An Approach to Environmental Monitoring in Sparse Linear Wireless Sensor Networks for Energy Conservation Using Dual Sinks

Pakdel, Hossein; Jahanshahi, Mohsen; Fotohi, Reza

doi:10.1007/s11277-022-09763-2

Cited by 8 publications

(2 citation statements)

References 25 publications

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“…Submetering systems involve the installation of additional meters at specific points within a facility to monitor energy consumption in different areas or with specific equipment. Wireless Sensor Networks use wireless communication to connect sensors and devices for monitoring various parameters, including electric quantities and energy consumption [ 3 , 4 ]. These sensors can be deployed in both indoor and outdoor environments.…”

Section: Introductionmentioning

confidence: 99%

Spectrogram Inversion for Reconstruction of Electric Currents at Industrial Frequencies: A Deep Learning Approach

Lalla,

Albini,

Di Barba

et al. 2024

Sensors

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In this paper, we present a deep learning approach for identifying current intensity and frequency. The reconstruction is based on measurements of the magnetic field generated by the current flowing in a conductor. Magnetic field data are collected using a magnetic probe capable of generating a spectrogram, representing the spectrum of frequencies of the magnetic field over time. These spectrograms are saved as images characterized by color density proportional to the induction field value at a given frequency. The proposed deep learning approach utilizes a convolutional neural network (CNN) with the spectrogram image as input and the current or frequency value as output. One advantage of this approach is that current estimation is achieved contactless, using a simple magnetic field probe positioned close to the conductor.

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Section: Introductionmentioning

confidence: 99%

Spectrogram Inversion for Reconstruction of Electric Currents at Industrial Frequencies: A Deep Learning Approach

Lalla,

Albini,

Di Barba

et al. 2024

Sensors

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“…Currently, manual monitoring suffers from high costs, low e ciency, and many errors [9,10], and sensor monitoring suffers from low monitoring e ciency, inability to process and analyze data, and inability to assess and provide early warning of the environmental quality in mining areas [11][12][13][14]. Meanwhile, automated monitoring suffers from high equipment costs, low coverage, and lack of analysis and prediction capabilities [15].…”

Section: Introductionmentioning

confidence: 99%

Distributed Environment Online Monitoring System Based on SOA

Chen

2023

Preprint

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Environmental contamination caused by energy extraction and processing is a persistent issue, and China’s environmental monitoring of mines is becoming increasingly rigorous as the 14th Five-Year Plan moves forward. This study proposes a distributed environmental online monitoring system to realize intelligent environmental monitoring in open-pit mines. The collection terminal utilizes PM2.5, PM10, temperature, and noise sensors for data collection. The hardware design employs an STM32 microcontroller as the control core. The communication design uses GPRS and RS-485 communication technologies to transmit data, and the software architecture utilizes a modular approach for data cleaning, data analysis, and data mining to achieve early warning and environmental quality evaluation in mining zones. The distributed environmental online monitoring system is tested in a mine, and the test data show the feasibility and effectiveness of the system. Good results on intelligently monitoring the environmental quality of the mine site are obtained.

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Load based dynamic channel allocation model to enhance the performance of device-to-device communication in WPAN

Logeshwaran,

Shanmugasundaram,

Lloret

2024

Wireless Netw

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The modern communication network has advanced to such an extent that it is now possible for devices within a wireless personal area network (WPAN) to communicate among themselves directly. However, the limited shared radio resources of a WPAN lead to numerous issues, such as cross-layer interference and data collisions, which wind up affecting the quality of communication. A load based dynamic channel allocation (LB-DCA) model has been proposed to enhance the performance of device-to-device communication in WPAN. This model uses several control schemes in collaboration with interference estimation and channel load balancing mechanisms to allocate and manage the radio resources efficiently. The objective of this model is to achieve high throughput, low interference and low energy consumption. The control schemes implemented are based on distributed coordination and a cell-splitting approach. These schemes are utilized to estimate the channel usage and number of active nodes in a network. The interference estimation is done by using a new efficiency formula. Further, channel load balancing takes into account the hops and load factor values. The proposed model obtained 98.58% CSI, 95.86% MCC, 96.35% delta-P, 97.96% FMI, 99.83% BMI, 21.52% enhanced spectrum efficiency, 16.38% enhanced scalability, 18.79% enhanced signal quality, 18.64% enhanced power control and 18.89% enhanced energy efficiency.

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An Approach to Environmental Monitoring in Sparse Linear Wireless Sensor Networks for Energy Conservation Using Dual Sinks

Cited by 8 publications

References 25 publications

Spectrogram Inversion for Reconstruction of Electric Currents at Industrial Frequencies: A Deep Learning Approach

Spectrogram Inversion for Reconstruction of Electric Currents at Industrial Frequencies: A Deep Learning Approach

Distributed Environment Online Monitoring System Based on SOA

Load based dynamic channel allocation model to enhance the performance of device-to-device communication in WPAN

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