Status | 已发表Published |
Title | Artificial intelligence-empowered path selection: A survey of ant colony optimization for static and mobile sensor networks |
Creator | |
Date Issued | 2020 |
Source Publication | IEEE Access
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ISSN | 2169-3536 |
Volume | 8Pages: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. |
Keyword | ant colony optimization transmission protocol Wireless sensor networks |
DOI | 10.1109/ACCESS.2020.2984329 |
URL | View source |
Indexed By | SCIE |
Language | 英语English |
WOS Research Area | Computer Science ; Engineering ; Telecommunications |
WOS Subject | Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications |
WOS ID | WOS:000530811900005 |
Scopus ID | 2-s2.0-85084192822 |
Citation statistics | |
Document Type | Journal article |
Identifier | http://repository.uic.edu.cn/handle/39GCC9TT/7070 |
Collection | Research outside affiliated institution |
Corresponding Author | Yu, 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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