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Smart Fire Alarm
Using Raspberry Pi and AWS
Advanced Distributed Systems Project
Outline
Introduction
Background and Related Work
Problem Statement
Proposed Solution
Architecture
Raspberry Pi
Amazon Web Services(AWS)
MQTT Protocol
Demo
Result, Conclusion and Future work
Introduction
• Safety and security have always been the prime concern of mankind.
• With so many advancements in the fields of machine learning,
computer vision, cloud infrastructure, it provides us with an
opportunity to leverage these techniques to address the concerns.
• We want to tap the potentials of cloud infrastructures and machine
learning to detect the fire and implement the smart fire detection
system so that we could alert the owner/residents as early as possible
to minimize the casualties.
Background and Related Work
• Fires are very common things happening in the world. Every 24
seconds, a fire department in the United States responds to a fire
somewhere in the nation. (Ref : Insurance Information Institute)[5]
• The thermal imaging approach, has lots of drawbacks like high initial
cost, images are difficult to interpret in specific objects having erratic
temperatures, etc. [6]
• In the earlier research, researcher has used raspberry pi with Arduino
board for smart fire alarms[1]
Problem
Statement
To build fire alert internet of
things (IoT) system using
Raspberry pi and cloud (AWS)
To reduce false positive alarms
and provide adequate alerts to
the users
Proposed Solution
• We proposed the architecture in alignment with our aim, by using
raspberry pi and Amazon Web Services (Cloud) and further
embedding the alert system in the smart home hub like Amazon Alexa
or Google Home
Architecture
Architecture
RASPBERRY PI AMAZON S3
BUCKET
AMAZON SNS
AMAZON SQS LAMBDA
FUNCTION
SMART HOME
HUB
Smart Fire Detection using Pi and AWS
Smart Fire Detection using Pi and AWS
Smart Fire Detection using Pi and AWS
Smart Fire Detection using Pi and AWS
Smart Fire Detection using Pi and AWS
Smart Fire Detection using Pi and AWS
Smart Fire Detection using Pi and AWS
Smart Fire Detection using Pi and AWS
Smart Fire Detection using Pi and AWS
Smart Fire Detection using Pi and AWS
Smart Fire Detection using Pi and AWS
Result
Expected : Fire Detected but
below threshold
Actual : Fire Detected above
threshold
Expected : Fire Detected above
threshold
Actual : Fire Detected above
threshold
Expected : Fire Detected but
below threshold
Actual : Fire Detected but
below threshold
Expected : No Fire Detected
Actual : No Fire Detected
Following testing on different images and videos, we came to conclusion that we could reduce the false positive alarms
to greater extent(35 – 40%).
Result
Images (Input)
Fire/ No Fire
Images (Output)
- Fire/ No Fire
Future Notes
• More efficient light-weight model is needed on the pi for more
efficient fire detection
• By integrating thermal imaging with machine learning approach, we
can further reduce false positive alarms
• We can use buzzers, connected to pi which will ring once the fire is
detected
Conclusion
WE SHOWED THAT FIRE ALERT SYSTEM
CAN BE IMPLEMENTED USING IOT
APPROACH
WE SUCCESSFULLY, INTEGRATED AWS
CLOUD SERVICES IN OUR SYSTEM
WE SUCCESSFULLY, INTEGRATED
DEEP LEARNING APPROACH FOR FIRE
DETECTION
References
1. FireNet: A Specialized Lightweight Fire & Smoke Detection Model for
Real-Time IoT Applications (https://arxiv.org/pdf/1905.11922.pdf)
2. FireNet is an artificial intelligence project for real-time fire detection.
3. (https://github.com/OlafenwaMoses/FireNET)
4. Fire Now, Fire Later: Alarm-Based Systems for Prescriptive Process
Monitoring(https://arxiv.org/pdf/1905.09568.pdf)
5. Insurance Information Institute (https://www.iii.org/fact-statistic/facts-
statistics-fire)
6. http://hpthermalcamera.com/advantages-disadvantages-infrared-
thermal-imaging-camera/
Thank you!
Any Questions?

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Smart Fire Detection using Pi and AWS

  • 1. Smart Fire Alarm Using Raspberry Pi and AWS Advanced Distributed Systems Project
  • 2. Outline Introduction Background and Related Work Problem Statement Proposed Solution Architecture Raspberry Pi Amazon Web Services(AWS) MQTT Protocol Demo Result, Conclusion and Future work
  • 3. Introduction • Safety and security have always been the prime concern of mankind. • With so many advancements in the fields of machine learning, computer vision, cloud infrastructure, it provides us with an opportunity to leverage these techniques to address the concerns. • We want to tap the potentials of cloud infrastructures and machine learning to detect the fire and implement the smart fire detection system so that we could alert the owner/residents as early as possible to minimize the casualties.
  • 4. Background and Related Work • Fires are very common things happening in the world. Every 24 seconds, a fire department in the United States responds to a fire somewhere in the nation. (Ref : Insurance Information Institute)[5] • The thermal imaging approach, has lots of drawbacks like high initial cost, images are difficult to interpret in specific objects having erratic temperatures, etc. [6] • In the earlier research, researcher has used raspberry pi with Arduino board for smart fire alarms[1]
  • 5. Problem Statement To build fire alert internet of things (IoT) system using Raspberry pi and cloud (AWS) To reduce false positive alarms and provide adequate alerts to the users
  • 6. Proposed Solution • We proposed the architecture in alignment with our aim, by using raspberry pi and Amazon Web Services (Cloud) and further embedding the alert system in the smart home hub like Amazon Alexa or Google Home
  • 8. Architecture RASPBERRY PI AMAZON S3 BUCKET AMAZON SNS AMAZON SQS LAMBDA FUNCTION SMART HOME HUB
  • 20. Result Expected : Fire Detected but below threshold Actual : Fire Detected above threshold Expected : Fire Detected above threshold Actual : Fire Detected above threshold Expected : Fire Detected but below threshold Actual : Fire Detected but below threshold Expected : No Fire Detected Actual : No Fire Detected Following testing on different images and videos, we came to conclusion that we could reduce the false positive alarms to greater extent(35 – 40%).
  • 21. Result Images (Input) Fire/ No Fire Images (Output) - Fire/ No Fire
  • 22. Future Notes • More efficient light-weight model is needed on the pi for more efficient fire detection • By integrating thermal imaging with machine learning approach, we can further reduce false positive alarms • We can use buzzers, connected to pi which will ring once the fire is detected
  • 23. Conclusion WE SHOWED THAT FIRE ALERT SYSTEM CAN BE IMPLEMENTED USING IOT APPROACH WE SUCCESSFULLY, INTEGRATED AWS CLOUD SERVICES IN OUR SYSTEM WE SUCCESSFULLY, INTEGRATED DEEP LEARNING APPROACH FOR FIRE DETECTION
  • 24. References 1. FireNet: A Specialized Lightweight Fire & Smoke Detection Model for Real-Time IoT Applications (https://arxiv.org/pdf/1905.11922.pdf) 2. FireNet is an artificial intelligence project for real-time fire detection. 3. (https://github.com/OlafenwaMoses/FireNET) 4. Fire Now, Fire Later: Alarm-Based Systems for Prescriptive Process Monitoring(https://arxiv.org/pdf/1905.09568.pdf) 5. Insurance Information Institute (https://www.iii.org/fact-statistic/facts- statistics-fire) 6. http://hpthermalcamera.com/advantages-disadvantages-infrared- thermal-imaging-camera/