IoT and digital twin enabled smart tracking for safety management

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.