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Firefly-inspired sensor network synchronicity with realistic radio effects
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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: Synchronization table of contents
Pages: 142 - 153  
Year of Publication: 2005
ISBN:1-59593-054-X
Authors
Geoffrey Werner-Allen  Harvard University
Geetika Tewari  Harvard University
Ankit Patel  Harvard University
Matt Welsh  Harvard University
Radhika Nagpal  Harvard University
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
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Downloads (6 Weeks): 29,   Downloads (12 Months): 208,   Citation Count: 16
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ABSTRACT

Synchronicity is a useful abstraction in many sensor network applications. Communication scheduling, coordinated duty cycling, and time synchronization can make use of a synchronicity primitive that achieves a tight alignment of individual nodes' firing phases. In this paper we present the Reachback Firefly Algorithm (RFA), a decentralized synchronicity algorithm implemented on TinyOS-based motes. Our algorithm is based on a mathematical model that describes how fireflies and neurons spontaneously synchronize. Previous work has assumed idealized nodes and not considered realistic effects of sensor network communication, such as message delays and loss. Our algorithm accounts for these effects by allowing nodes to use delayed information from the past to adjust the future firing phase. We present an evaluation of RFA that proceeds on three fronts. First, we prove the convergence of our algorithm in simple cases and predict the effect of parameter choices. Second, we leverage the TinyOS simulator to investigate the effects of varying parameter choice and network topology. Finally, we present results obtained on an indoor sensor network testbed demonstrating that our algorithm can synchronize sensor network devices to within 100 μsec on a real multi-hop topology with links of varying quality.


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  16

Collaborative Colleagues:
Geoffrey Werner-Allen: colleagues
Geetika Tewari: colleagues
Ankit Patel: colleagues
Matt Welsh: colleagues
Radhika Nagpal: colleagues