Abstract—Data dissemination is useful for many applications

Abstract—Today’s location-sensitive service relies on user’s mobile device to determine its location and send the location to the application. This approach allows the user to cheat by having his device transmit a fake location, which might enable the user to access a restricted resource erroneously or provide bogus alibis. To address this issue, we propose A Privacy-Preserving LocAtion proof Updating System (APPLAUS) in which co-located Bluetooth enabled mobile devices mutually generate location proofs, and update to a location proof server. Periodically changed pseudonyms are used by the mobile devices to protect source location privacy from each other, and from the untrusted location proof server. We also develop user-centric location privacy model in which individual users evaluate their location privacy levels in real-time and decide whether and when to accept a location proof exchange request based on their location privacy levels. APPLAUS can be implemented with the existing network infrastructure and the current mobile devices, and can be easily deployed in Bluetooth enabled mobile devices with little computation or power cost. Extensive experimental results show that our scheme, besides providing location proofs effectively, can significantly preserve the source location privacy. I.

Abstract—It is well known that the daily movement of people exhibits a high degree of repetition in which people usually stay at regular places for their daily activities. This paper presents a novel framework to construct a predictive model by exploiting the regularity of people movement found in the collected joint Wifi/Bluetooth trace. Our obtained predictive model is able to answer three fundamental questions: (1) where the person will stay at a future time, (2) how long she will stay at the location, and (3) who she will meet at a future time. In order to construct the predictive model, we first propose an efficient clustering algorithm to cluster Wifi access points in the Wifi trace into clusters and use these clusters to represent locations. Then, we construct a Naive Bayesian classifier to assign these locations to records in Bluetooth trace. The combined Wifi/Bluetooth trace with locations is used to construct the location predictor, stay duration predictor, and people predictor. Finally, we evaluate three predictors over the real Wifi/Bluetooth traces collected by 50 experiment participants in University of Illinois campus from March to August 2010. The results confirm that our predictors provide highly accurate predictions of location, stay duration, and people. I.

Abstract—Disruption Tolerant Networks (DTNs) are characterized by the low node density, unpredictable node mobility and lack of global network information. Most of current research efforts in DTNs focus on data forwarding, but only limited work has been done on providing effective data access to mobile users. In this paper, we propose a novel approach to support cooperative caching in DTNs, which enables the sharing and coordination of cached data among multiple nodes and reduces data access delay. Our basic idea is to intentionally cache data at a set of Network Central Locations (NCLs), which can be easily accessed by other nodes in the network. We propose an effective scheme which ensures appropriate NCL selection based on a probabilistic selection metric, and coordinate multiple caching nodes to optimize tradeoff between data accessibility and caching overhead. Extensive trace-driven simulations show that our scheme significantly improves data access performance compared to existing schemes. I.

Abstract—Opportunistic mobile networks consist of personal mobile devices which are intermittently connected with each other. Data access can be provided to these devices via cooperative caching without support from the cellular network infrastructure, but only limited research has been done on maintaining the freshness of cached data which may be refreshed periodically and is subject to expiration. In this paper, we propose a scheme to efficiently maintain cache freshness. Our basic idea is to let each caching node be only responsible for refreshing a specific set of caching nodes, so as to maintain cache freshness in a distributed and hierarchical manner. Probabilistic replication methods are also proposed to analytically ensure that the freshness requirements of cached data are satisfied. Extensive tracedriven simulations show that our scheme significantly improves cache freshness, and hence ensures the validity of data access provided to mobile users. I.

Abstract—Node mobility and end-to-end disconnections in Disruption Tolerant Networks (DTNs) greatly impair the effectiveness of data forwarding. Although social-based approaches can address the problem, most existing solutions only focus on forwarding data to a single destination. In this paper, we study multicast with single and multiple data items in DTNs from a social network perspective, develop analytical models for multicast relay selection, and furthermore investigate the essential difference between multicast and unicast in DTNs. The proposed approach selects relays according to their capabilities, measured by social-based metrics, for forwarding data to the destinations. The design of social-based metrics exploits social network concepts such as node centrality and social community, and the selected relays ensure achieving the required data delivery ratio within the given time constraint. Extensive tracedriven simulations show that the proposed approach has similar data delivery ratio and delay to that of Epidemic routing, but significantly reduces data forwarding cost, measured by the number of relays used. Index Terms—Multicast, Disruption Tolerant Network, social network, centrality, community.

Abstract — Accurately estimating the current positions of moving objects is a challenging task due to the various forms of data uncertainty (e.g. limited sensor precision, periodic updates from continuously moving objects). However, in many cases, groups of objects tend to exhibit similarities in their movement behavior. For example, vehicles in a convoy or animals in a herd both exhibit tightly coupled movement behavior within the group. While such statistical dependencies often increase the computational complexity necessary for capturing this additional structure, they also provide useful information which can be utilized to provide more accurate location estimates. In this paper, we propose a novel model for accurately tracking coordinated groups of mobile uncertain objects. We introduce an exact and more efficient approximate inference algorithm for updating the current location of each object upon the arrival of new (uncertain) location observations. Additionally, we derive probability bounds over the groups in order to process probabilistic threshold range queries more efficiently. Our experimental evaluation shows that our proposed model can provide 4X improvements in tracking accuracy over competing models which do not consider group behavior. We also show that our bounds enable us to prune up to 50 % of the database, resulting in more efficient processing over a linear scan. I.

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