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

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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.

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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.

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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

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  1. Samira Kalantary
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  2. Javad Akbari Torkestani
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  3. Ali Shahidinejad
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Correspondence to Ali Shahidinejad.

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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

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