Journal of Computer and Communication Networks
Research Article
Volume 01, 2025
Environmental and iNet Driven Evolution of Adaptive Agents in Self Organizing Networks
Journal of Computer and Communication Networks
Received On : 05 November 2024
Revised On : 20 December 2024
Accepted On : 06 January 2025
Published On : 02 February 2025
Volume 01, 2025
Pages : 013-021
Abstract
Self-Organizing Networks (SONs) represent a network design, which enables adaptive agents improve network performance through autonomous actions for self-management while reacting to shifting environmental conditions. The iNet mechanism provides the conceptual framework in this research. This research analyzes agent flexibility in SONs through evaluations of the iNet framework that operates with environmental assessment capabilities. The analysis uses simulated models to simulate agent conduct both with and without evolution in order to gauge how shifting network parameters affect performance indicators consisting of throughput, reaction time and load balancing. Both the agents' ability to adapt their behavior policies and the environmental assessment system jointly result in optimized resource allocation and improved management of tasks and better demand responsiveness. Research results prove that incorporating adaptive evolutionary mechanisms produces superior performance in SONs especially when the networks contain heterogeneous elements. The study introduces breakthrough findings about how evolutionary approaches and awareness of the environment help improve distributed network agents' behavior.
Keywords
Adaptive Agents, Self-Organizing Networks, Inet Evolution, Environmental Evaluation, Resource Utilization, Agent Adaptability, Simulation, Load Balancing, Performance Optimization.
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Cite this Article
Prabu Ragavendiran, “Environmental and iNet Driven Evolution of Adaptive Agents in Self Organizing Networks”, Journal of Computer and Communication Networks, pp. 013-021, 02 February 2025, doi: 10.64026/JCCN/2025002.
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© 2025 Prabu Ragavendiran. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.