Fault Pattern Diagnosis and Classification in Sensor Nodes Using Fall Curve

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Artículos y libros
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Abierto Inglés The rapid expansion of Internet of Things (IoT) devices deploys various sensors in different applications like homes, cities and offices. IoT applications depend upon the accuracy of sensor data. So, it is necessary to predict faults in the sensor and isolate their cause. A novel primitive technique named fall curve is presented in this paper which characterizes sensor faults. This technique identifies the faulty sensor and determines the correct working of the sensor. Different sources of sensor faults are explained in detail whereas various faults that occurred in sensor nodes available in IoT devices are also presented in tabular form. Fault prediction in digital and analog sensors along with methods of sensor fault prediction are described. There are several advantages and disadvantages of sensor fault prediction methods and the fall curve technique. So, some solutions are provided to overcome the limitations of the fall curve technique. In this paper, a bibliometric analysis is carried out to visually analyze 63 papers fetched from the Scopus database for the past five years. Its novelty is to predict a fault before its occurrence by looking at the fall curve. The sensing of current flow in devices is important to prevent a major loss. So, the fall curves of ACS712 current sensors configured on different devices are drawn for predicting faulty or non-faulty devices. The analysis result proved that if any of the current sensors gets faulty, then the fall curve will differ and the value will immediately drop to zero. Various evaluation metrics for fault prediction are also described in this paper. At last, this paper also addresses some possible open research issues which are important to deal with false IoT sensor data. metadata Uppal, Mudita; Gupta, Deepali; Anand, Divya; S. Alharithi, Fahd; Almotiri, Jasem; Ortega-Mansilla, Arturo; Singh, Dinesh y Goyal, Nitin mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, arturo.ortega@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2022) Fault Pattern Diagnosis and Classification in Sensor Nodes Using Fall Curve. Computers, Materials & Continua, 72 (1). pp. 1799-1814. ISSN 1546-2226

[img]
Vista Previa
Texto
TSP_CMC_46932.pdf - Versión Publicada
Available under License Creative Commons Attribution.

Descargar (725kB) | Vista Previa

Resumen

The rapid expansion of Internet of Things (IoT) devices deploys various sensors in different applications like homes, cities and offices. IoT applications depend upon the accuracy of sensor data. So, it is necessary to predict faults in the sensor and isolate their cause. A novel primitive technique named fall curve is presented in this paper which characterizes sensor faults. This technique identifies the faulty sensor and determines the correct working of the sensor. Different sources of sensor faults are explained in detail whereas various faults that occurred in sensor nodes available in IoT devices are also presented in tabular form. Fault prediction in digital and analog sensors along with methods of sensor fault prediction are described. There are several advantages and disadvantages of sensor fault prediction methods and the fall curve technique. So, some solutions are provided to overcome the limitations of the fall curve technique. In this paper, a bibliometric analysis is carried out to visually analyze 63 papers fetched from the Scopus database for the past five years. Its novelty is to predict a fault before its occurrence by looking at the fall curve. The sensing of current flow in devices is important to prevent a major loss. So, the fall curves of ACS712 current sensors configured on different devices are drawn for predicting faulty or non-faulty devices. The analysis result proved that if any of the current sensors gets faulty, then the fall curve will differ and the value will immediately drop to zero. Various evaluation metrics for fault prediction are also described in this paper. At last, this paper also addresses some possible open research issues which are important to deal with false IoT sensor data.

