IoT and digital twin enabled smart tracking for safety management

Zhiheng Zhao, Leidi Shen, Chen Yang, Wei Wu, Mengdi Zhang, George Q. Huang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

73 Citations (Scopus)

Abstract

Modern warehousing systems for fresh and cold-keeping storage, have presented characteristics of complex operation procedures, accelerated operating pace, and high labour intensity. Thus, the working environment has become complicated and hazardous. Two recent fatal accidents that occurred in cold warehouses have shifted wide focus to safety management. The invisibility of operators’ status and location causes late responsiveness for rescuing. This paper first proposes an IoT and digital twin-enabled tracking solution framework for safety management. Then an indoor safety tracking mechanism for detecting motionless behaviour and self-learning genetic positioning is developed for recognizing the abnormal condition and obtaining precise location information in a real-time manner. A real-life case study with physical and cyber world implementation is conducted to demonstrate the feasibility and effectiveness of our proposed techniques. The results show that the detection of abnormal motionless behaviour is fulfilled, and the indoor positioning algorithm with self-learning ability not only achieves high accuracy up to 96.5% but also ensures the long-term use through adaptation.

Original languageEnglish
Article number105183
JournalComputers and Operations Research
Volume128
DOIs
Publication statusPublished - Apr 2021
Externally publishedYes

Keywords

  • Abnormal state detection
  • Digital twin
  • Indoor positioning
  • Internet of things
  • Safety management

ASJC Scopus subject areas

  • General Computer Science
  • Modelling and Simulation
  • Management Science and Operations Research

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