A smart forest-fire early detection sensory system: Another approach of utilizing wireless sensor and neural networks

Soliman, Hamdy; Sudan, Komal; Mishra, Ashish

doi:10.1109/icsens.2010.5690033

Cited by 52 publications

(33 citation statements)

References 7 publications

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“…The authors of [6] combine a wireless sensor network with an artificial neural network (ANN) for forest fire detection. The system they propose collects data from a field via a WSN by sensors, e.g., temperature, light, and smoke.…”

Section: Related Workmentioning

confidence: 99%

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Data Mining Techniques Applied to Wireless Sensor Networks for Early Forest Fire Detection

Saoudi

Bounceur

Euler

et al. 2016

Proceedings of the International Conference on Internet of Things and Cloud Computing

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Nowadays, forest fires are a serious threat to the environment and human life. The monitoring system for forest fires should be able to make a real-time monitoring of the target region and the early detection of fire threats. In this paper, we propose a new approach based on the integration of Data Mining techniques into sensor nodes for forest fire detection. This approach is based on the clustered WSN where each sensor node will individually decide on detecting fire using a classifier of Data Mining techniques. When a fire is detected, the correspondent node will send an alert through its cluster-head which will pass through gateways and other cluster-heads until it will reach the sink in order to inform the firefighters. We use the CupCarbon simulator to validate and evaluate our proposed approach. Through extensive simulation experiments, we show that our approach can provide a fast reaction to forest fires while consuming energy e ciently.

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Section: Related Workmentioning

confidence: 99%

“…: systems employing charge-coupled device cameras and infrared detectors, satellite systems and images [2,3] and wireless sensor networks [4,5,6] .…”

Section: Doimentioning

confidence: 99%

Data Mining Techniques Applied to Wireless Sensor Networks for Early Forest Fire Detection

Saoudi

Bounceur

Euler

et al. 2016

Proceedings of the International Conference on Internet of Things and Cloud Computing

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“…Smart meters [9] are digital electronic devices that collect information about the use of electricity, water, or gas and that transmit it safely to utilities [10]. It can improve the network efficiency by monitoring contents in real time and estimating the future data transmission pattern through the data collected by the end user.…”

Section: Smart Metersmentioning

confidence: 99%

Dynamic Duty-Cycle MAC Protocol for IoT Environments and Wireless Sensor Networks

Kim

Kang

2019

Energies

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This paper proposes a new protocol that can be used to reduce transmission delay and energy consumption effectively. This will be done by adjusting the duty-cycle (DC) ratio of the receiver node and the contention window size of the sender node according to the traffic congestion for various devices in the Internet of Things (IoT). In the conventional duty-cycle MAC protocol, the data transmission delay latency and unnecessary energy consumption are caused by a high collision rate. This is because the receiver node cannot sufficiently process the data of the transmitting node during the traffic peak time when the transmission and reception have the same duty-cycle ratio. To solve this problem, this paper proposes an algorithm that changes the duty-cycle ratio of the receiver and broadcasts the contention window size of the senders through Early Acknowledgment (E-ACK) at peak time and off/peak time. The proposed algorithm, according to peak and off/peak time, can transmit data with fewer delays and minimizes energy consumption.Energies 2019, 12, 4069 2 of 13 an environment with traffic congestion causes problems of transmission delay, and the entire service life of the application is shortened due to the collision. Thus, the use of the existing duty-cycle (DC) MAC protocol [5] is not suitable. Therefore, it is necessary to develop a protocol for effective event detection [6] and data collection [7] considering various traffic conditions. For example, if the total number of wake-ups to listen to an application is determined [8], the existing duty-cycle MAC occurs irrespective of the congestion occurring at the time of the traffic. This reduces data collisions and transmission delays if the receiver wakes up frequently only at times in heavy traffic.The proposed algorithm increases the number of wake-up times by reducing the sleep time of the receiver at the peak traffic time and reduces the number of wake-up in the opposite case. This demonstrates reduced energy consumption. This paper proposes a new duty-cycle based MAC protocol suitable for congested WSNs and IoT. According to the degree of network traffic congestion, the duty-cycle ratio of the receiver and the contention window size information of the senders are broadcast through E-ACK (Early Acknowledgment), which reduces the transmission latency and waste of energy.The composition of the paper is described in detail in Section 1, followed by a summary of related work in Section 2. Section 3 proposes the operation procedure of the MAC layer. In Section 4, mathematical modeling is performed to predict the proposed algorithm and performance. In Section 5, we compare the performance of the proposed algorithm with the energy consumption and average service latency. Finally, Section 6 presents conclusions and future research topics.

