1,562 research outputs found
Fuzzy-based forest fire prevention and detection by wireless sensor networks
Forest fires may cause considerable damages both in ecosystems and lives.
This proposal describes the application of Internet of Things and wireless
sensor networks jointly with multi-hop routing through a real time and dynamic
monitoring system for forest fire prevention. It is based on gathering and
analyzing information related to meteorological conditions, concentrations of
polluting gases and oxygen level around particular interesting forest areas.
Unusual measurements of these environmental variables may help to prevent
wildfire incidents and make their detection more efficient. A forest fire risk
controller based on fuzzy logic has been implemented in order to activate
environmental risk alerts through a Web service and a mobile application. For
this purpose, security mechanisms have been proposed for ensuring integrity and
confidentiality in the transmission of measured environmental information.
Lamport's signature and a block cipher algorithm are used to achieve this
objective
A Wildfire Prediction Based on Fuzzy Inference System for Wireless Sensor Networks
The study of forest fires has been traditionally considered as an important
application due to the inherent danger that this entails. This phenomenon
takes place in hostile regions of difficult access and large areas. Introduction of
new technologies such as Wireless Sensor Networks (WSNs) has allowed us to
monitor such areas. In this paper, an intelligent system for fire prediction based
on wireless sensor networks is presented. This system obtains the probability of
fire and fire behavior in a particular area. This information allows firefighters to
obtain escape paths and determine strategies to fight the fire. A firefighter can
access this information with a portable device on every node of the network. The
system has been evaluated by simulation analysis and its implementation is being
done in a real environment.Junta de Andalucía P07-TIC-02476Junta de Andalucía TIC-570
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
A performance study of anomaly detection using entropy method
An experiment to study the entropy method for an anomaly detection system has
been performed. The study has been conducted using real data generated from the
distributed sensor networks at the Intel Berkeley Research Laboratory. The
experimental results were compared with the elliptical method and has been
analyzed in two dimensional data sets acquired from temperature and humidity
sensors across 52 micro controllers. Using the binary classification to
determine the upper and lower boundaries for each series of sensors, it has
been shown that the entropy method are able to detect more number of out
ranging sensor nodes than the elliptical methods. It can be argued that the
better result was mainly due to the lack of elliptical approach which is
requiring certain correlation between two sensor series, while in the entropy
approach each sensor series is treated independently. This is very important in
the current case where both sensor series are not correlated each other.Comment: Proceeding of the International Conference on Computer, Control,
Informatics and its Applications (2017) pp. 137-14
Temperature Monitoring and Forecast System in Remote Areas with 4.0G LTE Mobile Technologies
The need to monitor areas of high risk in terms of temperature indexes has included two important elements for its compliance: monitoring and forecast of records in an environment. Performing this procedure manually is inefficient as it provides a flat perspective and can’t predict the state of the environment with rigor. Software systems are contemporary elements in constant refinement, which satisfy emerging needs of a context, so that, in relation to monitoring and forecast an environment, it allows a sophisticated automation of the process, and that tends to lead to a better supervision of the risks in the environment. This article presents a proposal for the supervision of high-risk areas, through temperature registers, manageable through the design of a software system with the implementation of mobile 4.0G LTE technologies, aimed at efficiency and effectiveness in the notification of environmental status. Finally, I conclude with a remote temperature monitoring and forecast system, using mobile technologies, with a fuzzy logic prediction system with a quadratic error not greater than 2.6%, that is, on a fuzzy algebra system whose Numerical calculation does not exceed this error value in comparison with actual values; At the same time that the future works are presented from the approach of the research that is postulated, according to the emergence of new perspectives related to this developing software system
Peatland Forest Fire Prevention Using Wireless Sensor Network Based on Naïve Bayes Classifier
