A Practical Implementation of Optimal Telecommunication Tower Placement Strategy Using Data Science

  • Harsh AgarwalEmail author
  • Bhaskar Tejaswi
  • Debika Bhattacharya
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 714)


The exponential growth in the tele-density in India and around the world has put forth a lot of challenges for the network operators. The customers look for good signal reception, fast data speeds, and call quality while choosing their cell phone operators. The aim of this study is to obtain an optimum number of telecommunications towers using data science algorithms like mean shift, SVM classification, and K-means algorithm and practically implemented it using Android application. We propose a new method for optimizing the position of cell towers to get the coverage area of the widest service through three stages: Clustering, classification, and positioning. The proposed cell phone tower placement scheme involves data extraction from cell phone users through an Android application and the analysis of the data to obtain a set of possible candidate sites for establishing a base station.


Mobile communication Data science Tower placement 


  1. 1.
    TRAI Press Release on Telecom Subscription Data, 2016.Google Scholar
  2. 2.
    GSMA Association report The Mobile Economy, 2017.Google Scholar
  3. 3.
    Communications Consumer Panel report Mobile coverage: the consumer perspective, 2009.Google Scholar
  4. 4.
    Honghui Dong et al., “Urban residents travel analysis based on mobile communication data,” 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), The Hague, pp. 1487–1492. 2013.Google Scholar
  5. 5.
    J. Liao, Z. Wang, L. Wan, Q. C. Cao and H. Qi, “Smart Diary: A Smartphone-Based Framework for Sensing, Inferring, and Logging Users’ Daily Life,” in IEEE Sensors Journal, vol. 15, no. 5, pp. 2761–2773, May 2015.Google Scholar
  6. 6.
    S. Rallapalli, W. Dong, G. M. Lee, Y. C. Chen and L. Qiu, “Analysis and applications of smartphone user mobility,” 2013 Proceedings IEEE INFOCOM, Turin, pp. 3465–3470, 2013.Google Scholar
  7. 7.
    Church, R.L., ReVelle, C., The maximal covering location problem. Regional Science 30, 101–118, 1974.Google Scholar
  8. 8.
    M. B. Pereira, F. R. P. Cavalcanti and T. F. Maciel, “Particle Swarm Optimization for base station placement,” 2014 International Telecommunications Symposium (ITS), Sao Paulo, pp. 1–5, 2014.Google Scholar
  9. 9.
    D. Komnakos, A. Rouskas and A. Gotsis, “Energy Efficient Base Station Placement and Operation in Mobile Networks,” European Wireless 2013; 19th European Wireless Conference, Guildford, UK, pp. 1–5, 2013.Google Scholar
  10. 10.
    O. Celebi, E. Zeydan, O. Kurt, O. Dedeoglu, O. Iieri, B. Aykut Sungur, A. Akan, S. Ergut, On use of big data for enhancing network coverage analysis, in: 20th International Conference on Telecommunications (ICT), pp. 1– 5, 2013.Google Scholar
  11. 11.
    A. Karatepe, E. Zeydan, Anomaly detection in cellular network data using big data analytics, in: Proceedings of 20th European Wireless Conference, pp. 1–5, 2014.Google Scholar
  12. 12.
    D. Comaniciu and P. Meer. Mean shift: A robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Machine Intell. 24: pp. 603–619, 2002.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Harsh Agarwal
    • 1
    Email author
  • Bhaskar Tejaswi
    • 1
  • Debika Bhattacharya
    • 1
  1. 1.Department of CSEIEM KolkataKolkataIndia

Personalised recommendations