Paper Info
Title
An Effective Edge-Intelligent Service Placement Technology for 5G-and-Beyond Industrial IoT
Abstract
With the rapid development of wireless communication, traditional cloud computing cannot fully support low-latency services, especially in sensor networks. Mobile edge computing (MEC) can improve the quality of experience of end users and save the energy consumption of mobile end devices by providing computing resources and storage space. However, it may cause discontinuity of services if these mobile end devices roam around different MEC servers’ areas. To solve the aforementioned problem, in this article, we propose an effective edge-intelligent service placement algorithm (EISPA), which transforms the service placement problem into finding a globally optimal solution via nature-inspired particle swarm optimization (PSO). Moreover, we use a shrinkage factor and combine it with the simulated annealing (SA) algorithm to adjust the position of particles in our algorithm, which aims to avoid falling into an optimal local solution to a certain extent. Performance analysis results show that the EISPA is approaching the optimal enumeration collaborative computation offloading algorithm, and system cost under energy constraints is 83.6%, 20.4%, and 20.3% lower than that in Only Local, Finding the Nearest Edge, and the genetic SA-based PSO algorithms, respectively, which proves that the EISPA has better performance.
Year
DOI
Venue
2022
10.1109/TII.2021.3114300
IEEE Transactions on Industrial Informatics
Keywords
DocType
Volume
Edge-intelligent service,sensor networks,service placement,5G-and-beyond industrial Internet of Things (IIoT)
Journal
18
Issue
ISSN
Citations 
6
1551-3203
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Wang Tian11715.16
Yilin Zhang220.69
Naixue Xiong32413194.61
Shaohua Wan438248.34
Shigen Shen500.34
Shuqiang Huang6184.03