# A Review of Machine Learning-Based Routing Protocols for Wireless Sensor Network Lifetime +
## 7 Open Issues  
There are still a number of unresolved issues that need more WSN research. Here, we have enumerated a few open research problems related to WSNs that may be resolved with machine learning technique:  
_Localization:_ Creating effective route planning for the beacon nodes within the framework of mobile sensor nodes is crucial. As far as we are aware, mobile anchor nodes do not have access to any specific established path-planning approach. Moreover, ML has the potential to offer effective route-planning methods for all sensor network anchor nodes, leading to improved localization accuracy and reduced power consumption. While the majority of current localization algorithms only consider two-dimensional space, the majority of real-time applications deploy sensor nodes in three-dimensional space. As a result, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**ML.** & **Mobility** & **Environment** & **Complexity** & **Topology** & **Remarks** \\ \hline Regression & Static & Distributed/ Centralized & Moderate & Hybrid & Improved network lifetime \\ \hline k-NN & Static & Distributed & Moderate & Hybrid & Reliable data transmission \\ \hline Decision tree & Static & Distributed & Moderate & Tree & Improved network lifetime \\ \hline ANN & Static & Centralized & High & Hybrid & Improved network lifetime \\ \hline Decision tree & Static & Distributed & High & Tree & Reliable data transmission \\ \hline Bayesian & Static or mobile & Distributed/ Centralized & Moderate & Hybrid & Improved network lifetime \\  & & & Centralized & Moderate & Hybrid & Reliable data transmission \\  & & & Centralized & & Improved time complexity \\  & & & & & Improved accuracy \\ \hline k-means & Static & Distributed & Moderate & Tree & Efficient redundancy \\  & & & & & elimination \\ \hline Hierarchical & Static & Distributed or Centralized & Moderate & Hierarchical & Reduce unnecessary \\  & & & & & transmission \\ \hline PCA & Static & Distributed & Low & & Improved network lifetime \\  & & & Moderate & Hybrid & Efficient drift findings \\  & & & & High & Reduce unnecessary \\  & & & & transmission \\ \hline SVD & Static & Centralized & High & Hybrid & Reduce unnecessary \\  & & & & transmission \\ \hline Genetic Classifier & Static or mobile & Distributed & Moderate & Star & Improved network lifetime \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of ML-based techniques. both stationary and mobile WSNs require the creation of methods for three-dimensional space localization. _Coverage and connectivity:_ Although many new approaches to coverage and connectivity have been suggested, many unanswered questions remain. Another difficult problem is determining the ideal location for sensor nodes and predicting the minimal number of nodes needed to cover a specific area. The majority of sensor node deployments in real-time WSN systems are haphazard. A coverage hole could be caused by this haphazard deployment. This kind of coverage gap in the network is notoriously difficult to detect. Coverage gaps could emerge as a result of the ever-changing nature of the network. Additional study is needed to forecast these kinds of network issues and identify potential solutions. The optimal computing complexity for three-dimensional coverage has thus far remained undiscovered, while the majority of present-day researchers have focused on two-dimensional space. _Anomaly detection:_ One of the most exciting areas of WSN research is anomaly detection, and several researchers have come up with new methods to help with this problem. When something goes wrong with a WSN, it might cause transmission delays, increased communication overhead, or even data misinterpretation by the sensor nodes. While the authors of the aforementioned works did a good job of focusing on anomaly detection, there is still a need for more research on what to do once an anomaly has been detected and how to mitigate its effects.