Skip to main content
SpringerLink
Log in

An Efficient Intuitionistic Fuzzy Sets Base Stations Deployment Strategy in Internet of Things Systems

  • Published:
International Journal of Fuzzy Systems Aims and scope Submit manuscript

Abstract

The deployment of base stations (BSs) is an extremely important matter in an internet of things (IoT) system. When a BS location is poorly deployed, it will cause the overall coverage of sensor devices (SDs) to decrease. Hence, the more BSs are required to increase the number of covered SDs in IoT systems. It leads to a higher deployment budget of BSs. However, BSs cannot be deployed everywhere in real environments. Taking the SDs positions and BSs candidate locations into account, we design an efficient and novel BSs deployment strategy to maximize the number of covered SDs and to reduce the deployment budget of BSs in IoT systems. We employ the Intuitionistic Fuzzy Sets (IFS) to make an adjustment decision for determining suitable BSs deployment positions from the BS candidate locations. Simulation results demonstrate that our proposed IFS BS deployment strategy achieves a higher coverage ratio of SDs and a favorable deployment budget of BSs in IoT systems.

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

Similar content being viewed by others

References

  1. Dudak, G.J., Gaspar, G., Sedivy, S., Fabo, P., Pepucha, L., Tanuska, P.: Serial communication protocol with enhanced properties-securing communication layer for smart sensors applications. IEEE Sens. J. 19(1), 378–390 (2019)

    Article  Google Scholar 

  2. Seo, H., Park, J., Bennis, M., Choi, W.: Communication and consensus co-design for distributed, low-latency, and reliable wireless systems. IEEE Internet Things J. 8(1), 129–143 (2021)

    Article  Google Scholar 

  3. Huang, H., Li, H., Shao, C., Sun, T., Fang, W., Dang, S.: Data redundancy mitigation in V2X based collective perceptions. IEEE Access. 8, 13405–13418 (2020)

    Article  Google Scholar 

  4. Malik, A.W., Mahmood, I., Ahmed, N., Anwar, Z.: Big data in motion: a vehicle-assisted urban computing framework for smart cities. IEEE Access. 7, 55951–55965 (2019)

    Article  Google Scholar 

  5. Vincent, M., Babu, K. V., Arthi, M., Arulmozhivarman, P.: A novel fuzzy logic based relay station selection scheme for 4G cellular system. 2016 International Conference on Communication and Signal Processing (ICCSP), pp. 0158–0163, (2016)

  6. Chatterjee, S., Abdel-Rahman, M.J., MacKenzie, A.B.: Optimal base station deployment with downlink rate coverage probability constraint. IEEE Wirel. Commun. Lett. 7(3), 340–343 (2018)

    Article  Google Scholar 

  7. Dong, M., Kim, T., Wu, J., Wong, E.W.M.: Cost-efficient millimeter wave base station deployment in Manhattan-type geometry. IEEE Access. 7, 149959–149970 (2019)

    Article  Google Scholar 

  8. Cayirpunar, O., Tavli, B., Kadioglu-Urtis, E., Uludag, S.: Optimal mobility patterns of multiple base stations for wireless sensor network lifetime maximization. IEEE Sens. J. 17(21), 7177–7188 (2017)

    Article  Google Scholar 

  9. Zhang, Y., Dai, L., Wong, E.W.M.: Optimal BS deployment and user association for 5G millimeter wave communication networks. IEEE Trans. Wireless Commun. 20(5), 2776–2791 (2021)

    Article  Google Scholar 

  10. Mirza, S., Gujarathi, T., Bhole, K.: Cardiovascular risk assessment using intuitionistic fuzzy logic system. 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1–7, (2019)

  11. Liu, H., Tu, J., Sun, C.: Improved possibility degree method for intuitionistic fuzzy multi-attribute decision making and application in aircraft cockpit display ergonomic evaluation. IEEE Access. 8, 202540–202554 (2020)

    Article  Google Scholar 

  12. Liao, H., Mi, X., Xu, Z., Xu, J., Herrera, F.: Intuitionistic fuzzy analytic network process. IEEE Trans. Fuzzy Syst. 26(5), 2578–2590 (2018)

    Article  Google Scholar 

  13. Peng, H., et al.: Fault diagnosis of power systems using intuitionistic fuzzy spiking neural P systems. IEEE Transact. Smart Grid. 9(5), 4777–4784 (2018)

    Article  MathSciNet  Google Scholar 

  14. Hassan, S.G., Iqbal, S., Garg, H., Hassan, M., Shuangyin, L., Kieuvan, T.T.: Designing intuitionistic fuzzy forecasting model combined with information granules and weighted association reasoning. IEEE Access. 8, 41090–141103 (2020)

    Article  Google Scholar 

  15. Wei, A.P., Li, D.F., Jiang, B.Q., et al.: The novel generalized exponential entropy for intuitionistic fuzzy sets and interval valued intuitionistic fuzzy sets. Int. J. Fuzzy Syst. 21, 2327–2339 (2019)

    Article  MathSciNet  Google Scholar 

  16. Zhao, J., Lin, C.M.: An interval-valued fuzzy cerebellar model neural network based on intuitionistic fuzzy sets. Int. J. Fuzzy Syst. 19, 881–894 (2017)

    Article  Google Scholar 

  17. Liu, P., Wang, P.: Multiple attribute group decision making method based on intuitionistic fuzzy Einstein interactive operations. Int. J. Fuzzy Syst. 22, 790–809 (2020)

    Article  Google Scholar 

  18. Pliatsios, D., Sarigiannidis, P., Moscholios, I. D., Tsiakalos, A.: Cost-efficient remote radio head deployment in 5G networks under minimum capacity requirements. 2019 Panhellenic Conference on Electronics & Telecommunications (PACET), (2019)

  19. Wang, C.H., Lee, C.J., Wu, X.J.: A coverage-based location approach and performance evaluation for the deployment of 5G base stations. IEEE Access. 8, 123320–123333 (2020)

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the Ministry of Science and Technology, Taiwan, R.O.C., under Contract MOST-111-2221-E-150-047.

Author information

Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Formosa University, Hu-Wei, Yun-Lin, Taiwan

    Zhen-Yin Lin, Jau-Yang Chang & Jin-Tsong Jeng

Authors
  1. Zhen-Yin Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jau-Yang Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jin-Tsong Jeng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jau-Yang Chang.

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

Lin, ZY., Chang, JY. & Jeng, JT. An Efficient Intuitionistic Fuzzy Sets Base Stations Deployment Strategy in Internet of Things Systems. Int. J. Fuzzy Syst. 25, 1882–1894 (2023). https://doi.org/10.1007/s40815-023-01480-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40815-023-01480-7

Keywords

Search

Navigation