Iterative Clustering for Energy-Efficient Large-Scale Tracking Systems

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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.

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  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.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

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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.

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Correspondence to Uthman Baroudi.

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Alfares, H.K., Abu Elkhail, A. & Baroudi, U. Iterative Clustering for Energy-Efficient Large-Scale Tracking Systems. Wireless Pers Commun 110, 713–733 (2020).

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