NeTS-NOSS: SensorNet Architectures for Indoor Location Detection: From Resolution to Robustness

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Proposal Number: 0435312PI: David Starobinski Institution: Boston University Title: SensorNet Architectures of Indoor Location Detection: From Resolution to RobustnessAbstract: The ability to accurately and robustly determine the location of sensors or targets is fundamental to the operation of sensor networks. In open outdoor settings, this can be performed with trilateration techniques used by the Global Positioning System (GPS). However, in indoor or dense urban environments, such techniques face a wide variety of debilitating complications, including signal reflections, occlusions, and unpredictable dependencies (e.g., doors opening and closing). This project is investigating fundamental performance limits of indoor location detection systems and aims at prototyping sensor network architectures that approach these limits. The work focuses on tradeoffs between robustness and resolution of such systems, drawing on the literature in coding theory, large deviations, and mathematical programming. Paralleling the development of GPS, the results of this project could serve as a cornerstone technology for a wide variety of applications, including tracking of shared equipment, victim disaster rescue, or indoor navigation for the blind