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Optimizing Internet of Things Services Placement in Fog Computing Using Hybrid Recommendation System

Author

Listed:
  • Hanen Ben Rjeb

    (Miracl Lab, Higher Institute of Computer Science and Communication Technologies of Sousse, University of Sousse, Sousse RJQ4+5WW, Tunisia)

  • Layth Sliman

    (Efrei Research Lab, Paris Panthéon Assas University, 94800 Villejuif, France)

  • Hela Zorgati

    (Higher Institute of Computer Science and Multimedia of Sfax, University of Sfax, Sfax 3021, Tunisia)

  • Raoudha Ben Djemaa

    (Miracl Lab, Higher Institute of Computer Science and Communication Technologies of Sousse, University of Sousse, Sousse RJQ4+5WW, Tunisia)

  • Amine Dhraief

    (ESEN, Univesity of Manouba, Manouba CP 2010, Tunisia)

Abstract

Fog Computing extends Cloud computing capabilities by providing computational resources closer to end users. Fog Computing has gained considerable popularity in various domains such as drones, autonomous vehicles, and smart cities. In this context, the careful selection of suitable Fog resources and the optimal assignment of services to these resources (the service placement problem (SPP)) is essential. Numerous studies have attempted to tackle this issue. However, to the best of our knowledge, none of the previously proposed works took into consideration the dynamic context awareness and the user preferences for IoT service placement. To deal with this issue, we propose a hybrid recommendation system for service placement that combines two techniques: collaborative filtering and content-based recommendation. By considering user and service context, user preferences, service needs, and resource availability, the proposed recommendation system provides optimal placement suggestions for each IoT service. To assess the efficiency of the proposed system, a validation scenario based on Internet of Drones (IoD) was simulated and tested. The results show that the proposed approach leads to a considerable reduction in waiting time and a substantial improvement in resource utilization and the number of executed services.

Suggested Citation

  • Hanen Ben Rjeb & Layth Sliman & Hela Zorgati & Raoudha Ben Djemaa & Amine Dhraief, 2025. "Optimizing Internet of Things Services Placement in Fog Computing Using Hybrid Recommendation System," Future Internet, MDPI, vol. 17(5), pages 1-32, April.
    • Handle: RePEc:gam:jftint:v:17:y:2025:i:5:p:201-:d:1646672
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    References listed on IDEAS

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    1. Rui-Feng Wang & Wen-Hao Su, 2024. "The Application of Deep Learning in the Whole Potato Production Chain: A Comprehensive Review," Agriculture, MDPI, vol. 14(8), pages 1-30, July.
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