## 4 The Proposed Scheme: Architecture and Components
### The COVID-19 Layer
#### 4.3.2 COVID-19 Sanitization  
To ensure sanitization, we consider that \(E_{MC}\) demarcates a given region \(R\) into demarcations (red and orange zones), which are represented as \(Z_{r}\) and \(Z_{o}\), respectively. For sanitization purposes, a UAV swarm network is set up specifically that considers hexacopter drones, which are capable of vertical take-offs and landings. In such drones, a brushless direct current (DC) motor is present, and they are coupled with high-propulsion and thrust controllers. After analyzing the literature [44; 45; 46; 47], we observed that the hexacopters and octocopter drones are feasible for spraying purposes; the same drones are also used in the agriculture fields for insecticides and pesticides spraying. For example, the SYENA-H10 hexacopter is equipped with 5, 10, and 15 L of payload capacity that can cover 60, 30, and 10 acres of COVID-19 zones as per the volume spraying, i.e., ultra-low, low, and medium volume spraying, respectively [44]. A detailed comparison of different hexacopters with their technical specifications is illustrated in Table 3.  
A spray setup \(S_{p}\) is added as a payload to the drone. A ground controller station (\(E_{MC}\)) of that region sets up a swarm controller \(S_{c}\) to lead the swarm network for flying path and altitude setup. We consider that for a given region \(R\), we demarcate the region into specific zones \(Z_{r}\) and \(Z_{o}\), depending on the number of COVID-19 positive cases. In \(R\), we assume a group of swarm UAV networks \(\{N_{1},N_{2},\ldots,N_{s}\}\). In any network \(N_{s}\), we consider the coverage area \(\mathcal{C}_{area}\) of \(k^{th}\)\(E_{LAV}\). Based on captured feeds (distance violations), and COVID-19 central database counts, the drones form a long-term forecasting estimation of rising COVID-19 positive cases [48]. A specified threshold \(\delta\) for the number of distance violations triggers the UAVs to send a signal to their nearest station, which refers to the number of cases registered in the past 7-day window [39]. \(E_{HA}\) in that region cumulatively sends the bed and ventilator availability, and with high bottlenecks, the regions are flagged into \(Z_{r}\) and \(Z_{o}\)[49]. In these regions, the hexacopter drones perform sanitization on a regular basis to reduce the transmission rate of the virus. Algorithm 2 presents the UAV sanitization algorithm into demarcated regions based on COVID-19 cases. We consider a UAV swarm network, where the hexacopter drones are loaded with spray setups. We divide the entire region \(R\) into \(n\) sub-zones and assign \(k\) UAV swarms to monitor these zones. In this case, \(k>n\), as some zones might have more than one swarm network, based on the coverage range of the swarm network. In any area, \(C_{area}\), video feeds are captured, and social distancing is monitored as presented in Algorithm 1. If the social distancing between two persons is less than a specified threshold \(\delta\), we notify \(E_{HC}\) to send the resource availability (beds and ventilators) in a given region. Lines 1-9 present these conditions. If sufficient resources are present at health centers, we demarcate the zone as \(Z_{o}\), indicating a potential low COVID-19 hotspot. We monitor the contact chain of the person and flag all close contacts as potential COVID-19-positive cases. We initiate the swarm network to set up flight parameters and reach the \(Z_{o}\) region for sanitization. Lines 10-13 present these conditions. In case sufficient resources are not available at \(E_{HC}\), the hospital setups in the near future would face resource shortage, and hence \(E_{NC}\) flags the zone as \(Z_{r}\) (serious COVID-19 hotspot), where strict isolation and monitoring is followed by \(E_{NC}\). Lines 14-17 present this scenario. ```
1:UAV swarm network \(\{U_{1},U_{2},\ldots,U_{k}\}\). 2:Critical Boolean flag \(f_{2}\) for sanitization in a designated region \(R\).