Tipo de Documento: Artículo
Palabras Clave: Fault identification; fault classification; IoT sensor nodes; analog sensors; digital sensors; fall curve
Clasificación temática: Materias > Ingeniería
Divisiones: Universidad Europea del Atlántico > Investigación > Artículos y libros
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Depositado: 06 May 2022 23:56
Ultima Modificación: 12 Jul 2023 23:30
URI: https://repositorio.uneatlantico.es/id/eprint/652

Acciones (logins necesarios)

Ver Objeto Ver Objeto

en

close

Enzymatic treatment shapes in vitro digestion pattern of phenolic compounds in mulberry juice

The health benefits of mulberry fruit are closely associated with its phenolic compounds. However, the effects of enzymatic treatments on the digestion patterns of these compounds in mulberry juice remain largely unknown. This study investigated the impact of pectinase (PE), pectin lyase (PL), and cellulase (CE) on the release of phenolic compounds in whole mulberry juice. The digestion patterns were further evaluated using an in vitro simulated digestion model. The results revealed that PE significantly increased chlorogenic acid content by 77.8 %, PL enhanced cyanidin-3-O-glucoside by 20.5 %, and CE boosted quercetin by 44.5 %. Following in vitro digestion, the phenolic compound levels decreased differently depending on the treatment, while cyanidin-3-O-rutinoside content increased across all groups. In conclusion, the selected enzymes effectively promoted the release of phenolic compounds in mulberry juice. However, during gastrointestinal digestion, the degradation of phenolic compounds surpassed their enhanced release, with effects varying based on the compound's structure.

Artículos y libros

Peihuan Luo mail , Jian Ai mail , Qiongyao Wang mail , Yihang Lou mail , Zhiwei Liao mail , Francesca Giampieri mail francesca.giampieri@uneatlantico.es, Maurizio Battino mail maurizio.battino@uneatlantico.es, Elwira Sieniawska mail , Weibin Bai mail , Lingmin Tian mail ,

Luo

<a href="/17819/1/1-s2.0-S2214804325000679-main%20%281%29.pdf" class="ep_document_link"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>

en

open

What works in financial education? Experimental evidence on program impact

Financial education is increasingly essential for safeguarding both individual and corporate well-being. This study systematically reviews global financial education experiments using a dual-method framework that integrates a deep learning classifier with advanced multivariate statistical techniques. Our analysis indicates that while short-term improvements in financial literacy are common, such gains tend to diminish over time without ongoing reinforcement. Moreover, the limited impact of digital innovations and monetary incentives suggests that successful financial education depends on more than simply deploying technological solutions or extrinsic rewards. Overall, this review provides valuable insights into the evolving landscape of financial education in a dynamic economic context and underscores the need for sustainable strategies that secure lasting improvements in financial literacy.

Artículos y libros

Gonzalo Llamosas García mail , Cristina Mazas Pérez-Oleaga mail cristina.mazas@uneatlantico.es,

García

en

close

LC-MS and GC–MS analyses reveal that amino acid-induced ammoniation of EGCG in different tea types enhances its structural stability

Epigallocatechin gallate (EGCG) is the most abundant polyphenol in tea. Owing to the different fermentation degrees, differences in polyphenol composition of water extracts of green tea, white tea, oolong tea, and black tea occur, and affect health value. This study revealed that the content of EGCG decreases with the increase in the degree of fermentation. In tea with a high fermentation degree, EGCG was stably present in the form of ammoniation to yield nitrogen-containing EGCG derivative (N-EGCG). The content of N-EGCG in tea was negatively correlated with the content of EGCG. Furthermore, the content of l-serine and L-threonine in tea was positively and negatively correlated with N-EGCG and EGCG levels, respectively, suggesting that they may participate in the formation of N-EGCG as nitrogen sources. This study proposes a new fermentation-induced polyphenol-amino acid synergistic mechanism, which provides a theoretical basis for the study of the biotransformation reaction mechanism of tea polyphenols.

Artículos y libros

Yuxuan Zhao mail , Jingyimei Liang mail , Wanning Ma mail , Mohamed A. Farag mail , Chunlin Li mail , Jianbo Xiao mail ,

Zhao

<a class="ep_document_link" href="/17849/1/1-s2.0-S2590005625001043-main.pdf"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>

en

open

Ultra Wideband radar-based gait analysis for gender classification using artificial intelligence

