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Ran Duo

dblp:231/8623 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2020
—ORCID · none

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

Computer networks · 1 · 1 first-author

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 networks
1 paper
Vehicular, aerial and satellite networks · 56% Network management and operations · 36% Cellular and mobile networks · 8%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Network management and operations
network control
0.412020
Context-Aware Clustering for SDN Enabled Network · ICNP 2020
Vehicular, aerial and satellite networks › vehicular networks
vehicle clustering
0.412020
Context-Aware Clustering for SDN Enabled Network · ICNP 2020
Vehicular, aerial and satellite networks
vehicular networks
0.412020
Context-Aware Clustering for SDN Enabled Network · ICNP 2020
Cellular and mobile networks
5g
0.112020
Context-Aware Clustering for SDN Enabled Network · ICNP 2020
Network management and operations
quality of service management
0.112020
Context-Aware Clustering for SDN Enabled Network · ICNP 2020

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

simulation · 0.4
YearPublicationVenuePosition
2020 Context-Aware Clustering for SDN Enabled Network
abstract
Nowadays, fifth-generation (5G) technology is promoted to support massive data transmissions and low-latency communications, which enables diverse vehicular applications. There have been some studies discussing the use of vehicle clustering to save scarce spectrum resources, and prevent network congestions in hybrid cellular/IEEE 802.11p vehicular environments. However, different types of applications could have different levels of quality-of-service (QoS) requirements, and thus this issue should be considered in the clustering of vehicles. We propose a novel vehicle clustering scheme that utilizes the global view and programmable advantage of software defined networking (SDN) technology to conduct an efficient clustering in vehicular environments. The proposed scheme first classifies the existing applications to three different types, the delay-sensitive type, traffic-intensive type, and computation-intensive type, according to the QoS requirements. Then the scheme forms three different clusters based on this classification to satisfy the QoS requirement for each type. We use computer simulations to show the advantage of the proposed scheme over the conventional approach.
Ran Duo, Celimuge Wu, Tsutomu Yoshinaga, Yusheng Ji
ICNP1