[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Springer Nature Link
Log in

Resource discovery in the Internet of Things integrated with fog computing using Markov learning model

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

Abstract

Due to high and unpredictable connection delays, privacy gaps, and traffic load of networks connecting cloud computing to end users in many of the Internet of Things (IoT)-based services, some challenges have been created in cloud computing efficiency. Hence, fog computing has been proposed as a solution in order to bring the cloud service closer to the existing things in the ecosystem. Integrating IoT with fog computing is associated with many challenges, including the resource discovery process. In one sense, sensors, devices, and things are the resources in the IoT ecosystem that searching for them regarding the quality of the search and selection can be one of the challenges in the resource discovery process. In the present paper, the hidden Markov chain learning method has been used to cope with this challenge in the IoT ecosystem integrated with the fog computing, to determine the probability of the need for each thing or resource in the near future with the aim of reducing latency and increasing the network use. The simulation in this work has been performed in the Cloudsim platform, and the considered parameters in the proposed method have been compared with TOPSIS, VIKOR and SAW methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

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

Similar content being viewed by others

Notes

  1. Fog of Things.

  2. The TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method is a multi-criteria decision analysis (MCDA) method.

  3. The VIKOR (Vlsekriterijumska Optimizacija I KOmpromisno Resenje) method is another MCDA method.

  4. The simple additive weighting (SAW) method is one of the most popular MCDA methods.

  5. Cloud of Things refers to integration of Internet of things (IoT) with cloud computing (CC). Cloud of Things (CoT) is a high-performance cloud-based IoT application platform which allows to monitoring, managing, and controlling the IoT-enabled devices remotely.

  6. Multi-Criteria Decision Analysis.

  7. Quality of Context.

  8. Quality of Service.

  9. High-Resolution clustering.

  10. A workflow in cloud computing.

  11. Multiple-Criteria Decision Analysis.

References

  1. Al-Fuqaha A et al (2015) Internet of things: a survey on enabling technologies, protocols, and applications. IEEE Commun Surv Tutor 17(4):2347–2376

    Article  Google Scholar 

  2. Ghobaei-Arani M, Souri A, Rahmanian AA (2019) Resource management approaches in fog computing: a comprehensive review. J Grid Comput 18:1–42. https://doi.org/10.1007/s10723-019-09491-1

    Article  Google Scholar 

  3. Aazam M et al. (2014) Cloud of things: integrating Internet of Things and cloud computing and the issues involved. In: Proceedings of 2014 11th International Bhurban Conference on Applied Sciences and Technology (IBCAST) Islamabad, Pakistan, IEEE

  4. Shakarami A, Shahidinejad A, Ghobaei-Arani M (2020) A review on the computation offloading approaches in mobile edge computing: a game-theoretic perspective. Softw Pract Experience 50(9):1719–1759. https://doi.org/10.1002/spe.2839

    Article  Google Scholar 

  5. Shahidinejad A, Ghobaei-Arani M (2020) Joint computation offloading and resource provisioning for edge-cloud computing environment: a machine learning-based approach. Softw Pract Experience 50(12):2212–2230. https://doi.org/10.1002/spe.2888

    Article  Google Scholar 

  6. Bonomi F et al (2014) Fog computing: a platform for internet of things and analytics. Big data and internet of things: a roadmap for smart environments. Springer, Berlin, pp 169–186

    Chapter  Google Scholar 

  7. Bonomi F et al. (2012) Fog computing and its role in the internet of things. In: Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing. ACM

  8. 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 

  9. Marín-Tordera E et al (2017) Do we all really know what a fog node is? Current trends towards an open definition. Comput Commun 109:117–130

    Article  Google Scholar 

  10. Shakarami A, Ghobaei-Arani M, Shahidinejad A (2020) A survey on the computation offloading approaches in mobile edge computing: A machine learning-based perspective. Comput Netw. https://doi.org/10.1016/j.comnet.2020.107496

    Article  Google Scholar 

  11. Shakarami A, Ghobaei-Arani M, Masdari M, Hosseinzadeh M (2020) A survey on the computation offloading approaches in mobile edge/cloud computing environment: a stochastic-based perspective. J Grid Comput 18:639–671. https://doi.org/10.1007/s10723-020-09530-2

    Article  Google Scholar 

  12. Datta SK, Bonnet C, Haerri J (2015) Fog computing architecture to enable consumer centric internet of things services. In: 2015 International Symposium on Consumer Electronics (ISCE). IEEE

  13. Hu P et al (2017) Survey on fog computing: architecture, key technologies, applications and open issues. J Netw Comput Appl 98:27–42

    Article  Google Scholar 

  14. Verba N et al (2017) Platform as a service gateway for the Fog of Things. Adv Eng Inform 33:243–257

    Article  Google Scholar 

  15. Ghobaei-Arani M, Souri A, Safara F, Norouzi M (2020) An efficient task scheduling approach using moth-flame optimization algorithm for cyber-physical system applications in fog computing. Trans Emerg Telecommun Technol 31(2):e3770. https://doi.org/10.1002/ett.3770

    Article  Google Scholar 

  16. Nunes LH et al (2017) Multi-criteria IoT resource discovery: a comparative analysis. Softw Practice Exper 47(10):1325–1341

