## 4 Detection Method-Based Classification of Anomaly Detection Models in WSNs
### Classification-Based Anomaly Detection Models for WSNs
The data measurements were first encoded at each node using DWT and then sent to the base station where the SOM was applied on a batch of wavelet coefficients. The sensitivity of the SOM to the possible noise in the training data makes it difficult to differentiate the source of anomaly. Moreover, as this model adopts the centralized structure, high energy consumption is occurred due to high transmission of data to the base station. In addition, as DWT is used to encode data locally, it involves high computation cost. _[PERSON] and [PERSON]_[82]: a combination of DWT and one-class support vector machines (OCSVM) was proposed. In this model, DWT was used for encoding the data measurements at each node like in [81]. The encoded measurements are then examined for anomalies by the OCSVM at the base station. This model has also the same limitations of the model in [81] in terms of high computational cost of DWT in the nodes and the high communication overhead caused by transmitting the whole data to the base station. _[PERSON] et al._[83]: a method for real-time data quality control that utilizes the data spatial and temporal correlations to distinguish faulty sensor observations from valid observations was presented. The adaptability with environmental changes was achieved by using a Bayesian network structure that captures spatial relationships between sensors in the close neighborhood. Furthermore, the temporal correlations were also incorporated by extending the structure to a dynamic Bayesian network. This model was claimed to be able to detect faulty observations and even predict the missing or corrupted readings correctly. A dataset samples from the Sensorscope project were used to evaluate the performance of the model. Experimental results of this model showed that considering spatial and temporal correlations of sensor observations enhanced the result of detecting faulty observations compared to the models that consider either spatial or temporal correlations individually. _[PERSON] et al._[37]: proposed an in-network decentralized approach for event detection in WSNs based on machine learning techniques. This approach adopted decision trees for distributed event detection and a reputation-based voting method for detection results aggregation over the sensor nodes in order to reach a consensus decision. The authors stated that the use of decision tress achieves highly accurate results in terms of detection accuracy with simplicity. Although the time complexity of the machine learning techniques used to design this approach was thoroughly discussed, the communication overhead which is the main player of energy consumption was not investigated. _[PERSON] and [PERSON]_[84] and _[PERSON] et al._[85]: An energy efficient outlier detection approach that utilizes temporal and attribute correlations in WSNs was introduced based on the quarter sphere SVM (QSSVM) technique. It was reported that this approach increased outlier and event detection rates compared to previous models that use the same technique. Although, the communication overhead was reduced by exploiting temporal and attributes correlation, the computational complexity in individual sensors was increased. _[PERSON] et al._[86]: since the communication overhead incurred by the models proposed in [84] and [85] was significantly high, the authors in [86] present three partially online strategies to detect outliers and events based on the STA-QSSVM proposed in [85].The authors claimed that the adoption of the new strategies lead to a significant reduction in communication cost and comparable performance. Furthermore, it was claimed that these strategies are comparable to the STA-QSSVM in the existence of a higher fraction of outliers and events in the data set. _[PERSON] et al._[87]: in this work, various formulations of One-Class SVM such as hyper-plane, hyper-sphere, quarter-sphere and hyper-ellipsoidal were analyzed.