Anomaly detection using Machine Learning for temperature/ humidity/ leak detection IoT

Anomaly detection using Machine Learning for temperature/ humidity/ leak detection IoT


  • Harsh Yadav


Anomaly detection plays a pivotal role in ensuring the integrity, reliability, and security of IoT devices, particularly in critical applications such as temperature, humidity, and leak detection systems. This research paper investigates the application of machine learning techniques for anomaly detection in IoT devices deployed for monitoring environmental conditions. We explore the challenges associated with traditional threshold-based methods and propose a data-driven approach leveraging machine learning algorithms for more accurate and adaptive anomaly detection. The study involves collecting real-world sensor data from temperature, humidity, and leak detection IoT devices and developing supervised and unsupervised machine learning models to identify abnormal patterns and anomalies. Various algorithms such as Isolation Forest, One-Class SVM, and Autoencoders are evaluated for their effectiveness in detecting anomalies in sensor data streams. Experimental results demonstrate the superiority of machine learning-based approaches over traditional methods, with improved accuracy, sensitivity, and robustness in detecting anomalous events. The findings of this research contribute to advancing anomaly detection techniques in IoT devices and have significant implications for enhancing the reliability and efficiency of environmental monitoring systems in diverse domains, including smart buildings, industrial facilities, and agriculture.


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How to Cite

Yadav, H. (2024). Anomaly detection using Machine Learning for temperature/ humidity/ leak detection IoT . International Transactions in Artificial Intelligence, 8(8), 1–18. Retrieved from