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A Lightweight Association Rules Based Prediction Algorithm (LWRCCAR) for Context-Aware Systems in IoT Ubiquitous, Fog, and Edge Computing Environment

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Proceedings of the Future Technologies Conference (FTC) 2020, Volume 2 (FTC 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1289))

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Abstract

Proactive is one main aspect of ubiquitous context-aware systems in IoT environment. Ubiquitous context-aware systems in IoT environment needs a light-weight intelligent prediction techniques especially within fog and edge computing environment where technologies capabilities are poor. On the other hand, the data that ubiquitous context-aware systems depends on to learn is big. This paper suggests a light-weight prediction algorithm to help such system to work effectively. The proposed algorithm is improvement of RCCAR algorithm. RCCAR utilizes association rules for prediction. The contribution of this paper is minimize the number of association rules by giving a priority for associations that produced of high order itemsets before the lowest ones. The prediction is scored and formulated mathematically using confidence association rules measure. A real dataset is used in many different scenario experiments. The proposed algorithm achieves good with reasonable prediction score. For future work, extensive experiments with many datasets is recommended.

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Author information

Authors and Affiliations

  1. Information Technology Department, Faculty of Computer, Qassim University, Buraydah, Saudi Arabia

    Asma Abdulghani Al-Shargabi

  2. Software Technology Research Laboratory (STRL), De Montfort University, Leicester, UK

    Francois Siewe

Authors
  1. Asma Abdulghani Al-Shargabi
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  2. Francois Siewe
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Corresponding author

Correspondence to Asma Abdulghani Al-Shargabi .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

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Al-Shargabi, A.A., Siewe, F. (2021). A Lightweight Association Rules Based Prediction Algorithm (LWRCCAR) for Context-Aware Systems in IoT Ubiquitous, Fog, and Edge Computing Environment. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Proceedings of the Future Technologies Conference (FTC) 2020, Volume 2 . FTC 2020. Advances in Intelligent Systems and Computing, vol 1289. Springer, Cham. https://doi.org/10.1007/978-3-030-63089-8_2

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