Big Data and Cyber-Physical Systems Enabled Real-Time Sustainable Task Scheduling

Research Objective:

Over the last decade, the traditional industrial applications have evolved into the modern industry 4.0 systems with the convergence of cutting-edge technologies such as cloud-fog-edge computing, physical internet, cyber-physical systems, and big data analytics. Due to the nature of the modern technological involvements in project characteristics, customers’ complex specifications, frequent changes in requirements, time and resource constraints, and unexpected events, the ability to schedule and control projects has become more complex and troublesome. Researchers are motivated to design real-time and sustainable smart decision systems to address this overwhelming process by attaining digital interoperability, information visibility, transparency, and interconnectivity. However, to date, systems designed are almost the conceptual frameworks and simulation-based outcomes that lack real-time digital implementation and task optimisations in digital units. This research aims to design and implement a novel framework that can optimise the task scheduling process in real-time across the industrial partners as well as among multiple organisations subject to optimisation of resources in the technological stacks such as cloud-fog-edge computing.

This research will develop a real-time sustainable framework for the industry partners (suppliers, manufacturers, retailers, wholesalers, and customers) to share and access information and to avail smart, intelligent task scheduling among multiple organisations. Various computing resources such as load distribution, resource provisioning and cost manipulation on computing usage will also be optimised using IIOT, big data analytics and ensemble-based machine learning. Later, this research will investigate and reveal a pathway to deploy traditional or existing industrial applications into real-time resource and cost optimised sustainable systems.

Project start date: March 2022. 
Expected completion date: August 2025.