Abstract—IP address auto reconfiguration, which ensures the optimum routing, is individual of the most challenging challenges in Mobile Ad-hoc Networks (MANET). IP address reconfiguration protocols are divided into two categories: stateful and stateless. Addresses must be unique, and conflicts between addresses must be avoided. This paper offers the Hunger Games Search Improved Extreme Learning Machine (HGS-IELM) Method framework for IP address auto reconfiguration in MANET, which is based on the Hunger Games Search algorithm and the Improved Extreme Learning Machine. The HGS-IELM voting enforces ensuring a fresh read depending on each access. Both data consistency and message overhead are engineered to work together. The suggested HGS-IELM approach is scalable and does not need the use of a central server. According to the results of the experiments, the proposed HGS-IELM framework achieved decreased message overhead and latency. The suggested HGS-IELM approach exhibited enhanced address availability while maintaining appropriate redundancy.
 

Keywords—IP address, auto reconfiguration, cluster head, address borrowing, hunger games search algorithm, and improved extreme learning machine


Cite: Amit Gupta*, Movva Pavani, Shashi Kant Dargar3, Abha Dargar, and Arun Singh Chohan, “Improved Extreme Learning Machine Based Hunger Games Search for Automatic IP Configuration and Duplicate Node Detection," Journal of Communications, vol. 19, no. 3, pp. 152-160, 2024.


Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.