Konglin Zhu

SMART: A Social- and Mobile-Aware Routing Strategy for Disruption-Tolerant Networks

Disruption-tolerant networks (DTNs) are sparse mobile ad hoc networks where nodes connect with each other intermittently and end-to-end communication paths do not exist. Data routing in DTNs is challenging and has drawn much attention from research communities recently. Although many DTN routing strategies have been proposed in the past, they confront problems such as blind spots and dead ends and lack efficient implementation in a decentralized, large-scale, mobile, and dynamic environment. To overcome these difficulties, we introduce a new solution for DTNs that leverages the social properties and mobility characteristics of users. Our observation of the mobile trajectories of three data sets collected from real DTNs reveals that user movements appear locally and they tend to form communities correlated to geographic locations. Based on these findings, we propose a social- and mobile-aware routing strategy (SMART). It exploits a distributed community partitioning algorithm to divide the DTN into smaller communities regarding user locations and interaction routines. For intracommunity communications, a decayed routing metric convoluting social similarity and social centrality is calculated, which is used to decide forwarding node efficiently while avoiding the blind-spot and dead-end problems. To enable efficient intercommunity communications, we choose the fringe nodes that travel remotely as relays, and we propose the node-to-community utilities for routing decision across communities. We present empirical analysis to show that SMART reduces the occurrence of blind spots and dead ends to a level below 1%. The efficiency of SMART is evaluated by extensive trace-driven experiments, which illustrate that it outperforms other routing strategies in various real DTN traces.

Geo-based inter-domain routing (GIDR) protocol for MANETs

Inter-domain routing for MANETs (Mobile Ad Hoc Networks) draws increasing attention because of military and vehicular applications. The existing Border Gateway Protocol (BGP) is the de facto inter-domain routing protocol for the Internet. But BGP is not applicable to MANETs because the BGP design is based on a static Internet which does not support dynamic discovery of members, and cannot scale to mobile, dynamic topology environments.

Exploring user social behaviors in mobile social applications

Mobile social applications are popular as the proliferating of mobile devices. Understanding user social behaviors is important to improve mobile social applications and enhance its quality of service. However, there is still lack of data for real deployment mobile social application on data analysis of human interaction and social behaviors in mobile social networks. In this paper, we introduce the experiment methodology of deploying the Goose software in two campuses located in Germany and China respectively. Goose is a mobile social network application allows microblogging, message sending. With the help of volunteers, we collect user interaction data in the duration of 15 days. Based on the collected data, our observation reveals the following aspects of user interactions and their influences. First, user overall activities approximately match user daily life work pattern with a slightly longer time duration and periodically appearance. Second, user encounters in mobile social network follow the heavy tail distribution in small social communities, and user interactions follow the Pareto principle, where about 20% of users make close connections to the other users. Third, communication path between a pair of mobile nodes is mostly within 6 hops, and information diffusion using an epidemic strategy demonstrates that the informed population reaches to 50% in a short term and approaches to 80% in a long term.

Rethinking routing information in mobile social networks: Location-based or social-based?

With the rapid development of wireless communication technologies, mobile social networks (MSNs) become more and more popular and have attracted much attention from researchers. In this paper, we address the routing issue in MSNs where a set of social related mobile devices communicate opportunistically and intermittently. Existing routing strategies are mostly based on location information (i.e., geographical coordination and physical distance) and social information (i.e., the number of friends and the strength of social ties). These two kinds of information indeed represent two different levels of human behaviors: the location information is concrete and corresponds to the physical property of human activity; while the social information is logical and represents virtual human interactions. In the context of data routing, a rising question is: does the concrete location information outperform the logical social information in designing routing strategies in MSNs? To address the question, we devise a comprehensive social-based routing strategy called Soc and a general location-based strategy called Loc. We provide comprehensive performance comparisons of Soc and Loc together with other social-based and location-based strategies. Our experiment results show that the social-based strategy and the location-based strategy have no significant difference in routing performance: they perform closely in delivery ratio, delay and delivery cost with a slight difference less than 5% in most cases. This indicates that concrete location information is not always necessary to be the key consideration for routing design and logical social information could be potential substitute. Since collecting location information needs dedicated equipment and arguably violates user privacy, our work implies that the proposed social-based routing strategy is safe and effective for MSNs.

