# Multilayered Network Model for Mobile Network Infrastructure Disruption
## 3 Proposal of Multilayered Network Model for Mobile Network Infrastructure Disruption  
### Overview  
The proposed multilayered network model (MNM) is composed of three layers that accommodate three different types of wireless networks. The networks used in this multilayered concept are WSN, MANET, and DRONET. The main idea of this network model is to provide a backup network for destroying 5G and its infrastructure. In the case of MNM, WSN layer is supposed to provide IoT data collection functionality with the high number of static low energy wireless sensors. We assumed those data collected by the WSN network will be processed by cloud applications. Therefore, these data need to be transferred out of the WSN network to the Internet by gateways such as an Access Point (AP) of the Wi-Fi network. In the 5G network disruption scenario, we assumed that WSN gateways are unable to send data through AP since the fixed infrastructure is destroyed. In this case, data transfer to the part of the WSN network where Access Point is available could be very expensive in the term of energy consumption, data overload, delivery time, and so on. There is also a possibility that no functional Access Point exists in the WSN network. Therefore, the ability to transfer critical data to the Internet could be handled by mobile inertial sensors in the MANET network [10]. Nodes of MANET network are not strictly energy-constrained and with mobility, higher radio ranges and data transfers are able to transfer data to the functional AP. There is also a possibility that the MANET network could fall apart into isolated sub-networks because of the mobility of nodes. This disadvantage is handled in MNM by UAVs of DRONET network. UAVs with appropriate communication technologies are able to communicate over long distances in the air without obstacles. DRONET network in MNM si playing the role of backbone network, which can transfer critical data from MANET sub-network without functional AP to the part of MANET network where the functional AP is presented. The structure of MNM is displayed in Figure 1.  
Structure of MNM describes how this network model works. For example, WSN layer could be divided into three sub-WSN networks which all has its own connection to the Cloud on the Internet. The red area under WSN sub-network displayed at the left bottom expresses the part of fixed network that is disrupted. Since the AP connection to the Cloud is not functional, the special type of WSN sensor, called WSN gateway, passes critical data to the nearest mobile sensor of the MANET network. However, the isolated MANET sub-network could also suffer from not functional AP, so the node pass obtained data from the WSN network to the nearest MANET gateway - the MANET node chosen by network to be a gateway to the DRONET layer. This gateway is directly connected to the UAV of DRONET network. UAV than looks for the best opportunity to deliver critical data to the MANET sub-network with functional AP. Therefore, the data are transferred through another UAV to MANET sub-network, where AP connection to the Cloud is operational. The whole path from source WSN node to Cloud is displayed by orange colour. [PERSON] et al. [11] presented a survey that addresses optimal multilayered network design identified by flow and design connectivity. Based on this survey, the MNM can be identified as a three-layer network model with one-to-one flow-connectivity design, where each layer is supporting or is supported by only one other layer. Urgent data from the WSN layer can be passed to the MANET layer and then to DRONET layer, while direct flow from WSN layer to DRONET layer does not exist. In MNM, the only urgent data in network disruption scenario can pass between layers, which is commodity of WSN layer. Therefore, MNM falls into multilayer single flow-type network, where only one layer has a commodity to route. When disruption scenario does not occur, the layers act independently and do not communicate.