Titan: An Enabling Framework for Activity-Aware "Pervasive Apps" in Opportunistic Personal Area Networks
- PDF / 4,243,833 Bytes
- 22 Pages / 600.05 x 792 pts Page_size
- 29 Downloads / 152 Views
Research Article Titan: An Enabling Framework for Activity-Aware “Pervasive Apps ” in Opportunistic Personal Area Networks Daniel Roggen,1 Clemens Lombriser,1, 2 Mirco Rossi,1 and Gerhard Tr¨oster1 1 Wearable 2 IBM
Computing Laboratory, ETH Zurich, 8092 Z¨urich, Switzerland Zurich Research Laboratory, S¨aumerstrasse 4, 8803 R¨uschlikon, Switzerland
Correspondence should be addressed to Daniel Roggen, [email protected] Received 24 October 2010; Accepted 31 December 2010 Academic Editor: Arie Reichman Copyright © 2011 Daniel Roggen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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.
1. Introduction The famous “AppStores” are common nowadays to publish software (Apps) onto mobile phones. We envision that a similar development of “Pervasive AppStores” will commoditize the so-called Pervasive Apps. This work proposes a way to realize this idea. We present Titan, a service-oriented solution that comprises Internet application repositories storing applications in the form of dynamically composed service graphs, a mobile device managing the user’s Personal Area Network (PAN), and a service graph execution framework distributing service execution to available resources (sensors, mobile devices) in the user’s PAN. We focus on activity-aware applications, applications that use the physical activity of the user as a contextual source to provide an adapted pervasive computing experience, sometimes also called activi
Data Loading...