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“…-Al disiminuir la relación costo-mantenimiento y operación del sensor como lo reportan Solima et al en la detección del fuego en el bosque [49] y como analizan Buratti et al para diferentes ascenarios de aplicación de la RIdS [50] y como lo mencionan Yick et al [51] con respecto al consumo energético.…”

Section: Sensores Y Variables Para Facilitar La Dtifunclassified

Evaluación de tecnologías de sensores para la detección temprana de incendios forestales

Hernández-Hostaller

2017

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Los incendios forestales son fuegos sin control que se extienden libremente en el bosque. Las labores para el manejo y control del fuego forestal incluyen la detección como un primer paso La importancia de lograr la detección temprana de incendios forestales (DTIF) radica en que se facilita eliminarlos a un bajo costo, antes de que se quemen grandes extensiones de bosque. La DTIF está concebida para dar la alarma cuando se ha iniciado el fuego, y es por esta razón que se deben utilizar sensores para las variables indicadoras de la existencia del fuego: la variable química (emisiones de humo, CO, CO2, CH4) y la variable física (radiación infrarroja y el calor).Las características de los sensores utilizados son determinantes para descubrir el inicio del fuego en tiempo real, así como para mejorar la escalabilidad, disminuir el costo de la red de sensores, y lograr la DTIF en forma práctica.Los sensores para las emisiones infrarroja y térmica (variable física) tienen una alta sensibilidad que facilitan la detección desde gran distancia, pero al utilizarlos en tierra la desventaja es que el follaje y obstáculos del terreno atenúan y dispersan la emisión infrarroja y se requiere el emplazamiento de torres para mejorar la línea de vista.Los sensores de las emisiónes químicas tienen ventajas para la detección terrestre del inicio del fuego (las emanaciones del fuego en el bosque se difunden por el aire y pueden llegar a los rincones del bosque), no necesitan línea de vista y tampoco estar ubicados a gran altura, además el mercado ofrece soluciones para la detección del fuego que podrían adaptarse para utilizarlas dentro del bosque (por ejemplo sensores con alta sensibilidad y de costo variable, sistemas para la detección óptica del humo y sistemas multicriterio que disminuyen las falsas alarmas). Sus desventajas son que algunos sensores consumen energía de calentamiento (para elevar la temperatura y poder detectar el gas CO o CO2) y que se pueden dañar al sobrepasar los valores límites (de concentración de gas o humedad relativa). Además, la concentración de humo y gas cerca del punto de detección se reduce debido a la dispersión en el bosque y esto afecta el rendimiento de los sensores que requieren de una cantidad mínima de ppm o de porcentaje de oscurecimiento)Las emisiones o variables químicas (CO, humo y CO2) son detectadas principalmente mediante dos estrategias: -La respuesta de estas variables a fenómenos físicos (absorción de IR, dispersión IR, ionización, efectos fotoacústicos)-La interacción de estas variables con el material del sensor utilizado (absorción, reacción química o una combinación de ambas).

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A smart forest-fire early detection sensory system: Another approach of utilizing wireless sensor and neural networks

Cited by 52 publications

References 7 publications

Data Mining Techniques Applied to Wireless Sensor Networks for Early Forest Fire Detection

Data Mining Techniques Applied to Wireless Sensor Networks for Early Forest Fire Detection

Dynamic Duty-Cycle MAC Protocol for IoT Environments and Wireless Sensor Networks

Evaluación de tecnologías de sensores para la detección temprana de incendios forestales

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