Resilience of airborne networks
Hamed Ahmadi, Gianluca Fontanesi, Konstantinos Katzis, Muhammad, Zeeshan Shakir, Anding Zhu
TL;DR
This paper explores the resilience of airborne networks, focusing on how their unique features affect reliability and how machine learning and blockchain can enhance their robustness.
Contribution
It introduces the specific features of airborne networks influencing resilience and discusses innovative approaches using machine learning and blockchain technologies.
Findings
Airborne networks have unique features impacting their resilience.
Machine learning can predict and improve network robustness.
Blockchain can enhance security and trust in airborne networks.
Abstract
Networked flying platforms can be used to provide cellular coverage and capacity. Given that 5G and beyond networks are expected to be always available and highly reliable, resilience and reliability of these networks must be investigated. This paper introduces the specific features of airborne networks that influence their resilience. We then discuss how machine learning and blockchain technologies can enhance the resilience of networked flying platforms.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSoftware-Defined Networks and 5G · Vehicular Ad Hoc Networks (VANETs) · Smart Grid Security and Resilience