Hybridization of Brownboost and Random Forest Tree with Gradient Free Optimization for Route Selection

Abstract

MANETs are self-organizing network architectures of mobile nodes. Due to node mobility, wireless network topologies dynamically various over time.   A novel link stability estimation technique called Hybridization of Brownboost Cluster and Random Forest Decision Tree with Optimized Route Selection (HBCRFDT-GORS) technique is introduced for increasing the reliable data delivery by eliminating the stale routes in MANET. Brown Boost technique is applied to find the route paths having the smaller number of hop counts to perform the data transmission. After that, the status of the mobile nodes in the selected route paths is determined based on the residual energy and signal strength. Then, a random forest decision tree is applied to correctly identify the stale routes by finding the link failure due to the selfish node and the corruptive node along the route path. Then the broken link is removed from the route path. After eliminating the stale route from the path, the HBCRFDT-GORS technique finds the alternative optimal route through the gradient free optimization.  The proposed HBCRFDT-GORS technique performs stale route elimination and improves reliable data delivery from source to destination. Simulation is conducted on different performance metrics such as routing overhead, packet delivery ratio, packet drop rate, and delay with respect to the number of data packets. The Network simulation results indicate that the HBCRFDT-GORS technique is improving the data delivery and and minimizing the delay as well as reducing the packet losses when compared to the baseline approaches.