Vision Sensor-Based Real-Time Fire Detection in Resource-Constrained IoT Environments

Fire detection and management is very important to prevent social, ecological, and economic damages. However, achieving real-time fire detection with higher accuracy in an IoT environment is a challenging task due to limited storage, transmission, and computation resources. To overcome these challenges, early fire detection and automatic response are very significant. Therefore, we develop a novel framework based on a lightweight convolutional neural network (CNN), requiring less training time, and it is applicable over resource-constrained devices. The internal architecture of the proposed model is inspired by the block-wise VGG16 architecture with a significantly reduced number of parameters, input size, inference time, and comparatively higher accuracy for early fire detection. In the proposed model, small-size uniform convolutional filters are employed that are specifically designed to capture fine details of input fire images with a sequentially increasing number of channels to aid effective feature extraction. The proposed model is evaluated on two datasets such as a benchmark Foggia’s dataset and our newly created small-scaled fire detection dataset with extremely challenging real-world images containing a high-level of diversity. Experimental results conducted on both datasets reveal the better performance of the proposed model compared to state-of-the-art in terms of accuracy, false-positive rate, model size, and running time, which indicates its robustness and feasible installation in real-world scenarios.

Assessment on the Use of Meteorological and Social Media Information for Forest Fire Detection and Prediction in Riau, Indonesia

In this study, tweets related to fires in Riau, Sumatra, were identified using carefully selected keywords for the 2014–2019 timeframe. The TAGGS algorithm was applied, which allows for geoparsing based on the user’s nationality and hometown and on direct referrals to specific locations such as name of province or name of city in the message itself. Online newspapers covering Riau were analyzed for the year 2019 to provide additional information about the reasons why fires occurred and other factors, such as impact on people’s health, animal mortality related to ecosystem disruption, visibility, decrease in air quality and limitations in the government firefighting response. Correlation analysis between meteorological information, Twitter activity and satellite-derived hotspots was conducted. The existing approaches that BMKG and other Indonesian agencies use to detect fire activity are reviewed and a novel approach for early fire detection is proposed based on the crowdsourcing of tweets. The policy implications of these results suggest that crowdsourced data can be included in the fire management system in Indonesia to support early fire detection and fire disaster mitigation efforts.

A BIM-Based Smart System for Fire Evacuation

Building fires constitute a significant threat that affects property, the environment, and human health. The management of this risk requires an efficient fire evacuation system for buildings’ occupants. Therefore, a smart fire evacuation system that combines building information modeling (BIM) and smart technologies is proposed. The system provides the following capacities: (i) early fire detection; (ii) the evaluation of environmental data; (iii) the identification of the best evacuation path; and (iv) information for occupants about the best evacuation routes. The system was implemented in a research building at Lille University in France. The results show the system’s capacities and benefits, particularly for the identification of the best evacuation paths.

Alat Pendeteksi Kebakaran Menggunakan SMS

Lately, there are frequent fires caused by human factors. Because we cannot predict the process of fire in advance. And the delay in knowing the occurrence of a fire is very fatal to the safety of human life and property. With advances in technology, we can overcome fires by making early fire detection devices. With the presence of temperature and smoke detectors, we can detect fires as early as possible and be delivered quickly via alarms and SMS gateways. The main component of this fire detector is the Arduino Uno. This Arduino uno acts as the brain of the fire detection device. This tool works based on the detection of the temperature condition by the DHT11 temperature sensor, which is when the temperature is above normal, an alert notification will be sent via the SMS gateway and so will the MQ2 smoke and the buzzer will sound as a warning alarm.