Year: 2025
Type: article
Abstract: Reducing energy consumption in smart networks such as the Internet of Things (IoT), 5G and software-defined networks (SDN) is a major challenge. With the exponential increase in connected devices, network and energy resources are under severe strain. This paper proposes an innovative architecture, named SDN-Expert, integrating an expert system based on artificial intelligence (AI) with a hybrid inference engine combining decision trees and neural networks. This approach dynamically optimizes energy consumption by turning off unused router ports while maintaining optimal Quality of Service (QoS). Extended simulations performed on realistic large-scale network topologies using real-world traffic data (CAIDA datasets) demonstrate substantial energy savings (up to 32% improvement) and clearly illustrate the practicality and competitiveness of our solution compared to existing energy-efficient techniques in SDNs. Finally, we discuss potential applications of the proposed architecture to wireless sensor networks, highlighting its broader applicability and environmental impact.
Source: Journal of Sensor Technology
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4411500219