Skip to main content
SpringerLink
Log in

Markovian model based indoor location tracking for Internet of Things (IoT) applications

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

The monitoring of personnel movements, package tracking and other constructional material based tracking is a top concern in pervasive smart environment. Wireless sensor network (WSN) has given its own individuality in tracking scenario. The challenges faced in this paper deals about an effective 2-dimensional movable system tracking and finding the possible prediction of its exact location of the object in the sensing area. An indoor based WSN with wireless sensor nodes has been created, in which RSSI based location sensing methodology is used. The sensing area is classified as shells and the movement of the node is judged with markov model. The proposed algorithm is tested with various speed conditions suitable for IoT applications. Real study shows the effectiveness of the proposed two dimensional algorithms. The obtained results show minimal location error and accurate location of the object. The proposed methodology serves as the better solution for IoT applications. The proposed algorithm outperforms the existing algorithm with reduced error rate and computing iterations or complexity. A cloud enabled IoT based application is developed to location the elderly and post-surgical people. The developed application serves as a better solution for monitoring the elderly people inside the smart home environment without disturbing their privacy.

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

Similar content being viewed by others

References

  1. Xiong, Z., Song, Z., Scalera, A., Ferrera, E., Sottile, F., Brizzi, P., Tomasi, R., Spirito, M.A.: Hybrid WSN and RFID indoor positioning and tracking system. EURASIP J. Embed. Syst. 2013(1), 6 (2013)

    Article  Google Scholar 

  2. Bahl, P., Padmanabhan, V.N.: RADAR: an inbuilding RF-based user location and tracking system. In: Proceedings of IEEE INFOCOM, Israel, pp. 775–784. Tel Aviv (2000)

  3. Liu, H., Darabi, H., Banerjee, P., Liu, J.: Survey of wireless indoor positioning techniques and systems. IEEE Trans. Syst. Man Cybern. 37(6), 1067–1080 (2007)

    Article  Google Scholar 

  4. D’Souza, M., Schoots, B., Ros, M.: Indoor position tracking using received signal strength-based fingerprint context aware partitioning. IET Radar, Sonar & Navigation, Apr. 2016, pp. 1-9

  5. Au, A.W.S., Feng, C., Valaee, S., Reyes, S., Sorour, S., Markowitz, S., Gold, D., Gordon, K., Eizenman, M.: Indoor tracking and navigation using received signal strength and compressive sensing on a mobile device. IEEE Trans. Mob. Comput. 12(10), 2050–2062 (2013)

    Article  Google Scholar 

  6. Feng, C., Au, W.S.A., Valaee, S., Tan, Z.: Compressive sensing based positioning using RSS of WLAN access points. In: Proceedings of IEEE INFOCOM, pp. 1–9 (2010)

  7. Feng, C., Au, W.S.A., Valaee, S., Tan, Z.: Received-signal-strength-based indoor positioning using compressive sensing. IEEE Trans. Mob. Comput. 11(12), 1983–1993 (2012)

    Article  Google Scholar 

  8. Mikhail, M., Komarov, A.: 2D indoor positioning system based on a wireless sensor network technology for power adjustable solutions. IEEE 7th International Conference on Service-Oriented Computing and Applications, (2014)

  9. Huang, C.N., Chan, C.T.: ZigBee-based indoor location system by k-nearest neighbour algorithm with weighted RSSI. Proc. Comput. Sci. 5, 58–65 (2011)

    Article  Google Scholar 

  10. Zhang, T., Chen, Z.Y., Ouyang, Y.N., Hao, J.Y., Xiong, Z.: An improved RFID-based locating algorithm by eliminating diversity of active tags for indoor environment. Comput. J. 52(8), 902–909 (2008)

    Article  Google Scholar 

  11. Nguyen, N.T., Zheng, R., Liu, J., Han, Z.: GreenLocs: an energy-efficient indoor place identification framework. ACM Trans. Sens. Netw. 11(3), 43 (2015)

    Article  Google Scholar 

  12. Huang, C.N., Chiang, C.Y., Chang, J.S., Chou, Y.C., Hong, Y.X., Hsu, S.J. et al.: Location-aware fall detection system for medical care quality improvement. Proceedings of the 3rd International Conference on Multimedia and Ubiquitous Engineering, pp. 477-480 (2009)

  13. Jiang X.J., Liu, Y., Wang, X.L.: An enhanced approach of indoor location sensing using active RFID. In: Proceeding of the WASE International Conference on Information Engineering, pp. 169–172 (2009)

  14. Liu, H., Darabi, H., Banerjee, P., Liu, J.: Survey of wireless indoor positioning techniques and systems. IEEE Trans. Syst. Man Cybern. Part C 37(6), 1067–1080 (2007)

