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

TL;DR

This paper proposes a novel distributed intelligent routing method for IoT networks using Federated Deep Reinforcement Learning, enhancing performance and energy efficiency in softwarized IoT environments.

Contribution

It introduces a combined approach of distributed controller design and intelligent routing using FDRL to optimize IoT network performance and energy consumption.

Findings

FDRL outperforms traditional routing methods in simulations.

Distributed control improves scalability and energy efficiency.

Proposed method enhances IoT network performance.

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.