Reinforced Edge Selection using Deep Learning for Robust Surveillance in Unmanned Aerial Vehicles

Soohyun Park; Jeman Park; David Mohaisen; Joongheon Kim

doi:10.1109/ICTC49870.2020.9289219

Reinforced Edge Selection using Deep Learning for Robust Surveillance in Unmanned Aerial Vehicles

Soohyun Park, Jeman Park, David Mohaisen, Joongheon Kim

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

In this paper, we propose a novel deep Q-network (DQN)-based edge selection algorithm designed specifically for real-time surveillance in unmanned aerial vehicle (UAV) networks. The proposed algorithm is designed under the consideration of delay, energy, and overflow as optimizations to ensure real-time properties while striking a balance for other environment-related parameters. The merit of the proposed algorithm is verified via simulation-based performance evaluation.

Original language	English
Title of host publication	ICTC 2020 - 11th International Conference on ICT Convergence
Subtitle of host publication	Data, Network, and AI in the Age of Untact
Publisher	IEEE Computer Society
Pages	96-99
Number of pages	4
ISBN (Electronic)	9781728167589
DOIs	https://doi.org/10.1109/ICTC49870.2020.9289219
Publication status	Published - 21 Oct 2020
Event	11th International Conference on Information and Communication Technology Convergence, ICTC 2020 - Jeju Island, Korea, Republic of Duration: 21 Oct 2020 → 23 Oct 2020

Publication series

Name	International Conference on ICT Convergence
Volume	2020-October
ISSN (Print)	2162-1233
ISSN (Electronic)	2162-1241

Conference

Conference	11th International Conference on Information and Communication Technology Convergence, ICTC 2020
Country/Territory	Korea, Republic of
City	Jeju Island
Period	21/10/20 → 23/10/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

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10.1109/ICTC49870.2020.9289219

Cite this

Park, S., Park, J., Mohaisen, D., & Kim, J. (2020). Reinforced Edge Selection using Deep Learning for Robust Surveillance in Unmanned Aerial Vehicles. In ICTC 2020 - 11th International Conference on ICT Convergence: Data, Network, and AI in the Age of Untact (pp. 96-99). Article 9289219 (International Conference on ICT Convergence; Vol. 2020-October). IEEE Computer Society. https://doi.org/10.1109/ICTC49870.2020.9289219

@inproceedings{8685381f6170440993349794f7094938,

title = "Reinforced Edge Selection using Deep Learning for Robust Surveillance in Unmanned Aerial Vehicles",

abstract = "In this paper, we propose a novel deep Q-network (DQN)-based edge selection algorithm designed specifically for real-time surveillance in unmanned aerial vehicle (UAV) networks. The proposed algorithm is designed under the consideration of delay, energy, and overflow as optimizations to ensure real-time properties while striking a balance for other environment-related parameters. The merit of the proposed algorithm is verified via simulation-based performance evaluation.",

author = "Soohyun Park and Jeman Park and David Mohaisen and Joongheon Kim",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 11th International Conference on Information and Communication Technology Convergence, ICTC 2020 ; Conference date: 21-10-2020 Through 23-10-2020",

year = "2020",

month = oct,

day = "21",

doi = "10.1109/ICTC49870.2020.9289219",

language = "English",

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Park, S, Park, J, Mohaisen, D & Kim, J 2020, Reinforced Edge Selection using Deep Learning for Robust Surveillance in Unmanned Aerial Vehicles. in ICTC 2020 - 11th International Conference on ICT Convergence: Data, Network, and AI in the Age of Untact., 9289219, International Conference on ICT Convergence, vol. 2020-October, IEEE Computer Society, pp. 96-99, 11th International Conference on Information and Communication Technology Convergence, ICTC 2020, Jeju Island, Korea, Republic of, 21/10/20. https://doi.org/10.1109/ICTC49870.2020.9289219

Reinforced Edge Selection using Deep Learning for Robust Surveillance in Unmanned Aerial Vehicles. / Park, Soohyun; Park, Jeman; Mohaisen, David et al.
ICTC 2020 - 11th International Conference on ICT Convergence: Data, Network, and AI in the Age of Untact. IEEE Computer Society, 2020. p. 96-99 9289219 (International Conference on ICT Convergence; Vol. 2020-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - In this paper, we propose a novel deep Q-network (DQN)-based edge selection algorithm designed specifically for real-time surveillance in unmanned aerial vehicle (UAV) networks. The proposed algorithm is designed under the consideration of delay, energy, and overflow as optimizations to ensure real-time properties while striking a balance for other environment-related parameters. The merit of the proposed algorithm is verified via simulation-based performance evaluation.

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BT - ICTC 2020 - 11th International Conference on ICT Convergence

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Y2 - 21 October 2020 through 23 October 2020

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Park S, Park J, Mohaisen D, Kim J. Reinforced Edge Selection using Deep Learning for Robust Surveillance in Unmanned Aerial Vehicles. In ICTC 2020 - 11th International Conference on ICT Convergence: Data, Network, and AI in the Age of Untact. IEEE Computer Society. 2020. p. 96-99. 9289219. (International Conference on ICT Convergence). doi: 10.1109/ICTC49870.2020.9289219

Reinforced Edge Selection using Deep Learning for Robust Surveillance in Unmanned Aerial Vehicles

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