LoRa based architecture for smart town traffic management system

Abstract

While traffic congestion has been pointed out as everyday driving stress, few attempts are specialized in traffic management by using current IoT technology. In order to help alleviate traffic stress from drivers, this article proposes a cross-layer LoRa architecture and a machine-learning algorithm for smart town's traffic management systems. LoRa is selected since it has strengths in range and power when compared to other wireless communication technologies. We introduce the cross-layer LoRa architecture, which is devised to facilitate its cognitive analysis. By dynamically allocating network and information resources, it complements the limitations of the standard LoRa protocol. We also have designed the logistic regression algorithm-which runs above its cognitive engine. The proposed algorithm outputs traffic coefficients based on density and travel time. This algorithm has achieved 97% of accuracy in the simulation. With further research, we believe the proposed system could be an excellent solution for smart traffic management.