Applying Machine Learning in Managing Deployable Systems
Joseph Nygate, Clark G. Hochgraf, Mark Indelicato, William Johnson, Miguel Bazdresch, Romel Espinosa Reyes · 2018
The Next Generation First Responders program, run by the Department of Homeland Security, is responsible for developing and applying technologies to assist emergency responders of the future. Deployable Systems (DS) are a key technology in this program as they are critical in providing coverage when the availability of wireless resources is impacted during major disasters, in locations experiencing congestion due to large-scale incidents, or in remote areas where complete coverage is not feasible. The Public Safety Communications Research group that is conducting research into DS technology has described technology gaps in (·)Measuring, modeling and predicting network coverage (·)Allocating, prioritizing, and routing available bandwidth between different applications (·)Interworking DS from different vendors (·)Determining the coverage and bandwidth DS can provideWe will show how these gaps can be addressed by implementing two 3rd Generation Partnership Project (3GPP) specifications - Access Network Discovery and Selection Function (ANDSF) and Self-Organizing Networks (SON) across the wireless and DS networks using a common data repository. Moreover, we will show how Machine Learning algorithms can leverage this data to implement many additional use cases that will help emergency responders, and disaster response planners in developing and implementing effective and efficient disaster management strategies.