Wireless Personal Communications

, Volume 110, Issue 2, pp 713–733 | Cite as

Iterative Clustering for Energy-Efficient Large-Scale Tracking Systems

  • Hesham K. Alfares
  • Abdulrahman Abu Elkhail
  • Uthman BaroudiEmail author


A new technique is presented to design energy-efficient large-scale tracking systems based on mobile clustering. The new technique optimizes the formation of mobile clusters to minimize energy consumption in large-scale tracking systems. This technique can be used in large public gatherings with high crowd density and continuous mobility. Utilizing both Bluetooth and Wi-Fi technologies in smart phones, the technique tracks the movement of individuals in a large crowd within a specific area, and monitors their current locations and health conditions. The new system has several advantages, including good positioning accuracy, low energy consumption, short transmission delay, and low signal interference. Two types of interference are reduced: between Bluetooth and Wi-Fi signals, and between different Bluetooth signals. An integer linear programming model is developed to optimize the construction of clusters. In addition, a simulation model is constructed and used to test the new technique under different conditions. The proposed clustering technique shows superior performance according to several evaluation criteria.


Tracking systems Mobile networks Bluetooth and Wi-Fi interference Clustering algorithms Optimization Simulation 



The authors Abdulrahman Abu Elkhail and Uthman Baroudi would like to acknowledge the support provided by the Deanship of Scientific Research (DSR) at King Fahd University of Petroleum and Minerals, under the Grant RG1424-1.


  1. 1.
    Bluetooth SIG. (2017). Bluetooth specifications. Bluetooth Technology Website. Accessed 1 Nov 2018.
  2. 2.
    Jan, B., Farman, H., Javed, H., Montrucchio, B., Khan, M., & Ali, S. (2017). Energy efficient hierarchical clustering approaches in wireless sensor networks: A survey. In Wireless Communications and Mobile Computing.Google Scholar
  3. 3.
    Khanna, G., & Chaturvedi, S. K. (2018). A comprehensive survey on multi-hop wireless networks: milestones, changing trends and concomitant challenges. Wireless Personal Communications,101(2), 677–722.CrossRefGoogle Scholar
  4. 4.
    Weppner, J., Bischke, B., & Lukowicz, P. (2016). Monitoring crowd condition in public spaces by tracking mobile consumer devices with wifi interface. In Proceedings of the 2016 ACM international joint conference on pervasive and ubiquitous computing (pp. 1363–1371). ACM.Google Scholar
  5. 5.
    Chen, Z., Zhu, Q., Jiang, H., & Soh, Y. C. (2015). Indoor localization using smartphone sensors and iBeacons. In 2015 IEEE 10th conference on industrial electronics and applications (ICIEA), (pp. 1723–1728). IEEE.Google Scholar
  6. 6.
    Kim, H. S., Lee, J., & Jang, J. W. (2015). Blemesh: A wireless mesh network protocol for bluetooth low energy devices. In 2015 3rd international conference on future internet of things and cloud (FiCloud) (pp. 558–563). IEEE.Google Scholar
  7. 7.
    Mohandes, M., Haleem, M. A., Kousa, M., & Balakrishnan, K. (2013). Pilgrim tracking and identification using wireless sensor networks and GPS in a mobile phone. Arabian Journal for Science and Engineering,38(8), 2135–2141.CrossRefGoogle Scholar
  8. 8.
    Rostami, A. S., Mohanna, F., & Keshavarz, H. (2017). A novel energy-aware target tracking method by reducing active nodes in wireless sensor networks. Wireless Personal Communications,95(4), 3585–3599.CrossRefGoogle Scholar
  9. 9.
    Abe, R., Shimamura, J., Hayata, K., Togashi, H., & Furukawa, H. (2017). Network-based pedestrian tracking system with densely placed wireless access points. In Information search, integration, and personalization (pp. 82–96). Berlin: Springer.Google Scholar
  10. 10.
    Ashwin, M., Kamalraj, S., & Azath, M. (2017). Weighted clustering trust model for mobile ad hoc networks. Wireless Personal Communications,94(4), 2203–2212.CrossRefGoogle Scholar
  11. 11.
    Conti, M. (2017). Real time localization using bluetooth low energy. In International conference on bioinformatics and biomedical engineering (pp. 584–595). Berlin: Springer.CrossRefGoogle Scholar
  12. 12.
    Lu, X., Wang, J., Zhang, Z., Bian, H., & Yang, E. (2016). WIFI-Based Indoor positioning system with twice clustering and multi-user topology approximation algorithm. In International conference on geo-informatics in resource management and sustainable ecosystems (pp. 265–272). Berlin: Springer.Google Scholar
  13. 13.
    Lv, C., Zhu, J., & Tao, Z. (2018). An improved localization scheme based on PMCL method for large-scale mobile wireless aquaculture sensor networks. Arabian Journal for Science and Engineering,43(2), 1033–1052.CrossRefGoogle Scholar
  14. 14.
    Zhang, Q., Chen, G., Zhao, L., & Chang, C. Y. (2016). Piconet construction and restructuring mechanisms for interference avoiding in bluetooth PANs. Journal of Network and Computer Applications,75, 89–100.CrossRefGoogle Scholar
  15. 15.
    Yoo, J. W., & Park, K. H. (2011). A cooperative clustering protocol for energy saving of mobile devices with WLAN and bluetooth interfaces. IEEE Transactions on Mobile Computing,10(4), 491–504.CrossRefGoogle Scholar
  16. 16.
    GAMS Software GmbH. (2017). GAMS specifications. GAMS Website. Accessed 1 Nov 2018.
  17. 17.
    Golmie, N., Van Dyck, R. E., Soltanian, A., Tonnerre, A., & Rebala, O. (2003). Interference evaluation of Bluetooth and IEEE 802.11 b systems. Wireless Networks,9(3), 201–211.CrossRefGoogle Scholar
  18. 18.
    Santivanez, C., Ramanathan, R., Partridge, C., Krishnan, R., Condell, M., & Polit, S. (2006). Opportunistic spectrum access: Challenges, architecture, protocols. In Proceedings of the 2nd annual international workshop on wireless internet (p. 13). ACM.Google Scholar
  19. 19.
    Hu, Z., Susitaival, R., Chen, Z., Fu, I. K., Dayal, P., & Baghel, S. K. (2012). Interference avoidance for in-device coexistence in 3GPP LTE-advanced: Challenges and solutions. IEEE Communications Magazine,50(11), 60–67.CrossRefGoogle Scholar
  20. 20.
    Mathew, A., Chandrababu, N., Elleithy, K., & Rizvi, S. (2009). IEEE 802.11 & Bluetooth interference: simulation and coexistence. In Seventh annual communication networks and services research conference, 2009 (CNSR’09), (pp. 217-223). IEEE.Google Scholar
  21. 21.
    Chek, M. C. H., & Kwok, Y. K. (2007). Design and evaluation of practical coexistence management schemes for bluetooth and IEEE 802.11 b systems. Computer Networks,51(8), 2086–2103.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.King Fahd University of Petroleum and MineralsDhahranSaudi Arabia

Personalised recommendations