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FNSS: A Heuristics for Fog Node Site Selection

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Abstract

In this era of Information Technology (IT), services such as storage, computing, and networking offered by the Cloud and Fog computing, make life easier. With the introduction of the Internet of Things (IoT), activities pertaining to smart life are being automated. Many IoT applications are real-time in nature and therefore require quick processing and actuation. Researchers are working on planning and designing a network of Fog devices to offer the services close to the edge devices (data generation points). To serve such real-time applications, determining the optimal location to place the Fog devices is important. This work proposes a heuristic-based approach to solve a Fog Node Site Selection (FNSS) problem by formulating a mathematical model. This formulation considers the optimal location for the Fog device placement and interconnection between the Fog devices and the Cloud. The proposed model minimizes three important parameters: deployment cost, network latency, and communication traffic towards Cloud. The proposed model applies three multi-objective optimization methods, i.e., Weighted Sum, Null Constraint, and Sequential approach. The IBM CPLEX optimization tool is used to evaluate the model. A case study of the University automation, that leads to a smart university has been considered. The experimental results favour the Weighted Sum approach, out of three, which performs better than the other two methods.

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References

  1. Asghar A, et al. Fog based architecture and load balancing methodology for health monitoring systems. IEEE Access. 2021;9:96189–200. https://doi.org/10.1109/ACCESS.2021.3094033.

    Article  Google Scholar 

  2. Awaisi KS, et al. Towards a fog enabled efficient car parking architecture. IEEE Access. 2019;7:159100–11. https://doi.org/10.1109/ACCESS.2019.2950950.

    Article  Google Scholar 

  3. Balfaqih M, et al. Design and development of smart parking system based on fog computing and internet of things. Electron. 2021;10(24):1–18. https://doi.org/10.3390/electronics10243184.

    Article  Google Scholar 

  4. Bazaraa MS, et al. Linear programming and network flows. New York: John Wiley Sons; 2008.

    Google Scholar 

  5. Canali C, et al. A validated performance model for micro-services placement in fog systems. SN Comput Sci. 2023;4(4):417. https://doi.org/10.1007/s42979-023-01847-5.

    Article  Google Scholar 

  6. Celaya-Echarri M, et al. Building decentralized fog computing-based smart parking systems: from deterministic propagation modeling to practical deployment. IEEE Access. 2020;8:117666–88. https://doi.org/10.1109/ACCESS.2020.3004745.

    Article  Google Scholar 

  7. Deb K. Multi-objective optimization. In: Search methodologies. New York: Springer; 2014. p. 403–49.

    Chapter  Google Scholar 

  8. Garach PV, Thakkar R. A survey on FOG computing for smart waste management system. ICCT 2017 Int Conf Intell Commun Comput Tech. 2018. https://doi.org/10.1109/INTELCCT.2017.8324058.

    Article  Google Scholar 

  9. Haider F, et al. On the planning and design problem of fog computing networks. IEEE Trans Cloud Comput. 2021;9(2):724–36. https://doi.org/10.1109/TCC.2018.2874484.

    Article  MathSciNet  Google Scholar 

  10. Ibrar M, et al. SOSW: scalable and optimal nearsighted location selection for fog node deployment and routing in SDN-based wireless networks for IoT systems. Ann des Telecommun Telecommun. 2021;76(5–6):331–41. https://doi.org/10.1007/s12243-021-00845-z.

    Article  Google Scholar 

  11. Ijaz M, et al. Integration and applications of fog computing and cloud computing based on the internet of things for provision of healthcare services at home. Electron. 2021;10:9. https://doi.org/10.3390/electronics10091077.

    Article  Google Scholar 

  12. Maiti P, et al. QoS-aware fog nodes placement. Proc 4th IEEE Int Conf Recent Adv Inf Technol RAIT. 2018. https://doi.org/10.1109/RAIT.2018.8389043.

    Article  Google Scholar 

  13. Marler RT, Arora JS. The weighted sum method for multi-objective optimization: new insights. Struct Multidiscip Optim. 2010;41(6):853–62. https://doi.org/10.1007/S00158-009-0460-7.

    Article  MathSciNet  Google Scholar 

  14. Ning Z, et al. Vehicular fog computing: enabling real-time traffic management for smart cities. IEEE Wirel Commun. 2019;26(1):87–93. https://doi.org/10.1109/MWC.2019.1700441.

    Article  Google Scholar 

  15. OpenfogConsortium: OpenFog Reference Architecture for Fog Computing Produced. Ref. Archit. 2017;20817:1–162.

  16. Pahlavan K, Krishnamurthy P. Principles of wireless networks: a unified approach. USA: Prentice Hall PTR; 2011.

    Google Scholar 

  17. Rahmani AM, et al. Exploiting smart e-Health gateways at the edge of healthcare Internet-of-Things: A fog computing approach. Futur Gener Comput Syst. 2018;78:641–58. https://doi.org/10.1016/j.future.2017.02.014.

