The development of wireless sensor networks (WSNs) has given rise to smart systems that can be found in almost every industry, ranging from healthcare to transportation. Smart systems enable many organizations to execute their businesses efficiently and effectively in a timely manner. The high number of smart systems that exist in today’s society has been made possible by advancements being made in embedded systems and wireless networking. Since data is constantly being collected and analyzed in a WSN application, it’s important to ensure that the data being processed is precise, valid, consistent, and accurate. Therefore, in this survey, we are going to be examining the different types of anomalies that exist in WSNs, specifically focusing on data anomalies. In addition, we will also be discussing the different anomaly detection methods and further explore two specific anomaly detection methods, statistical-based and cluster-based, by examining two different implementations. The two different implementations used the temperature data of Laverton, VIC, Australia [14] and produced the same results, verifying the accuracy of the two anomaly detection methods.