Abstract-Disruption tolerant networks (DTNs) are a class of networks in which no contemporaneous path may exist between the source and destination at a given time. In such a network, routing takes place with the help of relay nodes and in a storeand-forward fashion. If the nodes in a DTN are controlled by rational entities, such as people or organizations, the nodes can be expected to behave selfishly and attempt to maximize their utilities and conserve their resources. Since routing is an inherently cooperative activity, system operation will be critically impaired unless cooperation is somehow incentivized. The lack of end-to-end paths, high variation in network conditions, and long feedback delay in DTNs imply that existing solutions for mobile ad-hoc networks do not apply to DTNs.In this paper, we propose the use of pair-wise tit-for-tat (TFT) as a simple, robust and practical incentive mechanism for DTNs. Existing TFT mechanisms often face bootstrapping problems or suffer from exploitation. We propose a TFT mechanism that incorporates generosity and contrition to address these issues. We then develop an incentive-aware routing protocol that allows selfish nodes to maximize their own performance while conforming to TFT constraints. For comparison, we also develop techniques to optimize the system-wide performance when all nodes are cooperative. Using both synthetic and real DTN traces, we show that without an incentive mechanism, the delivery ratio among selfish nodes can be as low as 20% as what is achieved under full cooperation; in contrast, with TFT as a basis of cooperation among selfish nodes, the delivery ratio increases to 60% or higher as under full cooperation. We also address the practical challenges involved in implementing the TFT mechanism. To our knowledge, this is the first practical incentive-aware routing scheme for DTNs.

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Proximity measures quantify the closeness or similarity between nodes in a social network and form the basis of a range of applications in social sciences, business, information technology, computer networks, and cyber security. It is challenging to estimate proximity measures in online social networks due to their massive scale (with millions of users) and dynamic nature (with hundreds of thousands of new nodes and millions of edges added daily). To address this challenge, we develop two novel methods to efficiently and accurately approximate a large family of proximity measures. We also propose a novel incremental update algorithm to enable near real-time proximity estimation in highly dynamic social networks. Evaluation based on a large amount of real data collected in five popular online social networks shows that our methods are accurate and can easily scale to networks with millions of nodes.To demonstrate the practical values of our techniques, we consider a significant application of proximity estimation: link prediction, i.e., predicting which new edges will be added in the near future based on past snapshots of a social network. Our results reveal that (i) the effectiveness of different proximity measures for link prediction varies significantly across different online social networks and depends heavily on the fraction of edges contributed by the highest degree nodes, and (ii) combining multiple proximity measures consistently yields the best link prediction accuracy.

-We present VCD, a novel system for enabling high-bandwidth content distribution in vehicular networks. In VCD, a vehicle opportunistically communicates with nearby access points (APs) to download the content of interest. To fully take advantage of such transient contact with APs, we proactively push content to the APs that the vehicles will likely visit in the near future. In this way, vehicles can enjoy the full wireless capacity instead of being bottlenecked by the Internet connectivity, which is either slow or even unavailable. We develop a new algorithm for predicting the APs that will soon be visited by the vehicles. We then develop a replication scheme that leverages the synergy among (i) Internet connectivity (which is persistent but has limited coverage and low bandwidth), (ii) local wireless connectivity (which has high bandwidth but transient duration), (iii) vehicular relay connectivity (which has high bandwidth but high delay), and (iv) mesh connectivity among APs (which has high bandwidth but low coverage). We demonstrate the effectiveness of VCD system using trace-driven simulation and Emulab emulation based on real taxi traces. We further deploy VCD in two vehicular networks: one using 802.11b and the other using 802.11n, to demonstrate its effectiveness.

Users today access a multitude of online services-among the most popular of which are online social networks (OSNs)-via both web sites and dedicated mobile applications (apps), using a range of devices (traditional PCs, tablets, and smartphones) that are connected via a variety of networks. The resulting infrastructure makes these services conveniently available anytime and anywhere, enabling them to become an integral part of daily life. As a consequence, users explicitly and implicitly provide a wealth of Personal Information (PI) that reflects several aspects of their life. Service providers monetize this information by selling to third parties (e.g., advertisers). Unfortunately, today, it remains difficult for end users to fully understand the amount and nature of the collected data.Our goal in this paper is to bring visibility into PI collected when accessing online services such as online social networks. This is a major challenge because PI is transferred in a proprietary way by each service. We develop a novel method that can automatically discover various types of PI carried within protocol fields of network traffic; the method includes techniques to filter out potential "containers" that do not actually carry PI and extend the set of containers initially found with additional ones. We evaluate the false positive/negative rates of our proposed method and show examples of interesting findings, including what kind of web sites or apps are more likely to transmit PI and which types of PI are most commonly collected.