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Status已发表Published
TitleArtificial intelligence-empowered path selection: A survey of ant colony optimization for static and mobile sensor networks
Creator
Date Issued2020
Source PublicationIEEE Access
ISSN2169-3536
Volume8Pages:71497-71511
Abstract

Artificial intelligence-empowered path selection plays an important role in wireless sensor networks (WSNs), because it can exceed the cognitive performance of humans and determine multiple aspects of the network performance. Ant colony optimization (ACO) is an effective intelligence algorithm which succeeds in addressing several issues of WSNs, including data transmission, node deployment, etc. There exist several ACO-based transmission strategies for WSNs, but the summary and comparison of such works are very limited. This paper provides a comprehensive overview of ACO-based transmission strategies for static and mobile WSNs. First, we provide a classification of existing ACO-based transmission methods, which distinguishes itself from other works in network types. Second, the highly typical ACO-based transmission strategies for WSNs are illustrated and discussed. Finally, we summarize the paper and present several open issues concerning the design of such networks. This survey contributes to system design guidance and network performance improvement.

Keywordant colony optimization transmission protocol Wireless sensor networks
DOI10.1109/ACCESS.2020.2984329
URLView source
Indexed BySCIE
Language英语English
WOS Research AreaComputer Science ; Engineering ; Telecommunications
WOS SubjectComputer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS IDWOS:000530811900005
Scopus ID2-s2.0-85084192822
Citation statistics
Cited Times:20[WOS]   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
Identifierhttp://repository.uic.edu.cn/handle/39GCC9TT/7070
CollectionResearch outside affiliated institution
Corresponding AuthorYu, Lei
Affiliation
1.School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, 510641, China
2.School of Medical Information Technology, Anhui University of Chinese Medicine, Hefei, 230012, China
3.College of Computer Science and Technology, Huaqiao University, Xiamen, 361021, China
4.School of Information Science and Engineering, Central South University, Changsha, 410083, China
5.Guangzhou Polytechnic of Sports, Guangzhou, 511400, China
6.School of Computer and Information, Hefei University of Technology, Hefei, 230601, China
7.School of Computer Science and Information Engineering, Luoyang Institute of Science and Technology, Luoyang, 471023, China
Recommended Citation
GB/T 7714
Chen, Xiaowei,Yu, Lei,Wang, Tianet al. Artificial intelligence-empowered path selection: A survey of ant colony optimization for static and mobile sensor networks[J]. IEEE Access, 2020, 8: 71497-71511.
APA Chen, Xiaowei., Yu, Lei., Wang, Tian., Liu, Anfeng., Wu, Xiaofeng., .. & Sun, Zeyu. (2020). Artificial intelligence-empowered path selection: A survey of ant colony optimization for static and mobile sensor networks. IEEE Access, 8, 71497-71511.
MLA Chen, Xiaowei,et al."Artificial intelligence-empowered path selection: A survey of ant colony optimization for static and mobile sensor networks". IEEE Access 8(2020): 71497-71511.
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