Recently, peatland forest fires happened massively and gave bad impact for environment. It is necessary to make efforts to reduce of peatland forest fires, Early Warning System (EWS) is one of the solutions. Here, we propose an EWS to prevent forest fire in peatland by using Wireless Sensor Network (WSN). It uses three significant parameters which are oxygen concentration, soil humidity, and environment temperature. Naïve bayes classifier processes the data parameters and then determines forest fire potential. Unusual measurement of the parameters will trigger the classifier decision. Forest fire potential will be displayed through web services. This EWS helps the authorities to monitor and detect forest fire potential in the peatland, so it can be prevented
Unmanned Aerial Systems for Wildland and Forest Fires
Wildfires represent an important natural risk causing economic losses, human
death and important environmental damage. In recent years, we witness an
increase in fire intensity and frequency. Research has been conducted towards
the development of dedicated solutions for wildland and forest fire assistance
and fighting. Systems were proposed for the remote detection and tracking of
fires. These systems have shown improvements in the area of efficient data
collection and fire characterization within small scale environments. However,
wildfires cover large areas making some of the proposed ground-based systems
unsuitable for optimal coverage. To tackle this limitation, Unmanned Aerial
Systems (UAS) were proposed. UAS have proven to be useful due to their
maneuverability, allowing for the implementation of remote sensing, allocation
strategies and task planning. They can provide a low-cost alternative for the
prevention, detection and real-time support of firefighting. In this paper we
review previous work related to the use of UAS in wildfires. Onboard sensor
instruments, fire perception algorithms and coordination strategies are
considered. In addition, we present some of the recent frameworks proposing the
use of both aerial vehicles and Unmanned Ground Vehicles (UV) for a more
efficient wildland firefighting strategy at a larger scale.Comment: A recent published version of this paper is available at:
https://doi.org/10.3390/drones501001
IoT-inspired Framework for Real-time Prediction of Forest Fire
Wildfires are one of the most devastating catastrophes and can inflict tremendous losses to life and nature. Moreover, the loss of civilization is incomprehensible, potentially extending suddenly over vast land sectors. Global warming has contributed to increased forest fires, but it needs immediate attention from the organizations involved. This analysis aims to forecast forest fires to reduce losses and take decisive measures in the direction of protection. Specifically, this study suggests an energy-efficient IoT architecture for the early detection of wildfires backed by fog-cloud computing technologies. To evaluate the repeatable information obtained from IoT sensors in a time-sensitive manner, Jaccard similarity analysis is used. This data is assessed in the fog processing layer and reduces the single value of multidimensional data called the Forest Fire Index. Finally, based on Wildfire Triggering Criteria, the Artificial Neural Network (ANN) is used to simulate the susceptibility of the forest area. ANN are intelligent techniques for inferring future outputs as these can be made hybrid with fuzzy methods for decision-modeling. For productive visualization of the geographical location of wildfire vulnerability, the Self-Organized Mapping Technique is used. Simulation of the implementation is done over multiple datasets. For total efficiency assessment, outcomes are contrasted in comparison to other techniqueS
Multi-Sensor System for Land and Forest Fire Detection Application in Peatland Area
Forest fire has a dangerous impact on environments and humans because of haze and carbon emitted from it. A common technology to detect fire hotspots is to use satellite images and then process them to determine the number of hotspots and their location. However, satellite systems cannot penetrate in bad weather or cloudy condition. This research proposes a ground sensor system, which uses several sensors related to the indicators of fire, especially fire in peatland area with unique characteristics. Common parameters of fire, such as temperature, smoke, haze, and carbon dioxide, are applied in this system. Indicators are measured using special sensors. Results of every sensor are analyzed by implementing intelligent computer programming, and an algorithm to determine fire hotspots and locations is applied. The fire hotspot location and intensity determined by integrated multiple sensors are more accurate than those determined by a single sensor. Data collected from every sensor are kept in a database, and a graph is generated for reporting and recording. In case of sensor readings with parameters, potential of fire and hotspots detected can be forwarded to the representative department for corresponding actions
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