Spectral analysis of forest fire noise for early detection using wireless sensor networks

Khamukhin, Alexander A.; Bertoldo, Silvano

doi:10.1109/sibcon.2016.7491654

Cited by 39 publications

(15 citation statements)

References 7 publications

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“…Over the last decade, sensor technology has become ubiquitous and has attracted a lot of attention. Sensors have been deployed in many areas such as healthcare [ 7 , 8 ], agriculture [ 9 , 10 ], and forest [ 11 , 12 ], vehicle and marine [ 13 , 14 ] monitoring. In transportation, sensor technology supports the design and development of a wide range of applications for traffic control, safety, and entertainment.…”

Section: Sensor Technologymentioning

confidence: 99%

Sensor Technologies for Intelligent Transportation Systems

Ibáñez

Zeadally

Contreras-Castillo

2018

Sensors

472

200

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Modern society faces serious problems with transportation systems, including but not limited to traffic congestion, safety, and pollution. Information communication technologies have gained increasing attention and importance in modern transportation systems. Automotive manufacturers are developing in-vehicle sensors and their applications in different areas including safety, traffic management, and infotainment. Government institutions are implementing roadside infrastructures such as cameras and sensors to collect data about environmental and traffic conditions. By seamlessly integrating vehicles and sensing devices, their sensing and communication capabilities can be leveraged to achieve smart and intelligent transportation systems. We discuss how sensor technology can be integrated with the transportation infrastructure to achieve a sustainable Intelligent Transportation System (ITS) and how safety, traffic control and infotainment applications can benefit from multiple sensors deployed in different elements of an ITS. Finally, we discuss some of the challenges that need to be addressed to enable a fully operational and cooperative ITS environment.

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Section: Sensor Technologymentioning

confidence: 99%

Sensor Technologies for Intelligent Transportation Systems

Ibáñez

Zeadally

Contreras-Castillo

2018

Sensors

472

200

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“…For fire monitoring and detection application of WSNs, several important design goals and features must be considered such as energy efficiency, accurate localization, early detection, forecast capability and adaptivity to harsh environment [9]. There is a considerable amount of research works from literature related to forest fires using WSNs [20][21][22][23][24][25][26]. The authors of [27] implemented a real-world forest fire monitoring and detection system in the San Francisco area.…”

Section: Related Workmentioning

confidence: 99%

“…The authors of [24] proposed a novel algorithm for smoke detection and designed a power-efficient smart imager tailored to the proposed algorithm. Reference [25] analyzed the spectral of forest fire noise to the wireless sensor networks. Through the early detection and analysis on the noise power spectrum of forest fires, a proper strategy can be adopted to fight the fire.…”

Section: Related Workmentioning

confidence: 99%

An Environmentally Aware Scheme of Wireless Sensor Networks for Forest Fire Monitoring and Detection

Sun

Xiao

2018

Future Internet

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Forest fires are a fatal threat to environmental degradation. Wireless sensor networks (WSNs) are regarded as a promising candidate for forest fire monitoring and detection since they enable real-time monitoring and early detection of fire threats in an efficient way. However, compared to conventional surveillance systems, WSNs operate under a set of unique resource constraints, including limitations with respect to transmission range, energy supply and computational capability. Considering that long transmission distance is inevitable in harsh geographical features such as woodland and shrubland, energy-efficient designs of WSNs are crucial for effective forest fire monitoring and detection systems. In this paper, we propose a novel framework that harnesses the benefits of WSNs for forest fire monitoring and detection. The framework employs random deployment, clustered hierarchy network architecture and environmentally aware protocols. The goal is to accurately detect a fire threat as early as possible while maintaining a reasonable energy consumption level. ns-2-based simulation validates that the proposed framework outperforms the conventional schemes in terms of detection delay and energy consumption.

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“…Nowadays, due to the rapid development of imaging techniques and the full use of the spectral camera, contemporaneously, optical spectrum analysis technology [4] is widely used for perceiving the ignition of wild land, such as wildfire video monitoring [5], remote sensing [6], etc. The working principle of these technologies is photogrammetry at visible and infrared bands on the forest surface, and automatic detection of fire evidence by fixed field cameras mounted on control towers or by radar, or by mobile cameras mounted on any aircraft (aircraft, unmanned aerial vehicles (UAVs), air balloons, or satellites).…”

Section: Introductionmentioning

confidence: 99%

Wildfire Detection Using Sound Spectrum Analysis Based on the Internet of Things

Zhang

Gao

Lin

et al. 2019

Sensors

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Wildfire is a sudden and hazardous natural disaster. Currently, many schemes based on optical spectrum analysis have been proposed to detect wildfire, but obstacles in forest areas can decrease the efficiency of spectral monitoring, resulting in a wildfire detection system not being able to monitor the occurrence of wildfire promptly. In this paper, we propose a novel wildfire detection system using sound spectrum analysis based on the Internet of Things (IoT), which utilizes a wireless acoustic detection system to probe wildfire and distinguish the difference in the sound between the crown and the surface fire. We also designed a new power supply unit: tree-energy device, which utilizes the biological energy of the living trees to generate electricity. We implemented sound spectrum analysis on the data collected by sound sensors and then combined our classification algorithms. The results describe that the sound frequency of the crown fire is about 0–400 Hz, while the sound frequency of the surface fire ranges from 0 to 15,000 Hz. However, the accuracy of the classification method is affected by some factors, such as the distribution of sensors, the loss of energy in sound transmission, and the delay of data transmission. In the simulation experiments, the recognition rate of the method can reach about 70%.

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Spectral analysis of forest fire noise for early detection using wireless sensor networks

Cited by 39 publications

References 7 publications

Sensor Technologies for Intelligent Transportation Systems

Sensor Technologies for Intelligent Transportation Systems

An Environmentally Aware Scheme of Wireless Sensor Networks for Forest Fire Monitoring and Detection

Wildfire Detection Using Sound Spectrum Analysis Based on the Internet of Things

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