Industrial Internet of Things PPT111.ppt

PPT for Industrial Internet of Things (IIOT)

1. 1. Title Slide (1 slide)
IOT BASED SMART AND
ADAPTIVE LIGHTING IN STREET
LIGHTS

2. Introduction
Introduces smart and weather-adaptive lighting
for street lights
Uses embedded system to control lights based on
sunlight detection
Incorporates camera surveillance and panic button
for safety
Aims to ensure safety and prevent energy wastage

3. Problems
High municipal expenses due to street lighting
Energy wastage in conventional street lighting systems
Lack of safety measures on streets
Manual switching of lights leading to inefficiency
High maintenance costs for traditional lighting systems
Inability to adapt to varying environmental conditions
Absence of real-time monitoring and control

4. Theories
IoT-based automation can reduce energy consumption
Smart lighting systems can cut municipal waste by 50-70%
Integrating surveillance and emergency features can
enhance public safety
Adaptive lighting can optimize energy use based on actual
needs
Real-time monitoring can improve maintenance efficiency
LED technology can significantly reduce power
consumption

5. Methods
Assessment of wireless communication protocols for inter-
light connectivity
Review of IoT technologies for lighting control
Examination of energy-saving potential in street lighting
Analysis of safety features integration in street light
systems
Comparative study of traditional vs. LED lighting
efficiency
Evaluation of different sensors and microcontrollers for
optimal performance

6. System Architecture
MSP430 Microcontroller as
the system brain
LDR sensor for light detection
Panic button for
emergency situations
IP65 CCTV camera for surveillance

7. Safety Features
Surveillance Cameras: Integrated IP65 CCTV cameras
provide continuous monitoring and store footage on a
server.
Emergency Panic Button: Sends alerts and real-time video
to the nearest police station when activated.
Automated Lighting Control: LDR sensors automatically
control street lights based on ambient light levels.
Remote Monitoring and Control: Allows for quick
adjustments and emergency responses via internet-based
remote control.

8. Implementation Benefits
Energy Efficiency: Reduces energy consumption with
adaptive lighting controlled by LDR sensors.
Cost Savings: Lowers operational and maintenance costs
through automated street light management.
Enhanced Public Safety: Improves surveillance and
emergency response with integrated CCTV cameras and
panic buttons.
Environmental Benefits: Decreases carbon footprint and
light pollution by optimizing lighting based on real-time
conditions.

9. Future Implications
The potential for integration with smart city initiatives is
immense, allowing for enhanced urban management and
improved quality of life for residents.
Scalability for large-scale urban deployment is crucial to
ensure that the solution can handle the complexities and
demands of a growing city infrastructure.
The possibility of adding more IoT features, such as air
quality sensors, opens up new avenues for real-time
environmental monitoring and proactive health measures.

10. Conclusion
The system addresses energy wastage and crime detection,
optimizing resource usage and enhancing public safety.
It creates a safer environment for pedestrians by
integrating advanced surveillance and alert mechanisms.
The technology provides valuable evidence for law
enforcement, aiding in the investigation and prevention of
criminal activities.
This initiative contributes significantly to smart city
development, fostering a more efficient and secure urban
landscape.

11. References
Archana. G, et al. "Intelligent Street Light System" (2015)
Akshay Balachandran, et al. "An Innovation in the Field of Street
Lighting System with Cost and Energy Efficiency" (2015)
Deepanshu Khandelwal, et al. "Sensor Based Automatic Street
Lighting system" (2015)
Isah Abdulazeez Watson, et al. "Design and Implementation of
an Automatic Street Light Control System" (2015)
Andrea Zanella, et al. "Internet of Things for Smart Cities" (2014)

12. Thank you!Thank you!