Efficient Forest Fire Detection System Based on Data Fusion Applied in Wireless Sensor Networks

Abbassi, Mohammed Anas El; Jilbab, Abdelilah; Bourouhou, Abdennaser

doi:10.15676/ijeei.2020.12.1.1

Cited by 12 publications

(3 citation statements)

References 10 publications

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“…Instead, there exist cases, like spreading fires, that are noisy and potentially chaotic systems in which transitions in dynamics are difficult to predict and demand a novel way of consideration [26]. Following the same rationale, in [27], a heterogeneous data fusion algorithm for detecting a fire in a defined area was proposed, which enables ignoring unnecessary and incorrect data that may influence the reliability of the detection. The magnitude of the event propagation is estimated at a second level using a Fuzzy Inference System.…”

Section: Event-triggered Traffic Modellingmentioning

confidence: 99%

Adaptive Spatial Scheduling for Event Traffic in LoRaWAN Networks

Asteriou,

Kantelis,

Beletsioti

et al. 2024

Sensors

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Low-Power Wide-Area Networks constitute a leading, emerging Internet-of-Things technology, with important applications in environmental and industrial monitoring and disaster prevention and management. In such sensor networks, external detectable events can trigger synchronized alarm report transmissions. In LoRaWANs, and more generally in networks with a random access-based medium access algorithm, this can lead to a cascade of frame collisions, temporarily resulting in degraded performance and diminished system operational capacity, despite LoRaWANs’ physical layer interference and collision reduction techniques. In this paper, a novel scheduling algorithm is proposed that can increase system reliability in the case of such events. The new adaptive spatial scheduling algorithm is based on learning automata, as well as previous developments in scheduling over LoRaWANs, and it leverages network feedback information and traffic spatial correlation to increase network performance while maintaining high reliability. The proposed algorithm is investigated via an extensive simulation under a variety of network conditions and compared with a previously proposed scheduler for event-triggered traffic. The results show a decrease of up to 30% in average frame delay compared to the previous approach and an order of magnitude lower delay compared to the baseline algorithm. These findings highlight the importance of using spatial information in adaptive schemes for improving network performance, especially in location-sensitive applications.

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Section: Event-triggered Traffic Modellingmentioning

confidence: 99%

Adaptive Spatial Scheduling for Event Traffic in LoRaWAN Networks

Asteriou,

Kantelis,

Beletsioti

et al. 2024

Sensors

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“…Song et al [12] propose a reference model for a fire detection and monitoring system using the MS/TP protocol and based on BACnet, aiming to meet response time and flexibility requirements. Jilbab et al [13] propose an intelligent model for fast and reliable fire detection in wireless sensor networks. Simulation results show robust performance in detection, rapid alerting, and energy efficiency.…”

Section: Related Workmentioning

confidence: 99%

Forest Fire Monitoring System Supported by Unmanned Aerial Vehicles and Edge Computing: A Performance Evaluation Using Petri Nets

Sabino,

Lima,

Brito

et al. 2023

Preprint

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Forest fires cause damage to life, biodiversity, and properties, affecting natural ecosystems, healthy and only. During the practical application of fire prevention, numerous detection techniques have been thoroughly researched to prevent devastating fires. The techniques improve early fire detection and accelerate emergency response, reducing damage and optimizing system containment operations. Wildfire detection requires a robust infrastructure for equipment, maintenance, and ongoing monitoring. Effective cooperation between components and integration of technologies is key to a consistent and comprehensive response to fires. This article proposes a fire monitoring system using drones, cameras, and edge computing technologies. We use Stochastic Petri Nets (SPN) to model the structure and evaluate the system's performance. The model is parameterizable, allowing adjustments to the components' resource capabilities and service time. Twenty-four parameters can be defined, making it possible to evaluate a wide variety of different scenarios. The results obtained in different scenarios in this work have the potential for auxiliary administrators of monitoring systems to estimate more than six analyses and plan more optimized architectures as needed.

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“…Their model considered three combustion phases: no fire, smoldering combustion and flaming combustion, and was able to obtain a consistent accuracy of >82% when multiple sensor input data were considered. Abbassi et al, implemented a multiple-level alert using a combination of KNN classifier and fuzzy logic [33]. In the first level, a cluster of neighboring nodes generates an alert using K-means clustering.…”

Section: Sensor Nodesmentioning

confidence: 99%

Early Wildfire Detection Technologies in Practice—A Review

Mohapatra

Trinh

2022

Sustainability

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As fires grow in intensity and frequency each year, so has the resistance from their anthropic victims in the form of firefighting technology and research. Although it is impossible to completely prevent wildfires, the potential devastation can be minimized if fires are detected and precisely geolocated while still in their nascent phases. Furthermore, automated approaches without human involvement are comparatively more efficient, accurate and capable of monitoring extremely remote and vast areas. With this specific intention, many research groups have proposed numerous approaches in the last several years, which can be grouped broadly into these four distinct categories: sensor nodes, unmanned aerial vehicles, camera networks and satellite surveillance. This review paper discusses notable advancements and trends in these categories, with subsequent shortcomings and challenges. We also describe a technical overview of common prototypes and several analysis models used to diagnose a fire from the raw input data. By writing this paper, we hoped to create a synopsis of the current state of technology in this emergent research area and provide a reference for further developments to other interested researchers.

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Efficient Forest Fire Detection System Based on Data Fusion Applied in Wireless Sensor Networks

Cited by 12 publications

References 10 publications

Adaptive Spatial Scheduling for Event Traffic in LoRaWAN Networks

Adaptive Spatial Scheduling for Event Traffic in LoRaWAN Networks

Forest Fire Monitoring System Supported by Unmanned Aerial Vehicles and Edge Computing: A Performance Evaluation Using Petri Nets

Early Wildfire Detection Technologies in Practice—A Review

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