The subject provides knowledge and skills to enable sensors and devices to become smart and connected. The learner will be exposed to the use of embedded systems to enable processing and data reduction to reduce communication. Data communication technologies for information exchange with other devices will also be covered. The learner will develop and test a smart sensor and device network for typical industrial application scenarios (e.g. predictive maintenance, process optimisation).

The subject covers knowledge and skills essential for integrating heterogeneous subsystems into a smart system. The subject will adopt a systems engineering approach to examine current and emerging trends, key techniques and strategies for developing system and network integration solutions. Students will be exposed to integration challenges such as legacy integration, human-system integration and system of system integration. The subject will cover knowledge and skills on commonly used IOT protocols and industrial connectivity standards and fieldbuses, as well as relevant hardware and software interfaces suitable for such integration. A mini-project will provide opportunity for the students to apply their learning to integrate heterogeneous subsystems.

This subject seeks to provide students with the knowledge and skills in managing structured, unstructured and semi-structured data generated by an IIOT system using appropriate cloud, database and layered databus technologies. It provides exposure to the need for alternative database management systems beyond traditional relational database systems in managing IIOT data. The subject covers data acquisition, data reduction, storage and retrieval for big data processing. These would provide the basis for analytics, machine learning, visualization, reports and alerts.

This subject provides knowledge of the concepts and skills in the tools used in data analytics and machine learning. It provides exposure to the process of data gathering, extraction and visualization. The subject covers the various stages of data analytics, from gathering data, asking the right questions to analysing and interpreting data to identify patterns and trends, that lead to intelligent actionable recommendations. The subject includes the deployment of machine learning models and algorithms, for e.g. predictive maintenance and big data-driven quality control.

This subject seeks to provide students with the knowledge and skills to integrate and apply relevant knowledge to a work-based problem or scenario while demonstrating appropriate project development skills, professional ethics and attitudes. The deliverable may be a solution to a problem, process /product improvements, development, design, evaluation or implementation of ideas.