Contemporary educational establishments are confronting a twofold challenge: ensuring student safety while reducing substantial electricity expenses. At present, many campuses depend on security personnel and manual controls to handle this, yet these approaches are sluggish, costly, and susceptible to human mistakes. This project introduces a Smart Campus Automation System that eliminates the necessity for manual management to tackle these inefficiencies. The system utilizes a network of Raspberry Pi controllers and ESP32 sensors to gather data in real-time. We incorporated two particular AI models: Convolutional Neural Networks (CNN) for automatic identification of security threats, and Deep Reinforcement Learning (DRL) for forecasting energy requirements based on occupancy in rooms. When compared with findings from ten recent studies, this automated method anticipated a decrease in energy usage of about 40%. At the same time, it enhanced security response times by 30%, identifying unauthorized access much quicker than human oversight. These results demonstrate that combining IoT with Artificial Intelligence provides a scalable, affordable solution that converts typical campuses into self-managing, secure, and sustainable settings.

This study addressed the essential inefficiencies inherent in conventional educational infrastructures, specifically regarding electricity waste and reactive security features. via designing and simulating a smart Campus Automation machine (SCAS), this research demonstrates that the convergence of net of factors (IoT) sensors and artificial Intelligence (AI) is not simply a theoretical idea, but a viable, high-effect solution. The implementation of a hybrid edge-Cloud structure—making use of ESP32 nodes for sensory information and Raspberry Pi-based totally edge AI for processing—proved superior to standard guide or cloud-best systems. The outcomes suggest that the machine can achieve an predicted 40% reduction in power intake by way of changing inflexible scheduling with Deep Reinforcement gaining knowledge of (DRL) based totally on real-time occupancy. furthermore, the combination of Convolutional Neural Networks (CNN) at the edge correctly decreased security alert latency to under zero.2 seconds, ensuring immediate hazard detection with out compromising user privacy thru mass video garage. In contrast to previous research that targeted on remoted elements of campus management, this framework offers a unified answer in which protection data actively informs strength decisions (e.g., turning off HVAC whilst a room is secured and empty). while challenges stay regarding huge-scale hardware retrofitting, the proposed modular node architecture gives a fee-powerful pathway for universities to transition into sustainable, self-regulating clever Campuses.

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