IoT edge computing-enabled collaborative tracking system for manufacturing resources in industrial park.

In manufacturing industry, the movement of manufacturing resources in production logistics often affects the overall efficiency. This research is motivated by a world-leading air-conditioner manufacturer. In order to provide the right manufacturing resources for subsequent production steps, excessive time and human effort has been consumed in locating the manufacturing resources in a huge industrial park. The development of Internet of Things (IoT) has made a profound impact on establish smart manufacturing workshop and tracking applications, however a growing trend of data quantity that generated from massive, heterogeneous and bottomed manufacturing resources objects pose challenge to centralized decision. In this study, the concept of edge-computing deeply integrated in collaborative tracking purpose in virtue of IoT technology. An IoT edge computing enabled collaborative tracking architecture is developed to offload the computation pressure and realize distributed decision making. A supervised learning of genetic tracking method is innovatively presented to ensure tracking accuracy and effectiveness. Finally, the research output is developed and implemented in a real-life industrial park for verification. The results show that the proposed tracking method not only performs constant improving accuracy up to 96.14% after learning compared to other tracking method, but also ensure quick responsiveness and scalability.