Transforming Higher Education: AI-Powered Smart University City System
Project Summary
The Smart University City System is an IoT-enabled platform designed for a large university with over 15,000 students and faculty, transforming its campus into a smart city hub. Deployed across academic buildings, dormitories, and public spaces, it integrates IoT sensors, AI-driven analytics, and a Flutter-based mobile app to provide real-time resource management, personalized services, and predictive insights. Built with Python for AI processing, Flutter for cross-platform mobile access, and AWS with Docker and Kubernetes for scalability, the system enhances campus efficiency, safety, and sustainability while integrating with broader smart city infrastructure.
Key Challenges Addressed
- Real-Time Campus Resource Management: Coordinating data from thousands of IoT devices to monitor classroom usage, energy consumption, and facility occupancy in real time.
- Predictive Analytics for Dynamic Campus Needs: Forecasting resource demands amidst fluctuating academic schedules, events, and student activities.
- Seamless Mobile Experience for Diverse Users: Delivering an intuitive, cross-platform mobile app for students, faculty, and staff with real-time updates and navigation.
Solutions Implemented
To address real-time resource management, we deployed IoT sensor nodes using STM32 microcontrollers with LoRa for long-range, low-power communication, enabling real-time monitoring of classroom occupancy, energy usage, and campus facilities across the university. These nodes, equipped with edge computing, transmitted data to AWS-hosted gateways, ensuring low-latency integration with campus and smart city systems. For predictive analytics, we developed AI models using Python, TensorFlow, and NLP to analyze usage patterns, academic calendars, and social media data for events, achieving 93% accuracy in forecasting resource demands. The Flutter-based mobile app for iOS and Android provided users with real-time schedules, campus navigation, and AI-driven personalized notifications, supported by Dockerized microservices and Kubernetes for seamless scalability and performance.
Technology Stack
- AI & Data Processing: Python, TensorFlow, NLP
- Mobile: Flutter, iOS, Android
- IoT: LoRa, STM32 microcontrollers
- Cloud & Deployment: AWS, Docker, Kubernetes
- APIs: RESTful APIs
- Tools: Visual Studio Code, Git
Client Benefits
- Optimized Resource Utilization: Reduced facility underutilization by 35% through AI-driven scheduling and real-time IoT monitoring.
- Enhanced User Engagement: Achieved 50% higher student and faculty satisfaction with personalized mobile app features and real-time notifications.
- Sustainable Campus Operations: Cut energy consumption by 25% with IoT-based monitoring and predictive analytics, aligning with smart city sustainability goals.
Approach
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IoT-Driven Resource MonitoringDeployed STM32-based sensor nodes with LoRa for real-time tracking of campus resources, integrated with AWS for secure, scalable data processing.
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AI-Powered Predictive InsightsBuilt TensorFlow and NLP-based models to forecast classroom and facility needs, optimizing resource allocation and energy efficiency.
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Cross-Platform Mobile AccessDeveloped a Flutter-based app for iOS and Android, offering live updates, navigation, and personalized services, deployed with Docker and Kubernetes.
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