Machine Learning of Semi-Autonomous Intelligent Mesh Networks Operation Expertise
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Authors
Bordetsky, Alex
Glose, Carsten
Mullins, Steven
Bourakov, Eugene
Subjects
Advisors
Date of Issue
2019
Date
2019
Publisher
HICSS
Language
Abstract
Operating networks in very dynamic environments makes network management both complex and difficult. It remains an open question how mesh or hastily formed networks with many nodes could be managed efficiently. Considering the various constraints such as limited communication channels on network management in dynamic environments, the need for semi-autonomous or autonomous networks is evident. Exploitation of machine learning techniques could be a way to solve this network management challenge. However, the need for large training datasets and the infrequency of network management events make it uncertain whether this approach is effective for highly dynamic networks and networks operating in unfriendly conditions, such as tactical military networks. This paper examines the feasibility of this approach by analyzing a recorded dataset of a mesh network experiment in a highly dynamic, austere military environment and derives conclusions for the design of future mesh networks and their network management systems.
Type
Conference Paper
Description
Proceedings of the 52nd Hawaii International Conference on System Sciences | 2019
The article of record at published may be found at https://hdl.handle.net/10125/59562.
The article of record at published may be found at https://hdl.handle.net/10125/59562.
Series/Report No
Department
Organization
Naval Postgraduate School (U.S.)
Identifiers
NPS Report Number
Sponsors
Funder
Format
8 p.
Citation
Distribution Statement
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.