Multiple Sensor on Clustering Wireless Sensor Network to Tackle Illegal Cutting

Mutiara, Giva Andriana; Suryana, Nanna; Othman, Mohd Fairuz Iskandar

doi:10.18517/ijaseit.10.1.8849

Cited by 3 publications

(5 citation statements)

References 9 publications

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“…It is because the farther the transmitter and receiver are, the more power the device must consume and the weaker the signal is. The comparison between the previous study [40] stated that LoRa has a more extended range of communication than ZigBee. LoRa also has better energy consumption than ZigBee.…”

Section: Discussionmentioning

confidence: 65%

Using Long-Range Wireless Sensor Network to Track the Illegal Cutting Log

2020

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Nowadays, the need for wireless sensing applications is increasing. Along with the increased illegal cutting of logs in the forest, however, it requires the integration application to tackle the illegal logging and forest preservation. The wireless sensor network is a suitable network architecture for remotely monitoring or tracking applications in the environment. This paper proposed an integrated system that can identify and track the position of a moving cutting log. An Arduino Uno, Raspberry Pi 3 B+, sound sensor, accelerometer sensor, LoRa GPS HAT Shield, and Outdoor LoRa Gateway OLG01 performed the hardware monitoring and tracking of the proposed system. The network of STAR topology configuration between master and slaves is represented by the LoRa Network embedded with the sensors, as an architecture of the wireless sensor network. The system was examined the performance of the network and the tracking process. The result determined that the LoRa can detect and identify the occurrence of the illegal cutting of logs in real-time. Meanwhile, in terms of the tracking performance, a duration of 5–46 s was required to track the new position of the moving cutting log.

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Section: Discussionmentioning

confidence: 65%

Using Long-Range Wireless Sensor Network to Track the Illegal Cutting Log

2020

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“…In [14], a system adopting sound and vibration sensors is presented. It uses a lowpower microcontroller and an Xbee Pro S2C module for communication.…”

Section: Illegal Tree-cutting Detectionmentioning

confidence: 99%

“…Main Contribution Main Limitations [14] Low-power microcontroller with sound/vibration sensors and Xbee…”

Section: Referencementioning

confidence: 99%

Monitoring Illegal Tree Cutting through Ultra-Low-Power Smart IoT Devices

Andreadis

Giambene

Zambon

2021

Sensors

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Forests play a fundamental role in preserving the environment and fighting global warming. Unfortunately, they are continuously reduced by human interventions such as deforestation, fires, etc. This paper proposes and evaluates a framework for automatically detecting illegal tree-cutting activity in forests through audio event classification. We envisage ultra-low-power tiny devices, embedding edge-computing microcontrollers and long-range wireless communication to cover vast areas in the forest. To reduce the energy footprint and resource consumption for effective and pervasive detection of illegal tree cutting, an efficient and accurate audio classification solution based on convolutional neural networks is proposed, designed specifically for resource-constrained wireless edge devices. With respect to previous works, the proposed system allows for recognizing a wider range of threats related to deforestation through a distributed and pervasive edge-computing technique. Different pre-processing techniques have been evaluated, focusing on a trade-off between classification accuracy with respect to computational resources, memory, and energy footprint. Furthermore, experimental long-range communication tests have been conducted in real environments. Data obtained from the experimental results show that the proposed solution can detect and notify tree-cutting events for efficient and cost-effective forest monitoring through smart IoT, with an accuracy of 85%.

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“…A complete presentation of methods and works on environmental sound recognition can be found in [12] with many works and studies on illegal logging in various urban and forest environments in [13]. Most systems have relied on wireless sensor networks (WSNs) and have utilized sound sensors to detect the operation of chainsaws, as well as vibration sensors to specify the exact position where logging was taking place in a forest [14,15].…”

Section: Related Workmentioning

confidence: 99%

“…In [15], the authors designed and introduced a system that was based on wireless sensor networks and various sensors to detect and recognize illegal cutting of trees. In the nodes of the network, sound and vibration sensors were employed.…”

Section: Related Workmentioning

confidence: 99%

Illegal Logging Detection Based on Acoustic Surveillance of Forest

et al. 2020

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In this article, we present a framework for automatic detection of logging activity in forests using audio recordings. The framework was evaluated in terms of logging detection classification performance and various widely used classification methods and algorithms were tested. Experimental setups, using different ratios of sound-to-noise values, were followed and the best classification accuracy was reported by the support vector machine algorithm. In addition, a postprocessing scheme on decision level was applied that provided an improvement in the performance of more than 1%, mainly in cases of low ratios of sound-to-noise. Finally, we evaluated a late-stage fusion method, combining the postprocessed recognition results of the three top-performing classifiers, and the experimental results showed a further improvement of approximately 2%, in terms of absolute improvement, with logging sound recognition accuracy reaching 94.42% when the ratio of sound-to-noise was equal to 20 dB.

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Multiple Sensor on Clustering Wireless Sensor Network to Tackle Illegal Cutting

Cited by 3 publications

References 9 publications

Using Long-Range Wireless Sensor Network to Track the Illegal Cutting Log

Using Long-Range Wireless Sensor Network to Track the Illegal Cutting Log

Monitoring Illegal Tree Cutting through Ultra-Low-Power Smart IoT Devices

Illegal Logging Detection Based on Acoustic Surveillance of Forest

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