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An Energy-Efficient Wireless Power Transmission-Based forest fire detection System

Computers, Materials & Continua 2022年第7期72卷 441-459页

作者： Arwa A.Mashat Niayesh Gharaei Aliaa M.Alabdali Faculty of Computing&Information Technology King Abdulaziz UniversityRabigh21911Saudi Arabia

Compared with the traditional techniques of forest fires detection,wireless sensor network(WSN)is a very promising green technology in detecting efficiently the ***,the power constraint of sensor nodes is one of the main design limitations of WSNs,which leads to limited operation time of nodes and late fire *** the past years,wireless power transfer(WPT)technology has been known as a proper solution to prolong the operation time of sensor *** WPT-based mechanisms,wireless mobile chargers(WMC)are utilized to recharge the batteries of sensor nodes ***,the energy of WMC is provided using energy-harvesting or energy-scavenging techniques with employing huge,and expensive ***,the high price of energy-harvesting devices hinders the use of this technology in large and dense networks,as such networks require multiple WMCs to improve the quality of service to the sensor *** solve this problem,multiple power banks can be employed instead of utilizing ***,the long waiting time of critical sensor nodes located outside the charging range of the energy transmitters is another limitation of the previous ***,the sensor nodes are equipped with radio frequency(RF)technology,which allows them to exchange energy ***,critical sensor nodes located outside the charging range of the WMC can easily receive energy from neighboring ***,in this paper,an energy-efficient and cost-effective wireless power transmission(ECWPT)scheme is presented to improve the network lifetime and performance in forest fire detection-based *** results exhibit that ECWPT scheme achieves improved network performance in terms of computational time(12.6%);network throughput(60.7%);data delivery ratio(20.9%);and network overhead(35%)as compared to previous related *** conclusion,the proposed scheme significantly improves network energy efficiency for WSN.

关键词： forest fire detection rechargeable wireless sensor networks wireless mobile charger power constraint sustainable network lifetime

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Fusion-Based Deep Learning Model for Automated forest fire detection

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Computers, Materials & Continua 2023年第10期77卷 1355-1371页

作者： Mesfer Al Duhayyim Majdy M.Eltahir Ola Abdelgney Omer Ali Amani Abdulrahman Albraikan Fahd N.Al-Wesabi Anwer Mustafa Hilal Manar Ahmed Hamza Mohammed Rizwanullah Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Information Technology College of ComputerQassim UniversityAl-Bukairiyah52571Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

Earth resource and environmental monitoring are essential areas that can be used to investigate the environmental conditions and natural resources supporting sustainable policy development,regulatory measures,and their implementation elevating the ***-scale forest fire is considered a major harmful hazard that affects climate change and life over the ***,the early identification of forest fires using automated tools is essential to avoid the spread of fire to a large ***,this paper focuses on the design of automated forest fire detection using a fusion-based deep learning(AFFD-FDL)model for environmental *** AFFDFDL technique involves the design of an entropy-based fusion model for feature *** combination of the handcrafted features using histogram of gradients(HOG)with deep features using SqueezeNet and Inception v3 ***,an optimal extreme learning machine(ELM)based classifier is used to identify the existence of fire or *** order to properly tune the parameters of the ELM model,the oppositional glowworm swarm optimization(OGSO)algorithm is employed and thereby improves the forest fire detection performance.A wide range of simulation analyses takes place on a benchmark dataset and the results are inspected under several *** experimental results highlighted the betterment of the AFFD-FDL technique over the recent state of art techniques.

关键词： Environment monitoring remote sensing forest fire detection deep learning machine learning fusion model

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forest fire detection Using Artificial Neural Network Algorithm Implemented in Wireless Sensor Networks

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ZTE Communications 2015年第2期13卷 12-16页

作者： Yongsheng Liu Yansong Yang Chang Liu Yu Gu Network Technology Research Institute China United Network Communications Corporation Limited Department of Computer Science Hefei University of Technology

A forest fire is a severe threat to forest resources and human life, In this paper, we propose a forest-fire detection system that has an artificial neural network algorithm implemented in a wireless sensor network （WSN）. The proposed detection system mitigates the threat of forest fires by provide accurate fire alarm with low maintenance cost. The accuracy is increased by the novel multi- criteria detection, referred to as an alarm decision depends on multiple attributes of a forest fire. The multi-criteria detection is implemented by the artificial neural network algorithm. Meanwhile, we have developed a prototype of the proposed system consisting of the solar batter module, the fire detection module and the user interface module.

