Location-Based Mobile Service (LBMS) is one of the most popular smartphone services. LBMS enables people to more easily connect with each other and analyze the aspects of their lives. However, sharing location data can leak people's privacy. We present PDVLoc, a controlled location data-sharing framework based on selectively sharing data through a Personal Data Vault (PDV). A PDV is a privacy architecture in which individuals retain ownership of their data. Data are routinely filtered before being shared with content-service providers, and users or data custodian services can participate in making controlled data-sharing decisions. Introducing PDVLoc gives users flexible and granular access control over their location data. We have implemented a prototype of PDVLoc and evaluated it using real location-sharing social networking applications, Google Latitude and Foursquare. Our user study of 19 participants over 20 days shows that most users find that PDVLoc is useful to manage and control their location data, and are willing to continue using PDVLoc.

Just as coordinate-oriented location-based applications have exploded recently with mapping services, new semantic location services will be critical for the next wave of killer applications. People are going to want everyday applications to have location-awareness that goes beyond simple numerical latitude and longitude. Loci is a new semantic location service layer that employs user feedback to bridge the gap between machine-learned and human-defined places. Advances in place learning techniques have provided us the tools to detect nearly 95% of the visits we make to places and the distances we travel. The difficulty of recovering the remaining 5% comes from designing parameters that work for every user in every place. Based on a user study with 29 participants over three weeks, we show that the level of user feedback required by the service is acceptable and most of the users are willing to provide help to improve their experiences with the service. Our results suggest that user feedback has the potential to significantly improve semantic location services, but requires well-timed prompting mechanisms to improve the quality of the feedback.

Continuously understanding a user's location context in colloquial terms and the paths that connect the locations unlocks many opportunities for emerging applications. While extensive research effort has been made on efficiently tracking a user's raw coordinates, few attempts have been made to efficiently provide everyday contextual information about these locations as places and paths. We introduce SensLoc, a practical location service to provide such contextual information, abstracting location as place visits and path travels from sensor signals. SensLoc comprises of a robust place detection algorithm, a sensitive movement detector, and an on-demand path tracker. Based on a user's mobility, SensLoc proactively controls active cycle of a GPS receiver, a WiFi scanner, and an accelerometer. Pilot studies show that SensLoc can correctly detect 94% of the place visits, track 95% of the total travel distance, and still only consume 13% of energy than algorithms that periodically collect coordinates to provide the same information.

Detecting visits to semantically meaningful places is important for many emerging mobile applications. We present PlaceSense, a place discovery algorithm suitable for mobile devices that exploits pervasive RF-beacons. By relying on separate mechanisms to detect entrance to and departure from a place and buffering overlapping data for subsequent visits, it is more robust than the state-of-the-art, especially in detecting short visits, places where people are mobile, or where inconsistent beacons are prevalent due to interference. We experimentally evaluate PlaceSense's effectiveness in discovering semantically meaningful places, and compare with other approaches that use coordinates or RF-beacon fingerprints. Our results demonstrate that PlaceSense correctly discovers 92% (compared to between 28% and 65% for previous work) of the visited places and accurately detects their entrance and departure times from both real-life and scripted data sets.

We present Castor, a secure code-update protocol for sensor networks that exploits symmetric cryptoystems. Through a synergistic combination of a one-way hash-chain, two one-way key-chains with the delayed disclosure of symmetric keys, and multiple message authentication codes (MACs), Castor enables untrusted sensor nodes to verify an update's authenticity and guarantees that no correct node will ever install or forward a compromised part of a code-update image. We describe an implementation of Castor that hardens the TinyOS-based update protocol, Deluge, against node compromise. We experimentally compare Castor's computational and communication costs with those of Deluge and with those of a contemporary secure update protocol, Sluice, that uses asymmetric cryptosystems (digital signatures) instead. Our results demonstrate that Castor incurs reasonable overheads as compared to Deluge, and lower resource usage as well as lower end-to-end update latency as compared to Sluice.