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Dashboard ui of proposed app
Inspiration
Streetlights are essential for public safety, but in most cities, they remain ON throughout the night—regardless of traffic or activity—leading to massive energy waste. Inspired by the need for smarter urban infrastructure, LumiSmart aims to optimize energy usage while maintaining safety by adapting lighting based on real-time conditions like vehicle presence and time of day.
What it does
LumiSmart automates and optimizes street lighting through: Motion-based lighting: Half the streetlights remain ON at low traffic, and more lights turn ON as vehicles pass. Remote monitoring and control via a web panel and Android app. Real-time status updates and alerts for faults or tampering. Scheduled automation (e.g., lights ON at sunset, OFF at sunrise). Map-based interface showing live streetlight status. My regularly detecting voltage difference we also find out when if anyone is stealing electricity or not.
How we built it
Backend: Django (Python) to manage APIs, logic, and database. Frontend: Django templates for web-based control panel. Mobile App: Android (Java/Kotlin + XML) for remote monitoring/control. Simulation: Motion sensors and light statuses simulated using logic and timed events. Map Integration: Streetlight locations and statuses shown using a simple coordinate-based map system.
Challenges we ran into
Simulating real-world sensors (motion, fault detection) without actual hardware. Managing real-time status updates across both mobile and web platforms. Designing an intuitive yet powerful interface for controlling and viewing streetlight data. Balancing energy-saving automation with reliable lighting for safety.
Accomplishments that we're proud of
Successfully built a working prototype that simulates motion-activated streetlight control. Integrated both web and mobile platforms with real-time communication. Demonstrated how smart automation can contribute to sustainable urban development. Designed a user-friendly system with real-time monitoring and alerts.
What we learned
Gained practical experience integrating frontend and backend systems using Django and Android. Learned how to simulate hardware-based features like sensors using software logic. Understood how small optimizations (like intelligent lighting) can significantly impact energy conservation. Improved our ability to think from a system-level perspective while building scalable solutions.
What's next for LumiSmart: Intelligent Streetlight Controller
Integrate actual IoT hardware (motion sensors, smart light controllers). Add AI-based traffic pattern analysis to further optimize lighting schedules. Use real-time geolocation and public datasets to make lighting context-aware. Partner with local municipalities or smart city pilots to test the system in real environments. Enable remote diagnostics and predictive maintenance features.
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