Localization to Enhance Security and Services in Wi-Fi Networks under Privacy Constraints

Developments of seamless mobile services are faced with two broad challenges, systems security and user privacy - access to wireless systems is highly insecure due to the lack of physical boundaries and, secondly, location based services (LBS) could be used to extract highly sensitive user information. In this paper, we describe our work on developing systems which exploit location information to enhance security and services under privacy constraints. We describe two complimentary methods which we have developed to track node location information within production University Campus Networks comprising of large numbers of users. The location data is used to enhance security and services. Specifically, we describe a method for creating geographic firewalls which allows us to restrict and enhance services to individual users within a specific containment area regardless of physical association. We also report our work on LBS development to provide visualization of spatio-temporal node distribution under privacy considerations

SmartMoveX on a graph -- an inexpensive active badge tracker

Measuring the locations of people and things in a building is an important part of ubiquitous computing. We present new hardware and software for this purpose. The hardware, called SmartMoveX, is an active badge system in which a small radio transmitter is attached to the person or thing being tracked. Receivers placed in the building’s existing offices, connected to existing PCs, transmit signal strength readings to a central PC using the building’s existing computer network. Combined with the low cost of the hardware, using the existing network makes this active badge system much less expensive than many others. To compute locations based on signal strength, we gathered signal strength readings from predefined location nodes in the building. We defined a graph on these nodes, which allowed us to enforce constraints on computed movements between nodes (e.g. cannot pass through walls) and to probabilistically enforce our expectations on transitions between connected nodes. Modeling the paths with a hidden Markov model, we used the Viterbi algorithm to compute optimal paths based on signal strengths over the node graph. The average location error was 3.05 meters, which compared favorably to a simple nearest neighbor algorithm’s average location error of 4.57 meters