LPWAN Based IoT Surveillance System for Outdoor Fire Detection

Roque, Gabriel; Padilla, Vladimir Sánchez

doi:10.1109/access.2020.3003848

Cited by 37 publications

(21 citation statements)

References 33 publications

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“…For fire detection, authors in [ 37 ] designed a wireless sensor network using multiple sensors for early detection of house fires using Global System for Mobile Communications (GSM) as communication system. Authors in [ 38 ] propose a prototype for fire detection in outdoor environments based on sensors and Low Power Wide Area Network (LPWAN). Authors focus on the accuracy in the temperature and gas measurements and the real-time detection of fire.…”

Section: Related Workmentioning

confidence: 99%

Efficient Anomaly Detection for Smart Hospital IoT Systems

Said

Yahyaoui

Abdellatif

2021

Sensors

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In critical Internet of Things (IoT) application domains, such as the Defense Industry and Healthcare, false alerts have many negative effects, such as fear, disruption of emergency services, and waste of resources. Therefore, an alert must only be sent if triggered by a correct event. Nevertheless, IoT networks are exposed to intrusions, which affects event detection accuracy. In this paper, an Anomaly Detection System (ADS) is proposed in a smart hospital IoT system for detecting events of interest about patients’ health and environment and, at the same time, for network intrusions. Providing a single system for network infrastructure supervision and e-health monitoring has been shown to optimize resources and enforce the system reliability. Consequently, decisions regarding patients’ care and their environments’ adaptation are more accurate. The low latency is ensured, thanks to a deployment on the edge to allow for a processing close to data sources. The proposed ADS is implemented and evaluated while using Contiki Cooja simulator and the e-health event detection is based on a realistic data-set analysis. The results show a high detection accuracy for both e-health related events and IoT network intrusions.

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Section: Related Workmentioning

confidence: 99%

Efficient Anomaly Detection for Smart Hospital IoT Systems

Said

Yahyaoui

Abdellatif

2021

Sensors

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“…Sensors-based events detection is not a novelty, with many works proposing some kind of sensing to detect an abnormal behavior that may indicate an emergency situation. In this context, some approaches have emerged to detect a particular emergency-for example, employing gas sensor [13], temperature sensor [14], sound sensor [15] or any sensor device to allow for a better perception of the environment. Although such specialized systems have contributed to the evolution of this research area, the unification of different detection systems in an integrated approach can be valuable for smart cities.…”

Section: Related Workmentioning

confidence: 99%

On the Use of Cameras for the Detection of Critical Events in Sensors-Based Emergency Alerting Systems

Costa

Vasques

Portugal

et al. 2020

JSAN

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The adoption of emergency alerting systems can bring countless benefits when managing urban areas, industrial plants, farms, roads and virtually any area that is subject to the occurrence of critical events, supporting in rescue operations and reducing their negative impacts. For such systems, a promising approach is to exploit scalar sensors to detect events of interest, allowing for the distributed monitoring of different variables. However, the use of cameras as visual sensors can enhance the detection of critical events, which can be employed along with scalar sensors for a more comprehensive perception of the environment. Although the particularities of visual sensing may be challenging in some scenarios, the combination of scalar and visual sensors for the early detection of emergency situations can be valuable for many scenarios, such as smart cities and industry 4.0, bringing promising results. Therefore, in this article, we extend a sensors-based emergency detection and alerting system to also exploit visual monitoring when identifying critical events. Implementation and experimental details are provided to reinforce the use of cameras as a relevant sensor unit, bringing promising results for emergencies management.

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“…These data are collected using Arduino board to be sent and analyzed by The Things Network (TTN) server, so that the result can be graphically presented in a website. In [4], a prototype for fire detection was built. Conventional sensors data, like temperature and gas measurement at the moment a fire starts, was collected to be processed using Arduino board.…”

Section: Introductionmentioning

confidence: 99%

A Privacy-Preserving Iot-Based Fire Detector

et al. 2021

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Fire detection has been an issue of interest to researchers due to its significant damage to lives and property within a very short time. One of the recent solutions developed to detect fire is to use Internetof-Things (IoT) devices equipped with cameras for surveillance. The captured videos of surroundings may be processed by the IoT devices themselves or at the cloud. The latter case is required if the detection algorithm is computationally demanding. However, the use of cloud has a flaw. In fact, using the cloud could pose the threat of having the privacy of a place violated, either through hacking or unauthorized access to the footage of the place where the cloud is installed. In this paper, a fire detection system that preserves the privacy of surroundings, while maintaining a high level of accuracy for fire detection is proposed. The proposed system makes use of the cloud for fire detection; and that is achieved by sending to the cloud features extracted from the video captured by the IoT device, instead of sending the actual footage. Binary video descriptors and Convolutional Neural Network (CNN) have been used to develop the fire detection algorithm. The video descriptors are used to extract features, while the CNN is used for classification. Videos with real fire and non-fire scenes have been used in this development. Results show that the performance of proposed fire detection algorithm can achieve 97.5% classification accuracy, that outperforms the state-of-the art algorithms which make direct use of raw videos. Therefore, the proposed fire detector is as reliable as other available systems, with the advantage of having a privacy-preserving capability. It is also demonstrated that the proposed video descriptors can be implemented for real-time processing using an IoT device, Raspberry Pi 4 platform, with an average processing speed of 100ms per frame, which well satisfies practical needs.

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LPWAN Based IoT Surveillance System for Outdoor Fire Detection

Cited by 37 publications

References 33 publications

Efficient Anomaly Detection for Smart Hospital IoT Systems

Efficient Anomaly Detection for Smart Hospital IoT Systems

On the Use of Cameras for the Detection of Critical Events in Sensors-Based Emergency Alerting Systems

A Privacy-Preserving Iot-Based Fire Detector

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