Paper Info
Title
6G-Enabled Short-Term Forecasting for Large-Scale Traffic Flow in Massive IoT Based on Time-Aware Locality-Sensitive Hashing
Abstract
With the advent of the Internet of Things (IoT) and the increasing popularity of the intelligent transportation system, a large number of sensing devices are installed on the road for monitoring traffic dynamics in real time. These sensors can collect streaming traffic data distributed across different traffic sites, which constitute the main source of big traffic data. Analyzing and mining such big traffic data in massive IoT can help traffic administrations to make scientific and reasonable traffic scheduling decisions, so as to avoid prospective traffic congestions in the future. However, the above traffic decision making often requires frequent and massive data transmissions between distributed sensors and centralized cloud computing centers, which calls for lightweight data integrations and accurate data analyses based on large-scale traffic data. In view of this challenge, a big data-driven and nonparametric model aided by 6G is proposed in this article to extract similar traffic patterns over time for accurate and efficient short-term traffic flow prediction in massive IoT, which is mainly based on time-aware locality-sensitive hashing (LSH). We design a wide range of experiments based on a real-world big traffic data set to validate the feasibility of our proposal. Experimental reports demonstrate that the prediction accuracy and efficiency of our proposal are increased by 32.6% and 97.3%, respectively, compared with the other two competitive approaches.
Year
DOI
Venue
2021
10.1109/JIOT.2020.3037669
IEEE Internet of Things Journal
Keywords
DocType
Volume
6G,intelligent transportation system (ITS),large-scale traffic management,massive Internet of Things (IoT),short-term traffic forecasting,time-aware locality-sensitive hashing (LSH)
Journal
8
Issue
ISSN
Citations 
7
2327-4662
1
PageRank 
References 
Authors
0.35
0
7
Name
Order
Citations
PageRank
Wang Fan192.47
Min Zhu210.35
Mao-Li Wang3132.52
Mohammad R. Khosravi4267.55
Qiang Ni525927.49
Shui Yu62365208.84
Qi Lianyong78012.65