    Article  Google Scholar 

  17. Romer K et al (2010) Real-time search for real-world entities: a survey. Proc IEEE 98(11):1887–1902

    Article  Google Scholar 

  18. Perera C et al (2013) Context aware computing for the internet of things: a survey. IEEE Commun Surv Tutor 16(1):414–454

    Article  Google Scholar 

  19. Bharti M, Kumar R, Saxena S (2018) Clustering-based resource discovery on Internet-of-Things. Int J Commun Syst 31(5):e3501

    Article  Google Scholar 

  20. Elahi BM et al. (2009) Sensor ranking: a primitive for efficient content-based sensor searching. In: Proceedings of the 2009 International Conference on Information Processing in Sensor Networks. IEEE Computer Society

  21. Lunardi WT et al. (2015) Context-based search engine for industrial IoT: discovery, search, selection, and usage of devices. In: 2015 IEEE 20th Conference on Emerging Technologies and Factory Automation (ETFA). IEEE

  22. Ostermaier B et al. (2010) A real-time search engine for the web of things. In: 2010 Internet of Things (IOT). IEEE

  23. Truong C, Römer K (2013) Content-based sensor searching for the Web of Things. In: 2013 IEEE Global Communications Conference (GLOBECOM). IEEE

  24. Carlson D, Schrader A (2014) Ambient ocean: a web search engine for context-aware smart resource discovery. In: 2014 IEEE International Conference on Internet of Things (iThings), and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom). IEEE

  25. Perera C et al. (2013) Context-aware sensor searching , selection and ranking model for internet of things middleware. In: 2013 IEEE 14th International Conference on Mobile Data Management. IEEE

  26. Eisenhauer M, Rosengren P, Antolin P (2010) Hydra: a development platform for integrating wireless devices and sensors into ambient intelligence systems. The Internet of Things. Springer, Berlin, pp 367–373

    Chapter  Google Scholar 

  27. Hachem S, Pathak A, Issarny V (2014) Service-oriented middleware for large-scale mobile participatory sensing. Pervasive Mob Comput 10:66–82

    Article  Google Scholar 

  28. Ghobaei-Arani M, Souri A, Baker T, Hussien A (2019) ControCity: an autonomous approach for controlling elasticity using buffer Management in Cloud Computing Environment. IEEE Access 7:106912–106924. https://doi.org/10.1109/ACCESS.2019.2932462

    Article  Google Scholar 

  29. Von Laszewski G et al. (2009) Power-aware scheduling of virtual machines in dvfs-enabled clusters. In: 2009 IEEE International Conference on Cluster Computing and Workshops. IEEE

  30. Etemadi M, Ghobaei-Arani M, Shahidinejad A (2020) A learning-based resource provisioning approach in the fog computing environment. J Exp Theor Artif Intell. https://doi.org/10.1080/0952813X.2020.1818294

    Article  Google Scholar 

  31. Calheiros RN et al (2011) CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw Pract Exper 41(1):23–50

    Article  MathSciNet  Google Scholar 

  32. Memariani A, Amini A, Alinezhad A (2009) Sensitivity analysis of simple additive weighting method (saw): the results of change in the weight of one attribute on the final ranking of alternatives. J Ind Eng 4:13–18

    Google Scholar 

  33. Abdullah L, Adawiyah CR (2014) Simple additive weighting methods of multi criteria decision making and applications: a decade review. Int J Inf Process Manag 5(1):39

    Google Scholar 

  34. Opricovic S, Tzeng GH (2004) Compromise solution by mcda methods: a comparative analysis of vikor and topsis. Eur J Oper Res 156(2):445–455

    Article  Google Scholar 

  35. Huang JJ, Tzeng GH, Liu HH (2009) A revised VIKOR model for multiple criteria decision making-the perspective of regret theory. Communications in computer and information science. Springer, Berlin, pp 761–768

    Google Scholar 

  36. Hurbungs V, Bassoo V, Fowdur T (2021) Fog and edge computing: concepts, tools and focus areas. Int J Inf Technol 6:1–12

    Google Scholar 

  37. Pradhan M, Poltronieri F, Tortonesi M (2019) Dynamic resource discovery and management for edge computing based on SPF for HADR operations. In: 2019 International Conference on Military Communications and Information Systems (ICMCIS). IEEE

  38. Murturi I et al. (2019) Edge-to-edge resource discovery using metadata replication. In: 2019 IEEE 3rd International Conference on Fog and Edge Computing (ICFEC). IEEE

  39. Kimovski D et al (2021) Cloud, Fog or Edge: Where to Compute? IEEE Internet Comput 3:71

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Qom Branch, Islamic Azad University, Qom, Iran

    Samira Kalantary & Ali Shahidinejad

  2. Department of Computer Engineering, Arak Branch, Islamic Azad University, Arak, Iran

    Javad Akbari Torkestani

Authors
  1. Samira Kalantary
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Javad Akbari Torkestani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ali Shahidinejad
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ali Shahidinejad.

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

Kalantary, S., Akbari Torkestani, J. & Shahidinejad, A. Resource discovery in the Internet of Things integrated with fog computing using Markov learning model. J Supercomput 77, 13806–13827 (2021). https://doi.org/10.1007/s11227-021-03824-2

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-021-03824-2

Keywords

Search

Navigation