Identity centric networks (IdCN), in which packets are destined to user identities, is one of the future network architectures under discussion. Since user mobility is already widely observed in the current Internet and seen as one of the main features of the future Internet, seamless mobility needs to be kept in mind for whatever routing mechanisms developed for IdCN. However, seamless handoff techniques from the IP world won't work because no Care-of-Address or the alike is available in IdCN to help quickly locating the mobile user's new position, therefore it is difficult to redirect the packets in transit before the routing protocol re-finds the moved users. In this paper a dynamic ring mechanism that does not rely on but can work with any IdCN routing protocol is proposed to support seamless handoff in IdCN. During a handoff, the mobile user makes use of this ring to keep its global connectivity, and leaves after the handoff finishes. Appropriate timing of hooking to and leaving the ring is crucial to keep the overhead at minimum. Experiments show that the proposed mechanism can indeed prevent most packet drops and keep the flow running during handoffs at reasonably lower cost than simple retransmission.

Joining objects from different web sources and returning top-k combinations is a research topic with much attention. Such techniques could be used in location based scenarios, which for one example, help plan a wonderful night by finding a good combination of hotel, restaurant and theater. Challenge to such techniques is that in a good combination, each individual object should be good enough and, of equal or even greater importance, subject to some given criteria, such as within a given range and close to each other. Proximity rank join method is one way to settle this problem. It takes advantage of sorted access of inputs, does not rely on specialize data structures to determine spatial closeness, and has efficient pull and bound strategies to avoid reading too much inputs before finding the top-k answers. In this paper, we propose a detailed scheme using the proximity rank join model that is optimized for location based purposes. It will be shown that our scheme indeed reflects user's desire better and outperform the naïve Euclidean distance scheme.

Popular online social networks (OSNs) like Facebook and Twitter are changing the way users communicate and interact with the Internet. A deep understanding of user interactions in OSNs can provide important insights into questions of human social behavior and into the design of social platforms and applications. However, recent studies have shown that a majority of user interactions on OSNs are latent interactions , that is, passive actions, such as profile browsing, that cannot be observed by traditional measurement techniques. In this article, we seek a deeper understanding of both active and latent user interactions in OSNs. For quantifiable data on latent user interactions, we perform a detailed measurement study on Renren, the largest OSN in China with more than 220 million users to date. All friendship links in Renren are public, allowing us to exhaustively crawl a connected graph component of 42 million users and 1.66 billion social links in 2009. Renren also keeps detailed, publicly viewable visitor logs for each user profile. We capture detailed histories of profile visits over a period of 90 days for users in the Peking University Renren network and use statistics of profile visits to study issues of user profile popularity, reciprocity of profile visits, and the impact of content updates on user popularity. We find that latent interactions are much more prevalent and frequent than active events, are nonreciprocal in nature, and that profile popularity is correlated with page views of content rather than with quantity of content updates. Finally, we construct latent interaction graphs as models of user browsing behavior and compare their structural properties, evolution, community structure, and mixing times against those of both active interaction graphs and social graphs.

Popular online social networks (OSNs) like Facebook and Twitter are changing the way users communicate and interact with the Internet. A deep understanding of user interactions in OSNs can provide important insights into questions of human social behavior, and into the design of social platforms and applications. However, recent studies have shown that a majority of user interactions on OSNs are latent interactions, passive actions such as profile browsing that cannot be observed by traditional measurement techniques. In this paper, we seek a deeper understanding of both visible and latent user interactions in OSNs. For quantifiable data on latent user interactions, we perform a detailed measurement study on Renren, the largest OSN in China with more than 150 million users to date. All friendship links in Renren are public, allowing us to exhaustively crawl a connected graph component of 42 million users and 1.66 billion social links in 2009. Renren also keeps detailed visitor logs for each user profile, and counters for each photo and diary/blog entry. We capture detailed histories of profile visits over a period of 90 days for more than 61,000 users in the Peking University Renren network, and use statistics of profile visits to study issues of user profile popularity, reciprocity of profile visits, and the impact of content updates on user popularity. We find that latent interactions are much more prevalent and frequent than visible events, non-reciprocal in nature, and that profile popularity are uncorrelated with the frequency of content updates. Finally, we construct latent interaction graphs as models of user browsing behavior, and compare their structural properties against those of both visible interaction graphs and social graphs.