Machine Learning based Intelligent Cognitive Network using Fog Computing

TL;DR

This paper proposes an AI-driven cognitive radio network utilizing fog computing and machine learning to enhance spectrum management, improve adaptability, and increase security by processing data locally and efficiently updating strategies.

Contribution

It introduces a novel fog computing framework with adaptive machine learning techniques for spectrum allocation in cognitive radio networks, improving efficiency and security.

Findings

Enhanced spectrum efficiency through local data analysis.

Increased system robustness to spectrum changes.

Reduced communication overhead and improved security.

Abstract

In this paper, a Cognitive Radio Network (CRN) based on artificial intelligence is proposed to distribute the limited radio spectrum resources more efficiently. The CRN framework can analyze the time-sensitive signal data close to the signal source using fog computing with different types of machine learning techniques. Depending on the computational capabilities of the fog nodes, different features and machine learning techniques are chosen to optimize spectrum allocation. Also, the computing nodes send the periodic signal summary which is much smaller than the original signal to the cloud so that the overall system spectrum source allocation strategies are dynamically updated. Applying fog computing, the system is more adaptive to the local environment and robust to spectrum changes. As most of the signal data is processed at the fog level, it further strengthens the system security by reducing the communication burden of the communications network.