• Medhavi Bhargava

In Mobile Ad hoc Network (MANET) normal routing protocols are not able to handle efficient route selection in terms of low load, higher energy, and higher signal strength. Routing methods are unable to handle these factors. The approach supported by machine learning plays a significant part in the process of anticipating the route selection and contributes to the route's increased reliability. The process of channel sensing is carried out by each and every route selection node, which helps to ensure that the route from the source to the destination is completed. Machine learning uses a decision tree as a model for classification and regression. This results in the formation of a tree structure, with each internal vertex reflecting a feature-based decision. Each branch in the tree represents a choice outcome, while each leaf node represents a final forecast or decision result. The decision tree is able to evaluate if a node is reliable, unreliable, or unreachable based on the distance value that is sent to it when we send the information about the distance. The intensity of the signal is another factor to take into account. In the event that the signal strength of the device is greater than the output of the decision tree, the node is considered reliable; otherwise, it is considered unreliable or inaccessible. We evaluate the developed ESPR technique's performance against OLSR, DSR, AODV, and EAPS. As a result of the machine learning technique that was taken in routing, the performance measurements indicate that ESPR performance is improved.

Energy, Machine Learning, MANET, Routing, ESPR

"Machine Learning based Route Prediction Approach for Reliable Routing in MANET", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.d206-d213, May-2025, Available :http://www.jetir.org/papers/JETIR2505358.pdf

"Machine Learning based Route Prediction Approach for Reliable Routing in MANET", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. ppd206-d213, May-2025, Available at : http://www.jetir.org/papers/JETIR2505358.pdf