A Support System Development for Healthcare System using FL and FC Techniques

This has been increasing in difficulty for accomplishing time-consuming and complex tasks with the rise in the number of Internet of Things (IoT) devices and the generated big data, along with problems of expanding concerns often in the form of latency and use of power. Fog computing is the potential solution advanced by a dispersed form of computing and has a solution to these issues. IoT-Fog applications are limited in meeting time constraints to reduce service latency and energy consumption, due to the limited processing capacity that fog computing devices have. Based on this factor, this paper now proposes an alternative approach by using Fuzzy Logic to find the solution based on Dynamic Integer Linear Programming to reduce the workload in the clinical decision support system by efficient allocation of resources to the IoT devices from the Fog compute layer while accounting for the system constraints and resource availability. The proposed task-priority scheme is integrated into the proposed clinical decision support system and reduces the latency and energy consumption in fog nodes. Experiments show that the proposed method outperforms the benchmark methods in terms of energy consumption and service delay.