An Energy Efficient Scheduling in Industrial Internet of Things (IIoT)

Fog computing, extends the capabilities of cloud computing to the network’s edge. This paradigm aims to minimize latency and facilitate real-time processing for edge devices, offering a more efficient and responsive computing environment. It decentralizes computing resources, enhancing scalability, security, and resource efficiency while optimizing data privacy. By bringing computer resources and services closer to devices and endpoints, it seeks to overcome the limits of centralized cloud architecture. Effectively scheduling tasks in fog computing remains challenged due to the dynamic nature of industrial IOT (IIoT) devices nature and their limited resources. Ant Colony optimization (ACO) plays a major role in addressing fog computing’s task scheduling difficulties. In this study, an optimisation method called ant colony optimisation (ACO) is used to propose a fog computing task scheduling solution for Industrial Internet of Things (IIoT) applications. By mimicking the searching behaviour of ants, it finds optimal task scheduling by considering the factors like time taken for execution, network utilisation, and consumption of energy. In order to evaluate the performance, the Ant colony optimized scheduler, the proposed method has to be compared with extant methods using iFogSim simulator. When compared with First come first serve (FCFS) based scheduling, the Ant colony optimization (ACO) based scheduling attains 14.34% reduction in energy consumption, 44.74% reduction in network usage and a 50% faster in executing the tasks.