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Exploring Unknown Topologies of Wire...

Bouchoucha, Taha.

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Exploring Unknown Topologies of Wireless and Social Networks.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Exploring Unknown Topologies of Wireless and Social Networks./
作者:	Bouchoucha, Taha.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	126 p.
附註:	Source: Dissertations Abstracts International, Volume: 82-04, Section: B.
Contained By:	Dissertations Abstracts International82-04B.
標題:	Electrical engineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27741438
ISBN:	9798672161136

Exploring Unknown Topologies of Wireless and Social Networks.
Bouchoucha, Taha.

Exploring Unknown Topologies of Wireless and Social Networks. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 126 p.

Source: Dissertations Abstracts International, Volume: 82-04, Section: B.

Thesis (Ph.D.)--University of California, Davis, 2020.

This item must not be sold to any third party vendors.

Future generations of communication networks, beyond 5G, will continue the phenomenal expansion of mobile communications to many fields like environment monitoring, self driving cars, healthcare, smartcity and industrial IoT. We are witnessing the rapid growth of heterogeneous, dense and dynamic networks. The dynamic nature of these networks, because of user mobility and network reconfiguration, introduces time-varying and service-driven topologies. As a result, learning and exploring the connectivity of such networks represent an important problem in practical applications of communication networks as well as online social networks. Modeling large scale networks as connected graphs, one well known task in network analysis and resource allocation is to extract their connectivity information among nodes to visualize network topology, disseminate data, and improve routing efficiency. This work investigates a simple measurement model in which a small subset of source nodes collect hop distance information from networked nodes in order to generate a virtual coordinate system (VCS) for networks of unknown topology. We establish a VCS to define logical distance among nodes based on principal component analysis (PCA) and to determine connectivity relationship and effective routing methods. To address the issue of incomplete and erroneous data, we present a robust analytical algorithm to derive VCS against practical issues of missing and corrupted measurements. We also develop a node selection method to designate anchor nodes in order to improve network connectivity inference and VCS accuracy and reliability. Seeing strong similarities, we expand our study to the case of online social networks where we establish a robust network inference algorithm as well as an efficient rumor blocking technique that hinders the propagation of misinformation in social-media.

ISBN: 9798672161136Subjects--Topical Terms:

649834
Electrical engineering.
Subjects--Index Terms:

Network topology

Exploring Unknown Topologies of Wireless and Social Networks.
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506 $a This item must not be sold to any third party vendors.
520 $a Future generations of communication networks, beyond 5G, will continue the phenomenal expansion of mobile communications to many fields like environment monitoring, self driving cars, healthcare, smartcity and industrial IoT. We are witnessing the rapid growth of heterogeneous, dense and dynamic networks. The dynamic nature of these networks, because of user mobility and network reconfiguration, introduces time-varying and service-driven topologies. As a result, learning and exploring the connectivity of such networks represent an important problem in practical applications of communication networks as well as online social networks. Modeling large scale networks as connected graphs, one well known task in network analysis and resource allocation is to extract their connectivity information among nodes to visualize network topology, disseminate data, and improve routing efficiency. This work investigates a simple measurement model in which a small subset of source nodes collect hop distance information from networked nodes in order to generate a virtual coordinate system (VCS) for networks of unknown topology. We establish a VCS to define logical distance among nodes based on principal component analysis (PCA) and to determine connectivity relationship and effective routing methods. To address the issue of incomplete and erroneous data, we present a robust analytical algorithm to derive VCS against practical issues of missing and corrupted measurements. We also develop a node selection method to designate anchor nodes in order to improve network connectivity inference and VCS accuracy and reliability. Seeing strong similarities, we expand our study to the case of online social networks where we establish a robust network inference algorithm as well as an efficient rumor blocking technique that hinders the propagation of misinformation in social-media.
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