Smart_Street_Lighting_System

1. Smart Street Lighting System
• Energy-Efficient and Motion-Sensor Based
Street Lighting
• Presented by: [Your Name]
• Course: Smart City

2. Introduction
• Challenges of Traditional Lighting:
• - High energy consumption.
• - Limited adaptability to varying conditions.
• Why Smart Lighting?
• - Supports smart city objectives.
• - Improves energy efficiency and public safety.

3. Project Objectives
• 1. Replace traditional streetlights with LED
systems.
• 2. Incorporate motion sensors for adaptive
lighting.
• 3. Save energy while ensuring adequate
illumination.

4. Background
• - Smart Cities: Leveraging technology for
sustainable urban development.
• - Smart Lighting: Enhancing infrastructure
efficiency and sustainability.
• - Examples: Successful implementations in
Copenhagen and Barcelona.

5. Proposed System Overview
• Key Components:
• - LED lights.
• - Motion sensors.
• - Central monitoring system.
• Functionality: Adaptive lighting based on real-
time conditions.

6. System Features
• - Energy-efficient LEDs.
• - Motion-triggered brightness adjustments.
• - Weather-responsive illumination.
• - Data collection and remote monitoring.

7. Technical Design
• Block Diagram:
• - Motion sensors detect activity.
• - Controller adjusts light intensity.
• - Cloud platform for data processing.

8. Motion Sensor Selection
• Types Considered:
• - PIR sensors.
• - Ultrasonic sensors.
• - Microwave sensors.
• Chosen Sensor: Passive Infrared (PIR) for
reliability and cost-effectiveness.

9. System Operation
• 1. Normal Mode: Dim light during inactivity.
• 2. Motion Mode: Bright light upon detecting
movement.
• 3. Sequential Lighting: Activation of adjacent
lights for enhanced visibility.

10. Weather-Adaptive Lighting
• Sensors Used:
• - Light Dependent Resistors (LDRs).
• - Rain sensors.
• Behavior: Brightens during cloudy or adverse
weather conditions.

11. Energy Efficiency Calculations
• Comparison:
• - Traditional lighting: 150W/lamp.
• - LED with motion sensor: 40W idle, 100W
active.
• Sample Savings for 100 Lamps:
• - Daily: X kWh.
• - Monthly: Y kWh.
• - Yearly: Z kWh.

12. Cloud Monitoring System
• Data Collected:
• - Energy usage.
• - Motion patterns.
• - System diagnostics.
• Dashboard: Real-time monitoring and
reporting.

13. Implementation Plan
• 1. Pilot project with 100 lamps.
• 2. Install and configure sensors.
• 3. Integrate cloud-based monitoring.
• 4. Evaluate and optimize performance.

14. Budget Estimation
• LED Lamps: $X per unit.
• Sensors: $Y per unit.
• Cloud Infrastructure: $Z initial setup.
• Comparison: Initial investment vs. long-term
savings.

15. Environmental Impact
• Energy Savings: Reduced consumption by X%.
• Carbon Emissions: Lowered by Y metric tons
annually.

16. Case Study/Simulation
• Scenario: Comparison of one street with
traditional vs. smart lighting.
• Results:
• - Energy consumption reduced by X%.
• - Improved illumination during critical hours.

17. Challenges
• - Sensor calibration and sensitivity
adjustments.
• - High initial investment.
• - Maintenance and data security.

18. Future Enhancements
• AI Integration: Predictive maintenance and
adaptive lighting schedules.
• Solar Panels: Further reduce energy reliance.
• Expansion: Integrate with smart traffic and
safety systems.

19. Conclusion
• Summary of benefits:
• - Significant energy savings.
• - Enhanced public safety.
• - Contribution to sustainability goals.
• Scalability for widespread adoption.

20. References and Acknowledgments
• References: List of studies, articles, and tools
used.
• Acknowledgments: Thanks to professors,
peers, and collaborators.