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Generic and efficient connectivity determination for IoT applications

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Generic and efficient connectivity determination for IoT applications. / He, X.; Peng, Z.; Wang, J. et al.
In: IEEE Internet of Things Journal, Vol. 7, No. 6, 12.06.2020, p. 5291-5301.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

He, X, Peng, Z, Wang, J & Yang, G 2020, 'Generic and efficient connectivity determination for IoT applications', IEEE Internet of Things Journal, vol. 7, no. 6, pp. 5291-5301. https://doi.org/10.1109/JIOT.2020.2976685

APA

He, X., Peng, Z., Wang, J., & Yang, G. (2020). Generic and efficient connectivity determination for IoT applications. IEEE Internet of Things Journal, 7(6), 5291-5301. https://doi.org/10.1109/JIOT.2020.2976685

Vancouver

He X, Peng Z, Wang J, Yang G. Generic and efficient connectivity determination for IoT applications. IEEE Internet of Things Journal. 2020 Jun 12;7(6):5291-5301. Epub 2020 Feb 27. doi: 10.1109/JIOT.2020.2976685

Author

He, X. ; Peng, Z. ; Wang, J. et al. / Generic and efficient connectivity determination for IoT applications. In: IEEE Internet of Things Journal. 2020 ; Vol. 7, No. 6. pp. 5291-5301.

Bibtex

@article{ec8b3181a7f94f579f480d2aa4b14169,
title = "Generic and efficient connectivity determination for IoT applications",
abstract = "Network connectivity, with its significant application value for data transmission and node cooperation, has drawn a great concern in recent years. Facing the heterogeneity and complexity of the IoT system, the connectivity determination between nodes in the network is a big challenge. In view of this, this article proposes a generic and efficient connectivity determination method for IoT applications. This method first characterizes the connectivity parameters of nodes, including the direct connection probabilities between nodes, the degree centrality, and the betweenness centrality of nodes, and based on them, then constructs a node connectivity random graph (NCRG) and splits the NCRG into separate components. Furthermore, it converts the connectivity between nodes located in different components into the connectivity between these components and provides an algorithm to determine their connectivity. Specifically, three testing rules are defined in the algorithm to rank the testing priorities of these components and testing edges between these components. The simulation results show that the proposed method can efficiently achieve high accuracy with less cost.",
keywords = "Connectivity determination, Network connectivity, Random graph, Testing rules, Graph theory, Betweenness centrality, Connection probability, Degree centrality, Determination methods, IOT applications, Node connectivity, Node cooperation, Internet of things",
author = "X. He and Z. Peng and J. Wang and G. Yang",
year = "2020",
month = jun,
day = "12",
doi = "10.1109/JIOT.2020.2976685",
language = "English",
volume = "7",
pages = "5291--5301",
journal = "IEEE Internet of Things Journal",
issn = "2327-4662",
publisher = "IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC",
number = "6",

}

RIS

TY - JOUR

T1 - Generic and efficient connectivity determination for IoT applications

AU - He, X.

AU - Peng, Z.

AU - Wang, J.

AU - Yang, G.

PY - 2020/6/12

Y1 - 2020/6/12

N2 - Network connectivity, with its significant application value for data transmission and node cooperation, has drawn a great concern in recent years. Facing the heterogeneity and complexity of the IoT system, the connectivity determination between nodes in the network is a big challenge. In view of this, this article proposes a generic and efficient connectivity determination method for IoT applications. This method first characterizes the connectivity parameters of nodes, including the direct connection probabilities between nodes, the degree centrality, and the betweenness centrality of nodes, and based on them, then constructs a node connectivity random graph (NCRG) and splits the NCRG into separate components. Furthermore, it converts the connectivity between nodes located in different components into the connectivity between these components and provides an algorithm to determine their connectivity. Specifically, three testing rules are defined in the algorithm to rank the testing priorities of these components and testing edges between these components. The simulation results show that the proposed method can efficiently achieve high accuracy with less cost.

AB - Network connectivity, with its significant application value for data transmission and node cooperation, has drawn a great concern in recent years. Facing the heterogeneity and complexity of the IoT system, the connectivity determination between nodes in the network is a big challenge. In view of this, this article proposes a generic and efficient connectivity determination method for IoT applications. This method first characterizes the connectivity parameters of nodes, including the direct connection probabilities between nodes, the degree centrality, and the betweenness centrality of nodes, and based on them, then constructs a node connectivity random graph (NCRG) and splits the NCRG into separate components. Furthermore, it converts the connectivity between nodes located in different components into the connectivity between these components and provides an algorithm to determine their connectivity. Specifically, three testing rules are defined in the algorithm to rank the testing priorities of these components and testing edges between these components. The simulation results show that the proposed method can efficiently achieve high accuracy with less cost.

KW - Connectivity determination

KW - Network connectivity

KW - Random graph

KW - Testing rules

KW - Graph theory

KW - Betweenness centrality

KW - Connection probability

KW - Degree centrality

KW - Determination methods

KW - IOT applications

KW - Node connectivity

KW - Node cooperation

KW - Internet of things

U2 - 10.1109/JIOT.2020.2976685

DO - 10.1109/JIOT.2020.2976685

M3 - Journal article

VL - 7

SP - 5291

EP - 5301

JO - IEEE Internet of Things Journal

JF - IEEE Internet of Things Journal

SN - 2327-4662

IS - 6

ER -