Secure Coordination for Vertiport Sequencing in Advanced Air Mobility

TL;DR

This paper addresses secure and robust sequencing mechanisms for vertiport operations in advanced air mobility, considering potential misreporting and malicious spoofing under sensing uncertainties.

Contribution

It introduces a robust sequencing framework that integrates surveillance data with self-reported information, accounting for strategic misreporting and adversarial attacks.

Findings

Formulates sequencing as a robust optimization problem under uncertainty.

Models misreporting as strategic deviation and spoofing as adversarial disturbance.

Proposes robust sequencing rules to improve security and reliability.

Abstract

Advanced air mobility operations will require reliable coordination mechanisms for managing dense traffic near vertiports. However, sequencing decisions may become vulnerable when they rely on potentially falsified self-reported information such as estimated time of arrival. Self-interested vehicles may misreport their arrival times to obtain favorable landing priority, while malicious actors may spoof information to disrupt sequencing decisions or induce unnecessary congestion. This paper studies secure coordination for vertiport sequencing under sensing uncertainty. We consider a coordinator that combines self-reported Remote-ID information with externally obtained surveillance measurements to check reports and assign separation-feasible arrival schedules. Since surveillance-based estimates are uncertain, falsified reports may remain consistent with the sensing uncertainty region and cannot always be rejected outright. We therefore formulate sequencing as a robust design problem over this uncertainty region. Self-interested misreporting is modeled as a strategic deviation that improves the reporting vehicle's own sequencing outcome, whereas malicious spoofing is modeled as an adversarial disturbance that degrades the system-level objective. The final paper will develop robust sequencing rules over surveillance-consistent uncertainty sets and evaluate their performance in representative vertiport sequencing scenarios.