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Resource aware programming in the Pixie OS
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Conference On Embedded Networked Sensor Systems archive
Proceedings of the 6th ACM conference on Embedded network sensor systems table of contents
Raleigh, NC, USA
SESSION: Architecture aspects of sensor networks table of contents
Pages 211-224  
Year of Publication: 2008
ISBN:978-1-59593-990-6
Authors
Konrad Lorincz  Harvard University, Cambridge, MA, USA
Bor-rong Chen  Harvard University, Cambridge, MA, USA
Jason Waterman  Harvard University, Cambridge, MA, USA
Geoff Werner-Allen  Harvard University, Cambridge, MA, USA
Matt Welsh  Harvard University, Cambridge, MA, USA
Sponsors
SIGCOMM: ACM Special Interest Group on Data Communication
SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing
SIGOPS: ACM Special Interest Group on Operating Systems
SIGMETRICS: ACM Special Interest Group on Measurement and Evaluation
ACM: Association for Computing Machinery
SIGARCH: ACM Special Interest Group on Computer Architecture
SIGBED: ACM Special Interest Group on Embedded Systems
Publisher
ACM  New York, NY, USA
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ABSTRACT

This paper presents Pixie, a new sensor node operating system designed to support the needs of data-intensive applications. These applications, which include high-resolution monitoring of acoustic, seismic, acceleration, and other signals, involve high data rates and extensive in-network processing. Given the fundamentally resource-limited nature of sensor networks, a pressing concern for such applications is their ability to receive feedback on, and adapt their behavior to, fluctuations in both resource availability and load.

The Pixie OS is based on a dataflow programming model based on the concept of resource tickets, a core abstraction for representing resource availability and reservations. By giving the system visibility and fine-grained control over resource management, a broad range of policies can be implemented. To shield application programmers from the burden of managing these details, Pixie provides a suite of resource brokers, which mediate between low-level physical resources and higher-level application demands. Pixie is implemented in NesC and supports limited backwards compatibility with TinyOS.

We describe Pixie in the context of two applications: limb motion analysis for patients undergoing treatment for motion disorders, and acoustic target detection using a network of microphones. We present a range of experiments demonstrating Pixie's ability to accurately account for resource availability at runtime and enable a range of both generic and application-specific adaptations.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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Collaborative Colleagues:
Konrad Lorincz: colleagues
Bor-rong Chen: colleagues
Jason Waterman: colleagues
Geoff Werner-Allen: colleagues
Matt Welsh: colleagues