文献类型：期刊文献

英文题名：An Effective Edge-Intelligent Service Placement Technology for 5G-and-Beyond Industrial IoT

作者：Wang, Tian[1,2];Zhang, Yilin[3];Xiong, Neal N.[4];Wan, Shaohua[5];Shen, Shigen[6];Huang, Shuqiang[7]

机构：[1]Beijing Normal Univ Zhuhai, BNU UIC Inst Artificial Intelligence & Future Net, Zhuhai 519085, Peoples R China;[2]BNU HKBU United Int Coll, Guangdong Key Lab Artificial Intelligence & Multi, Zhuhai 519000, Peoples R China;[3]Huaqiao Univ, Coll Comp Sci & Technol, Xiamen 361000, Peoples R China;[4]Sul Ross State Univ, Dept Comp Sci & Math, Alpine, TX 79830 USA;[5]Zhongnan Univ Econ & Law, Sch Informat & Safety Engn, Wuhan 430073, Peoples R China;[6]Shaoxing Univ, Dept Comp Sci & Engn, Shaoxing 312000, Peoples R China;[7]Jinan Univ, Coll Sci & Engn, Guangzhou 510632, Peoples R China

年份：2022

卷号：18

期号：6

起止页码：4148

外文期刊名：IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS

收录：SCI-EXPANDED(收录号：WOS:000761218600058)、、EI(收录号：20213910954656)、Scopus(收录号：2-s2.0-85115727573)、WOS

基金：This work was supported in part by the Natural Science Foundation of Fujian Province of China under Grant 2020J06023, in part by the National Natural Science Foundation of China under Grant 62172046, Grant 61772233, and Grant 62172438, and in part by the UIC Start Up Research Fund under Grant R72021202. Paper no. TII-21-0409.

语种：英文

外文关键词：Servers; Industrial Internet of Things; Delays; Costs; Task analysis; Heuristic algorithms; Energy consumption; Edge-intelligent service; sensor networks; service placement; 5G-and-beyond industrial Internet of Things (IIoT)

外文摘要：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.