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Machine Learning of Semi-Autonomous Intelligent Mesh Networks Operation Expertise

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Title:Machine Learning of Semi-Autonomous Intelligent Mesh Networks Operation Expertise
Authors:Bordetsky, Alex
Glose, Carsten
Mullins, Steve
Bourakov, Eugene
Keywords:Decision Support for Complex Networks
Decision Analytics, Mobile Services, and Service Science
MANET, Network Management, Tactical Networks, Machine Learning Applications, Expert Mobile Systems
Date Issued:08 Jan 2019
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.
Pages/Duration:8 pages
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
Appears in Collections: Decision Support for Complex Networks

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