关键词： forest fire detection artificial neural network wireless sensor network

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Performance Analysis of Support Vector Machine (SVM) on Challenging Datasets for forest fire detection

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International Journal of Communications, Network and System Sciences 2024年第2期17卷 11-29页

作者： Ankan Kar Nirjhar Nath Utpalraj Kemprai Aman Department of Computer Science Chennai Mathematical Institute Chennai India Department of Mathematics & Computer Science Chennai Mathematical Institute Chennai India Department of Data Science Chennai Mathematical Institute Chennai India

This article delves into the analysis of performance and utilization of Support Vector Machines (SVMs) for the critical task of forest fire detection using image datasets. With the increasing threat of forest fires to ecosystems and human settlements, the need for rapid and accurate detection systems is of utmost importance. SVMs, renowned for their strong classification capabilities, exhibit proficiency in recognizing patterns associated with fire within images. By training on labeled data, SVMs acquire the ability to identify distinctive attributes associated with fire, such as flames, smoke, or alterations in the visual characteristics of the forest area. The document thoroughly examines the use of SVMs, covering crucial elements like data preprocessing, feature extraction, and model training. It rigorously evaluates parameters such as accuracy, efficiency, and practical applicability. The knowledge gained from this study aids in the development of efficient forest fire detection systems, enabling prompt responses and improving disaster management. Moreover, the correlation between SVM accuracy and the difficulties presented by high-dimensional datasets is carefully investigated, demonstrated through a revealing case study. The relationship between accuracy scores and the different resolutions used for resizing the training datasets has also been discussed in this article. These comprehensive studies result in a definitive overview of the difficulties faced and the potential sectors requiring further improvement and focus.

关键词： Support Vector Machine Challenging Datasets forest fire detection Classification

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A UAV-based forest fire detection Algorithm Using Convolutional Neural Network

A UAV-based Forest Fire Detection Algorithm Using Convolutio...

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第37届中国控制会议

作者： Yanhong Chen Youmin Zhang Jing Xin Yingmin Yi Ding Liu Han Liu Xi'an University of Technology Concordia University

With the new development in Unmanned Aerial Vehicle（UAV） in recent years, UAV equipped with color and infrared cameras becomes a new tool for carrying out the forest fire detection and fighting missions due to its advantages of low price,high maneuverability, and easy use. In order to detect a potential fire in its early stage, a UAV-based forest fire detection method using a convolutional neural network method is proposed in this paper. The effectiveness of the proposed fire detection algorithm is verified by using simulated flames in an indoor experimental testbed.

关键词： forest fire detection Convolutional Neural Network Unmanned Aerial Vehicle

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MsfireD-Net:A lightweight and efficient convolutional neural network for flame and smoke segmentation

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Journal of Automation and Intelligence 2023年第3期2卷 130-138页

作者： F.M.Anim Hossain Youmin Zhang Department of Mechanical Industrial and Aerospace EngineeringConcordia UniversityMontréalQuébecCanada

With the rising frequency and severity of wildfires across the globe,researchers have been actively searching for a reliable solution for early-stage forest fire *** recent years,Convolutional Neural Networks(CNNs)have demonstrated outstanding performances in computer vision-based object detection tasks,including forest fire *** CNNs to detect forest fires by segmenting both flame and smoke pixels not only can provide early and accurate detection but also additional information such as the size,spread,location,and movement of the ***,CNN-based segmentation networks are computationally demanding and can be difficult to incorporate onboard lightweight mobile platforms,such as an Uncrewed Aerial Vehicle(UAV).To address this issue,this paper has proposed a new efficient upsampling technique based on transposed convolution to make segmentation CNNs *** proposed technique,named Reversed Depthwise Separable Transposed Convolution(RDSTC),achieved F1-scores of 0.78 for smoke and 0.74 for flame,outperforming U-Net networks with bilinear upsampling,transposed convolution,and CARAFE ***,a Multi-signature fire detection Network(MsfireD-Net)has been proposed in this paper,having 93%fewer parameters and 94%fewer computations than the RDSTC *** being such a lightweight and efficient network,MsfireD-Net has demonstrated strong results against the other U-Net-based networks.

关键词： forest fire detection Convolutional neural network Semantic segmentation UAV Efficient upsampling

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Smoke detection Algorithm Based on Wavelet Transformation and Energy Analysis

Smoke Detection Algorithm Based on Wavelet Transformation an...

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2012 National Conference on Information Technology and Computer Science

作者： Xu Yijun School of Technology Beijing forestry University Beijing,China HAN Ning ﹡ School of Technology Beijing forestry University Beijing,China

A novel algorithm to detect the forest fire smoke based on the wavelet transformation and high-frequency energy analysis is *** conventional color-based RGB algorithm has the difficulty to distinguish the smoke from other objects with the same color,such as clouds and fogs,so it leads to some false *** the shortages of the RGB algorithm,we propose a wavelet-based algorithm to improve the detection *** been transformed by the wavelet,the original picture will be divided into four *** of the subimages can reflect the low-frequency energy information of the original image and the other three can offer the high-frequency energy information of the original information in horizontal,vertical and diagonal directions *** the wavelet coefficients in the four sub-images,the energy value can be *** this way,the energy feature of the picture can be distracted and the energy parameters in frequency domain can be figured *** comparing the ratios of high-frequency energy to the whole energy in the pictures of the smoke,cloud,and fog respectively,evident distinctions can be ***,a special range of the smoke’s high-frequency energy ratio can be set by large experimental *** comparing the data obtained from the camera with the range for the smoke,we can make sure whether the object is smoke or something just with the similar *** experimental results we obtained can accurately distinguish the smoke from the none-smoke ***,this algorithm can improve the traditional color-based RGB algorithm and the false alarm rate can be reduced

关键词： forest fire detection RGB algorithm Wavelet transformation High-frequency energy ratio

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