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arxiv logo>cs> arXiv:2306.16304
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Computer Science > Networking and Internet Architecture

arXiv:2306.16304 (cs)
[Submitted on 28 Jun 2023]

Title:Dual Identities Enabled Low-Latency Visual Networking for UAV Emergency Communication

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Abstract:The Unmanned Aerial Vehicle (UAV) swarm networks will play a crucial role in the B5G/6G network thanks to its appealing features, such as wide coverage and on-demand deployment. Emergency communication (EC) is essential to promptly inform UAVs of potential danger to avoid accidents, whereas the conventional communication-only feedback-based methods, which separate the digital and physical identities (DPI), bring intolerable latency and disturb the unintended receivers. In this paper, we present a novel DPI-Mapping solution to match the identities (IDs) of UAVs from dual domains for visual networking, which is the first solution that enables UAVs to communicate promptly with what they see without the tedious exchange of beacons. The IDs are distinguished dynamically by defining feature similarity, and the asymmetric IDs from different domains are matched via the proposed bio-inspired matching algorithm. We also consider Kalman filtering to combine the IDs and predict the states for accurate mapping. Experiment results show that the DPI-Mapping reduces individual inaccuracy of features and significantly outperforms the conventional broadcast-based and feedback-based methods in EC latency. Furthermore, it also reduces the disturbing messages without sacrificing the hit rate.
Comments:6 pages, 6 figures
Subjects:Networking and Internet Architecture (cs.NI)
Cite as:arXiv:2306.16304 [cs.NI]
 (orarXiv:2306.16304v1 [cs.NI] for this version)
 https://doi.org/10.48550/arXiv.2306.16304
arXiv-issued DOI via DataCite
Journal reference:GLOBECOM 2022 - 2022 IEEE Global Communications Conference, Rio de Janeiro, Brazil, 2022, pp. 474-479
Related DOI:https://doi.org/10.1109/GLOBECOM48099.2022.10001505
DOI(s) linking to related resources

Submission history

From: Yanpeng Cui [view email]
[v1] Wed, 28 Jun 2023 15:27:49 UTC (31,504 KB)
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