TY - JOUR
T1 - A Dynamic Evolution Mechanism for IoV Community in an Urban Scene
AU - Cheng, Jiujun
AU - Cao, Chunrong
AU - Zhou, Mengchu
AU - Liu, Cong
AU - Gao, Shangce
AU - Jiang, Changjun
N1 - Funding Information:
Manuscript received August 27, 2020; revised October 29, 2020; accepted November 12, 2020. Date of publication November 23, 2020; date of current version April 23, 2021. This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFB2100801; in part by the Ministry of Science and Higher Education of the Russian Federation as part of World-Class Research Center Program: Advanced Digital Technologies under Contract 075-15-2020-903; in part by NSFC under Grant 61872271 and Grant 61902222; in part by the Fundamental Research Funds for the Central Universities under Grant 22120190208; and in part by the Open Foundation of State Key Laboratory of Networking and Switching Technology (Beijing University of Posts and Telecommunications) under Grant SKLNST-2020-1-20. (Corresponding authors: MengChu Zhou; Cong Liu; Shangce Gao.) Jiujun Cheng, Chunrong Cao, and Changjun Jiang are with the Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai 200092, China (e-mail: chengjj@tongji.edu.cn; 1930765@tongji.edu.cn; cjjiang@tongji.edu.cn).
Publisher Copyright:
© 2014 IEEE.
PY - 2021/5/1
Y1 - 2021/5/1
N2 - Existing work on the Internet-of-Vehicles (IoV) community mainly focuses on the detection of IoV community using static network detection and evolution methods of complex networks. These methods are prone to over-centralization, high computational complexity, and poor stability during the evolution of a community. In this work, we present an IoV community model and its evolution mechanism in an urban scene. More specifically, we first propose an IoV community detection model based on node similarity merging. Then, we use a network increment-based strategy to analyze node increment, edge increment, and weight increment. Finally, we give a dynamic evolution mechanism of an IoV community. Simulation-based experimental evaluation results show that the proposed mechanism achieves better real-time performance and accuracy than existing methods.
AB - Existing work on the Internet-of-Vehicles (IoV) community mainly focuses on the detection of IoV community using static network detection and evolution methods of complex networks. These methods are prone to over-centralization, high computational complexity, and poor stability during the evolution of a community. In this work, we present an IoV community model and its evolution mechanism in an urban scene. More specifically, we first propose an IoV community detection model based on node similarity merging. Then, we use a network increment-based strategy to analyze node increment, edge increment, and weight increment. Finally, we give a dynamic evolution mechanism of an IoV community. Simulation-based experimental evaluation results show that the proposed mechanism achieves better real-time performance and accuracy than existing methods.
KW - Community model
KW - Internet of Vehicles (IoV)
KW - dynamic evolution
KW - network incremental-based
KW - urban scene
UR - http://www.scopus.com/inward/record.url?scp=85097143775&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85097143775&partnerID=8YFLogxK
U2 - 10.1109/JIOT.2020.3039775
DO - 10.1109/JIOT.2020.3039775
M3 - Article
AN - SCOPUS:85097143775
SN - 2327-4662
VL - 8
SP - 7521
EP - 7530
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
IS - 9
M1 - 9266057
ER -