WirelessDT: A Digital Twin Platform for Real- Time Evaluation of Wireless Software Applications

Wireless technology has become one of the most important parts of our daily routine. Besides being used for communication, the wireless signal has been applied to various Wireless Software Applications (WSAs). The signal fluctuation caused by the measurement system or the environmental dy-namic can significantly influence WSAs' performance, making it challenging to evaluate WSAs in real-world scenarios. To overcome these challenges, we propose WirelsssDT, a wireless digital twin platform, using digital twin and real-time ray tracing technologies to emulate the wireless signals propagation and generate emulation data for real-time WSAs evaluation. In this demonstration, we evaluate a wireless indoor localisation mobile application with two typical prediction algorithms: 1) Kalman Filter-based Trilateration and 2) Deep Recurrent Neural Network, as a case study to demonstrate the capabilities of WirelessDT. The source code is available at https://github.com/codelzz/WirelessDT, and the demonstration video is available at https://Iyoutu.be/9KI-3jgMBUA