Intelligent Edge Computing Architecture: Low-Latency Transmission in an Intelligent Transport System for IoT Applications

Latency is a determining factor in an IoT-enabled Intelligent Transportation System. To solve the latency issue in an edge computing system connected to the cloud, where the primary challenge is the distance between the end device and the cloud server, an implementation in a real urban environment is presented to illustrate the architecture of Intelligent Edge Computing. The IEC design is scalable through a communication system that incorporates latency and distance measurements in the transmission of a detection signal using deep learning at the edge node. This enabled the transmission of 2-byte detection signals to the fog node, where the received information was processed to count vehicles on up to three streets near the intersection. The vehicle detection signal is transmitted between two different embedded platforms. This architecture enabled an average transmission latency of 15.45 ms and a total end-to-end latency of 47.9087 ms over a distance of 600 m in a real-world urban environment. The IEC system leverages this low latency and offers intelligent processing closer to the data source and, therefore, to the user.