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A measurement study of interference modeling and scheduling in low-power wireless networks
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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: Selected problems in WSNs table of contents
Pages 141-154  
Year of Publication: 2008
ISBN:978-1-59593-990-6
Authors
Ritesh Maheshwari  Stony Brook University, Stony Brook, NY, USA
Shweta Jain  Stony Brook University, Stony Brook, NY, USA
Samir R. Das  Stony Brook University, Stony Brook, NY, 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

Accurate interference models are important for use in transmission scheduling algorithms in wireless networks. In this work, we perform extensive modeling and experimentation on two 20-node TelosB motes testbeds -- one indoor and the other outdoor -- to compare a suite of interference models for their modeling accuracies. We first empirically build and validate the physical interference model via a packet reception rate vs. SINR relationship using a measurement driven method. We then similarly instantiate other simpler models, such as hop-based, range-based, protocol model, etc. The modeling accuracies are then evaluated on the two testbeds using transmission scheduling experiments. We observe that while the physical interference model is the most accurate, it is still far from perfect, providing a 90-percentile error about 20-25% (and 80 percentile error 7-12%), depending on the scenario. The accuracy of the other models is worse and scenario-specific. The second best model trails the physical model by roughly 12-18 percentile points for similar accuracy targets. Somewhat similar throughput performance differential between models is also observed when used with greedy scheduling algorithms. Carrying on further, we look closely into the the two incarnations of the physical model -- 'thresholded' (conservative, but typically considered in literature) and 'graded' (more realistic). We show via solving the one shot scheduling problem, that the graded version can improve `expected throughput' over the thresholded version by scheduling imperfect links.


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:
Ritesh Maheshwari: colleagues
Shweta Jain: colleagues
Samir R. Das: colleagues