Aerial-based Crisis Management Center (ACMC)

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 will provide the required communications services and the computational resources that are critically needed by first responders. In our approach to develop an ACMC architecture, we utilize a set of flexible Unmanned Aerial Systems (UAS) that can be dynamically composed to meet the communications and computational requirements of CM tasks. The ACMC services will be modeled as a deep neural network (DNN) mass transport approach to cover a distributed target in a decentralized manner. This is indeed a new decentralized coverage approach with time-varying communication weights. Furthermore, our analysis proves the stability and convergence of the proposed DNN-based mass transport for a team of UAS (e.g., quadcopters), where each quadcopter uses a feedback nonlinear control to independently attain the intended coverage trajectory in a decentralized manner.

Figures (6)

• Figure 1: The proposed architecture for Aerial-based Crisis Management center (ACMC) for simultaneous provision computation resources for $n$ applications, defined by $\mathcal{A}=\left\{1,\cdots,n\right\}$, by $N$ Aerial Virtual Data Centers (AVDCs), defined by $\mathcal{V}=\left\{1,\cdots,N\right\}$.
• Figure 2: The block diaram of the trajectory tracking control used by every AVDC $i\in \mathcal{V}$.
• Figure 3: The initial formation of the AVDC team and inter-agent communication links.
• Figure 4: The structure of the DNN assigned based on the reference configuration of the AVDC team.
• Figure 5: The quintic polynomial used for defining $\beta$ versus ${t-t_0\over t_f-t_0}$.