| Title |
Vehicle-Assisted Data Delivery in Smart City: A Deep Learning Approach |
| ID_Doc |
38518 |
| Authors |
Liu, W; Watanabe, Y; Shoji, Y |
| Title |
Vehicle-Assisted Data Delivery in Smart City: A Deep Learning Approach |
| Year |
2020 |
| Published |
Ieee Transactions On Vehicular Technology, 69, 11 |
| DOI |
10.1109/TVT.2020.3028576 |
| Abstract |
Collecting the massive internet of things data produced in a large smart city is quite challenging, and recent advances in vehicle-to-everything communication makes urban vehicles to be a good candidate to conduct this task. Hence, this paper proposes a novel deep learning algorithm called DeepVDD to facilitate vehicle-assisted data delivery. First, a theoretical analysis is presented to quantitatively reveal the correlation between vehicle mobility and the success ratio of vehicle-assisted data delivery. Based on the findings in analysis, DeepVDD adopts a novel multi-headed neural network to determine the strategies for vehicles to deliver data. Comprehensive evaluations have been executed based on the real taxi mobility data in Tokyo, Japan. The results have validated that, compared with other state-of-art algorithms, DeepVDD not only improves the success ratio of data delivery, but also significantly reduces the communication overhead of vehicular networks. |
| Author Keywords |
Sensors; Smart cities; Machine learning; Trajectory; Data models; Space vehicles; Vehicle-assisted data delivery; vehicle-to-everything communication; deep learning |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000589638700112 |
| WoS Category |
Engineering, Electrical & Electronic; Telecommunications; Transportation Science & Technology |
| Research Area |
Engineering; Telecommunications; Transportation |
| PDF |
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