Early forest fire detection by vision-enabled wireless sensor networks

Fernández-Berni, Jorge; Carmona-Galán, Ricardo; Martínez-Carmona, Juan F.; Rodríguez-Vázquez, Á.

doi:10.1071/wf11168

Cited by 40 publications

(33 citation statements)

References 16 publications

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“…Such system collects contextual data from temperature, humidity, light, and air quality sensors and then centrally infer events. Moreover, the centralized context reasoning systems in [6,12], and [23] provide early inference of forest fire events based on visionenabled sensors, home monitoring based on the received signal strength of sensors, and surveillance of critical areas, respectively. In wireless sensors network deployments, e.g., [2,5,11,16], the back-end systems centrally provide event inference for specific areas by minimizing false alerts.…”

Section: Related Workmentioning

confidence: 99%

Predictive intelligence to the edge through approximate collaborative context reasoning

Anagnostopoulos

Kolomvatsos

2017

Appl Intell

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We focus on Internet of Things (IoT) environments where a network of sensing and computing devices are responsible to locally process contextual data, reason and collaboratively infer the appearance of a specific phenomenon (event). Pushing processing and knowledge inference to the edge of the IoT network allows the complexity of the event reasoning process to be distributed into many manageable pieces and to be physically located at the source of the contextual information. This enables a huge amount of rich data streams to be processed in real time that would be prohibitively complex and costly to deliver on a traditional centralized Cloud system. We propose a lightweight, energy-efficient, distributed, adaptive, multiple-context perspective event reasoning model under uncertainty on each IoT device (sensor/actuator). Each device senses and processes context data and infers events based on different local context perspectives: (i) expert knowledge on event representation, (ii) outliers inference, and (iii) deviation from locally predicted context. Such novel approximate reasoning paradigm is achieved through a contextualized, collaborative belief-driven clustering process, where clusters of devices are formed according to their belief on the presence of events. Our distributed and federated intelligence model efficiently identifies any localized abnormality on the contextual data in light of event reasoning through aggregating local degrees of belief, updates, and adjusts its knowledge to contextual data outliers and novelty detection. We provide comprehensive experimental and comparison assessment of our model over real contextual data with other localized and centralized event detection models and show the benefits stemmed from its adoption by achieving up to three orders of magnitude less energy consumption and high quality of inference.Keywords Collaborative event inference · Federated reasoning · Edge predictive intelligence · Optimal stopping theory · Adaptive vector quantization · Type-2 fuzzy logic inference

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Section: Related Workmentioning

confidence: 99%

Predictive intelligence to the edge through approximate collaborative context reasoning

Anagnostopoulos

Kolomvatsos

2017

Appl Intell

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“…Moreover, it is a low-cost and low-power consumption technology. These features have accelerated the application of WSNs in many areas including precision agriculture [24][25][26], wildfire detection and reporting [27][28][29], and PV condition monitoring and reporting [30][31][32]. The reviewed literature is focused primarily on analysing performance and demonstrates the effectiveness of the WSN technology.…”

Section: Remote Monitoring Technologymentioning

confidence: 99%

Small Scale Rollout of PV Systems in Chikwawa District, Malawi: Remote Monitoring System Effectiveness

Mafuta

Dauenhauer²,

Chadza

2017

Journal of Solar Energy

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Off-grid solar photovoltaic systems in Malawi are deployed increasingly as the primary option for rural public infrastructure such as primary schools and health centres. Overall, grid-connected electricity access has remained stagnant at around 9% with only 1% of rural population connected. To improve the technical sustainability of such systems, a novel remote monitoring technology utilising Wireless Sensor Networks was installed and the systems were monitored over roughly one year. This paper has described the technical design, performance, and benefits received from deployment of the technology. Furthermore, it has evaluated the cost implications for a larger scale rollout and potential benefits.We dedicate this work to Mr. McEnroe Mulera who was a great asset to the project and will be missed

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“…Unfortunately, they are expensive, difficult to handle, and fragile (Briz et al 2003). For this reason, CMOS cameras are still preferred for some applications where it is not strictly necessary operating in the whole infrared band, (Cheon et al 2009, Naoult et al 2007, Bendiscio et al 1998, Fernandez-Berni et al 2012. Silicon can detect Near Infrared Radiation (NIR) within the band [700 1,100] nm.…”

Section: Introductionmentioning

confidence: 99%

Fire detection with a frame-less vision sensor working in the NIR band

Leñero‐Bardallo¹,

Fernández-Berni²,

Rodríguez-Vázquez³

et al. 2014

Advances in Forest Fire Research

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This paper draws the attention of the community about the capabilities of an emerging generation of bio-inspired vision sensors to be used in fire detection systems. Their principle of operation will be described. Moreover experimental results showing the performance of an eventbased vision sensor will be provided. The sensor was intended to monitor flames activity without using optic filters. In this article, we will also extend this preliminary work and explore how its outputs can be processed to detect fire in the environment. IntroductionInfrared cameras can easily detect fire and hot spots. Unfortunately, they are expensive, difficult to handle, and fragile (Briz et al. 2003). For this reason, CMOS cameras are still preferred for some applications where it is not strictly necessary operating in the whole infrared band, (Cheon et al. 2009, Naoult et al. 2007, Bendiscio et al. 1998, Fernandez-Berni et al. 2012. Silicon can detect Near Infrared Radiation (NIR) within the band [700 1,100] nm. This property can be exploited to monitor flame and fire activity.Flames have a very characteristic oscillatory behavior. They flicker with certain frequency components that depend on their nature. The study of these frequency components is interesting for industrial processes and applications. Traditionally CMOS cameras with NIR filters have been employed (Yan et al. 2006). As an alternative to this traditional approach, recently we proposed an bio-inspired event-based system to monitor flame activity without using optic filters (Leñero-Bardallo et al. 2013). The idea behind this method was to compare the photocurrents of stacked photodiodes at different depths. The top one was more sensitive to shorter wavelengths and the bottom one was more sensitive to longer wavelengths, having a very good sensitivity within the NIR band. Just comparing the difference between their photocurrents, we were able to determine if the incident radiation was within the NIR band without optic filters. Moreover, we could process with a real-time algorithm the temporal variations of the NIR levels in the visual scene provoked by flames and compute their frequency components values. This paper studies how the prior work to monitor flame activity could be extended to detect fire in the environment and emit an alarm in case. We will show that the previously implemented algorithm outputs can be processed to detect fire with a low computational load. Up

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Early forest fire detection by vision-enabled wireless sensor networks

Cited by 40 publications

References 16 publications

Predictive intelligence to the edge through approximate collaborative context reasoning

Predictive intelligence to the edge through approximate collaborative context reasoning

Small Scale Rollout of PV Systems in Chikwawa District, Malawi: Remote Monitoring System Effectiveness

Fire detection with a frame-less vision sensor working in the NIR band

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