 

  18. Chen Z, Xia F, HuangFanyuWang TBuH (2013) A localization method for the internet of things. J Supercomput 63:657–674. https://doi.org/10.1007/s11227-011-0693-2

    Article  Google Scholar 

  19. Zhou M, Wang Y, Tian Z, Lian Y, Wang Y, Wang B (2019) Calibrated data simplification for energy-efficient location sensing in internet of things. IEEE Internet Things J 6:6125–6133. https://doi.org/10.1109/JIOT.2018.2869671

    Article  Google Scholar 

  20. Capurso N, Song T, Cheng W, Yu J, Cheng X (2017) An android-based mechanism for energy efficient localization depending on indoor/outdoor context. IEEE Internet Things J 4:299–307. https://doi.org/10.1109/JIOT.2016.2553100

    Article  Google Scholar 

  21. Niu J, Wang B, Shu L, Duong TQ, Chen Y (2015) ZIL: an energy-efficient indoor localization system using ZigBee radio to detect WiFi fingerprints. IEEE J Sel Areas Commun 33:1431–1442. https://doi.org/10.1109/JSAC.2015.2430171

    Article  Google Scholar 

  22. Salazar González JL, Soria Morillo LM, Álvarez-García JA, Enríquez De Salamanca Ros F, Jiménez Ruiz AR (2019) Energy-efficient indoor localization WiFi-fingerprint system: an experimental study. IEEE Access 7:162664–162682. https://doi.org/10.1109/ACCESS.2019.2952221

    Article  Google Scholar 

  23. Gu Y, Ren F (2015) Energy-efficient indoor localization of smart hand-held devices using bluetooth. IEEE Access 3:1450–1461. https://doi.org/10.1109/ACCESS.2015.2441694

    Article  Google Scholar 

  24. Kwak M, Park Y, Kim J, Han J, Kwon T (2018) An energy-efficient and lightweight indoor localization system for internet-of-things (IoT) environments. Assoc Comput Mach 2:1–28. https://doi.org/10.1145/3191749

    Article  Google Scholar 

  25. Guidara A, Derbel F, Fersi G, Bdiri S, Jemaa MB (2019) Energy-efficient on-demand indoor localization platform based on wireless sensor networks using low power wake up receiver. Ad Hoc Netw 93:101902. https://doi.org/10.1016/j.adhoc.2019.101902

    Article  Google Scholar 

  26. Choi T, Chon Y, Cha H (2017) Energy-efficient WiFi scanning for localization. Pervasive Mob Comput 37:124–138. https://doi.org/10.1016/j.pmcj.2016.07.005

    Article  Google Scholar 

  27. Debbiche A, Msadaa IC, Grayaa K (2022) EIPSO: an energy efficient indoor positioning system based on game theory. Mob Netw Appl 81:1–12. https://doi.org/10.1007/s11036-022-02041-2

    Article  Google Scholar 

  28. Yaghoubi F, Abbasfar A-A, Maham B (2014) Energy-efficient RSSI-based localization for wireless sensor networks. IEEE Commun Lett 18(6):973–976. https://doi.org/10.1109/LCOMM.2014.2320939

    Article  Google Scholar 

  29. Wang J, Gao Q, Yu Y, Zhang X, Feng X (2016) Time and energy efficient TOF-based device-free wireless localization. IEEE Trans Industr Inf 12(1):158–168. https://doi.org/10.1109/TII.2015.2501225

    Article  Google Scholar 

  30. Pan Wu, Xiaobing Wu, Chen G, Shan M, Zhu X (2016) A few bits are enough: energy efficient device-free localization. Comput Commun 83:72–80. https://doi.org/10.1016/j.comcom.2016.01.010

    Article  Google Scholar 

  31. Sadowski S, Spachos P, Plataniotis KN (2020) Memoryless techniques and wireless technologies for indoor localization with the internet of things. IEEE Internet Things J 7(11):10996–11005. https://doi.org/10.1109/JIOT.2020.2992651

