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Disens: scalable distributed sensor network simulation
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Principles and Practice of Parallel Programming archive
Proceedings of the 12th ACM SIGPLAN symposium on Principles and practice of parallel programming table of contents
San Jose, California, USA
SESSION: Parallel applications table of contents
Pages: 24 - 34  
Year of Publication: 2007
ISBN:978-1-59593-602-8
Authors
Ye Wen  University of California, Santa Barbara, CA
Rich Wolski  University of California, Santa Barbara, CA
Gregory Moore  University of California, Santa Barbara, CA
Sponsors
ACM: Association for Computing Machinery
SIGPLAN: ACM Special Interest Group on Programming Languages
Publisher
ACM  New York, NY, USA
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ABSTRACT

Simulation is widely used for developing, evaluating and analyzing sensor network applications, especially when deploying a large scale sensor network remains expensive and labor intensive. However, due to its computation intensive nature, existent simulation tools have to make trade-offs between fidelity and scalability and thus offer limited capabilities as design and analysis tools. In this paper, we introduce DiSenS (DIstributed SENsor network Simulation) -- a highly scalable distributed simulation system for sensor networks. DiSenS does not only faithfully emulates an extensive set of sensor hardware and supports extensible radio/power models, so that sensor network applications can be simulated transparently with high fidelity, but also employs distributed-memory parallel cluster system to attack the complex simulation problem. Combining an efficient distributed synchronization protocol and a sophisticated node partitioning algorithm (based on existent research), DiSenS achieves greater scalability than even many discrete event simulators. On a small to medium size cluster (16-64 nodes), DiSenS is able to simulate hundreds of motes in realtime speed and scale to thousands in sub-realtime speed. To our knowledge, DiSenS is the first full-system sensor network simulator with such scalability.


REFERENCES

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Collaborative Colleagues:
Ye Wen: colleagues
Rich Wolski: colleagues
Gregory Moore: colleagues