A cognitive-inspired model for self-organizing networks

TL;DR

This paper introduces a cognitive-inspired computational model for self-organizing networks that reduces resource use and adapts dynamically, improving network topology over randomized methods in large, complex environments.

Contribution

The paper presents a novel algorithm inspired by human cognition that enables dynamic self-organization of networks, outperforming randomized approaches in resource-constrained scenarios.

Findings

Cognitive-inspired algorithm improves network topology.

Outperforms randomized methods in ecological ICT scenarios.

Reduces computational resources needed for network management.

Abstract

In this work we propose a computational scheme inspired by the workings of human cognition. We embed some fundamental aspects of the human cognitive system into this scheme in order to obtain a minimization of computational resources and the evolution of a dynamic knowledge network over time, and apply it to computer networks. Such algorithm is capable of generating suitable strategies to explore huge graphs like the Internet that are too large and too dynamic to be ever perfectly known. The developed algorithm equips each node with a local information about possible hubs which are present in its environment. Such information can be used by a node to change its connections whenever its fitness is not satisfying some given requirements. Eventually, we compare our algorithm with a randomized approach within an ecological scenario for the ICT domain, where a network of nodes carries a certain set of objects, and each node retrieves a subset at a certain time, constrained with limited resources in terms of energy and bandwidth. We show that a cognitive-inspired approach improves the overall networks topology better than a randomized algorithm.