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Federated, secure, standardized, scalable, and transparent mechanism to access and share resources, particularly data resources, across organizational boundaries that does not require application modification and does not disrupt existing data access patterns has been needed for some time in the computational science community. The Global Federated File System (GFFS) addresses this need and is a foundational component of the NSF-funded eXtreme Science and Engineering Discovery Environment (XSEDE) program. The GFFS allows user applications to access (create, read, update, delete) remote resources in a location-transparent fashion. Existing applications, whether they are statically linked binaries, dynamically linked binaries, or scripts (shell, PERL, Python), can access resources anywhere in the GFFS without modification (subject to access control). In this paper we present an overview of the GFFS and its most common use cases: accessing data at an NSF center from a home or campus, accessing data on a campus machine from an NSF center, directly sharing data with a collaborator at another institution, accessing remote computing resources, and interacting with remote running jobs. We present these uses cases and how they are realized using the GFFS.

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A large amount of energy could be saved by detecting home occupancy and automatically controlling the lights, and HVAC. Existing occupancy sensors can detect the motion of people but cannot detect people when they are stationary. In this paper, we present a system called Peripheral WiFi Vision (PeriFi), which exploits multipath reflections as individual spatial sensors to increase the sensitivity of the conventional approaches. PeriFi analyzes each multipath component independently, increasing sensitivity so it can directly sense both moving and non-moving occupants.Our evaluations for 6 physical configurations with 11 different occupancy states show that PeriFi can achieve 96.7% accuracy, which translates to nearly 30% improvement over the conventional approaches.

Doorway tracking systems track people’s room location by instrumenting the doorways rather than instrumenting the rooms themselves—resulting in fewer sensors and less monitoring while still providing location information on occupants. In this article, we explore what is required to make doorway tracking a practical solution. We break a doorway tracking system into multiple independent design components, including both sensor and algorithmic design. Informed by this design, we construct a doorway tracking system and analyze how different combinations of these design components affect tracking accuracy. We perform a six-day in situ study in a ten-room house with two volunteers to analyze how these design components respond to the natural types and frequencies of errors in a real-world setting. To reflect the needs of different application classes, we analyze these design components using three different evaluation metrics: room accuracy, duration accuracy, and transition accuracy. Results indicate that doorway tracking can achieve 99.5% room accuracy on average in controlled settings and 96% room accuracy in in situ settings. This is contrasted against the 76% in situ setting room accuracy of Doorjamb, a doorway tracking system whose design implements only a limited number of components in our proposed doorway tracking system design space. We describe the differences between the data in the in situ and controlled settings, and provide guidelines about how to design a doorway tracking system for a given application’s accuracy requirements.