# Unmanned Aerial Vehicles Enabled IoT Platform for Disaster Management
## 3 Architecture and Key Challenges
### Challenges  
We categorize challenges associated with the UAV-enabled IoT platform for disaster management into four areas: (i) efficient connectivity solutions; (ii) data analytics; (iii) law enforcement and technology; and (iv) sensor network deployment. The flight time of a UAV is limited by its battery capacity that needs to be charged at regular intervals. It raises various challenges for the emergency communication systems where the UAVs are utilized in disaster recovery network. The key issues include seamless handover between the UAVs to maintain user sessions, managing charging cycles of UAVs, and maintaining uninterrupted connectivity with the radio access network. Selecting the optimal position for a UAV-based data relay is also a major challenge that is complicated by the lack of knowledge about the user distribution and damaged network infrastructure in the disaster area. Moreover, due to the movement of UAVs, interference management in such networks is another main concern. As time is critical in rescue operations and since the topology of UAV based emergency and public safety network is not fixed, efficient dynamic routing protocols are required that can rapidly adapt to the topology changes to maintain connectivity and minimize the network delay. There are also unique challenges in terms of service requirement of different users in a disaster scenario. The traffic post-disaster is mostly bursty in nature and most users are concentrated in a small area, which can overload the network. For the first responders and rescue personnel, the network must be able to support broadband data rates such that the critical voice, video, data and control information can be sent and received. UAV-enabled IoT platforms for disaster management typically generate large-scale streaming data from heterogeneous sources tagged with time and space information. Ubiquitous knowledge of disaster further requires data fusion and sharing, which is especially important in time-sensitive IoT applications such as wildfire management. Traditional IoT data analytics and learning methods are not suitable to handle such a task. This calls for fast and streaming data analytics to predict future insights and to enable timely decisions that are critical in case of a disaster scenario. UAV-enabled disaster management systems in the literature typically consider cloud infrastructure for data analysis where the UAVs relay sensory data to the cloud, which communicates back the decision. This adds extra latency to the response time which can have a serious effect in certain cases. To minimize delay, fast data analytics close to the source is required. Legal and technological challenges associated with the deployment of UAVs are mainly concerned with their impact on the privacy of the victims and administrative issues such as the laws governing the use of UAVs. These government regulations vary with country and define size, range and maximum flight height of the UAVs along with their use of airspace to ensure safe operation in the environment. Data protection is a main concern with the use of UAVs as the data acquired from various sensors could possibly include sensitive information. Therefore, UAV-based disaster management system must have a high level of integrity and secure data links to avoid misuse. Security is another major issue with UAV-based systems as they are vulnerable to hacking and hijacking. This requires implementation of high standards of encryption and authentication. The deployment of the sensor network is still a major concern as it affects coverage and connectivity. Sensor node placement is particularly challenging in disaster management because accurate environmental information is required for useful results. The problem is further complicated as the sensor nodes are subject to random failures due to factors such as harsh environment, hardware failure, and battery depletion. For reliable sensor network, nodes should be deployed to get maximum coverage with minimum overlap and excellent connectivity with the sink.