Aerial-based Crisis Management Center (ACMC)
Hossein Rastgoftar, Salim Hariri

TL;DR
This paper proposes an aerial-based crisis management system utilizing autonomous drones to provide communication and computational support during disasters, employing a decentralized deep neural network approach for dynamic coverage.
Contribution
It introduces a novel decentralized coverage method using DNNs and UAS for crisis management, with proven stability and convergence in a dynamic environment.
Findings
Proposed a DNN-based mass transport approach for UAS coverage.
Proved stability and convergence of the decentralized control system.
Demonstrated dynamic composition of UAS for crisis response.
Abstract
Crisis management (CM) for critical infrastructures, natural disasters such as wildfires and hurricanes, terrorist actions, or civil unrest requires high speed communications and connectivity, and access to high performance computational resources to deliver timely dynamic responses to the crisis being managed by different first responders. CM systems should detect, recognize, and disseminate huge amounts of heterogeneous dynamic events that operate at different speeds and formats. Furthermore, the processing of crisis events and the development of real-time responses are major research challenges when the communications and computational resources needed by CM stakeholders are not available or severely degraded by the crisis. The main goal of the research presented in this paper is to utilize Unmanned Autonomous Systems (UAS) to provide Aerial-based Crisis Management Center (ACMC) that…
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Taxonomy
TopicsUAV Applications and Optimization · Air Traffic Management and Optimization · Opportunistic and Delay-Tolerant Networks
MethodsSparse Evolutionary Training · SPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
