Vehicle-Assisted Data Delivery in Smart City: A Deep Learning Approach

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 t...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on vehicular technology 2020-11, Vol.69 (11), p.13849-13860
Main Authors: Liu, Wei, Watanabe, Yoshito, Shoji, Yozo
Format: Article
Language:English
Subjects:
Algorithms
Data models
Deep learning
Internet of Things
Machine learning
Neural networks
Sensors
Smart cities
Space vehicles
Trajectory
Vehicle-assisted data delivery
vehicle-to-everything communication
Vehicles
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2020.3028576