Location-based services are growing in popularity due to the ubiquity of smartphone users. The relevance of location-based query results is very important, especially for mobile phones with limited screen size. Location-based data frequently changes; this introduces challenges in indexing and ranking places. The growing popularity of mobile social networks, such as Twitter, FourSquare and Facebook Places, presents an opportunity to build better location-based services by leveraging user interactions on these networks. In this paper, we present SocialTelescope, a location-based service that automatically compiles, indexes and ranks locations, based on user interactions with locations in mobile social networks. We implemented our system as a location-based search engine that uses geo-tweets by Twitter users to learn about places. We evaluated the coverage and relevance of our system by comparing it against current state-of-the-art approaches including page-rank (Google Local Search), expert-based (Zagat) and user-review based (Yelp). Our results show that a crowd-sourced location-based service returns results that match those returned by current approaches in relevance, at a substantially lower cost.

Social hierarchy and stratification among humans is a well studied concept in sociology. The popularity of online social networks presents an opportunity to study social hierarchy for different types of networks and at different scales. We adopt the premise that people form connections in a social network based on their perceived social hierarchy; as a result, the edge directions in directed social networks can be leveraged to infer hierarchy. In this paper, we define a measure of hierarchy in a directed online social network, and present an efficient algorithm to compute this measure. We validate our measure using ground truth including Wikipedia notability score. We use this measure to study hierarchy in several directed online social networks including Twitter, Delicious, YouTube, Flickr, LiveJournal, and curated lists of several categories of people based on different occupations, and different organizations. Our experiments on different online social networks show how hierarchy emerges as we increase the size of the network. This is in contrast to random graphs, where the hierarchy decreases as the network size increases. Further, we show that the degree of stratification in a network increases very slowly as we increase the size of the graph.

People waste many hours driving each day. Although unavoidable, this time can be very boring to motorists. Similar to people taking mass transit who often pass the time socializing with those around them, motorists could benefit from social interactions if they were given broader social opportunities. Unfortunately, existing Multiparty Voice Communication (MVC) systems do not scale to large numbers of users and do not provide adequate access controls. We present RoadSpeak, a scalable MVC system that allows motorists to automatically join Voice Chat Groups (VCGs) along popular roadways. RoadSpeak achieves interruption-free communication through the use of voice chat message buffering, flow control and in-order delivery of voice messages to participants. We have implemented a RoadSpeak prototype on Nokia N95 smart phones using 3G cellular networking for voice message transfer. We have also built an MVC simulator to perform large-scale simulations that compare RoadSpeak with existing MVC systems. The results of our evaluation prove the feasibility of RoadSpeak and demonstrate that it performs similarly to a traditional MVC systems while supporting substantially larger groups of users.

To usefully query a location-based service, a mobile device must typically present its own location in its query to the server. This may not be acceptable to clients that wish to protect the privacy of their location. This paper presents the design and implementation of SybilQuery, a fully decentralized and autonomous k-anonymization-based scheme to privately query location-based services. SybilQuery is a client-side tool that generates k-1 Sybil queries for each query by the client. The location-based server is presented with a set of k queries and is unable to distinguish between the client's query and the Sybil queries, thereby achieving k-anonymity. We tested our implementation of SybilQuery on real mobility traces of approximately 500 cabs in the San Francisco Bay area. Our experiments show that SybilQuery can efficiently generate Sybil queries and that these queries are indistinguishable from real queries.

Traffic querying, road sensing and mobile content delivery are emerging application domains for vehicular networks whose performance depends on the throughput these networks can sustain. Rate adaptation is one of the key mechanisms at the link layer that determine this performance. Rate adaptation in vehicular networks faces the following key challenges: (1) due to the rapid variations of the link quality caused by fading and mobility at vehicular speeds, the transmission rate must adapt fast in order to be effective, (2) during infrequent and bursty transmission, the rate adaptation scheme must be able to estimate the link quality with few or no packets transmitted in the estimation window, (3) the rate adaptation scheme must distinguish losses due to environment from those due to hidden-station induced collision. Our extensive outdoor experiments show that the existing rate adaptation schemes for 802.11 wireless networks under utilize the link capacity in vehicular environments. In this paper, we design, implement and evaluate CARS, a novel context-aware rate selection algorithm that makes use of context information (e.g. vehicle speed and distance from neighbor) to systematically address the above challenges, while maximizing the link throughput. Our experimental evaluation in real outdoor vehicular environments with different mobility scenarios shows that CARS adapts to changing link conditions at high vehicular speeds faster than existing rate-adaptation algorithms. Our scheme achieves significantly higher throughput, up to 79%, in all the tested scenarios, and is robust to packet loss due to collisions, improving the throughput by up to 256% in the presence of hidden stations.

VANETs (vehicular ad hoc networks) are emerging as a new network environment for intelligent transportation systems. Many of the applications built for VANETs will depend on the data push communication model, where information is disseminated to a group of vehicles. In this paper, we present a formal model of data dissemination in VANETs and study how VANET characteristics, specifically the bidirectional mobility on well defined paths, affects the performance of data dissemination. We study the data push model in the context of TrafficView, a system we have implemented to disseminate information about the vehicles on the road. Traffic data could be disseminated using vehicles moving on the same direction, vehicles moving in the opposite direction, or vehicles moving in both directions. Our analysis as well as simulation results show that dissemination using only vehicles in the opposite direction increases the data dissemination performance significantly