Work

Routing Optimization Based on Distributed Intelligent Network Softwarization for the Internet of Things

Year: 2024

Type: conference-paper

Abstract: The Internet of Things (IoT) establishes connectivity between billions of heterogeneous devices that provide a variety of essential everyday services. The IoT faces several challenges, including energy efficiency and scalability, that require consideration of enabling technologies such as network softwarization. This technology is an appropriate solution for IoT, leveraging Software Defined Networking (SDN) and Network Function Virtualization (NFV) as two main techniques, especially when combined with Machine Learning (ML). Although many efforts have been made to optimize routing in softwarized IoT, the existing solutions do not take advantage of distributed intelligence. In this paper, we propose to optimize routing in softwarized IoT networks using Federated Deep Reinforcement Learning (FDRL), where distributed network softwarization and intelligence (i.e., FDRL) join forces to improve routing in constrained IoT networks. Our proposal introduces the combination of two novelties (i.e., distributed controller design and intelligent routing) to meet the IoT requirements (mainly performance and energy efficiency). The simulation results confirm the effectiveness of our proposal compared to the conventional counterparts.

Source: ACM/SIGAPP Symposium on Applied Computing (SAC)

DOI: 10.1145/3605098.3635995

Authors: Mohamed Ali Zormati, Hicham Lakhlef, Sofiane Ouni

Institutions: Université de Technologie de Compiègne, University of Carthage, Heuristics and Diagnostics for Complex Systems, National Institute of Applied Science and Technology

Language: English

Topic: Software-Defined Networks and 5G

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: green

OpenAlex ID: W4398186345

FWCI: 4.155

Cites: 12

Cited by: 9

Related to: 10