    Article  Google Scholar 

  15. Xiao, J., Zhou, Z., Yi, Y., Ni, L.M.: A survey on wireless indoor localization from the device perspective. ACM Comput. Surv. 49(2), 25 (2016)

    Article  Google Scholar 

  16. Huang, H., Zhou, J., Li, W., Zhang, J., Zhang, X., Hou, G.: Wearable indoor localisation approach in Internet of Things. IET Netw. 5(5), 1–5 (2016)

    Google Scholar 

  17. Hsu, P.W., Lin, T.H., Chang, H.H., Chen, Y.T., Tseng, Y.J., Hsiao, C.H., et al.: Simulations and experiments for optimal deployment of an RFID-based location-aware system. Wirel. Commun. Mob. Comput. 11(6), 697–691 (2009)

    Google Scholar 

  18. Thomas, F., Ros, L.: Revisiting trilateration for robot localization. IEEE Trans. Robot. 21(1), 93–101 (2005)

    Article  Google Scholar 

  19. Kousalya, G., Narayanasamy, P., Park, J.H., Kim, T.H.: Predictive handoff mechanism with real-time mobility tracking in a campus wide wireless network considering ITS. Comput. Commun. 31(12), 2781–2789 (2008)

    Article  Google Scholar 

  20. Azizyan, M., Constandache, I., Roy Choudhury, R.: Surroundsense: Mobile phone localization via ambience fingerprinting. In: Proceedings of ACM MobiCom, New York, pp. 261–272 (2009)

  21. Wu, C., Yang, Z., Liu, Y., Xi, W.: WILL: wireless indoor localization without site survey. In: Proceedings of IEEE INFOCOM, pp. 64–72 (2012)

  22. Kim, Y., Chon, Y., Cha, H.: Smartphone-based collaborative and autonomous radio fingerprinting. IEEE Trans. Syst. Man Cybern. Part C 42(1), 112–122 (2012)

    Article  Google Scholar 

  23. Yen, L.H., Yang, C.C.: Mobility profiling using Markov chains for tree-based object tracking in wireless sensor networks. IEEE Conf. Sens. Netw. 6, 220–225 (2006)

    Google Scholar 

  24. Lloret, J., Tomas, J., Garcia, M., Canovas, A.: A hybrid stochastic approach for self-location of wireless sensors in indoor environments. Sensors 9, 3695–3712 (2009)

    Article  Google Scholar 

  25. Bettstetter, C., Hartenstein, H., Pérez-Costa, X.: Stochastic properties of the random waypoint mobility model: epoch length, direction distribution, and cell change rate. Proceedings of the 5th ACM International Workshop on Modeling Analysis and Simulation of Wireless and Mobile Systems (2002)

  26. Pasricha, S., Ugave, V, Anderson, C.W., Han,Q.: LearnLoc: a framework for smart indoor localization with embedded mobile devices. Proceedings of the 10th International Conference on Hardware/Software Code sign and System Synthesis, IEEE Press (2015)

  27. Quwaider, M., Biswas, S.: Body posture identification using hidden Markov model with a wearable sensor network. In: Proceedings of the ICST 3rd International Conference on Body Area Networks (2008)

  28. Kamthe, A., Carreira-Perpinan, M.A., Cerpa, A.E.: M&M: multi-level Markov model for wireless link simulation. Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems (2009)

  29. Ni, L.M., Liu, Y.H., Lau, Y.C., Patil, A.P.: LANDMARC: indoor location sensing using active RFID. In: Proceedings of the 1st IEEE International Conference on Pervasive Computing and Communications, pp. 407-415 (2003)

  30. Wang, W., Figueiredo e Silva, P., Lohan, E.S.: Investigations on mobility models and their impact on indoor positioning. Proceedings of the 5th International Workshop on Mobile Entity Localization and Tracking in GPS-less Environments (2015)

Download references

Author information

Authors and Affiliations

  1. Department of Information Technology, Sri Krishna College of Technology, Coimbatore, 641 042, India

    A. Christy Jeba Malar

  2. Department of CSE, Coimbatore Institute of Technology, Coimbatore, 641 014, India

    G. Kousalya

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang Avenue, Singapore, 639798, Singapore

    Maode Ma

Authors
  1. A. Christy Jeba Malar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. G. Kousalya
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Maode Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. Christy Jeba Malar.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jeba Malar, A.C., Kousalya, G. & Ma, M. Markovian model based indoor location tracking for Internet of Things (IoT) applications. Cluster Comput 22 (Suppl 5), 11805–11812 (2019). https://doi.org/10.1007/s10586-017-1494-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-017-1494-z

Keywords

Search

Navigation