Towards a dynamic social-network-based approach for service composition in the Internet of Things

2011 ◽  
Author(s):  
Wen Xu ◽  
Zheng Hu ◽  
Tao Gong ◽  
Zhengzheng Zhao
Keyword(s):  
Social Network ◽  
Internet Of Things ◽  
Service Composition ◽  
The Internet ◽  
Dynamic Social Network ◽  
The Internet Of Things

scholarly journals MultiCuckoo: Multi-Cloud Service Composition Using a Cuckoo-Inspired Algorithm for the Internet of Things Applications

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 56737-56749 ◽  
Author(s):  
Heba Kurdi ◽  
Fadwa Ezzat ◽  
Lina Altoaimy ◽  
Syed Hassan Ahmed ◽  
Kamal Youcef-Toumi
Keyword(s):  
Internet Of Things ◽  
Service Composition ◽  
Cloud Service ◽  
The Internet ◽  
Cloud Service Composition ◽  
The Internet Of Things ◽  
Multi Cloud

scholarly journals Community privacy estimation method based on key node method in space social Internet of Things

2019 ◽  
Vol 15 (10) ◽  
pp. 155014771988313 ◽  
Author(s):  
Guobin Chen ◽  
Tangsen Huang
Keyword(s):  
Social Network ◽  
Internet Of Things ◽  
Estimation Method ◽  
Public Information ◽  
Clustering Coefficient ◽  
The Internet ◽  
The Core ◽  
Social Internet Of Things ◽  
Internet Of Things Technology ◽  
The Internet Of Things

Based on the research of social network and the Internet of Things, a new research topic in the field of Internet of Things, Social Internet of Things is gradually formed. The SIoT applies the research results of SIoT from different aspects of the Internet of Things, and solves the specific problems in the research of Internet of Things, which brings new opportunities for the development of the Internet of Things. With the development of the Internet of Things technology, in the spatial social Internet of Things structure, user information includes sensitive attributes and non-sensitive attribute information. This information can be inferred from public user information to infer the information of the private user and even speculate on sensitive attributes. This article proposes an information speculation method based on the core users of spatial social networks, and estimates the non-core user information through the core user public information. First, the user’s spatial social network is divided into communities, and the core nodes of the community in the spatial social network are calculated by PageRank algorithm and the convergence of the algorithm is proved. Then, through the public information of the core nodes divided by the community in the space social network, the private information of relevant users to these core nodes can be speculated. Finally, by experimental analyzing the community structures of SIoT (Social Internet of Things) like Twitter, Sina Weibo, ER random networks, and NW small-world network, and making 5%, 10%, 15%, 20% information anonymous respectively in these four kinds of networks, we can analyze their clustering coefficient, Q-modularity and properties. Finally, the key node information of the four spatial social structures is speculated to analyze the effectiveness of the proposed method. Compared with the non-core speculation method, this method has advantages in speculative information integrity and time.

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