Multi-Layer Secured Edge-AI Enabled Model for Consumer Health Systems

The Internet of Medical Things (IoMT) and edge computing technologies have significantly advanced the healthcare industry by enabling real-time monitoring and data processing at the network edges. Medical devices are combined with consumer electronics to measure the physiological factors of the human body and transfer them to cloud storage for further analysis and processing. However, optimizing routing to account for limited processing power and network constraints of the consumer healthcare system poses a significant research challenge. Moreover, due to the restricted environment, several privacy and security issues arise with integrating heterogeneous technologies over wireless systems. In this work, we proposed a multi-layer secured-edged-enabled AI model for health systems to provide real-time data analysis and transmission decisions at the edges. Firstly, the intelligent algorithm employs dynamic and trusted routing strategies at the edges to control the data flow and minimize the network congestion on consumers’ data. Secondly, in the event of data and integrity breaches, randomized security patterns are investigated to build trust among devices using edge management and raise the degree of data privacy. The randomized security patterns improve the analysis of data security by integrating the concept of rotating encryption keys and their management. Such a paradigm decreases the probabilities of network threats and generates unpredictable patterns for increasing the resilience of IoT systems in the existence of harmful devices. The proposed model is tested using various simulation-based experiments, revealing its significant improvement over existing studies for packet drop ratio, data delay, system uptime, data breaches, and security overhead.