Modeling population growth in online social networks

PurposeOnline social networks (OSNs) are now among the most popular applications on the web offering platforms for people to interact, communicate and collaborate with others. The rapid development of OSNs provides opportunities for people’s daily communication, but also brings problems such as burst network traffic and overload of servers. Studying the population growth pattern in online social networks helps service providers to understand the people communication manners in OSNs and facilitate the management of network resources. In this paper, we propose a population growth model for OSNs based on the study of population distribution and growth in spatiotemporal scale-space.MethodsWe investigate the population growth in three data sets which are randomly sampled from the popular OSN web sites including Renren, Twitter and Gowalla. We find out that the number of population follows the power-law distribution over different geographic locations, and the population growth of a location fits a power function of time. An aggregated population growth model is conducted by integrating the population growth over geographic locations and time.ResultsWe use the data sets to validate our population growth model. Extensive experiments also show that the proposed model fits the population growth of Facebook and Sina Weibo well. As an application, we use the model to predict the monthly population in three data sets. By comparing the predicted population with ground-truth values, the results show that our model can achieve a prediction accuracy between 86.14% and 99.89%.ConclusionsWith our proposed population growth model, people can estimate the population size of an online social network in a certain time period and it can also be used for population prediction for a future time.

How do online social networks grow

Online social networks such as Facebook, Twitter and Gowalla allow people to communicate and interact across borders. In past years online social networks have become increasingly important for studying the behavior of individuals, group formation, and the emergence of online societies. Here we focus on the characterization of the average growth of online social networks and try to understand which are possible processes behind seemingly long-range temporal correlated collective behavior. In agreement with recent findings, but in contrast to Gibrats law of proportionate growth, we find scaling in the average growth rate and its standard deviation. In contrast, Renren and Twitter deviate, however, in certain important aspects significantly from those found in many social and economic systems. Whereas independent methods suggest no significance for temporally long-range correlated behavior for Renren and Twitter, a scaling analysis of the standard deviation does suggest long-range temporal correlated growth in Gowalla. However, we demonstrate that seemingly long-range temporal correlations in the growth of online social networks, such as in Gowalla, can be explained by a decomposition into temporally and spatially independent growth processes with a large variety of entry rates. Our analysis thus suggests that temporally or spatially correlated behavior does not play a major role in the growth of online social networks.

Geo-Assisted Multicast Inter-Domain Routing (GMIDR) Protocol for MANETs

Large military ad hoc networks are often characterized by the interconnection of heterogeneous domains. The same trend is emerging in civilian MANETs (e.g., search and rescue, vehicular networks). In these networks it is important to be able to efficiently propagate information across domains in multicast mode (e.g., situation awareness dissemination, commands, streams). Several multicast protocols have been developed for single domain MANET. However, few can be extended to inter-domain operation. In fact, multicast routing across different MANET domains faces the challenges of node motion, topology changes, dynamic gateway election and, possibly, connectivity interruption. To overcome these challenges, especially to achieve routing scalability and at the same time maintains efficient routing, this paper proposes the Geo-assisted Multicast Inter-domain Routing (GMIDR) protocol based on geographical assistance and cluster technology. Intensive simulation results show that the GMIDR protocol is scalable and stable with various numbers of multicast group members, and it outperforms other multicast protocols. A military use case scenario simulation shows that GMIDR can be utilized efficiently in the large scale networks crossing multiple domains. Geocast by applying GMIDR shows the flexibility of the protocol.

Exploring mobile data on smartphones from collection to analysis

The explosive increasing data on mobile terminals has multiple perspective of value for users and service providers, such as improving user experience, resource allocation and mobile devices marketing, etc. However, it lacks massive fine scale mobile data for studies. Previous research still limited in a grain scale, making analysis in a static way to the data obtained from Internet Service Providers (ISPs). In this paper, we propose a dynamic data analysis system to explore the mobile data from collection to analysis. It achieves data collection in a fine scale with dynamic manner. Accordingly, the analysis function is also formed to adapt to dynamic increments of data. Besides, this system can be easily extended with different modules. To show these characteristics of this system, we elaborate several demonstrations of analysis results in aspects of user usage pattern. Furthermore, clustering analysis is made to some specific smartphone applications that contribute to the rapid growth of mobile data.

Thumb: A real-time resource information sharing application over mobile phones

Information sharing plays a significant role in human daily life. With the development of the Internet and applications over it, we share almost all kinds of information in a much wider space and time scope that we never had. However, when it comes to sharing information about resources which are available in an instant manner and expire in a very short time, such as the cases in carpooling, train ticket resale and short-term discount information, the Internet is not that all-powerful. On the other hand, we have witnessed the rapid development of smart mobile phones over recent years, and which is gradually reshaping our way to learn and communicate. To the best of our knowledge, instant resource information sharing systems have not been available in this new environment. In this work, we develop and demonstrate Thumb, a system aiming to leverage the new functionalities of smart mobile phones and the Internet to allow users to share instant availability of resource information, especially for resources with extreme short life time.

Chapter 12 Population Growth in Online Social Networks

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