# Fault Detection in Wireless Sensor Networks through the Random Forest Classifier
## 2 Related Work
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Techniques** & **Contributions** & **Limitations** & **Future Work** \\ \hline SVM and Statistical Learning Theory [1] & Fault detection in WSNs & Upcoming faults are not identified, predicted, and quantified; it does not consider single-hop indoor datasets & Forecasting of upcoming faults \\ \hline OCSVM and SCD [3] & Anomaly detection in WSNs & No proposed scheme for anomaly & Multi-class, one-class classification \\  & & identification mechanism & problems for efficient anomaly detection \\ \hline LNSM and PSO-ANN [4] & Measured the RSSI in real environments, determined the distance between sensor and anchor noises, WSNs, and improved the accuracy of estimated distance & For outdoor environments, the effect of anchor noise & Algorithms can be evaluated through more metrics such as MAPE and MAE \\ \hline SVR and Neighbor Coordination [5] & Fault detection for WSNs & The proposed algorithm is specific to meteorological elements, and there is no comparison with any other classifier & Comparisons with more than two classifiers for validation \\ \hline CNN [9] & Image classification and object detection & No mechanism for fault identification & Fault identification classifiers can be incorporated with CNN \\ \hline RF and XGBout [10] & To detect fault in wind turbines & The number of features is limited to 10 & More comprehensive training data in multiple wind turbine working conditions, including all the unconsidered faults \\ \hline SVM and TA [11] & Improvised sensor fault diagnosis & Single type of ICO coding matrix in both feature extraction and fault classification & Parameter optimization processes \\ \hline RF and SVR [12] & Anomaly detection in energy & No comparison with other hybrid classifiers that are & More robust voting techniques should be trained on different datasets and features & explored such as weighted voting \\ \hline Stochastic systems [13] & Distributed soft fault detection in WSNs & Limitation of the network-induced delay and event-triggered mechanism & Non-linear systems with complex and limited communication \\ \hline PNN [14] & Heterogeneous fault diagnosis & No variation in the classifier & Further, this methodology can be applied on body as an sensor networks, vehicular ad-hoc networks, and UWSN \\ \hline SVM and Statistical Time-Domain Features & Sensor fault classification & The scheme is implemented on only five faults & More faults should be considered \\ \hline MMSE, Multiple Hypothesis Test, and GLR & Wireless sensor fault detection, & A large number of time-synchronized samples are required for MMSE identification & Future work in energy efficiency of WSNs \\ Test [16] & Identification, and quantification & & \\ \hline DNN and Conventional RF [17] & Fault detection in a direct drive & No evaluation through comparisons & Comparison of DNN with other neural network algorithms \\ \hline GA [18] & Fault detection in cluster head and cluster members & Limited use of the optimization technique & For better evaluation, multiple optimization techniques can be used to validate the scheme \\ \hline LSVM [31] & A data-driven framework for reliable link classification of WSNs & Net suitable for unsupervised data & The technique can be enhanced for anomaly identification in link failure \\ \hline SVM, GA, and Tukey Test [23] & To adjust the transmission rate in WSNs & No scheme for identifying and directing anomalies in the transmission or congestion rate & Multi-classification to detect faulty nodes \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of related work.