Mesh-IoT Based System for Large-Scale Environment

This paper presents an Internet-of-Things (IoT) architecture by integrating a Synology cloud server, edge computing systems, and physical networks. More specifically, we have established a physical network combining two subsystems 1) non-standardized Bluetooth Low Energy (BLE) Mesh network, and 2) a security monitoring system. The BLE Mesh system has one IoT host connected with three BLE devices, enabling to extend the communication distance by using one or two relays. The monitoring system consists of a Passive Infrared Sensor (PIR) and a webcam with multiple solutions for recognizing a human face. Two algorithms, Low Binary Pattern Histogram (LBPH) and Deep Metric Learning (DML), have been implemented and evaluated on different benchmarks. Experimental results show that the DML-based computation can reach 99.38% accuracy with almost 400 ms latency for recognizing a single face in frames of images. The future work will focus on testing the cloud service by integrating a Synology D218+ server, as well as improving the computation speed of facial recognition on pure hardware design on field-programmable gate array (FPGA). The aim of our work is to provide a robust IoT-Edge-Cloud system which can be deployed on the large-scale applications and processes data much faster compared to traditional cloud computing system due to the perks of parallel computing on FPGAs at the network edge.