# Unmanned Aerial Vehicles Enabled IoT Platform for Disaster Management
## 4 Case Study: NDN Architecture for Ground IoT Network
### Proposed Methodology  
Figure 3 shows the method adopted to achieve the NDN-based wildfire objective. Here, we use multiple sensors such as flame and smoke sensors to detect a wildfire disaster. However, these two sensors are attached with the controller with WiFi module (IEEE 801.11b/g/n) to transmit the critical information towards all nearby consumer nodes. We use different consumer nodes (such as Consumers A, B, C,..., H) to gather \"Interest\" messages from respective consumers that ultimately reach the desired destination with the help of a centralized server. Each consumer node collects information in its network domain, which finally interface with the central server. Moreover, the nodes continuously send their collected data (values of sensors) to the server. The significant feature of the application and its design is the merging of hybrid push-pull-based communication to enable reliable and efficient message propagation in the network. In our wildfire scenario, we need a push-based communication mechanism to increase the robustness and reliability of a disaster management system. On the other hand, the pull-based communication system is enabled when we have a disaster-free situation. Hence, the recipient has control over what it receives. From the security aspect, it also has a large time window to verify the identity of the sender, from the time elapsed between the demand request and receipt of content at the receiving end. According to the push-pull-based communication mechanism, whenever a client sends an interest packet to get the server for checking the status of nodes, it replies with an acknowledgment signal. It then checks the values of the respective node, i.e., whether they are within the threshold limit or not. Furthermore, a particular calibrated value for every sensor is set already. If the values coming from the sensor of the respective node are within the threshold, it sends the values to the respective consumer. On the other hand, if the value exceeds the threshold, the server generates an alert by broadcasting a message via an NDN Android application (App), indicating that damage could occur due to fire. Figure 4 shows the block diagram of the NDN wildfire detection mechanism. In a wildfire scenario, NDN architecture becomes more reliable as compared to IP-based system due to the presence of a three-level data caching structure (CS, PIT and FIB). A disastrous state most likely disconnects the network into multiple fragments, thus communication is the most challenging task, which NDN provides with robustness and with esteem surety. Furthermore, in NDN mechanism, we do not need to retransmit data in the case of network fragmentation directly from the source; instead,  
Figure 4: Block diagram of teh NDN wildfire detection mechanism. Figure 3: The NDN IoT-based wildfire scenario. we use on the path nodes to act as source to transmit information stored in their data structures. Further, we consider with two parameters in wildfire scenario, i.e., average delay (transmission + propagation) between nodes and the reliability of the system. We use an NFD library in our controller and an Android App, which provide us the functionality of the NDN mechanism and generate log files. We tested our modules according to the different disaster impacts, i.e., 10%, 20%, 30%, 40% and 50%, with different numbers of nodes. The hardware implementation consisted of flame and smoke sensors that were connected to the micro-controller that sent the data to the IoT cloud server using the WiFi module, as shown in Figure 5. The Arduino Mega was used in fire detection module, which is a micro-controller board based on the ATmega2560. The MQ-2 smoke sensor and flame/fire sensor (YG1006) were used for fire detection. We used cellular and WiFi technologies for the fire detection module. The values of the sensors were continuously monitored and, when they exceeded the threshold value, an alert message was broadcasted to all users through the NDN-based Android App.