Abstract
Due to their deployment in harsh situations with hundreds to thousands of nodes, wireless sensor networks, or WSNs, have proven vital in a variety of applications, from military monitoring to healthcare. But these networks have a lot of security issues, such outlier identification, which makes it difficult to find nodes that behave abnormally. With an emphasis on the benefits of Bayesian Networks in precisely identifying outlier nodes and calculating missing data values, this study explores machine learning-based outlier identification techniques. Furthermore, a viable answer in face of security issues is the proposal of the Bayesian classification method for assessing the conditional reliance of nodes in WSNs. This survey article also examines several machine learning algorithms used for outlier detection in WSN data, highlighting requirement for accurate anomaly identification without sacrificing data quality in the face of issues like energy efficiency and data integrity. The goal of the study is to give an overview of methods that provide better performance while using less network resources, with a focus on offline and online detection modes. Using expertise obtained from the development of Internet of Things (IoT) systems, this paper underlines significance of anomaly detection in WSNs for industries such as industry, healthcare, and agriculture. It explores MLA-based methods, showing how well they work to precisely detect abnormalities and improve data integrity in Internet of Things applications.