IoT edge computingenabled collaborative tracking system for manufacturing resources in industrial park Abstract   In manufacturing industry, the movement of manufacturing resources in production logistics often affects the overall efficiency. This research is motivated by a worldleading airconditioner 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 edgecomputing 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 reallife 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.

Edge Computing for the Production Industry A Systematic Approach to Enable Decision Support and Planning of Edge Because the Edge Computing (EC) paradigm allows processing of vast amounts of data in proximity to the respective source, latency and quantity constraints are no longer a limiting factor. That enables the development of novel datadriven applications and the extension of the solutions space for valueadded services in production. The complexity and diversity of factories, combined with the continuing discovery of new datadriven solutions, poses a challenge for practitioners to thoroughly determine where, which, and how data should be processed. This, however, is crucial for deciding how and whether to invest in EC. This paper proposes a multiphase concept for the systematic assessment of whether and where EC is most beneficial in a given production environment. It is comprised of human and machine interpretable functions. Combining multiple functions leads to a datadriven solution, which forms links between the data sources (assets) of a production environment and the desired outcome (goals). Four main criteria for EC are derived to enable the exposure of areas with increased EC potential, forming the baseline for a scoring system. The concept is designed so that its application is feasible within an industrial context. First analyses show the prospect of the approach and suggest potential benefits for providing practical implementation guidance.