Supervised and unsupervised outlier detection machine learning algorithms in wireless sensor networks

Outliers influence the quality and reliability of data obtained from wireless sensor networks (WSNs) for a variety of reasons, including the effects of harsh environments and the limitations of processing and communication capacities. In this paper, we implemented and evaluated ten machine learning techniques (both supervised and unsupervised) in terms of their ability to detect outliers in WSNs. A real-world dataset was used to evaluate the results of our study. Our findings show that the best-performing unsupervised algorithms are comparable to supervised algorithms and can be used in real-world applications. This can be highly useful because unsupervised methods overcome the limitations of supervised machine learning in WSNs by not requiring historical data to build a model to detect abnormalities quickly and accurately. As a result, communication overhead will be reduced, resulting in energy savings and increased network lifetime. In addition, the results of our research show that some algorithms outperform others. This is extremely beneficial because the selection of detection algorithms is essential in improving the performance of outlier detectors.