Peli-Remo Optimization Algorithm for Reliable routing in MANET: K-means node clustering process and CNN-based Energy Prediction

MANET, an ad hoc wireless network is made up of several nodes connected, governed by certain rules for packet movement from one node point to another. The structure of communication may be designed using a variety of protocols. For an ad hoc network, routing protocols are used to manage the connections. To improve communication, this article suggests an effective MANET cluster-based routing paradigm for multimedia communication. The clustering process is started using the K-means clustering method. The Deep Convolutional Neural Network (DCNN) model will first predict the node energy. The cluster head will be chosen using the Hybrid Remora with Pelicon Optimization technique (HRPOA), which integrates the Remora optimization technique and the Pelican optimization algorithm. The HRPOA model's channel quality for round 500 is around 1.56, whereas the conventional techniques obtained the lowest channel capacity, with BWO=~0.5, GWO=~0.78, BA=~0.98, POA=~0.57, and ROA=~0.78, in that order.