Skip to main content

Advertisement

SpringerLink
Log in

Access point selection in the network of Internet of things (IoT) considering the strategic behavior of the things and users

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

With the expansion in the use of IoT, increasing the efficiency of these networks has become even more significant. Objects need reliable communications at suitable speed to be able to reach the expected performance. In a heterogeneous network of IoT, the objects can include users and related devices. Despite the hybrid Li-Fi and Wi-Fi networks, IoT needs were somewhat met. However, the efficiency of these networks depends on the choice of access points at the network level. In this study, a new algorithm was proposed based on the access point selection model, considering the strategic behavior of the objects and users. In this algorithm, inspired by access point selection model, a new adaptive algorithm was selected by choosing the access point by Markov game to enhance the load balancing and efficiency in IoT networks based on Wi-Fi and Li-Fi combination. According to the simulation results, it is seen that the proposed method could greatly increase the efficiency of IoT network and better distributes network load between access points with different technology. Overall network throughput was estimated at an average of at least 10% compared to the load-balancing method with fuzzy logic approach proposed in before research.

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

Similar content being viewed by others

References

  1. Wang Y, Haas H (2015) Dynamic load balancing with handover in hybrid Li-Fi and Wi-Fi networks. J Lightwave Technol 33(22):4671–4682

    Article  Google Scholar 

  2. Tsonev D, Stefan V, Harald H (2014) “Light fidelity (Li-Fi): towards all optical networking.” broadband access communication technologies VIII. Int Soc Optics Photon 9007:627

    Google Scholar 

  3. Harald H, Liang Y, Yunlu W and Cheng C (2015) What is Li-Fi?’ Journal Of Lightwave Technology; 2015 IEEE

  4. Tsonev D, Videv S, Haas H (2013) Light fidelity (Li-Fi): towards all optical networking. In: Proc. SPIE 9007, Broadband Access Communication Technologies VIII, 900702 (1 February 2014). https://doi.org/10.1117/12.2044649

  5. Wu X, Majid S, Harald H (2017) Access point selection for hybrid Li-Fi and Wi-Fi networks. IEEE Trans Commun 65:5375–5385

    Article  Google Scholar 

  6. Li X, Zhang R, Hanzo L (2015) Cooperative load balancing in hybrid visible light communications and Wi-Fi. IEEE Trans Commun 63(4):1319–1329

    Article  Google Scholar 

  7. Wang Y, Dushyantha AB, and Harald H (2015) "Dynamic load balancing for hybrid Li-Fi and RF indoor networks." Communication Workshop (ICCW), 2015 IEEE International Conference on. IEEE

  8. Hansen CJ (2011) WiGiG: Multi-gigabit wireless communications in the 60 GHz band. IEEE Wireless Commun 18(6):6–7

    Article  Google Scholar 

  9. Wu X et al (2016) "Two-stage access point selection for hybrid VLC and RF networks." Personal, Indoor, and Mobile Radio Communications (PIMRC), 2016 IEEE 27th Annual International Symposium on. IEEE

  10. Hasan MK, et al (2018) "Fuzzy logic based network selection in hybrid OCC/Li-Fi communication system." 2018 Tenth International Conference on Ubiquitous and Future Networks (ICUFN). IEEE

  11. Souri A et al (2019) A systematic review of IoT communication strategies for an efficient smart environment. Trans Emerg Telecommun Technol 6:e3736

    Google Scholar 

  12. Murugaveni S, Mahalakshmi K (2020) Optimal frequency reuse scheme based on cuckoo search algorithm in Li-Fi fifth-generation bidirectional communication. IET Commun 14(15):2554–2563

    Article  Google Scholar 

  13. Etemadi M, Ghobaei-Arani M, Shahidinejad A (2020) Resource provisioning for IoT services in the fog computing environment: An autonomic approach. Comput Commun 161:109–131

    Article  Google Scholar 

  14. Le S-P et al (2020) Enabling wireless power transfer and multiple antennas selection to IoT network relying on NOMA. Elektronika ir Elektrotechnika 26(5):59–65

    Article  Google Scholar 

  15. Liu W-Y et al (2011) An approach for multi-objective categorization based on the game theory and Markov process. Appl Soft Comput 11(6):4087–4096

    Article  Google Scholar 

  16. Hao, J et al (2015) "An Adaptive Markov Strategy for Effective Network Intrusion Detection." Tools with Artificial Intelligence (ICTAI), 2015 IEEE 27th International Conference on. IEEE

  17. Wang X, Tuomas S (2003) Reinforcement learning to play an optimal Nash equilibrium in team Markov games. Adv Neural Inf Process Syst 15:1603–1610

    Google Scholar 

  18. Lei C, Ma D-H, Zhang H-Q (2017) Optimal strategy selection for moving target defense based on Markov game. IEEE Access 5:156–169

    Article  Google Scholar 

  19. Stefan I, Burchardt H, and Haas H (2013) “Area Spectral Efficiency Performance Comparison between VLC and RF Femtocell Networks,” in Communications (ICC), 2013 IEEE International Conference on, pp. 3825–3829

  20. Forecast, Global Mobile Data Traffic (2019) "Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2017–2022." Update 201

  21. Wang Y, Xiping W, Harald H (2015) "Distributed load balancing for Internet of Things by using Li-Fi and RF hybrid network." Personal, Indoor, and Mobile Radio Communications (PIMRC), 2015 IEEE 26th Annual International Symposium on. IEEE

  22. Littman ML (1994) "Markov games as a framework for multi-agent reinforcement learning." Machine Learning Proceedings 1994. 157–163

  23. Anbalagan S et al (2020) SDN-assisted efficient LTE-Wi-Fi aggregation in next generation IoT networks. Future Gener Computer Syst 107:898–908

    Article  Google Scholar 

  24. Zhang W, Yu K, Wang W, Li X (2021) A self-adaptive ap selection algorithm based on multiobjective optimization for indoor WiFi positioning. IEEE Int Things J 8(3):1406–1416. https://doi.org/10.1109/JIOT.2020.3011402

    Article  Google Scholar 

  25. Mitate, S, et al (2021) "Wireless System selection with spectrum database for IoT." 2021 International Conference on Information Networking (ICOIN). IEEE,

  26. Priya B, Malhotra J (2021) QAAs: QoS provisioned artificial intelligence framework for AP selection in next-generation wireless networks. Telecommun Syst 76(2):233–249

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Payam Porkar Rezaeiye & Arash Sharifi

  2. Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin, 64002, Taiwan

    Amir Masoud Rahmani

  3. Computer Engineering Department, Amirkabir University of Technology, Tehran, Iran

    Mehdi Dehghan

Authors
  1. Payam Porkar Rezaeiye
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Arash Sharifi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Amir Masoud Rahmani
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mehdi Dehghan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mehdi Dehghan.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Porkar Rezaeiye, P., Sharifi, A., Rahmani, A.M. et al. Access point selection in the network of Internet of things (IoT) considering the strategic behavior of the things and users. J Supercomput 77, 14207–14229 (2021). https://doi.org/10.1007/s11227-021-03788-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-021-03788-3

Keywords

Search

Navigation