An IoT-Edge-Server System with BLE Mesh Network, LBPH, and Deep Metric Learning - Gajjar, Archit; Dave, Shivang; Yang, T. Andrew; Wu, Lei; Yang, Xiaokun (Advances in Artificial Intelligence and Applied Cognitive Computing, 2021)
This paper presents a hardware architecture, IoT-Edge-Server, of a diverse embedded system including a wide variety of applications such as smart city, smart building, or smart agricultural farm. First of all, we improve computation time by integrating the idea of edge computing on Raspberry Pi and CPU, which processes different algorithms. Second, the hardware processors are connected to a server that can manipulate the entire system and also possess storage capacity to save the system’s important data and log files. Specifically, the hardware computes data from (1) a non-standardized Bluetooth Low Energy (BLE) mesh system and (2) a surveillance system. The BLE mesh system has one master and three slave devices, while the surveillance system has a passive infrared (PIR) sensor and a camera to detect motion. Experimental results prove that using the phenomena of edge computing demonstrates an improvement in computation speed and data privacy. Our vision is thus to create a system as a case study, capable of sensing the surrounding environment, and more importantly, directing different types of sensor data to the optimal place, in terms of computing devices, for analysis and making decisions autonomous at the proximity of the network edge to improve data privacy, latency, and bandwidth usage.