Towards Automation of Management and Planning for Future Military Tactical Networks

Chiang, C.-Y.J.; Chadha, Ritu; Newman, Scott; Lo, Richard

doi:10.1109/milcom.2006.302136

Cited by 3 publications

(1 citation statement)

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“…Although some progress has been made [5], further automation of network management is vital even in standard and non-military networks, as various efforts show [9].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning of Semi-Autonomous Intelligent Mesh Networks Operation Expertise

Bordetsky¹,

Glose²,

Mullins³

et al. 2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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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.

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“…Although some progress has been made [5], further automation of network management is vital even in standard and non-military networks, as various efforts show [9].…”

Section: Introductionmentioning

confidence: 99%