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Role of machine learning in configuration management of ad hoc wireless networks
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Source Joint International Conference on Measurement and Modeling of Computer Systems archive
Proceedings of the 2005 ACM SIGCOMM workshop on Mining network data table of contents
Philadelphia, Pennsylvania, USA
SESSION: Routing & configuration management table of contents
Pages: 223 - 224  
Year of Publication: 2005
ISBN:1-59593-026-4
Authors
Sung-eok Jeon  Georgia Institute of Technology
Chuanyi Ji  Georgia Institute of Technology
Sponsors
SIGCOMM: ACM Special Interest Group on Data Communication
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 4,   Downloads (12 Months): 23,   Citation Count: 0
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ABSTRACT

In this work, we show that machine learning, e.g., graphical models, plays an important role for the self-configuration of ad hoc wireless network. The role of such a learning approach includes a simple representation of complex dependencies in the network and a distributed algorithm which can adaptively find a nearly optimal configuration.


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.

 
1
S. Geman, and D. Geman, "Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images," IEEE Trans. PAMI vol. 6, pp. 721--741, 1984
 
2
K. Huang, "Statistical Mechanics," John Wiley & Sons
Collaborative Colleagues:
Sung-eok Jeon: colleagues
Chuanyi Ji: colleagues

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