Titan: an enabling framework for activity-aware "pervasive apps" in opportunistic personal area networks

  • Authors:

  • Daniel Roggen;Clemens Lombriser;Mirco Rossi;Gerhard Tröster

  • Affiliations:

  • Wearable Computing Laboratory, ETH Zurich, Zürich, Switzerland;Wearable Computing Laboratory, ETH Zurich, Zürich, Switzerland and 2IBM Zurich Research Laboratory, Rüschlikon, Switzerland;Wearable Computing Laboratory, ETH Zurich, Zürich, Switzerland;Wearable Computing Laboratory, ETH Zurich, Zürich, Switzerland

  • Venue:
  • EURASIP Journal on Wireless Communications and Networking - Special issue on towards the connected body: advances in body communications
  • Year:
  • 2011

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Abstract

Upcoming ambient intelligence environments will boast ever larger number of sensor nodes readily available on body, in objects, and in the user's surroundings. We envision "Pervasive Apps", user-centric activity-aware pervasive computing applications. They use available sensors for activity recognition. They are downloadable from application repositories, much like current Apps for mobile phones. A key challenge is to provide Pervasive Apps in open-ended environments where resource availability cannot be predicted. We therefore introduce Titan, a service-oriented framework supporting design, development, deployment, and execution of activity-aware Pervasive Apps. With Titan, mobile devices inquire surrounding nodes about available services. Internet-based application repositories compose applications based on available services as a service graph. The mobile device maps the service graph to Titan Nodes. The execution of the service graph is distributed and can be remapped at run time upon changing resource availability. The framework is geared to streaming data processing and machine learning, which is key for activity recognition. We demonstrate Titan in a pervasive gaming application involving smart dice and a sensorized wristband. We comparatively present the implementation cost and performance and discuss how novel machine learning methodologies may enhance the flexibility of the mapping of service graphs to opportunistically available nodes.