The Adaptive Strategies Improving Digital Twin Using the Internet of Things

Abstract

AbstractDigital twins for factories and processes are becoming more prevalent and more valuable as a result of recent technological breakthroughs and the rise of smart manufacturing. There are also more potential for closed-loop analytics with digital twins, as well as with the rise of connection, data storage, and the Industrial Internet of Things (IIoT). Some factories have employed discrete event simulations (DES) to construct digital twins that are connected to the manufacturing floor and can be monitored in real time. However, it is difficult to quantify the advantages of a digital twin that is linked to the real world. With the emergence of the new generation of mobile network (5G), Tactile Internet, as well as the deployment of Industry 4.0 and Health 4.0, multimedia systems are moving towards immersed haptic-enabled human–machine interaction systems such as the digital twin (DT). Specifically, Industry 4.0 will be using DT and robots on a large scale. This will increase human–machine and interaction to a great extent. There will be multimodal communications used to interact with digital twins and robots, especially haptics. Hence, Tactile Internet will replace the conventional Internet today. In fact, a DT system can also be extended in Health 4.0 domain to act as a COVID-19 is a COVID-19 early warning system. When a person's temperature and other symptom data are tracked in real time, it may be determined whether or not it is time to see a doctor or undergo a COVID examination. In conjunction with a COVID tracing programme, the digital twin may be able to provide further information about the virus in relation to the individual. Since there are currently no well-recognized models to evaluate the performance of these systems, to address this research lacuna, we proposed a Quality of Experience (QoE) model for DT systems con-training multi-levels of subjective, objective, and physiopsychological influencing factors. The model is itemized through a fully detailed taxonomy that deduces the perceived user’s emotional and physical states during and after consuming spatial, temporal, proximal, and abstracted multi-modality media between humans and machines.KeywordsInternet of ThingsDigital twinRecommendation systemVirtual and health care