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Industrial IoT

This project is to develop Wireless Positioning and Sensing Network for Industry 4.0 in collaboration with Peytec Inc. Automation of modern industrial plants require real-time tracking of object locations and sensing of local and ambient parameters for variety of applications such as counting and tracking of objects in assembly line, detection and positioning of failures of machines etc. Mostly, discrete Real Time Location System (RTLS) performs object tracking in existing industrial automation without its integration with the sensing and control network, which constrains application’s responsiveness. In this project, we propose an integrated solution of Wireless Positioning Sensor Network (WPSN) that is designed for accuracy, reliability, scalability and optimal network operation. The proposed WPSN is applicable for harsh indoor industrial environments for not only monitoring and control of plant operations but also for identification, localization and tracking of assets and inventory in industrial warehouses. The indoor industrial environment pose challenging conditions for radio signal propagation that adversely affects reliability of communication of sensing and location data. We approach reliability by incorporating redundancy and making our network reconfigurable through adaptive intelligent learning process. We employ adaptive clustering technique to address the need of scalable deployment for varied industrial scenarios. We include hybrid localization scheme to provide high precision positioning but with fallback reduced precision operation to deal with long-term channel impairment. The WPSN is connected with backend cloud infrastructure for low cost monitoring and control. ​

Team
  • Xiaofeng Li 
  • Ryan Murari
  • Nourin Kadir
  • Dr. Bahauddin Kazi
  • Dr. S. M. Kamruzzaman
Partner
  • Dr. Xavier Fernando
  • Peytec Inc.

Selected Publications

  • S. M. Kamruzzaman, M. Jaseemuddin, X. Fernando, and P. Moeini, Wireless Positioning Sensor Network Integrated with Cloud for Industrial Automation, Proceedings of IEEE Local Computer Network Conference (LCN), Singapore, October 2017.