Gender classification plays a vital role in various applications, particularly in security and healthcare. While several biometric methods such as facial recognition, voice analysis, activity monitoring, and gait recognition are commonly used, their accuracy and reliability often suffer due to challenges like body part occlusion, high computational costs, and recognition errors. This study investigates gender classification using gait data captured by Ultra-Wideband radar, offering a non-intrusive and occlusion-resilient alternative to traditional biometric methods. A dataset comprising 163 participants was collected, and the radar signals underwent preprocessing, including clutter suppression and peak detection, to isolate meaningful gait cycles. Spectral features extracted from these cycles were transformed using a novel integration of Feedforward Artificial Neural Networks and Random Forests , enhancing discriminative power. Among the models evaluated, the Random Forest classifier demonstrated superior performance, achieving 94.68% accuracy and a cross-validation score of 0.93. The study highlights the effectiveness of Ultra-wideband radar and the proposed transformation framework in advancing robust gender classification.

Artículos y libros

Adil Ali Saleem mail , Hafeez Ur Rehman Siddiqui mail , Muhammad Amjad Raza mail , Sandra Dudley mail , Julio César Martínez Espinosa mail ulio.martinez@unini.edu.mx, Luis Alonso Dzul López mail luis.dzul@uneatlantico.es, Isabel de la Torre Díez mail ,

Saleem

<a href="/17856/1/fpubh-13-1654645.pdf" class="ep_document_link"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>

en

open

Children's and adolescents' lifestyle factors associated with physical activity in five Mediterranean countries: the DELICIOUS project

Background: Physical activity in children and adolescents represents one of the most important lifestyle factors to determine current and future health. Aim: The aim of the study is to assess the lifestyle and dietary factors linked to physical activity in younger populations across five countries in the Mediterranean region. Design: A total of 2,011 parents of children and adolescents (age range 6–17 years) participating to a preliminary survey of the DELICIOUS project were investigated to determine children's adequate physical activity level (identified using the short form of the international physical activity questionnaire) as well as diet quality parameters [measured as Youth-Healthy Eating Index (Y-HEI)] and eating and lifestyle factors (i.e., meal habits, sleep duration, screen time, etc.). Logistic regression analyses were performed to assess the odds ratios (ORs) and 95% confidence intervals (CIs) for the associations between variables of interest. Results: Younger children of younger parents currently working had higher rates and probability to have adequate physical activity. Multivariate analysis showed that children and adolescents who had breakfast (OR = 1.88, 95% CI: 1.38, 2.56) and often ate with their family (OR = 1.80, 95% CI: 0.90, 3.61) were more likely to have an adequate level of physical activity. Children and adolescents who reported a sleep duration (8–10 h) closest to the recommended one were significantly more likely to achieve adequate levels of physical activity (OR = 1.88, 95% CI: 1.38, 2.56). Conversely, those with more than 4 h of daily screen time were less likely to engage in adequate physical activity (OR = 0.77, 95% CI: 0.54, 1.10). Furthermore, children and adolescents in the highest tertile of YEHI scores showed a 60% greater likelihood of engaging in adequate physical activity (OR = 1.60, 95% CI: 1.27, 2.01). Conclusion: These results emphasize the importance of promoting healthy diet and lifestyle habits, including structured and high quality shared meals, sufficient sleep, and screen time moderation, as key strategies to support active behaviors in younger populations. Future interventions should focus on reinforcing these behaviors through parental guidance and community-based initiatives to foster lifelong healthy habits.

Artículos y libros

Alice Rosi mail , Francesca Scazzina mail , Maria Antonieta Touriz Bonifaz mail , Francesca Giampieri mail francesca.giampieri@uneatlantico.es, Achraf Ammar mail , Khaled Trabelsi mail , Osama Abdelkarim mail , Mohamed Aly mail , Evelyn Frias-Toral mail , Juancho Pons mail , Laura Vázquez-Araújo mail , Josep Alemany Iturriaga mail josep.alemany@uneatlantico.es, Lorenzo Monasta mail , Nunzia Decembrino mail , Ana Mata mail , Adrián Chacón mail , Pablo Busó mail , Giuseppe Grosso mail ,

Rosi