    Article  Google Scholar 

  18. Saroa MK, Aron R.: Fog computing and its role in development of smart applications. Proc. 16th IEEE Int. Symp. Parallel Distrib. Process. with Appl. 17th IEEE Int. Conf. Ubiquitous Comput. Commun. 8th IEEE Int. Conf. Big Data Cloud Comput. 11:1120–1127 (2019). https://doi.org/10.1109/BDCloud.2018.00166.

  19. Shaheen Q, et al. A lightweight location-aware fog framework (LAFF) for QoS in internet of things paradigm. Mob Inf Syst. 2020. https://doi.org/10.1155/2020/8871976.

    Article  Google Scholar 

  20. Sham EE, Vidyarthi DP. Admission control and resource provisioning in fog-integrated cloud using modified fuzzy inference system. J Supercomput. 2022. https://doi.org/10.1007/s11227-022-04483-7.

    Article  Google Scholar 

  21. Sham EE, Vidyarthi DP. CoFA for QoS based secure communication using adaptive chaos dynamical system in fog-integrated cloud. Digit Signal Process. 2022;126: 103523. https://doi.org/10.1016/j.dsp.2022.103523.

    Article  Google Scholar 

  22. Sham EE, Vidyarthi DP. Intelligent admission control manager for fog-integrated cloud: a hybrid machine learning approach. Concur Comput. 2021. https://doi.org/10.1002/cpe.6687.

    Article  Google Scholar 

  23. da Silva RAC, da Fonseca NLS. On the location of fog nodes in fog-cloud infrastructures. Sensors. 2019. https://doi.org/10.3390/s19112445.

    Article  Google Scholar 

  24. Da Silva RAC, Da Fonseca NLS. Location of fog nodes for reduction of energy consumption of end-user devices. IEEE Trans Green Commun Netw. 2020;4(2):593–605. https://doi.org/10.1109/TGCN.2020.2986753.

    Article  Google Scholar 

  25. Singh S, Vidyarthi DP, et al. Designing fog device network for digitization of University Campus. In: Patel KK, et al., editors. Soft computing and its engineering applications. Cham: Springer Nature; 2023. p. 123–34.

    Chapter  Google Scholar 

  26. Sohag MU, Podder AK. Smart garbage management system for a sustainable urban life: An IoT based application. Internet of Things. 2020;11: 100255. https://doi.org/10.1016/J.IOT.2020.100255.

    Article  Google Scholar 

  27. Srikanth CS, et al. Smart waste management using internet-of-things (IoT). Int J Innov Technol Explor Eng. 2019;8(9):2518–22. https://doi.org/10.35940/ijitee.g5334.078919.

    Article  Google Scholar 

  28. Tang C, et al. Towards smart parking based on fog computing. IEEE Access. 2018;6:70172–85. https://doi.org/10.1109/ACCESS.2018.2880972.

    Article  Google Scholar 

  29. Tomovic S, et al. Software-defined fog network architecture for IoT. Wirel Pers Commun. 2017;92(1):181–96. https://doi.org/10.1007/s11277-016-3845-0.

    Article  MathSciNet  Google Scholar 

  30. Vilela PH, et al. Performance evaluation of a fog-assisted IoT solution for e-Health applications. Futur Gener Comput Syst. 2019;97:379–86. https://doi.org/10.1016/j.future.2019.02.055.

    Article  Google Scholar 

  31. Yadav P, Vidyarthi DP. Analyzing the behavior of real-time tasks in fog-cloud architecture. New York: Springer International Publishing; 2022. https://doi.org/10.1007/978-3-030-96040-7_18.

    Book  Google Scholar 

  32. Zhang D, et al. Model and algorithms for the planning of fog computing networks. IEEE Internet Things J. 2019;6(2):3873–84. https://doi.org/10.1109/JIOT.2019.2892940.

    Article  Google Scholar 

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Acknowledgements

The authors would like to acknowledge Jawaharlal Nehru University, New Delhi, INDIA, for the facility in carrying out the experiments. One of the authors would acknowledge UGC, India, for the JRF fellowship.

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  1. School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, 110067, India

    Satveer Singh & Deo Prakash Vidyarthi

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  1. Satveer Singh
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Contributions

The idea is conceptualized by both SS and DPV. SS: wrote the main manuscript text and prepared all figures and Tables. DPV: seconded the manuscript with major modifications. SS: performed experiments. All authors reviewed the manuscript.

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Correspondence to Satveer Singh.

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This article is part of the topical collection “Soft Computing in Engineering Applications”? guest edited by Kanubhai K. Patel.

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Singh, S., Vidyarthi, D.P. FNSS: A Heuristics for Fog Node Site Selection. SN COMPUT. SCI. 5, 146 (2024). https://doi.org/10.1007/s42979-023-02468-8

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