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Atipong Suriya. Comparative analysis of machine learning techniques for effective anomaly detection in humidity and temperature data in WSNs. (). King Mongkut's University of Technology North Bangkok. Central Library. : , 2025.
Comparative analysis of machine learning techniques for effective anomaly detection in humidity and temperature data in WSNs
Name: Apiwut Kaewsong
Organization :
Ubon Ratchathani University. Faculty of Engineering
Email :
apiwut.ka.64@ubu.ac.th
Name: Atipong Suriya
Organization :
Ubon Ratchathani University. Faculty of Engineering
Email :
atipong.s@ubu.ac.th
keyword:
Humidity measuring instruments
LCSH:
Wireless sensor networks
LCSH:
Machine learning
LCSH:
Temperature measurements
LCSH:
Environmental monitoring
Abstract:
Wireless Sensor Networks (WSNs) are vital for monitoring environmental parameters like temperature and humidity. Accurate anomaly detection in sensor data is crucial for maintaining data reliability and application efficiency. This research conducts a comparative analysis of five machine learning techniquesSupport Vector Machines (SVM), k-Nearest Neighbors (k-NN), Random Forests (RF), Naive Bayes (NB), and Long Short-Term Memory (LSTM)to evaluate their effectiveness in detecting anomalies in WSN sensor readings. We analyze various performance indicators, encompassing accuracy, precision, recall, F1-score, and processing time, utilizing a labeled dataset from existing literature. Results indicate that while LSTM excels in accuracy with larger training samples, RF and k-NN consistently demonstrate strong performance across metrics. This study highlights the strengths and limitations of each technique, providing insights for the selection of suitable methods in anomaly detection within WSN applications
King Mongkut's University of Technology North Bangkok. Central Library
Address:
BANGKOK
Email:
library@kmutnb.ac.th
Created:
2025
Modified:
2025-06-18
Issued:
2025-06-18
บทความ/Article
application/pdf
BibliograpyCitation :
In Electrical Engineering Academic Association (Thailand) and King Mongkut's University of Technology North Bangkok. Department of Electrical and Computer Engineering. 13th International Electrical Engineering Congress (iEECON 2025) (P06287). Bangkok : Electrical Engineering Academic Association (Thailand), 2025
eng
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0.029768 วินาที
Apiwut Kaewsong
| Title | Contributor | Type |
|---|---|---|
| Comparative analysis of machine learning techniques for effective anomaly detection in humidity and temperature data in WSNs
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Apiwut Kaewsong;Atipong Suriya | บทความ/Article |
Atipong Suriya
| Title | Contributor | Type |
|---|---|---|
| A comparative study of wind speed measurement from meteorological mast and LiDAR
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Bongkoj Sookananta;Prasit Nakonrat;Atipong Suriya;Mongkol Pusayatanont;Sumet Suttapukti;Puettipon Asksonmee | บทความ/Article | |
| Comparative analysis of machine learning techniques for effective anomaly detection in humidity and temperature data in WSNs
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Apiwut Kaewsong;Atipong Suriya | บทความ/Article |