Heuristic-Based Genetic Algorithm with Batesian Mimic Features for Secure Energy Efficient QoS Multicast Routing in Manet

Abstract

This study intends to ensure that data is delivered reliably from the source node to the destination node. By implementing the multicast routing protocol, the MANET network’s reliability may be improved significantly.  Evaluation of multicast routing for quality of service (QoS) is the primary goal of this study.  In multicasting, data packets from one node are transmitted to a set of receiver nodes at a time, simultaneously.  First, the method discovers the list of routes between any source and destination. For each mobile node, the trustworthiness is verified by considering the location, mobility speed, energy, several transmissions involved, and their neighbour list and so on.  In this method, a scheme strives to predict a stable route by selecting the most stable neighbour relative to the transmitter of the route request message with secure route support and nodes impending during the route discovery process. This minimizes the contention phase, to predict the route lifetime and the transmission time of the data packet to reduce lost data packets and route error messages. The optimal path selection algorithm is used to find the path between sources and destinations. This algorithm, suggests the methodology based on the maximum energy of the node and minimum hops between the nodes. To improve the lifetime of the networks, the research introduced Taylor Kernel Fuzzy C-means clustering and Weighted Clustering Algorithm (WCA) to elect a node by having weighted probabilities. To solve the energy efficiency problem with optimal multicast routing, the research proposed QoS Aware routing of an improved Heuristic-based Genetic algorithm with Mimicking Batesian features (HG-MBF) to improve packet transmission. Further, to tackle the security attack problem, the research proposed the Gradient Deep Belief Classifier (GDBC) to detect multiple attacks in the network. From the result and discussion, the proposed method increases the attack detection rate by 82% as compared to existing works. The memory consumption and computational time of the proposed method are reduced when compared to existing methods.