Paper Info
Title
Edge Server Quantification and Placement for Offloading Social Media Services in Industrial Cognitive IoV
Abstract
The automotive industry, a key part of industrial Internet of Things, is now converging with cognitive computing (CC) and leading to industrial cognitive Internet of Vehicles (CIoV). As the major data source of industrial CIoV, social media has a significant impact on the quality of service (QoS) of the automotive industry. To provide vehicular social media services with low latency and high reliability, edge computing is adopted to complement cloud computing by offloading CC tasks to the edge of the network. Generally, task offloading is implemented based on the premise that edge servers (ESs) are appropriately quantified and located. However, the quantification of ESs is often offered according to empirical knowledge, lacking analysis on real condition of intelligent transportation system (ITS). To address the abovementioned problem, a collaborative method for the quantification and placement of ESs, named CQP, is developed for social media services in industrial CIoV. Technically, CQP begins with a population initializing strategy by Canopy and K-medoids clustering to estimate the approximate ES quantity. Then, nondominated sorting genetic algorithm III is adopted to achieve solutions with higher QoS. Finally, CQP is evaluated with a real-world ITS social media data set from China.
Year
DOI
Venue
2021
10.1109/TII.2020.2987994
IEEE Transactions on Industrial Informatics
Keywords
DocType
Volume
Edge computing,industrial cognitive Internet of Vehicles (CIoV),multiobjective optimization,server placement
Journal
17
Issue
ISSN
Citations 
4
1551-3203
7
PageRank 
References 
Authors
0.46
0
7
Name
Order
Citations
PageRank
Xu Xiaolong142464.23
Bowen Shen2181.60
Xiaochun Yin3233.71
Mohammad R. Khosravi4267.55
Hua-Ming Wu57913.85
Lianyong Qi656057.12
Shaohua Wan738248.34