Abstract
In high-speed vehicular ad-hoc networks (VANETs), cooperative data dissemination is an effective solution to amend the limited connection time of communication links between roadside units (RSUs) and vehicles. Existing data dissemination strategies utilize efficient cooperation of vehicle-to-vehicle (V2V) and vehicle- to-infrastructure (V2I) communication links to maintain the data transmissions and thus improve the system performance. Recently, unmanned aerial vehicle (UAV) is widely utilized in communication systems, which has a high probability of line-of-sight (LoS) links with better channel quality and can be dynamically deployed. In this paper, we propose a novel UAV-assisted data dissemination scheduling strategy in VANETs. The recursive least squares (RLS) algorithm is utilized to predict the vehicle mobility with low complexity and high prediction accuracy. To enhance the transmission utilities of the UAVs, we further propose a maximum vehicle coverage (MVC) algorithm to schedule the two- dimensional (2D) movements of the UAVs during the process of data dissemination. Simulations in both urban and highway scenarios verify that the proposed UAV-assisted data dissemination strategy achieves a significant reduction of data dissemination delay and an improvement of system throughput.
| Original language | English (US) |
|---|---|
| Title of host publication | 2018 IEEE International Conference on Communications, ICC 2018 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Print) | 9781538631805 |
| DOIs | |
| State | Published - Jul 27 2018 |
| Externally published | Yes |
| Event | 2018 IEEE International Conference on Communications, ICC 2018 - Kansas City, United States Duration: May 20 2018 → May 24 2018 |
Publication series
| Name | IEEE International Conference on Communications |
|---|---|
| Volume | 2018-May |
| ISSN (Print) | 1550-3607 |
Other
| Other | 2018 IEEE International Conference on Communications, ICC 2018 |
|---|---|
| Country/Territory | United States |
| City | Kansas City |
| Period | 5/20/18 → 5/24/18 |
Bibliographical note
Funding Information:ACKNOWLEDGEMENTS This work was jointly supported by the National Natural Science Foundation of China (Grant No. 61622101 and 61571020), the National Science and Technology Major Project (Grant No. 2018ZX03001031), the open research fund of the State Key Laboratory of Integrated Services Networks (Grant No. ISN18-14), Xidian University, and the National Science Foundation under grant number CNS-1343189.
Publisher Copyright:
© 2018 IEEE.