Intelligent Transportation Systems' Orchestration: Lessons Learned & Potential Opportunities

TL;DR

This paper reviews the challenges and lessons learned in orchestrating Intelligent Transportation Systems within 5G and 6G networks, highlighting potential data-driven research opportunities like reinforcement and federated learning.

Contribution

It provides a comprehensive analysis of ITS orchestration challenges, lessons from current deployments, and proposes future research directions using advanced machine learning paradigms.

Findings

Identified key challenges in ITS orchestration.

Lessons learned from current deployment efforts.

Proposed research opportunities with reinforcement and federated learning.

Abstract

The growing deployment efforts of 5G networks globally has led to the acceleration of the businesses/services' digital transformation. This growth has led to the need for new communication technologies that will promote this transformation. 6G is being proposed as the set of technologies and architectures that will achieve this target. Among the main use cases that have emerged for 5G networks and will continue to play a pivotal role in 6G networks is that of Intelligent Transportation Systems (ITSs). With all the projected benefits of developing and deploying efficient and effective ITSs comes a group of unique challenges that need to be addressed. One prominent challenge is ITS orchestration due to the various supporting technologies and heterogeneous networks used to offer the desired ITS applications/services. To that end, this paper focuses on the ITS orchestration challenge in detail by highlighting the related previous works from the literature and listing the lessons learned from current ITS deployment orchestration efforts. It also presents multiple potential data-driven research opportunities in which paradigms such as reinforcement learning and federated learning can be deployed to offer effective and efficient ITS orchestration.