Aerial-borne Data Management Center (ADMC) - Tsai, Chieh; Rastgoftar, Hossein; Hariri, Salim (Proceedings of the AAAI Symposium Series, 2026)
Crisis management (CM) for critical infrastructures, naturaldisasters such as wildfires and hurricanes, terroristactions,or civil unrest requires high-speed communications andconnectivity,and access to high-performance computational resourcesto deliver timely dynamic responses to the crisis beingmanaged by different first responders.CMsystems shoulddetect, recognize, and disseminate huge amounts ofheterogeneousdynamic events that operate at different speeds andformats. Furthermore, the processing of crisis events andthe development of real-time responses are major researchchallenges when the communications and computationalresourcesneeded by CM stakeholders are not available orseverely degraded by the crisis. The main goal of theresearchpresented in this paper is to utilize Unmanned AutonomousSystems (UAS) to provide an Aerial-borne Data ManagementCenter (ADMC) that will provide the required communicationsservices and the computational resources that are criticallyneeded by first responders. In our approach to developan ADMC architecture, we utilize a set of flexible UnmannedAerial Systems (UAS) that can be dynamically composed tomeet the communications and computational requirements ofCM tasks. The ADMC services will be modeled as a deepneural network (DNN) mass transport approach to cover adistributed target in a decentralized manner. Furthermore,ouranalysis proves the stability and convergence of theproposedDNN-based mass transport for a team of UAS (e.g.,quadcopters),where each quadcopter uses a feedback nonlinearcontrol to independently attain the intended coveragetrajectoryin a decentralized manner.