TY - GEN
T1 - Towards Self-Organizing UAV Ad-Hoc Networks Through Collaborative Sensing and Deployment
AU - Batsoyol, Narangerelt
AU - Lee, Hyungjune
N1 - Funding Information:
This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2015R1D1A1A01057902 and NRF-2018R1A2B6004006).
Publisher Copyright:
© 2018 IEEE.
PY - 2018
Y1 - 2018
N2 - In this paper, we consider an aerial ad-hoc network construction problem using UAVs in a disaster scenario. We aim to reconnect the communication-wise isolated urban area with the outside communication infrastructure. Our main goal is to perform both network exploration and relay deployment tasks at the same time by taking a progressive optimization toward a self-organizing network construction. We propose a novel UAV exploration-and-deployment algorithm that gradually explores the region of interests and achieves full network coverage in a fast manner. Then, we present an effective network refinement algorithm based on clustering that minimizes the number of UAVs for deployment by finding out essential UAVs, while keeping the similar network coverage performance. Simulation results demonstrate that our proposed scheme significantly reduces the execution time for network exploration and deployment compared to a baseline counterpart. Also, our cluster-based network refinement algorithm provides a very lightweight yet effective solution, well-balancing between UAV resource and computation overhead.
AB - In this paper, we consider an aerial ad-hoc network construction problem using UAVs in a disaster scenario. We aim to reconnect the communication-wise isolated urban area with the outside communication infrastructure. Our main goal is to perform both network exploration and relay deployment tasks at the same time by taking a progressive optimization toward a self-organizing network construction. We propose a novel UAV exploration-and-deployment algorithm that gradually explores the region of interests and achieves full network coverage in a fast manner. Then, we present an effective network refinement algorithm based on clustering that minimizes the number of UAVs for deployment by finding out essential UAVs, while keeping the similar network coverage performance. Simulation results demonstrate that our proposed scheme significantly reduces the execution time for network exploration and deployment compared to a baseline counterpart. Also, our cluster-based network refinement algorithm provides a very lightweight yet effective solution, well-balancing between UAV resource and computation overhead.
UR - http://www.scopus.com/inward/record.url?scp=85063440561&partnerID=8YFLogxK
U2 - 10.1109/GLOCOM.2018.8647985
DO - 10.1109/GLOCOM.2018.8647985
M3 - Conference contribution
AN - SCOPUS:85063440561
T3 - 2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings
BT - 2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE Global Communications Conference, GLOBECOM 2018
Y2 - 9 December 2018 through 13 December 2018
ER -