Mobile online social network services have seen a rapid increase, in which the huge amount of user-generated social media contents propagating between users via social connections has significantly challenged the traditional content delivery paradigm: First, replicating all of the contents generated by users to edge servers that well "fit" the receivers becomes difficult due to the limited bandwidth and storage capacities. Motivated by device-to-device (D2D) communication that allows users with smart devices to transfer content directly, we propose replicating bandwidth-intensive social contents in a device-to-device manner. Based on large-scale measurement studies on social content propagation and user mobility patterns in edge-network regions, we observe that (1) Device-to-device replication can significantly help users download social contents from nearby neighboring peers; (2) Both social propagation and mobility patterns affect how contents should be replicated; (3) The replication strategies depend on regional characteristics ( e.g. , how users move across regions). Using these measurement insights, we propose a joint content replication strategy for edge-network regions, in which social contents are assigned to users in edge-network regions according to a joint consideration of social graph, content propagation and user mobility. We formulate the replication scheduling as an optimization problem and design distributed algorithm only using historical, local and partial information to solve it. Trace-driven experiments further verify the superiority of our proposal: compared with conventional pure movement-based and popularity-based approach, our design can significantly ( @math times) improve the amount of social contents successfully delivered by device-to-device replication.
@cite_40: Recent years have witnessed the emergence of numerous new Internet services for mobile users. Supporting mobile applications via public WiFi networks has received significant research attention due to the drastic increase of penetration rate of 802.11-based networks. Nevertheless, recent empirical studies showed that unplanned WiFi networks cannot provide satisfactory Quality of Service for interactive mobile applications due to intermittent network connectivity. In this paper, we exploit realistic mobility characteristics of users to deploy WiFi Access Points (APs) for continuous service for mobile users. We study two AP deployment problems that aim to maximize the continuous user coverage and to minimize the AP deployment cost, respectively. Both problems are formulated based on mobility graphs that capture the statistical mobility patterns of users. We prove that both problems are NP-hard. We develop several optimal and approximation algorithms with provable performance bounds for different topologies of mobility graphs. The effectiveness of our approaches is validated by extensive simulations using real user mobility traces.
@cite_34: Our understanding of how individual mobility patterns shape and impact the social network is limited, but is essential for a deeper understanding of network dynamics and evolution. This question is largely unexplored, partly due to the difficulty in obtaining large-scale society-wide data that simultaneously capture the dynamical information on individual movements and social interactions. Here we address this challenge for the first time by tracking the trajectories and communication records of 6 Million mobile phone users. We find that the similarity between two individuals' movements strongly correlates with their proximity in the social network. We further investigate how the predictive power hidden in such correlations can be exploited to address a challenging problem: which new links will develop in a social network. We show that mobility measures alone yield surprising predictive power, comparable to traditional network-based measures. Furthermore, the prediction accuracy can be significantly improved by learning a supervised classifier based on combined mobility and network measures. We believe our findings on the interplay of mobility patterns and social ties offer new perspectives on not only link prediction but also network dynamics.
@cite_7: Even though human movement and mobility patterns have a high degree of freedom and variation, they also exhibit structural patterns due to geographic and social constraints. Using cell phone location data, as well as data from two online location-based social networks, we aim to understand what basic laws govern human motion and dynamics. We find that humans experience a combination of periodic movement that is geographically limited and seemingly random jumps correlated with their social networks. Short-ranged travel is periodic both spatially and temporally and not effected by the social network structure, while long-distance travel is more influenced by social network ties. We show that social relationships can explain about 10 to 30 of all human movement, while periodic behavior explains 50 to 70 . Based on our findings, we develop a model of human mobility that combines periodic short range movements with travel due to the social network structure. We show that our model reliably predicts the locations and dynamics of future human movement and gives an order of magnitude better performance than present models of human mobility.
When jointly studying mobility and social network of users, @cite_7 observed that though human movement and mobility patterns have a high degree of variation, they exhibit structural patterns due to geographic and social constraints. In particular, short-ranged travel is periodic both spatially and temporally, and not affected by the social network structure. Recently, such mobility studies have improved the edge network and content delivery design, @cite_40 studies the mobility characteristics of people to guide wireless network deployment. @cite_34 found the similarity between individuals' mobility patterns and their social proximities.