Intelligent Urban Expressway Managing Architecture Using LoRaWAN and Edge Computing

With the rapid growth of smart cities, Intelligent Transportation Systems (ITS) are playing an increasingly important role. However, with the rapid expansion of city boundaries, ITS faces scalability and energy consumption challenges. The wide range and the massive number of nodes have become significant issues for network technology in ITS. This study proposes an urban expressway managing architecture using LoRaWAN and edge computing. The traffic network is divided into different sections. A LoRa WAN network is established for monitoring and controlling each section by exploiting its low-power, long-range characteristics. Moreover, an edge computing-based traffic state encoder model has been proposed to handle the large amount of data generated by the massive number of nodes. The architecture's procedure allocates tasks to LoRaWAN devices by exploiting their different computing capabilities. Simulation results on real maps of both Abu Dhabi and Beijing demonstrate the high performance and scalability of the architecture. Numerical results also show that the encoder model can effectively reduce network packet size by extracting data features.