An Intelligent Autonomous Parking System by Analyzing Stream Data generated by Sensors using Neural Networks Model

smart cities has been envisioned long before widespread Internet connectivity became a reality. In the present scenario, where the Internet of Things (IoT) is revolutionizing various domains, the development of smart cities and smart nations is becoming increasingly achievable. Urban challenges such as traffic congestion, limited parking spaces, and road safety can be effectively addressed using IoT and Artificial Intelligence (AI). In recent times, the most critical issue arising due to overpopulation in cities is the lack of an efficient parking system. This project proposes an IoT-based cloud-integrated smart parking system that utilizes Convolutional Neural Networks (CNNs) for enhanced image-based vehicle detection and parking space management. The system employs IoT equipment such as IR sensors, microprocessors, and cameras, where CNN algorithms process real-time image data to accurately detect vehicle occupancy, predict parking availability, and optimize space utilization. By integrating CNN with IoT, the system enhances automation, reduces human intervention, and provides real-time parking insights, contributing to the development of smart and efficient urban infrastructure.