scholarly journals LoRaWAN Network for Fire Monitoring in Rural Environments

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 531 ◽  
Author(s):  
Sandra Sendra ◽  
Laura García ◽  
Jaime Lloret ◽  
Ignacio Bosch ◽  
Roberto Vega-Rodríguez
Keyword(s):  
Forest Fires ◽  
Rural Areas ◽  
Low Cost ◽  
Fire Risk ◽  
Fire Detection ◽  
Circular Area ◽  
Fire Monitoring ◽  
Rural Environments ◽  
The Internet Of Things ◽  
Different Parts

The number of forest fires that occurred in recent years in different parts of the world is causing increased concern in the population, as the consequences of these fires expand beyond the destruction of the ecosystem. However, with the proliferation of the Internet of Things (IoT) industry, solutions for early fire detection should be developed. The assessment of the fire risk of an area and the communication of this fact to the population could reduce the number of fires originated by accident or due to the carelessness of the users. This paper presents a low-cost network based on Long Range (LoRa) technology to autonomously evaluate the level of fire risk and the presence of a forest fire in rural areas. The system is comprised of several LoRa nodes with sensors to measure the temperature, relative humidity, wind speed and CO2 of the environment. The data from the nodes is stored and processed in a The Things Network (TTN) server that sends the data to a website for the graphic visualization of the collected data. The system is tested in a real environment and, the results show that it is possible to cover a circular area of a radius of 4 km with a single gateway.

scholarly journals Internet of Trees

2021 ◽  
Vol 10 (2) ◽  
pp. 750-753
Keyword(s):  
Forest Fires ◽  
Low Cost ◽  
Web Server ◽  
Fire Risk ◽  
Fire Detection ◽  
Natural Habitats ◽  
The Public ◽  
Recorded Data ◽  
The Impact ◽  
The Internet Of Things

The frequency of the forest fires that have occurred in the different parts of the world, In recent decades significant population problems and causing the death if the wild animals as the impact of these fires extend beyond the destruction of the natural habitats. The proliferation of the Internet of Things industry, resolutions for initial fire detection should be developed. The valuation of the fire risk of an area and communication of this realities to the population could reduce the amount of fires originated by accident or due to carelessness of the public user. This paper proposes a low-cost network based on NXP Rapid IOT kit and Long Range (Lora) technology to autonomously estimate the level of fire risk in the forest. The system comprises of NXP Rapid IOT kit which humidity, air quality and detection of the tree fall. The data from each node stored and processed in a in a web server or the mobile application that sendsthe recorded data to a web server for graphical conception of collected data.

Detection and monitoring of fire and gas leakage systems in industries

2018 ◽  
Vol 7 (2.8) ◽  
pp. 419
Author(s):  
K Geetha ◽  
P Prabha ◽  
C Preetha Devi ◽  
S Priyadharshini ◽  
S Tamilselvan
Keyword(s):  
Temperature Sensor ◽  
Traditional Method ◽  
Automatic System ◽  
Continuous Monitoring ◽  
Low Cost ◽  
Fire Detection ◽  
Wireless Sensor ◽  
Quality Service ◽  
Fire Monitoring ◽  
Gas Leakage

Now a days, Industries are more equipped with automatic system. Fire monitoring is one of the applications where continuous monitoring of temperature and humidity is essential to detect the fire in the industry. Fire detection is very much necessary to protect both the industry and to conserve environment and livelihood of human. This paper presents an algorithm to detect the fire in the industry based on ZigBee and GPRS wireless sensor network which provides low cost, low maintenance and good quality service when compared with the traditional method. The hardware circuitry of proposed solution is based on microcontroller, temperature sensor along with ZigBee and GPRS modules.

scholarly journals Fires Monitoring by Means of Spectral Indices from Sentinel-2 Data: the Case Study of the Vesuvius and Lattari Mountains, Campania (Italy)

2020 ◽  
Author(s):  
Domenico Antonio Giuseppe Dell'Aglio ◽  
Carmine Gambardella ◽  
Massimiliano Gargiulo ◽  
Antonio Iodice ◽  
Rosaria Parente ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Forest Fires ◽  
European Space Agency ◽  
Fire Risk ◽  
Fire Detection ◽  
Spectral Indices ◽  
Active Fire ◽  
Space Agency ◽  
Repeat Time ◽  
Sentinel 2

Forest fires are part of a set of natural disasters that have always affected regions of the world typically characterized by a tropical climate with long periods of drought. However, due to climate change in recent years, other regions of our planet have also been affected by this phenomenon, never seen before. One of them is certainly the Italian peninsula, and especially the regions of southern Italy. For this reason, the scientific community, as well as remote sensing one, is highly concerned in developing reliable techniques to provide useful support to the competent authorities. In particular, three specific tasks have been carried out in this work: (i) fire risk prevention, (ii) active fire detection, and (iii) post-fire area assessment. To accomplish these analyses, the capability of a set of spectral indices, derived from spaceborne remote sensing (RS) data, is assessed to monitor the forest fires. The spectral indices are obtained from Sentinel-2 multispectral images of the European Space Agency (ESA), which are free of charge and openly accessible. Moreover, the twin Sentinel-2 sensors allow to overcome some restrictions on time delivery and observation repeat time. The performance of the proposed analyses were assessed experimentally to monitor the forest fires occurred in two specific study areas during the summer of 2017: the volcano Vesuvius, near Naples, and the Lattari mountains, near Sorrento (both in Campania, Italy).

