Hybrid Fog-Edge-IoT Architecture for Real-time Data Monitoring
International journal of intelligent engineering and systems · 2023
The Internet of Things (IoT) has recently transformed many lives into comfort zones.The IoT concepts and their exponential growth are burning research issues that need more space for processing and monitoring.To meet the explosive growth of IoT data, edge and fog computing is being deployed for better data analysis with minimum computational complexity.The data can be gathered and analyzed at the fog or edge layer to maximize data utilization.This paper presents a novel prediction and resource allocation using Q-based deep extreme neural network learning algorithms suitable for smart healthcare applications.Resource allocation and effective prediction represent challenging missions involving various resources and IoT nodes to achieve effective computations, leading to realtime computational complexity.The proposed system is composed of four modules: (1) the data collection unit (DCU), (2) the data processing unit (DPU), (3) the intelligent prediction module (IPM), and (4) the adaptive resource allocation module (ARAM).The proposed algorithm is trained using the medical training data, which consists of different heart arrhythmias, specifically heart attacks.The proposed algorithm will be tested using the user's sensing data from the IoT layer to predict the probability of a heart attack and then decide accordingly.The system aims to achieve an improved quality of service (QoS) with less latency.Extensive experimentation results demonstrate that the proposed algorithm has outperformed real-time data monitoring with prediction and resource allocation.