Energy-Efficient Deep CNN for Smoke Detection in Foggy IoT Environment

Smoke detection in Internet of Things (IoT) environment is a primary component of early disaster-related event detection in smart cities. Recently, several smoke and fire detection methods are presented with reasonable accuracy and running time for normal IoT environment. However, these methods are...

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Bibliographic Details
Published in:IEEE internet of things journal 2019-12, Vol.6 (6), p.9237-9245
Main Authors: Khan, Salman, Muhammad, Khan, Mumtaz, Shahid, Baik, Sung Wook, de Albuquerque, Victor Hugo C.
Format: Article
Language:English
Subjects:
Accuracy
Artificial neural networks
Convolutional neural networks (CNNs)
disaster management
Energy efficiency
False alarms
Feature extraction
fire alert
Fire detection
Fire protection
Fires
foggy Internet of Things (IoT) environment
image classification
Image color analysis
Internet of Things
Run time (computers)
Shape
Smart cities
Smoke
smoke detection
surveillance
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Summary:Smoke detection in Internet of Things (IoT) environment is a primary component of early disaster-related event detection in smart cities. Recently, several smoke and fire detection methods are presented with reasonable accuracy and running time for normal IoT environment. However, these methods are unable to detect smoke in foggy IoT environment, which is a challenging task. In this paper, we propose an energy-efficient system based on deep convolutional neural networks for early smoke detection in both normal and foggy IoT environments. Our method takes advantage of VGG-16 architecture, considering its sensible stability between the accuracy and time efficiency for smoke detection compared to the other computationally expensive networks, such as GoogleNet and AlexNet. Experiments performed on benchmark smoke detection datasets and their results in terms of accuracy, false alarms rate, and efficiency reveal the better performance of our technique compared to state-of-the-art and verifies its applicability in smart cities for early detection of smoke in normal and foggy IoT environments.
ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2019.2896120