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Qing Wang 0014

dblp:97/6505-14 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2020
0000-0002-7631-129XORCID · conflict

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Cloud and datacenter computing · 100%
Computer networks
1 paper
Internet of things and sensor networks · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Cloud and datacenter computing
cloud architecture
0.412020
Research on the Architecture and its Implementation for Instrumentation and Measurement Cloud · IEEE Trans. Serv. Comput. 2020
Cloud and datacenter computing › virtualization
resource virtualization
0.412020
Research on the Architecture and its Implementation for Instrumentation and Measurement Cloud · IEEE Trans. Serv. Comput. 2020
Internet of things and sensor networks › wireless sensor network
sensor management
0.112020
Research on the Architecture and its Implementation for Instrumentation and Measurement Cloud · IEEE Trans. Serv. Comput. 2020

Methods — techniques the papers use, named apart from their topics

service wrapping · 0.9resource virtualization · 0.9
YearPublicationVenuePosition
2020 Research on the Architecture and its Implementation for Instrumentation and Measurement Cloud
abstract
Cloud computing has brought a new method of resource utilization and management. Nowadays some researchers are working on cloud-based instrumentation and measurement systems designated as Instrumentation and Measurement Clouds (IMCs). However, until now, no standard definition or detailed architecture with an implemented system for IMC has been presented. This paper adopts the philosophy of cloud computing and brings forward a relatively standard definition and a novel architecture for IMC. The architecture inherits many key features of cloud computing, such as service provision on demand, scalability and so on, for remote Instrumentation and Measurement (IM) resource utilization and management. In the architecture, instruments and sensors are virtualized into abstracted resources, and commonly used IM functions are wrapped into services. Users can use these resources and services on demand remotely. Platforms implemented under such architecture can reduce the investment for building IM systems greatly, enable remote sharing of IM resources, increase utilization efficiency of various resources, and facilitate processing and analyzing of Big Data from instruments and sensors. Practical systems with a typical application are implemented upon the architecture. Results demonstrate that the novel IMC architecture can provide a new effective and efficient framework for establishing IM systems.
Hengjing He, Wei Zhao 0031, Songling Huang, Geoffrey C. Fox, Qing Wang 0014
IEEE Trans. Serv. Comput.5