Integrated IoT-Driven System with Fuzzy Logic and V2X Communication for Real-Time Speed Monitoring and Accident Prevention in Urban Traffic

Road safety is a critical concern globally, with speeding being a leading cause of traffic accidents. Leveraging advanced technologies can significantly enhance the ability to monitor and control vehicle speeds in real time. Traditional methods of speed monitoring are often limited in their ability to provide real-time, adaptive interventions. Existing systems do not adequately integrate sensor data and decision-making processes to prevent speeding-related accidents effectively. This paper aims to address these limitations by proposing a novel system that utilizes Internet of Things (IoT) technology combined with fuzzy logic to monitor vehicle speeds and prevent accidents in real time. The proposed system integrates IoT sensors for continuous vehicle speed monitoring and employs a Fuzzy Inference System (FIS) to make decisions based on variables such as speed, alcohol presence, and driver fitness. The system also facilitates interaction between drivers and law enforcement through Vehicleto-Everything (V2X) communication. The FIS implementation demonstrated effective speed control capabilities, accurately assessing and responding to various risk levels, thereby reducing the likelihood of speeding-related accidents. This research contributes to the advancement of road safety systems by integrating IoT and fuzzy logic technologies, offering a more adaptive and responsive approach to traffic management and accident prevention. Future enhancements will focus on incorporating machine learning techniques to dynamically adjust FIS rules based on real-time data and improve sensor network reliability to ensure more accurate and comprehensive monitoring.