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Sympathy for the sensor network debugger
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Source Conference On Embedded Networked Sensor Systems archive
Proceedings of the 3rd international conference on Embedded networked sensor systems table of contents
San Diego, California, USA
SESSION: Software support table of contents
Pages: 255 - 267  
Year of Publication: 2005
ISBN:1-59593-054-X
Authors
Nithya Ramanathan  UCLA Center for Embedded Network Sensing
Kevin Chang  UCLA Center for Embedded Network Sensing
Rahul Kapur  UCLA Center for Embedded Network Sensing
Lewis Girod  UCLA Center for Embedded Network Sensing
Eddie Kohler  UCLA Center for Embedded Network Sensing
Deborah Estrin  UCLA Center for Embedded Network Sensing
Sponsors
SIGARCH: ACM Special Interest Group on Computer Architecture
SIGBED: ACM Special Interest Group on Embedded Systems
ACM: Association for Computing Machinery
SIGCOMM: ACM Special Interest Group on Data Communication
SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing
SIGMETRICS: ACM Special Interest Group on Measurement and Evaluation
SIGOPS: ACM Special Interest Group on Operating Systems
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 21,   Downloads (12 Months): 121,   Citation Count: 29
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ABSTRACT

Being embedded in the physical world, sensor networks present a wide range of bugs and misbehavior qualitatively different from those in most distributed systems. Unfortunately, due to resource constraints, programmers must investigate these bugs with only limited visibility into the application. This paper presents the design and evaluation of Sympathy, a tool for detecting and debugging failures in sensor networks. Sympathy has selected metrics that enable efficient failure detection, and includes an algorithm that root-causes failures and localizes their sources in order to reduce overall failure notifications and point the user to a small number of probable causes. We describe Sympathy and evaluate its performance through fault injection and by debugging an active application, ESS, in simulation and deployment. We show that for a broad class of data gathering applications, it is possible to detect and diagnose failures by collecting and analyzing a minimal set of metrics at a centralized sink. We have found that there is a tradeoff between notification latency and detection accuracy; that additional metrics traffic does not always improve notification latency; and that Sympathy's process of failure localization reduces.


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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CITED BY  29

Collaborative Colleagues:
Nithya Ramanathan: colleagues
Kevin Chang: colleagues
Rahul Kapur: colleagues
Lewis Girod: colleagues
Eddie Kohler: colleagues
Deborah Estrin: colleagues