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A Novel Resilient and Intelligent Predictive Model for CPS-Enabled E-Health Applications

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Abstract

Cyber-physical-social-systems interconnect diverse technologies and communication infrastructure to the Internet for environmental sensing and computation. They offer many real-time autonomous services for smart cities, industry, transportation, medical systems, etc. The Internet of Medical Things (IoMT) has gained the potential for developing cyber-physical system (CPS) to facilitate healthcare applications and analyze the records of patients. Such a communication paradigm is integrated into many wireless standards for managing crucial data with cloud computing. However, the limitations of low-powered resources of such healthcare infrastructures increase the complexity level of sustainable growth. Wireless connectivity in next-generation networks is another research goal due to unbalanced load distribution. Furthermore, low-powered computing devices can be easily accessible by intruders and eliminate the confidentiality of any data transmission, so privacy is another research concern for healthcare systems. Therefore, using intelligent computing, this paper proposed a novel resilient predictive model for e-health sensing. The proposed model provides an efficient CPS-enabled automated routing system by exploring the optimization process with edge intelligence. This particular solution increases the level of cooperation between communication devices with intelligent data processing and higher predictive services. Moreover, by offering a trustworthy scheme, it seeks to enhance digital communication, data aggregation, and data breach prevention. The experimental findings highlight significant outcomes of the proposed model for packet reception, network overhead, data delay, and reliability as compared to alternative solutions.

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Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

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Acknowledgements

This work was supported by Artificial Intelligence & Data Analytics (AIDA) Lab CCIS Prince Sultan University, Riyadh, Saudi Arabia. The authors are thankful for their support.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

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Authors and Affiliations

  1. Artificial Intelligence & Data Analytics (AIDA) Lab CCIS, Prince Sultan University, Riyadh 12435, Saudi Arabia

    Amjad Rehman, Khalid Haseeb, Tanzila Saba & Gwanggil Jeon

  2. Department of Industrial Engineering, College of Engineering, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Kingdom of Saudi Arabia

    Teg Alam

  3. Azad Institute of Engineering and Technology, Azad Puram, Chandrawal via Bangla Bazar & Bijnour, Near CRPF Camp, Lucknow 226002, India

    Teg Alam

  4. Department of Embedded Systems Engineering, College of Information Technology, Incheon National University, Incheon 22012, Korea

    Gwanggil Jeon

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  1. Amjad Rehman
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  4. Tanzila Saba
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Correspondence to Gwanggil Jeon.

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Rehman, A., Haseeb, K., Alam, T. et al. A Novel Resilient and Intelligent Predictive Model for CPS-Enabled E-Health Applications. Cogn Comput (2024). https://doi.org/10.1007/s12559-024-10278-0

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