A survey on technologies for automatic forest fire monitoring, detection, and fighting using unmanned aerial vehicles and remote sensing techniques

2015 ◽  
Vol 45 (7) ◽  
pp. 783-792 ◽  
Author(s):  
Chi Yuan ◽  
Youmin Zhang ◽  
Zhixiang Liu
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Forest Fire ◽  
Forest Fires ◽  
Cooperative Control ◽  
Fire Detection ◽  
Fire Monitoring ◽  
Diagnosis And Prognosis ◽  
Aerial Vehicles ◽  
Remote Sensing Techniques ◽  
Operational Range

Because of their rapid maneuverability, extended operational range, and improved personnel safety, unmanned aerial vehicles (UAVs) with vision-based systems have great potential for monitoring, detecting, and fighting forest fires. Over the last decade, UAV-based forest fire fighting technology has shown increasing promise. This paper presents a systematic overview of current progress in this field. First, a brief review of the development and system architecture of UAV systems for forest fire monitoring, detection, and fighting is provided. Next, technologies related to UAV forest fire monitoring, detection, and fighting are briefly reviewed, including those associated with fire detection, diagnosis, and prognosis, image vibration elimination, and cooperative control of UAVs. The final section outlines existing challenges and potential solutions in the application of UAVs to forest firefighting.

scholarly journals Early forest fire detection by vision-enabled wireless sensor networks

2012 ◽  
Vol 21 (8) ◽  
pp. 938 ◽  
Author(s):  
Jorge Fernández-Berni ◽  
Ricardo Carmona-Galán ◽  
Juan F. Martínez-Carmona ◽  
Ángel Rodríguez-Vázquez
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Forest Fires ◽  
Prescribed Burning ◽  
Low Cost ◽  
Fire Detection ◽  
Wireless Sensor ◽  
Radio Communication ◽  
Power Efficient ◽  
Autonomous Operation

Wireless sensor networks constitute a powerful technology particularly suitable for environmental monitoring. With regard to wildfires, they enable low-cost fine-grained surveillance of hazardous locations like wildland–urban interfaces. This paper presents work developed during the last 4 years targeting a vision-enabled wireless sensor network node for the reliable, early on-site detection of forest fires. The tasks carried out ranged from devising a robust vision algorithm for smoke detection to the design and physical implementation of a power-efficient smart imager tailored to the characteristics of such an algorithm. By integrating this smart imager with a commercial wireless platform, we endowed the resulting system with vision capabilities and radio communication. Numerous tests were arranged in different natural scenarios in order to progressively tune all the parameters involved in the autonomous operation of this prototype node. The last test carried out, involving the prescribed burning of a 95 × 20-m shrub plot, confirmed the high degree of reliability of our approach in terms of both successful early detection and a very low false-alarm rate.

Fire Detection Using Both Infrared and Visual Images With Application to Unmanned Aerial Vehicle Forest Fire Surveillance

2019 ◽  
Author(s):  
Chi Yuan ◽  
Zhixiang Liu ◽  
Anim Hossain ◽  
Youmin Zhang
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Forest Fire ◽  
Forest Fires ◽  
Low Cost ◽  
Fire Detection ◽  
Visual Images ◽  
Camera System ◽  
Real Time Processing ◽  
Computational Performance ◽  
Aerial Vehicle

Abstract Forest fires are a universal problem that destroy a large amount of natural resources and creates environmental pollution. Forest firefighting is one of today’s most important events for natural and environmental resources protection and conservation. Unmanned aerial vehicle (UAV) with remote sensing system can offer a rapid, safe and low-cost approach for effective forest fire detection which have attracted researchers attention worldwide. In this paper, automatic detection of fire regions using both visual and infrared images is investigated. In order to improve the computational performance to satisfy the requirement of real-time processing, a reduced complexity fusion method is adopted in this research. Through testing the proposed approach on real video sequences, good detection performance is achieved and it is indicated that using multi-modal camera system to detect forest fire with application to firefighting UAV is very promising.

Computer Vision Method for Forest Fires Detection Based on RGB Images Obtained by Unmanned Motor Glider

2021 ◽  
pp. 71-78
Author(s):  
Michael Yu. Kataev ◽  
Eugene Yu. Kartashov
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Forest Fire ◽  
Forest Fires ◽  
Fire Detection ◽  
Monitoring Systems ◽  
Colour Space ◽  
Fire Monitoring ◽  
Aerial Vehicle ◽  
Rgb Images ◽  
Forest Fire Detection

The article proposes a method (algorithm) of forest fire detection by means of RGB images obtained by using an unmanned aerial vehicle (motor glider). It includes several stages associated with background detection and subtraction and recognition of fire areas by means of RGB colour space. The proposed method was tested using images of forest fires. It is proposed to use unmanned aerial vehicles capable to monitor large areas continuously for several hours. The results of calculations are shown, which demonstrate that the proposed method allows us to detect areas of images occupied by forest fires and may be used in automatic forest fire monitoring systems.

