Teoría central: la evaluación y el aprendizaje en educación inicial

As the objects to be processed, the properties and requirement of content should be considered during the content dissemination process in mobile social networks as well. In many cases, there is no restriction on the content, which can be called free content. The free content can be received and edited by any users. And any connections can be utilized to transmit the content, regardless the bandwidth and duration provided by the connection.

However, some other content has their dissemination requirements. In this section, we

analyze the interesting content types that have specific properties and requirements

2.3.1 Simple Content

Simple content is the most common content type existing in content dissemination applications. Most content generated in daily social networks can be categorized as simple content, such as messages, news, pictures and so on. There is no limitation and requirement to disseminate simple content. Every user in social networks has the right to copy, edit and delete the open source content. And the simple content can be transmitted through almost any connections, regardless the connection condition, duration and distance.

To disseminate simple content in mobile social networks, the content providers need to consider more constraints from network condition and user preference, instead of content perspective. Researchers have proposed various studies on the dissemination problem for simple content. Gao et. al.[40] propose to efficiently maintain the cache freshness by or- ganizing the caching users as a tree structure during content access. Focusing on similar

problem, Chen et. al.[41] deduce the optimal file replication strategy by further considering users’ ability to meet others as a resource.

2.3.2 Streaming Content

Streaming content is also a challenging content type which has particular requiremen- t on the connection condition during content dissemination. Different from free content, streaming content has the properties of large size, long transmission duration and high data completeness requirement. To efficiently disseminate streaming content in mobile social net- works, the network should provider high speed and long duration streaming data transmission paths[42]. However, because of the wireless and mobility environment of Mobile Social Net- works, many existing connections disobey the requirements. The mobile ad hoc connection quality is highly limited by distance and the power of mobile devices. In additional, user be- haviors and mobility would disrupt the connection as well. Hence, to disseminate streaming content in MSNs, we need to detect the reliable dissemination paths which providers long duration and high speed transmission performance. It is an essential challenging on how to detect the reliable streaming data transmission paths in MSNs.

User preference analysis can be leveraged to predict the possible behavior acted dur- ing streaming content dissemination. By analyzing the mobility, interests on content and attitude on social influence, we can estimate the possible behavior towards the streaming content dissemination, and further detect the users who would be active during the dissem- ination process. The authors in [43] propose a collaborative mobile architecture to model user behaviors and stimulate user cooperation in multicast live streaming. In [44], the au- thors analyze user activities on live video streaming systems and identify the impact of those activities on performance. A Bayesian network is built to model user behaviors and help to enhance the live streaming system.

Studied have been taken to solve the streaming dissemination problem by optimize the caching and streaming schemes. To avoid disconnection and service breakdown caused by users’ mobility in the network, Wu et. al.[45] propose a two level framework for cooperative

media streaming in MSNs. In the framework, headlight prefetching and dynamic chaining are designed to deal with the uncertainty of user movement and maximize cache utilization and streaming benefit.

Chapter 3

BAYESIAN-BASED CONTENT DISSEMINATION FRAMEWORK

In this chapter, we study the dissemination problem of large size of content such as multimedia videos.

Since videos carry abundant visual contents and information, the video content is an attractive content type deployed in plenty of applications. However, video dissemination in MSNs encounters more significant challenges in the distribution process than other types of contents such as text and picture do. First, it requires high bandwidth and reliable routing paths, which are difficult to be guaranteed in dynamic unstable mobile ad hoc networks. Second, the nature of the social networks such as user interests, connections and behaviors may have a great impact on how the video is disseminated across the physical network. For example, how one can motivate a neighbor mobile user to participant or help forwarding the video content if a direct link to the video source does not exist. Social users can be affected by many factors (e.g, battery running down) and may respond dramatically different to the same factor.

In this work, we propose a novel framework for effective video content dissemination in mobile social networks, which takes into consideration both the social features and the limited physical resources[46]. Different from other related work, our framework captures the individual user’s personal decision and interaction during the process of video distribution. We analyze the factors that impact users’ choices, including video content matching with users’ interests, the decision or choices of other neighbor users and the physical resources (e.g., battery and bandwidth). By synthesizing those factors, we develop an effective Bayesian network model which enables each user to calculate its probability to request the video (i.e., video request probability). This probability is then used to select the optimized routing path for video sharing and transmissions. To the best of our knowledge, this is the first

work in modeling the social characteristics and network resources for video sharing using the Bayesian technique.