onr:"swepub:oai:DiVA.org:uu-500246"
Search: onr:"swepub:oai:DiVA.org:uu-500246" > Innovative soft com...
- 1 of 1
- Previous record
- Next record
- To hitlist
Innovative soft computing-enabled cloud optimization for next-generation IoT in digital twins
- Feng, Hailin (author)
- Qiao, Liang (author)
-
- Lv, Zhihan, Dr. 1984- (author)
- Uppsala universitet,Institutionen för speldesign
- (creator_code:org_t)
- Elsevier, 2023
- 2023
- English.
- In: Applied Soft Computing. - : Elsevier. - 1568-4946 .- 1872-9681. ; 136
- Journal article (peer-reviewed)
Abstract
Subject headings
Close
- The research aims to reduce the network resource pressure on cloud centers (CC) and edge nodes, to improve the service quality and to optimize the network performance. In addition, it studies and designs a kind of edge–cloud collaboration framework based on the Internet of Things (IoT). First, raspberry pi (RP) card working machines are utilized as the working nodes, and a kind of edge–cloud collaboration framework is designed for edge computing. The framework consists mainly of three layers, including edge RP (ERP), monitoring & scheduling RP (MSRP), and CC. Among the three layers, collaborative communication can be realized between RPs and between RPs and CCs. Second, a kind of edge–cloud matching algorithm is proposed in the time delay constraint scenario. The research results obtained by actual task assignments demonstrate that the task time delay in face recognition on edge–cloud collaboration mode is the least among the three working modes, including edge only, CC only, and edge–CC collaboration modes, reaching only 12 s. Compared with that of CC running alone, the identification results of the framework rates on edge–cloud collaboration and CC modes are both more fluent than those on edge mode only, and real-time object detection can be realized. The total energy consumption of the unloading execution by system users continuously decreases with the increase in the number of users. It is assumed that the number of pieces of equipment in systems is 150, and the energy-saving rate of systems is affected by the frequency of task generation. The frequency of task generation increases with the corresponding reduction in the energy-saving rate of systems. Based on object detection as an example, the system energy consumption is decreased from 18 W to 16 W after the assignment of algorithms. The included framework improves the resource utility rate and reduces system energy consumption. In addition, it provides theoretical and practical references for the implementation of the edge–cloud collaboration framework.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Keyword
- Cloud optimization
- Edge computing
- Edge–cloud collaboration
- Internet of things
- Energy conservation
- Energy utilization
- Face recognition
- Object detection
- Soft computing
- Time delay
- Unloading
- Collaboration framework
- Collaboration modes
- Edge clouds
- Objects detection
- Optimisations
- Three-layer
- Time-delays
Publication and Content Type
- ref (subject category)
- art (subject category)
Find in a library
- Applied Soft Computing (Search for host publication in LIBRIS)
To the university's database
- 1 of 1
- Previous record
- Next record
- To hitlist
Find more in SwePub
- By the author/editor
- Feng, Hailin
- Qiao, Liang
- Lv, Zhihan, Dr. ...
- About the subject
-
- ENGINEERING AND TECHNOLOGY
- ENGINEERING AND ...
- and Electrical Engin ...
- and Computer Systems
-
- NATURAL SCIENCES
- NATURAL SCIENCES
- and Computer and Inf ...
- and Computer Science ...
- Articles in the publication
- Applied Soft Com ...
- By the university
- Uppsala University
Search outside SwePub
- Extend your search to:
- Google Book Search
- Google Scholar
Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.
- Copyright © LIBRIS - National Library Systems
- LIBRIS.kb.se
