Current Status and Prospects of Research on Sensor Fault Diagnosis of Agricultural Internet of Things

Zou, Xiuguo; Li, Wenchao; Huo, Zhiqiang; Wang, Sunyuan; Chen, Zhilong; Xin, Chengrui; Bai, Yungang; Liang, Zhenyu; Yan, Gong; Yan, Qingzeng; Shu, Lei

doi:10.3390/s23052528

Cited by 16 publications

(2 citation statements)

References 111 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, distributed sensor failures, such as breaking of the optic fiber, were not modeled, but could be easily inferred. The sensor failures that were considered in this work were partial failures [ 55 , 56 , 57 , 58 , 59 ]: Bias. Drifting.…”

Section: Methodsmentioning

confidence: 99%

Assessing Sensor Integrity for Nuclear Waste Monitoring Using Graph Neural Networks

Hembert,

Ghnatios,

Cotton

et al. 2024

Sensors

View full text Add to dashboard Cite

A deep geological repository for radioactive waste, such as Andra’s Cigéo project, requires long-term (persistent) monitoring. To achieve this goal, data from a network of sensors are acquired. This network is subject to deterioration over time due to environmental effects (radioactivity, mechanical deterioration of the cell, etc.), and it is paramount to assess each sensor’s integrity and ensure data consistency to enable the precise monitoring of the facilities. Graph neural networks (GNNs) are suitable for detecting faulty sensors in complex networks because they accurately depict physical phenomena that occur in a system and take the sensor network’s local structure into consideration in the predictions. In this work, we leveraged the availability of the experimental data acquired in Andra’s Underground Research Laboratory (URL) to train a graph neural network for the assessment of data integrity. The experiment considered in this work emulated the thermal loading of a high-level waste (HLW) demonstrator cell (i.e., the heating of the containment cell by nuclear waste). Using real experiment data acquired in Andra’s URL in a deep geological layer was one of the novelties of this work. The used model was a GNN that inputted the temperature field from the sensors (at the current and past steps) and returned the state of each individual sensor, i.e., faulty or not. The other novelty of this work lay in the application of the GraphSAGE model which was modified with elements of the Graph Net framework to detect faulty sensors, with up to half of the sensors in the network being faulty at once. This proportion of faulty sensors was explained by the use of distributed sensors (optic fiber) and the environmental effects on the cell. The GNNs trained on the experimental data were ultimately compared against other standard classification methods (thresholding, artificial neural networks, etc.), which demonstrated their effectiveness in the assessment of data integrity.

show abstract

Section: Methodsmentioning

confidence: 99%

Assessing Sensor Integrity for Nuclear Waste Monitoring Using Graph Neural Networks

Hembert,

Ghnatios,

Cotton

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Similarly, technological development today is one of the greatest sources of wealth for nations, as long as it is applied to the service of solutions to social, economic, and environmental problems [14][15][16]. In addition, the Internet of Things (IoT) represents a potential system to collect information in agriculture situations in real time [17][18][19][20][21]. In this sense, the IoT system continuously monitors and maintains the necessary parameters at optimal levels, reducing the need for human involvement in the process of vermicomposting [22].…”

Section: Introductionmentioning

confidence: 99%

A Data Analytic Monitoring with IoT System of the Reproductive Conditions of the Red Worm as a Product Diversification Strategy

Sánchez-Mojica,

Pérez-Domínguez,

Gutiérrez Londoño

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

The Internet of Things (IoT) is becoming increasingly important due to the ability to collect data in real time and monitor the performance of systems. In this sense, the objective of the project is to create an IoT system to monitor and enhance red boll worm farming conditions in California as part of a strategy to diversify annelid-based goods. Therefore, the goal is to expand this animal’s productivity so that additional items can be made from California red worms. Furthermore, the method used implies a research design that uses an experimental approach to obtain data based on the variable conditions identified in the literature review. The analysis of the data will allow determination of the factors that result in optimization of production, and at the same time creation of a production estimation in the network platform. Finally, this project proposes to facilitate the monitoring and control of the variables that interfere in the earthworm reproduction process to increase the production of annelids in pursuit of product diversification. In addition, we put it into practice in real life to demonstrate its applicability and efficacy. In this mode, the results indicate potential findings about IoT application in agriculture situations.

show abstract

Applying Machine Learning to Minimize the Impact of Sensor Failures to RTOS Based Internet of Things Systems

Sharma

Chmaj

Selvaraj

2023

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

Current Status and Prospects of Research on Sensor Fault Diagnosis of Agricultural Internet of Things

Cited by 16 publications

References 111 publications

Assessing Sensor Integrity for Nuclear Waste Monitoring Using Graph Neural Networks

Assessing Sensor Integrity for Nuclear Waste Monitoring Using Graph Neural Networks

A Data Analytic Monitoring with IoT System of the Reproductive Conditions of the Red Worm as a Product Diversification Strategy

Applying Machine Learning to Minimize the Impact of Sensor Failures to RTOS Based Internet of Things Systems

Contact Info

Product

Resources

About