TY - JOUR
T1 - Rendezvous
T2 - Opportunistic Data Delivery to Mobile Users by UAVs through Target Trajectory Prediction
AU - Yoon, Jin Yi
AU - Lee, A. Hyun
AU - Lee, Hyung June
N1 - Funding Information:
Manuscript received April 5, 2019; revised October 11, 2019; accepted December 10, 2019. Date of publication December 27, 2019; date of current version February 12, 2020. This work was supported by Samsung Research Funding Center of Samsung Electronics under Project SRFC-IT1803-00. The review of this article was coordinated by Dr. F. Tang. (Corresponding author: HyungJune Lee.) J. Yoon and H. Lee are with the Department of Computer Science and Engineering, Ewha Womans University, Seoul, Republic of Korea (e-mail: yjin3012@gmail.com; hyungjune.lee@ewha.ac.kr).
Publisher Copyright:
© 1967-2012 IEEE.
PY - 2020/2
Y1 - 2020/2
N2 - Reliable and timely delivery of data to mobile targets is a challenging problem in mobile ad-hoc networks, because of the opportunistic and unpredictable nature of the problem. Finding a complete series of mobile-to-mobile contacts and forwarding data toward targets within a designated deadline is even more difficult. We leverage unmanned aerial vehicles (UAVs) as message ferries to travel over highly probable rendezvous points for over-the-air delivery to target users. We propose Rendezvous, an opportunistic yet disciplined data delivery scheme based on trajectory prediction of users and UAV path planning. During the offline learning phase, we identify both temporal and spatial regularities of mobile users from real-world trajectories using sequence-wise clustering, and construct a compact yet well-summarized cluster signature that enables efficient search. In the data delivery phase, given recent movement history of users, UAVs perform their own distributed path planning collaboratively over time. Each UAV finds a chronological sequence of future visiting points where it can make consecutive data delivery efforts to users to overcome the uncertainty in mobility and ensure timely delivery. Real-world trace-driven simulation experiments demonstrate that Rendezvous achieves reliable and punctual data delivery using only few UAVs compared with existing algorithms.
AB - Reliable and timely delivery of data to mobile targets is a challenging problem in mobile ad-hoc networks, because of the opportunistic and unpredictable nature of the problem. Finding a complete series of mobile-to-mobile contacts and forwarding data toward targets within a designated deadline is even more difficult. We leverage unmanned aerial vehicles (UAVs) as message ferries to travel over highly probable rendezvous points for over-the-air delivery to target users. We propose Rendezvous, an opportunistic yet disciplined data delivery scheme based on trajectory prediction of users and UAV path planning. During the offline learning phase, we identify both temporal and spatial regularities of mobile users from real-world trajectories using sequence-wise clustering, and construct a compact yet well-summarized cluster signature that enables efficient search. In the data delivery phase, given recent movement history of users, UAVs perform their own distributed path planning collaboratively over time. Each UAV finds a chronological sequence of future visiting points where it can make consecutive data delivery efforts to users to overcome the uncertainty in mobility and ensure timely delivery. Real-world trace-driven simulation experiments demonstrate that Rendezvous achieves reliable and punctual data delivery using only few UAVs compared with existing algorithms.
KW - Opportunistic data delivery
KW - mobile Ad-Hoc Network (MANET)
KW - path planning
KW - trajectory prediction
KW - unmanned aerial vehicles (UAVs)
UR - http://www.scopus.com/inward/record.url?scp=85079770223&partnerID=8YFLogxK
U2 - 10.1109/TVT.2019.2962391
DO - 10.1109/TVT.2019.2962391
M3 - Article
AN - SCOPUS:85079770223
SN - 0018-9545
VL - 69
SP - 2230
EP - 2245
JO - IEEE Transactions on Vehicular Technology
JF - IEEE Transactions on Vehicular Technology
IS - 2
M1 - 8944158
ER -