# Unmanned Aerial Vehicles Enabled IoT Platform for Disaster Management
## 3 Architecture and Key Challenges
### Architecture  
Figure 2 presents the architecture of UAV-enabled IoT platform for disaster management. The IoT platform consists of three main components: (i) ground IoT network; (ii) ground and aerial connectivity of ground IoT network and UAVs; and (iii) data analytics. The information regarding disaster and emergency situation can be collected from the ground IoT network and patrolling UAVs. Moreover, UAVs can act as base station or relays in disaster situation when the wireless infrastructure is damaged. Current trends in research and implementation are to use human controlled UAVs for IoT platforms. Moreover, these UAVs are not connected via cellular network for data collection and relay. It is important to incorporate autonomous UAVs in IoT platform for disaster management as it offers many advantages such as improved tactical decision making and response time. However, these advantages come with challenges including privacy issues, crashes, close encounters with manned vehicles (aircrafts), shoot down, and inappropriate use [51]. For example, according to Transport Canada, the number of close encounters with aircraft has tripled since 2014 [52]. In a recent incident in  
Edmonton, an aircraft pilot reported that he noticed a drone 2000 feet below his left wing in 2018. It is extremely dangerous when UAVs are flying that close to an aircraft. The loss of connectivity is reported as the major cause behind the UAV crashes [52]. Therefore, wireless connectivity is the key to safe operation of UAVs. Earlier generations of cellular networks were solely designed to support users on the ground. Beyond 5G cellular networks need to be investigated to have inherent features to support both aerial and ground users. In addition, how UAVs can effectively act as a base station in the affected region should also be studied. Particularly, focus should be given on spectrum allocation and interference management to improve the performance of the IoT platform and to avoid any unexpected interruptions in communication. Further, a framework should be defined based on the input from wildfire management department and the rules and regulations of Transport Canada (or similar authorities in other countries) for the operations of UAVs. The data collected by the UAVs need to be analyzed using machine learning techniques to effectively predict the wildfire ahead of time. This, for example, can help to control the wildfire by determining the rate and direction of fire. In addition, the ground sensors data can be used to predict the forest fire in advance. Therefore, data collection using UAVs (from the ground IoT network and from the patrolling UAVs) and their analysis is a unique research area especially for wildfire management. Further, data analytics can provide great insight to reduce the socioeconomic damage caused by wildfire. Data analysis can certainly help to predict the fire which can significantly reduce the ongoing total cost. Machine learning and artificial intelligence techniques can also play a significant role in design optimization to enhance the performance of UAV-enabled IoT platform. They can be used to validate the disaster management architecture under realistic wildfire scenarios. For data acquisition from ground IoT network, routing path and deployment of UAVs has been investigated in [17]. Flight performance of UAV is also investigated in [53]. The authors studied data mapping and efficiency of multi-UAV system in terms of the number of UAVs required to serve a disaster area. The role of data analytics is highlighted for wildfire prevention to reduce environmental as well as socioeconomic losses in [54]. Machine learning and artificial intelligence can play a crucial role to predict wildfire. A wildfire prediction model is proposed in [55] to determine the direction and rate of fire spread. The advent of UAVs has shifted the focus of aviation laws and regulations enforcement [56].