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

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Amjad Rehman, Khalid Haseeb, Teg Alam, Tanzila Saba, Gwanggil Jeon
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引用次数: 0

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.

Abstract Image

用于 CPS 电子健康应用的新型弹性智能预测模型
网络-物理-社会-系统将各种技术和通信基础设施与互联网互联,用于环境传感和计算。它们为智能城市、工业、交通、医疗系统等提供许多实时自主服务。医疗物联网(IoMT)具有开发网络物理系统(CPS)的潜力,以促进医疗保健应用和分析病人记录。这种通信范例已被纳入许多无线标准,用于通过云计算管理关键数据。然而,此类医疗基础设施的低功率资源限制增加了可持续发展的复杂性。由于负载分布不平衡,下一代网络中的无线连接是另一个研究目标。此外,低功耗计算设备很容易被入侵者访问,并消除任何数据传输的保密性,因此隐私问题是医疗保健系统的另一个研究关注点。因此,本文利用智能计算技术,为电子健康传感提出了一种新型弹性预测模型。所提出的模型通过利用边缘智能探索优化过程,提供了一个高效的 CPS 自动化路由系统。这一特殊解决方案通过智能数据处理和更高的预测服务提高了通信设备之间的合作水平。此外,通过提供一种可信赖的方案,它还能增强数字通信、数据聚合和数据泄露预防。与其他解决方案相比,实验结果凸显了所提模型在数据包接收、网络开销、数据延迟和可靠性方面的显著成果。
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来源期刊
Cognitive Computation
Cognitive Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-NEUROSCIENCES
CiteScore
9.30
自引率
3.70%
发文量
116
审稿时长
>12 weeks
期刊介绍: Cognitive Computation is an international, peer-reviewed, interdisciplinary journal that publishes cutting-edge articles describing original basic and applied work involving biologically-inspired computational accounts of all aspects of natural and artificial cognitive systems. It provides a new platform for the dissemination of research, current practices and future trends in the emerging discipline of cognitive computation that bridges the gap between life sciences, social sciences, engineering, physical and mathematical sciences, and humanities.
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