    Article  Google Scholar 

  32. Khatab ZE, Gazestani AH, Ghorashi SA, Ghavami M (2021) A fingerprint technique for indoor localization using autoencoder based semi-supervised deep extreme learning machine. Signal Process 181:107915. https://doi.org/10.1016/j.sigpro.2020.107915

    Article  Google Scholar 

  33. Labinghisa BA, Lee DM (2021) Neural network-based indoor localization system with enhanced virtual access points. J Supercomput 77:638–651. https://doi.org/10.1007/s11227-020-03272-4

    Article  Google Scholar 

  34. Guidara A, Fersi G, Jemaa MB, Derbel F (2021) A new deep learning-based distance and position estimation model for range-based indoor localization systems. Ad Hoc Netw 114:102445. https://doi.org/10.1016/j.adhoc.2021.102445

    Article  Google Scholar 

  35. Varma PS, Anand V (2022) Fault-tolerant indoor localization based on speed conscious recurrent neural network using Kullback–Leibler divergence. Peer-to-Peer Netw 15:1370–1384. https://doi.org/10.1007/s12083-022-01301-y

    Article  Google Scholar 

  36. Quanyi Hu, Feng Wu, Wong RK, Millham RC, Fiaidhi J (2021) A novel indoor localization system using machine learning based on bluetooth low energy with cloud computing. Comput. https://doi.org/10.1007/s00607-020-00897-4

    Article  Google Scholar 

  37. Roy P, Chowdhury C, Kundu M, Ghosh D, Bandyopadhyay S (2021) Novel weighted ensemble classifier for smartphone based indoor localization. Expert Syst Appl 164:113758. https://doi.org/10.1016/j.eswa.2020.113758

    Article  Google Scholar 

  38. Soyer MS, Abdel-Qader A, Onbaşlı MC (2021) An efficient and low-latency deep inertial odometer for smartphone positioning. IEEE Sens J 21:27676–27685. https://doi.org/10.1109/JSEN.2021.3122815

    Article  Google Scholar 

  39. Varma PS, Anand V (2021) Random forest learning based indoor localization as an IoT service for smart buildings. Wirel Pers Commun 117:3209–3227. https://doi.org/10.1007/s11277-020-07977-w

    Article  Google Scholar 

  40. Zhao L, Chunhua Su, Dai Z, Huang H, Ding S, Huang X, Han Z (2020) Indoor device-free passive localization with DCNN for location-based services. J Supercomput 76:8432–8449. https://doi.org/10.1007/s11227-019-03110-2

    Article  Google Scholar 

  41. Roy P, Chowdhury C (2021) A survey of machine learning techniques for indoor localization and navigation systems. J Intell Robot Syst 101:63. https://doi.org/10.1007/s10846-021-01327-z

    Article  Google Scholar 

  42. Rohra JG, Perumal B, Narayanan SJ, Thakur P, Bhatt RB (2017) User localization in an indoor environment using fuzzy hybrid of particle swarm optimization & gravitational search algorithm with neural networks. In: Proceedings of Sixth International Conference on Soft Computing for Problem Solving, pp. 286–295. https://doi.org/10.1007/978-981-10-3322-3_27

  43. Bhatt. R (2005) Fuzzy-Rough approaches for pattern classification: hybrid measures, mathematical analysis, Feature selection algorithms. Decision tree algorithms, Neural learning, and Applications, Amazon Books

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology, Raipur, Chhattisgarh, India

    Pothuri Surendra Varma & Veena Anand

Authors
  1. Pothuri Surendra Varma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Veena Anand
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pothuri Surendra Varma.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Varma, P.S., Anand, V. Intelligent scanning period dilation based Wi-Fi fingerprinting for energy efficient indoor positioning in IoT applications. J Supercomput 79, 7736–7761 (2023). https://doi.org/10.1007/s11227-022-04980-9

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-022-04980-9

Keywords

Search

Navigation