A Novel Gradient Boosted Energy Optimization Model (GBEOM) for MANET

Abstract

Mobile Ad Hoc Network (MANET) is an infrastructure-less network that is comprised of a set of nodes that move randomly. In MANET, the overall performance is improved through multipath multicast routing to achieve the quality of service (quality of service). In this, different nodes are involved in the information data collection and transmission to the destination nodes in the network. The different nodes are combined and presented to achieve energy-efficient data transmission and classification of the nodes. The route identification and routing are established based on the data broadcast by the network nodes. In transmitting the data packet, evaluating the data delivery ratio is necessary to achieve optimal data transmission in the network. Furthermore, energy consumption and overhead are considered essential factors for the effective data transmission rate and better data delivery rate. In this paper, a Gradient-Based Energy Optimization model (GBEOM) for the route in MANET is proposed to achieve an improved data delivery rate. Initially, the Weighted Multi-objective Cluster-based Spider Monkey Load Balancing (WMC-SMLB) technique is utilized for obtaining energy efficiency and load balancing routing. The WMC algorithm is applied to perform an efficient node clustering process from the considered mobile nodes in MANET. Load balancing efficiency is improved with a higher data delivery ratio and minimum routing overhead based on the residual energy and bandwidth estimation. Next, the Gradient Boosted Multinomial ID3 Classification algorithm is applied to improve the performance of multipath multicast routing in MANET with minimal energy consumption and higher load balancing efficiency. The proposed GBEOM exhibits ∼4% improved performance in MANET routing.