Anomaly detection using Machine Learning for temperature/ humidity/ leak detection IoT
Abstract
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.
References
Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015). Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications. IEEE Communications Surveys & Tutorials, 17(4), 2347-2376.
Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys (CSUR), 41(3), 1-58.
Géron, A. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O'Reilly Media, Inc.
Hodge, V. J., & Austin, J. (2004). A survey of outlier detection methodologies. Artificial Intelligence Review, 22(2), 85-126.
Khan, J. Y., Yuce, M. R., & Bulger, G. (2018). Internet of Things (IoT) Wireless Sensor Networks (WSNs): A Survey. IEEE Access, 6, 43019-43034.
Liu, L., Wang, F. Y., & Zhou, X. (2017). A survey of deep neural network architectures and their applications. Neurocomputing, 234, 11-26.
Mekala, P. D., & Vohra, R. (2017). Anomaly Detection in IoT Data Using Deep Learning Techniques: A Survey. In International Conference on Intelligent Data Communication Technologies and Internet of Things (pp. 158-167). Springer.
Rassam, M., Al-Dubai, A., Alzahrani, A., & Radi, N. (2017). A Review of Anomaly Detection Techniques in Financial Domain. Journal of Big Data, 4(1), 30.
Reddy, S., & Reddy, G. M. (2013). A review on machine learning algorithms for anomaly detection. International Journal of Computer Applications, 80(12), 10-17.
Ren, J., Zhang, Y., & Yu, X. (2019). Anomaly Detection for Internet of Things: A Survey, Challenges, and Opportunities. IEEE Internet of Things Journal, 6(5), 8284-8299.
Sutskever, I., Vinyals, O., & Le, Q. V. (2014). Sequence to sequence learning with neural networks. In Advances in Neural Information Processing Systems (pp. 3104-3112).
Tan, P. N., Steinbach, M., & Kumar, V. (2013). Introduction to Data Mining. Pearson Education.
Thakur, N., Sharma, S. K., & Chen, J. (2017). Anomaly Detection in IoT using Machine Learning Techniques: A Review. In 2017 14th IEEE India Council International Conference (INDICON) (pp. 1-5). IEEE.
Tripathi, S., & Kumar, S. (2017). A survey of outlier detection techniques in data mining. IETE Technical Review, 34(5), 443-459.
Varghese, B., & K, B. (2017). A survey on outlier detection methods in cloud data using data mining techniques. Procedia Computer Science, 115, 583-590.
Vats, A., & Singh, R. (2019). Anomaly detection in IoT: Techniques, challenges, and recent trends. Journal of Network and Computer Applications, 131, 60-75.
Verma, A., Kumar, V., & Singh, S. (2019). A review of machine learning based anomaly detection techniques for insider threat detection in cloud computing. Computers & Security, 82, 279-301.
Wang, S., Zhang, C., & Wang, X. (2016). A survey of anomaly detection in Internet of Things. Journal of Network and Computer Applications, 74, 24-36.
Yaseen, M., Ahmed, K., & Shafiq, M. (2020). Anomaly detection in internet of things: Techniques, challenges, and future directions. Journal of Network and Computer Applications, 150, 102495.
Zhang, C., Patras, P., & Haddadi, H. (2017). Deep learning in mobile and wireless networking: A survey. IEEE Communications Surveys & Tutorials, 20(3), 2224-2287.
Bhanushali, A., Singh, K., & Kajal, A. (2024). Enhancing AI Model Reliability and Responsiveness in Image Processing: A Comprehensive Evaluation of Performance Testing Methodologies. International Journal of Intelligent Systems and Applications in Engineering, 12(15s), 489-497.
Singh, K., Bhanushali, A., & Senapati, B. (2024). Utilizing Advanced Artificial Intelligence for Early Detection of Epidemic Outbreaks through Global Data Analysis. International Journal of Intelligent Systems and Applications in Engineering, 12(2), 568-575.
Singh, K. Artificial Intelligence & Cloud in Healthcare: Analyzing Challenges and Solutions Within Regulatory Boundaries.