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Using statistical design of experiments for analyzing mobile ad hoc networks
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Source International Workshop on Modeling Analysis and Simulation of Wireless and Mobile Systems archive
Proceedings of the 8th ACM international symposium on Modeling, analysis and simulation of wireless and mobile systems table of contents
Montréal, Quebec, Canada
SESSION: Analysis of wireless networks: tools and techniques table of contents
Pages: 159 - 168  
Year of Publication: 2005
ISBN:1-59593-188-0
Authors
Michael W. Totaro  University of Louisiana at Lafayette, Lafayette, LA
Dmitri D. Perkins  University of Louisiana at Lafayette, Lafayette, LA
Sponsors
ACM: Association for Computing Machinery
SIGSIM: ACM Special Interest Group on Simulation and Modeling
Publisher
ACM  New York, NY, USA
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ABSTRACT

The performance of mobile ad hoc networks can be influenced by numerous factors, including protocol design at every layer; parameter settings such as retransmission limits and timers; system factors such as network size and traffic load; as well as environmental factors such as channel fading. In this work, we are concerned with understanding the functional relationship between these influential factors and performance of mobile ad hoc networking systems. We show how a systematic statistical design of experiments (DOE) strategy can be used to analyze network system and protocol performance, leading to more objective conclusions valid over a wide range of network conditions and environments. Using a DOE strategy and a 2k factorial design, we quantify the main and interactive effects of five factors (i.e., network density, node mobility, traffic load, network size, and medium access control scheme) on two response metrics (i.e., packet delivery ratio and end-to-end delay). Using these effects measures, we then develop two first-order linear regression models that define the functional relationship between the influential factors and two performance metrics.


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:
Michael W. Totaro: colleagues
Dmitri D. Perkins: colleagues