The Design and Implementation of Edge ComputingBased Intelligent Ashcan Management System for Smart Community This paper designs an Intelligent Ashcan Management System (IAMS) which is one of the most important part in a smart city. Traditional ashcan management is inefficient and has many disadvantages, because managers cannot obtain the realtime state of ashcans efficiently. As a result, it often happens that ashcans are full but not collected in time. Moreover, in special cases that ashcans fall, catch fire, etc., managers should find these ashcans and handle these emergencies as soon as possible. To manage ashcans efficiently, economically and intelligently, in this paper, we propose an edge computing based IAMS. Specifically, in IAMS, each ashcan has an intelligent user equipment (UE) equipped with sensors to measure distance, temperature, smog and tilt. For data transmission, IAMS uses Narrow Band Internet of Things (NBIoT), which has advantages of large transmission range and low cost. Combined with edge computing, the collected data can be processed rapidly and managers can obtain the realtime state of each ashcan. Moreover, managers can view global information through web browser and mobile devices. According to the realtime state of ashcans, we also design an IAMS algorithm to get an efficient garbage collection path based on genetic algorithm. Finally, we deployed the proposed IAMS in Soochow University and experimental results show its efficiency and stability.

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.