###### Abstract
The proposed method in this work employs multi-user microgrids to generate electricity concurrently with cellular-based communications, which are dynamically re-adjusted into a mesh network along with local edge computing to control/operate smart critical infrastructures. The main aim of the framework is to construct districts within a mid size zone that act as secure area, including essential critical infrastructure functions operating at limited but acceptable levels. Guaranteeing that the combination of microgrids, cellular-based catastrophe recovery mesh network, and edge computing are geographically located in this \"socially planned\" fashion will assist in reducing at-risk districts and bolster the economic argument for microgrids. Related work on enhancing critical infrastructure resilience has focused on hardening [14; 15] the essential elements in every infrastructure. Various techniques have been introduced in the literature for discovering the most vital components in a critical infrastructure, such as graph theoretic analysis, simulation-based analysis, stochastic modeling, and expert judgment [14]. Graph centrality measures have been utilized in developing critical infrastructure protection strategies, including vulnerability studies of power systems. A heuristic approach introduced in [16] uses five graph centrality measures, namely degree centrality, between centrality, centered centrality, eccentricity centrality, and radially. The authors evaluated the nodes with each of the five centrality measures. If a node is highly ranked by at least two measures, it is considered a critical node. The model was applied to assess the impact of potential attacks on the Swiss power network. However, the author utilized the proposed model considering the impact on the power network's infrastructure, neglecting the dependency on other infrastructures that could potentially have a higher effect. In [17], the authors introduced a scheme to determine the critical nodes of a smart power network according to the highest power flow in the system. However, their investigation concentrated on the betweenness centrality as the primary graph measure, which could lead to a bias in the outcome by neglecting other essential centrality measures in networks, such as node degrees and closeness. The authors of [18] considered the centrality metrics of a dependency risk graph, exploring the connection between dependency risk paths and graph centrality. They mapped different critical infrastructures into one graph and applied the centrality metrics on each node, assuming that the links are represented by the escalating failure values between nodes from different infrastructures. The primary motivation for that was to identify the critical infrastructure nodes between interdependent critical infrastructures that noticeably impact the essential routes of risk in the network and then to analyze cascading failures to different nodes or links in the network. In [19], the authors proposed a model to identify the most critical nodes in interdependent critical infrastructures. They developed an integer linear programming optimization formulation that models the approach of an attacker who targets a collection of nodes with the intention of compromising or damaging them. They assumed that the attacker is motivated by three objectives: (i) minimizing the size of the largest connected component, (ii) maximizing the number of disconnected components, and (iii) minimizing the cost of an attack. All three objectives are based on graph theory metrics and can be used to determine where to hardened the infrastructure. Here, the problem of where to harden multiple infrastructures as a group using a microgrid is considered. Noting that microgrids are the most costly element in our framework, our focus is on where to place a microgrid in order to promote smart critical infrastructure operations post-disaster.