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A social internet of things (IoT) system can be viewed as a mix of traditional peer-to-peer networks and social networks, where “things” autonomously establish social relationships according to the owners’ social networks, and seek trusted “things” that can provide services needed when they come into contact with each other opportunistically. We propose and analyze the design notion of adaptive trust management for social IoT systems in which social relationships evolve dynamically among the owners of IoT devices. We reveal the design tradeoff between trust convergence versus trust fluctuation in our adaptive trust management protocol design. With our adaptive trust management protocol, a social IoT application can adaptively choose the best trust parameter settings in response to changing IoT social conditions such that not only trust assessment is accurate but also the application performance is maximized. We propose a table-lookup method to apply the analysis results dynamically and demonstrate the feasibility of our proposed adaptive trust management scheme with two real-world social IoT service composition applications.
Rumpel is a “hyper data” web browser that lets you take control of your personal data.
Human Computer Interaction: Concepts, Methodologies, Tools, and Applications penetrates the human computer interaction (HCI) field with more breadth and depth of comprehensive research than any other publication. The four-volume set contains more than 200 authoritative works from over 250 leading experts in the field of human computer interaction. This groundbreaking collection contains significant chapters in topics such as Web logs, technology influences, and human factors of information systems and technologies.
The Structure of Human Civilization
Offers a profound understanding of how we create a social reality–a reality of money, property, governments, marriages, stock markets and cocktail parties
Explains how a single linguistic operation, repeated over and over, is used to create and maintain the elaborate structures of human social institutions
A new trust framework is emerging, fuelled by social, economic and technological forces that will profoundly change not just how we are trusted in the world, but how we view trust in the world.
Welcome to the 1st International Conference on Social Networking and Computing (MIC-Social 2015). This event is intended to represent a major forum for researchers, engineers and students from all over the world to meet in Milan to present their latest research results and to exchange new ideas and practical experience in the following major areas:
SNMA: Social Network Models and Architectures
SNTA: Social Networking Techniques and Applications
SNIP: Social Network Information Processing
SNSP: Social Network Security and Privacy
SNME: Social Network Management and Economics
Social sensing has emerged as a new paradigm for collecting sensory measurements by means of “crowd-sourcing” sensory data collection tasks to a human population. Humans can act as sensor carriers (e.g., carrying GPS devices that share location data), sensor operators (e.g., taking pictures with smart phones), or as sensors themselves (e.g., sharing their observations on Twitter). The proliferation of sensors in the possession of the average individual, together with the popularity of social networks that allow massive information dissemination, heralds an era of social sensing that brings about new research challenges and opportunities in this emerging field.
Online Social networks (OSNs) are one of the latest revolutions that have taken the world by storm; they are so popular nowadays that people want to use them anytime and everywhere through heterogeneous devices in order to facilitate human interaction as well as creating professional relationships. Their use within vehicles has attracted many companies to develop and to integrate basic social features to navigation applications and services.
Churn prediction, recognition, and mitigation have become essential topics in various industries. As a means for forecasting and managing risk, further research in this field can greatly assist companies in making informed decisions based on future possible scenarios.
Developing Churn Models Using Data Mining Techniques and Social Network Analysis provides an in-depth analysis of attrition modeling relevant to business planning and management. Through its insightful and detailed explanation of best practices, tools, and theory surrounding churn prediction and the integration of analytics tools, this publication is especially relevant to managers, data specialists, business analysts, academicians, and upper-level students.