Course Details

Subject {L-T-P / C} : EC6320 : Smart Industrial IoT { 3-0-0 / 3}

Subject Nature : Theory

Coordinator : Prof. Umesh Chandra Pati

Syllabus

Module 1: Introduction: Internet of Things (IoT) Sensors and Actuators IoT Communication IoT networking. (3 hours)

Module 2: Industry 4.0 and Introduction to Industry 5.0: Fourth Industrial Revolution Globalization and Emerging Issues LEAN Production Systems Smart and Connected Business Perspective Smart Factories Cyber-Physical Systems and Next Generation Sensors Collaborative Platform and Product Lifecycle Management Augmented Reality and Virtual Reality Artificial Intelligence Big Data and Advanced Analysis Cybersecurity in Industry 4.0. Introduction to Industry 5.0 and its concepts, core values, enabling technologies, challenges, and responses. (7 hours)

Module 3: Industrial Internet of Things (IIoT): Introduction to Industrial IoT Difference between IoT and IIoT Industrial Processes Industrial Sensing and Actuation IIoT Business Model Industrial Internet Systems IIoT Reference Architecture Key enablers of IIoT/ IIoT Layers: IIoT Sensing, IIoT Processing, IIoT Communication, IIoT Networking. (7 hours)

Module 4: AI Framework for IIoT: Convergence of Industrial Internet of Things and Industrial Artificial Intelligence Architecture Viewpoints Business Viewpoints Usage Viewpoints: Industrial AI Market, Usage Considerations, Trustworthiness (Security, Privacy, Confidentiality, Explainability, and Controllability), Ethical and Societal Concerns (Ethics, bias, and safety), Impact on Labor Force, AI as a Force for Good Functional Viewpoint: Architecture Objectives and Constraints, Data Concerns, Learning Techniques, General Industrial AI Functional Architecture, System of Systems Issues, Horizon of Industrial AI Implementation Viewpoint: Implementation Guidance and Implementation Considerations such as Scope, Response Time, Reliability, Bandwidth and Latency, Capacity, Security, Data Properties, Temporal Data Correlation, Interoperability, Running Systems in Parallel, Dealing with Technical Debt, Portability and Reusability of AI Systems. (10 hours)

Module 5: IIoT Analytics and Data Management: Big Data Analytics in IIoT IIoT Analytics using machine learning, deep learning, and data sciences Cloud computing in IIoT Fog Computing in IIoT Data Management with Hadoop Data Center Networks Software Defined Networks (SDN) in IIoT Security in IIoT. (8 hours)

Module 6: IIoT Application Domains: Factories and Assembly Line Food Industry Healthcare Power Plants Inventory Management and Quality Control Plant Safety and Security Facility Management Oil, Chemical and Pharmaceutical Industries Applications of UAVs in Industries Future of IIoT. (5 hours)

Course Objectives

Course Outcomes

After the completion of this course, students should be able to: <br /> <br />CO1: Distinguish between IoT and IIoT. <br />CO2: Comprehend the basics of Smart IIoT with respect to Industry 4.0 and Industry 5.0. <br />CO3: Analyze the different aspects of smart IIoT and subsequently apply the knowledge for the advancement in Industry 4.0 and Industry 5.0. <br />CO4: Apply different AI techniques for big data analytics in the Smart IIoT framework. <br />CO5: Development of various practical applications of Smart IIoT.

Essential Reading

Supplementary Reading