Establishing a Dynamic Traffic Monitoring System through Integration of Wireless Sensor Networks and Edge Computing

Abstract

Traffic monitoring plays a very important role in effective traffic management and planning. Here, we suggest a different monitoring system at  real time traffic that combines edge computing and wireless sensor networks (WSNs) to provide real time traffic data in a precise and timely manner. An approach that combines IoT (e.g., Wireless Sensor Networks — WSN) with edge computing has been suggested to overcome the drawbacks of such typical traffic monitoring techniques and thus to provide multiple benefits. One of the main difficulties in traffic monitoring is the need of data collection and data processing on real time. Our system employs WSN for collecting traffic data from a number of sensors such as audio sensor real time, without flooding a highly centralized servers with large volume of data. The data gathered is processed and evaluated at the edge node, this leads to reduced latency and enhancement in the scalability. The suggested system identifies and tracks cars based on both sensor data that were gathered and sophisticated algorithms suggested. The results show that the proposed system has a good relation to the existing conventional loop detector system, and the system's efficiency is proved. A couple benefits of the suggested approach include affordability, scalability, and the ability to customize. It provides a complete take on obtaining and processing real time traffic information for preemptive traffic management and decision making. Traffic monitoring through the technology is effective and traffic flow is improved across highways to urban areas.