Optimization of Task Scheduling Based on Marine Predators’Algorithm for Fog Computing Based Healthcare System

Abstract

High-speed development of Internet of Things (IoT) and fog computing has improved the real-time healthcare monitoring system to great extent because it allows processing the data immediately and with low latency as well as utilization of the resources efficiently. Nevertheless, scheduling of tasks in fogs is a serious issue because of the dynamic workloads, non-homogeneous devices, and high Quality of Service (QoS). This paper presents an optimal way of scheduling the tasks in a healthcare system that utilizes fogs by using the Marine Predators Algorithm (MPA). The given approach utilizes exploration and exploitation opportunities of MPA to make task distribution, latency minimization, and even load distribution over fog nodes efficient. Under the three-layer IoT to fog to cloud architecture, the algorithm will be tested on Fog-Simulator in different scenarios with different numbers of sensors. As experimental findings prove, the proposed method is better than current methods, including MPSO, BLA and traditional MPA, in the aspects of the reduction of latency and scalability. The findings also verify that the suggested model enhances the overall performance of the system and promotes the provision of trustworthy real-time healthcare.