Internet of Things

Objective 1

 

To analyze the vulnerabilities and build secure systems using blockchain architecture (Cyber Manufacturing Security).

Challenges

  • Lack of strong encryption and authentication-based access of connected manufacturing assets
  • Differentiating process changes indicative of cyber-attacks from common variations in manufacturing due to inherent system variability
  • Lack of coordinated data recovery mechanism in case of a total system outage.
 Research Opportunities

  • Exploring blockchain’s potential for decentralization of critical manufacturing design and process-related data that enables highly secure peer-to-peer data transmission. Application areas: minimization of supply chain-related disruption, secure platform for shareable, immutable, and irreplaceable CAD files, the secured network for IoT data transmission
  • Exploring authentication mechanisms for access to the manufacturing assets. Generation of secure one-time use tokens, biometric scans, AI-based recognition tools, etc.
  • Developing an AI-based monitoring system for continuous tracking, detection, and alert system for cyber security threats. 
Objectives

  • Exploring applications of blockchain in cyber security, the integrity of information in the manufacturing domain
  • Understanding vulnerabilities of IIoT at the individual machine level and exploring preventive technologies 
  • Understanding various breach response systems depending upon the nature of the breach
 Equipment / Software

  • Computer systems 
  • Biometric kit
  • Cloud SAAS Subscription
  • LORA Modules
  • Measurement and data analysis
Collaborators: NIST USA

 

Objective 2

Build human-in-loop cognitive systems to bridge operation technology to information technology through Industrial Internet of Things (Cognitive IoT)

Challenges

  • Integration of the human factor in Cyber-Physical systems.
  • Interaction with ERP, MES, SCADA, simulation, analytics, data management, as well as lower-level shop floor activities delivering flexibility in CPS-enabled manufacturing environments.
  • Extraction of contextual relevant data for data or service consumption for efficient aggregation and processing.
 Research Opportunities

  • Cognitive contribution of human activities towards the operation of technical systems.
  • Integration of design stage – FMECA – Failure Modes, Effects, and Criticality Analysis with technical stage operating systems.
  • Visual analytics for human-machine interaction. 
  • Developing solutions for scalable data processing.
Objectives

  • Human empowerment for industry 4.0 technologies with more effective integration of human cognitive capabilities within technical systems.
  • Engaging HIL with visual analytics to communicate condition monitoring outcomes in visually relevant ways for augmenting human capabilities when performing analytics-driven decision tasks.
  • Developing semantic-based solutions for interacting with the machine and humans.
 Equipment / Software

  • Workstations 
  • Eurotech ESF 
  • IBM Cognos Analytics 
  • Cluvio Software 
  • Wincc Software 
  • Smart Factory Testbed