scholarly journals Assessment of Fire Risk and Forest Fires in Rural Areas Using Long Range Technology

2020 ◽  
Vol 2 (1) ◽  
pp. 38-48
Author(s):  
Dr. Smys S. ◽  
Dr. Jennifer S. Raj
Keyword(s):  
Long Range ◽  
Forest Fire ◽  
Forest Fires ◽  
Rural Areas ◽  
Fire Risk ◽  
Cost Effective ◽  
Battery Life ◽  
Network Simulator ◽  
Sensing Element ◽  
Effective Network

The occurrences of forest fires is not only a progressing concern in the lives of the people but also in the deterioration of the environment. Since the emergence of the internet of things, new methodologies are being continuously devised to have an early knowledge about the occurrence of the forest fires. The identifying the areas with the fire risks and the intimating it to the public would minimize the death rate caused due to these types of fire accidents. So the paper utilizes the cost- effective network that is centered on long range technology to automatically assess the degree of fire risky and forest fire rural areas and transmit to the website for public vision using the things network server. The proposed method includes many long range nodes and the sensing element to measure the atmospheric changes and the CO2 level in the environment. The long range based sensor network used in the detection of the fire risky and the forest fire areas is evaluated using the network simulator-2 and was found to provide an enhanced service quality by providing a better coverage, battery life, latency, cost and as well as efficiency.

scholarly journals Low-Cost Implementation of Reactive Jammer on LoRaWAN Network

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 864
Author(s):  
Toni Perković ◽  
Hrvoje Rudeš ◽  
Slaven Damjanović ◽  
Antun Nakić
Keyword(s):  
Low Cost ◽  
Fire Detection ◽  
Wide Area ◽  
Area Network ◽  
Wide Area Network ◽  
Jamming Attacks ◽  
Package Delivery ◽  
Parking Lot ◽  
Lightning Fire ◽  
The Internet Of Things

The Low-Power Wide-Area Network (LPWA) has already started to gain a notorious adoption in the Internet of Things (IoT) landscape due to its enormous potential. It is already employed in a wide variety of scenarios involving parking lot occupancy, package delivery, smart irrigation, smart lightning, fire detection, etc. If messages from LPWA devices can be manipulated or blocked, this will violate the integrity of the collected information and lead to unobserved events (e.g., fire, leakage). This paper explores the possibility that violates message integrity by applying a reactive jamming technique that disrupts a Long Range Wide Area Network (LoRaWAN) network. As shown in this paper, using low-cost commodity hardware based on Arduino platform, an attacker can easily mount such an attack that would result in completely shutting down the entire LoRaWAN network with high probability. Several countermeasures are introduced to reduce the possibility of jamming attacks.

scholarly journals A Collaborative Region Detection and Grading Framework for Forest Fire Smoke Using Weakly Supervised Fine Segmentation and Lightweight Faster-RCNN

Forests ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 768
Author(s):  
Jin Pan ◽  
Xiaoming Ou ◽  
Liang Xu
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Learning Strategy ◽  
Early Stage ◽  
Fire Detection ◽  
Surveillance Systems ◽  
Region Detection ◽  
Decision Network ◽  
Fire Smoke ◽  
Weakly Supervised

Forest fires are serious disasters that affect countries all over the world. With the progress of image processing, numerous image-based surveillance systems for fires have been installed in forests. The rapid and accurate detection and grading of fire smoke can provide useful information, which helps humans to quickly control and reduce forest losses. Currently, convolutional neural networks (CNN) have yielded excellent performance in image recognition. Previous studies mostly paid attention to CNN-based image classification for fire detection. However, the research of CNN-based region detection and grading of fire is extremely scarce due to a challenging task which locates and segments fire regions using image-level annotations instead of inaccessible pixel-level labels. This paper presents a novel collaborative region detection and grading framework for fire smoke using a weakly supervised fine segmentation and a lightweight Faster R-CNN. The multi-task framework can simultaneously implement the early-stage alarm, region detection, classification, and grading of fire smoke. To provide an accurate segmentation on image-level, we propose the weakly supervised fine segmentation method, which consists of a segmentation network and a decision network. We aggregate image-level information, instead of expensive pixel-level labels, from all training images into the segmentation network, which simultaneously locates and segments fire smoke regions. To train the segmentation network using only image-level annotations, we propose a two-stage weakly supervised learning strategy, in which a novel weakly supervised loss is proposed to roughly detect the region of fire smoke, and a new region-refining segmentation algorithm is further used to accurately identify this region. The decision network incorporating a residual spatial attention module is utilized to predict the category of forest fire smoke. To reduce the complexity of the Faster R-CNN, we first introduced a knowledge distillation technique to compress the structure of this model. To grade forest fire smoke, we used a 3-input/1-output fuzzy system to evaluate the severity level. We evaluated the proposed approach using a developed fire smoke dataset, which included five different scenes varying by the fire smoke level. The proposed method exhibited competitive performance compared to state-of-the-art methods.

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