Title: MANET routing with deep maxout-based energy prediction using optimisation

Authors: Kingston Albert Dhas Y; S. Jerine

Addresses: Department of Computer Applications, Noorul Islam Centre for Higher Education, Kumaracoil, Thuckalay, Kanyakumari, Tamilnadu-629180, India ' Department of Software Engineering, Noorul Islam Centre for Higher Education, Kumaracoil, Thuckalay, Kanyakumari, Tamilnadu-629180, India

Abstract: MANET is a group of transportable mobile nodes that faces difficulties in maximising energy consumption due to its dynamic nature. The main aim of this work is to develop an efficient MANET routing protocol for adopting the changes in network topology. This work intends to offer a novel deep maxout-based energy prediction for MANET routing with hybrid butterfly and bald eagle search algorithm (DMMR-HBBES). This process is of two phases, energy prediction, cluster head selection and routing. The energy of the nodes is predicted by the deep maxout model. The choice of CH is then made while taking into account factors like energy, distance, and security. The optimal routing is performed based on parameters like link quality, energy, distance, and security. A novel HBBES algorithm is presented to address the previously discussed optimisation problem. In node 200, the DMMR-HBBES model acquired longer link quality of 0.92 than traditional models.

Keywords: MANET; deep maxout; routing; CH selection; HBBES.

DOI: 10.1504/IJBIC.2025.143689

International Journal of Bio-Inspired Computation, 2025 Vol.25 No.1, pp.32 - 42

Received: 16 Jan 2023
Accepted: 29 Feb 2024
Published online: 03 Jan 2025 *

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