Integration of Internet of Things and Social Network

2019 ◽  
pp. 312-324
Author(s):  
Halima Bouazza ◽  
Laallam Fatima Zohra ◽  
Bachir Said
Keyword(s):  
Social Network ◽  
Internet Of Things

scholarly journals RE-ENGINEERING OF SMART CITY'S BUSINESS PROCESSES BASED ON SOCIAL NETWORKS AND INTERNET OF THINGS

2018 ◽  
Vol 16 (3) ◽  
pp. 275
Author(s):  
Emir Ugljanin ◽  
Dragan Stojanović ◽  
Ejub Kajan ◽  
Zakaria Maamar
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Internet Of Things ◽  
Smart City ◽  
Business Processes ◽  
Smart Cities ◽  
Successful Management ◽  
Complete Integration ◽  
Social Actions

This paper reports our experience with developing a Business-2-Social (B2S) platform that provides necessary support to all this platform’s constituents, namely business processes, social media (e.g., social network), and Internet of Things (IoT). This platform is exemplified with smart cities whose successful management requires a complete integration of IoT and social media capabilities into the business processes implementing user services. To ensure a successful integration, social actions, that a smart city would allow citizens execute, are analyzed in terms of impact of these smart city’s business processes. Reactions to these actions are tracked and then analyzed to improve user services.

scholarly journals Social Security and Privacy for Social IoT Polymorphic Value Set: A Solution to Inference Attacks on Social Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-16
Author(s):  
Yunpeng Gao ◽  
Nan Zhang
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Internet Of Things ◽  
Wide Distribution ◽  
Security And Privacy ◽  
Ranking Functions ◽  
Retrieval Models ◽  
Utility Loss ◽  
Inference Attacks ◽  
Real World Datasets

Social Internet of Things (SIoT) integrates social network schemes into Internet of Things (IoT), which provides opportunities for IoT objects to form social communities. Existing social network models have been adopted by SIoT paradigm. The wide distribution of IoT objects and openness of social networks, however, make it more challenging to preserve privacy of IoT users. In this paper, we present a novel framework that preserves privacy against inference attacks on social network data through ranked retrieval models. We propose PVS, a privacy-preserving framework that involves the design of polymorphic value sets and ranking functions. PVS enables polymorphism of private attributes by allowing them to respond to different queries in different ways. We begin this work by identifying two classes of adversaries, authenticity-ignorant adversary, and authenticity-knowledgeable adversary, based on their knowledge of the distribution of private attributes. Next, we define the measurement functions of utility loss and propose PVSV and PVST that preserve privacy against authenticity-ignorant and authenticity-knowledgeable adversaries, respectively. We take into account the utility loss of query results in the design of PVSV and PVST. Finally, we show that PVSV and PVST meet the privacy guarantee with acceptable utility loss in extensive experiments over real-world datasets.

scholarly journals SPY-BOT: Machine learning-enabled post filtering for Social Network-Integrated Industrial Internet of Things

2021 ◽  
pp. 102588
Author(s):  
Md Arafatur Rahman ◽  
Nafees Zaman ◽  
A. Taufiq Asyhari ◽  
S.M. Nazmus Sadat ◽  
Prashant Pillai ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Network ◽  
Internet Of Things ◽  
Industrial Internet Of Things ◽  
Industrial Internet

Research on the Content-Based Video Indexing for Smart Grid

2014 ◽  
Vol 945-949 ◽  
pp. 3391-3395
Author(s):  
Ming Liang Yan
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Information Retrieval ◽  
Social Network ◽  
Data Analysis ◽  
Internet Of Things ◽  
Video Retrieval ◽  
Video Data ◽  
Intelligent Data Analysis ◽  
Engine System

Data has become the fundamental resource by the emerging new services such as cloud computing, internet of things and social network. In the electric power applications, the video data mining plays an important role in the intelligent data analysis. With growth of video data in such an amazing speed, the information retrieval is becoming more and more important. This paper focuses on the analysis of the content-based video retrieval and proposes the design of a uniformed search engine system. The system is oriented to the retrieval of both the unstructured video contents and structured tags, which helps to achieve the integration of the heterogeneity data resources. In this paper, a retrieval framework is discussed and several problems are addressed.

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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