Abstract
Context recognition, such as gesture or activity recognition, is a key mechanism that enables ubiquitous computing systems to proactively support users. It becomes challenging in unconstrained environments such as those encountered in daily living, where it has to deal with heterogeneous networks, changing sensor availability, communication capabilities, and available processing power.
This paper describes Titan, a new framework that is specifically designed to perform context recognition in such dynamic sensor networks. Context recognition algorithms are represented by interconnected data processing tasks forming a task network. Titan adapts to different context recognition algorithms by dynamically reconfiguring individual sensor nodes to update the network wide algorithm execution.
We demonstrate the applicability of Titan for activity recognition on Tmote Sky sensor nodes and show that Titan is able to perform processing of sensor data sampled at 100 Hz and can reconfigure a sensor node in less than 1ms. This results in a better tradeoff between computational speed and dynamic reconfiguration time.
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Lombriser, C., Roggen, D., Stäger, M., Tröster, G. (2007). Titan: A Tiny Task Network for Dynamically Reconfigurable Heterogeneous Sensor Networks. In: Braun, T., Carle, G., Stiller, B. (eds) Kommunikation in Verteilten Systemen (KiVS). Informatik aktuell. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69962-0_11
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DOI: https://doi.org/10.1007/978-3-540-69962-0_11
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