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Efficient Remote Identification for Drone Swarms

Kang-Moon Seo1, Jane Kim1, Soojin Lee1, Jun-Woo Kwon1, Seung-Hyun Seo1,2,*

1 The Department of Electronic & Electrical Engineering, Graduate School, Hanyang University, Ansan, Korea
2 School of Electrical Engineering, Hanyang University ERICA, Ansan, Korea

* Corresponding Author: Seung-Hyun Seo. Email: email

(This article belongs to this Special Issue: Advances in Information Security Application)

Computers, Materials & Continua 2023, 76(3), 2937-2958. https://doi.org/10.32604/cmc.2023.039459

Abstract

With the advancement of unmanned aerial vehicle (UAV) technology, the market for drones and the cooperation of many drones are expanding. Drone swarms move together in multiple regions to perform their tasks. A Ground Control Server (GCS) located in each region identifies drone swarm members to prevent unauthorized drones from trespassing. Studies on drone identification have been actively conducted, but existing studies did not consider multiple drone identification environments. Thus, developing a secure and effective identification mechanism for drone swarms is necessary. We suggested a novel approach for the remote identification of drone swarms. For an efficient identification process between the drone swarm and the GCS, each Reader drone in the region collects the identification information of the drone swarm and submits it to the GCS for verification. The proposed identification protocol reduces the verification time for a drone swarm by utilizing batch verification to verify numerous drones in a drone swarm simultaneously. To prove the security and correctness of the proposed protocol, we conducted a formal security verification using ProVerif, an automatic cryptographic protocol verifier. We also implemented a non-flying drone swarm prototype using multiple Raspberry Pis to evaluate the proposed protocol’s computational overhead and effectiveness. We showed simulation results regarding various drone simulation scenarios.

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Cite This Article

K. Seo, J. Kim, S. Lee, J. Kwon and S. Seo, "Efficient remote identification for drone swarms," Computers, Materials & Continua, vol. 76, no.3, pp. 2937–2958, 2023.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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