# UAV IoT Framework Views and Challenges: Towards Protecting Drones as \"Things\"
## 2 Overview on UAVs as Members of IoT
### UAVs for Wireless Networks
#### 2.1.2 UAV Types and Classifications  
Different types of UAVs with distinctive characteristics, such as supported altitude, speed, and energy autonomy, are suitable for different applications. Generally, UAVs are classified according to their supported altitudes into Low-Altitude Platforms (LAP) and High-Altitude Platforms [11]. Furthermore, UAVs can be classified into rotary-wing and fixed-wing. The former are appropriate for cases that require UAVs that can remain at steady positions, whereas the latter are suitable for applications that demand UAVs travelling at high speeds and covering large distances [12]. In an IoT environment, due to the limited energy capacity of the participating devices, suitable LAP UAVs of the rotary-wing type can be efficiently and dynamically positioned to allow IoT devices to transmit with minimum power. A related framework towards this direction is introduced in Reference [13], while authors in Reference [14] introduce a resource-allocation scheme for improving energy consumption at cluster heads that use aerial base stations.  
1.3 Interference Management, Deployment, Path Planning, and Energy Consumption of UAVs in IoT Networks  
The use of UAVs as flying relays for IoT networks has numerous advantages, such as energy conservation and reliability; however, there are also some significant challenges that need to be addressed. Among those challenges, interference management, UAV deployment, and path planning are considered of major importance. The authors in Reference [15] propose and analyze an efficient deployment scheme for multiple UAVs using circle parking theory.  
Regarding interference management, the findings revealed that UAVs' altitude needs to be adjusted according to the coverage requirements and the beamwidth of their directional antennas. A related work presented in Reference [16] concluded on the optimum placement of UAVs as relay nodes that the decode-and-forward approach outperforms the amplify-and-forward one. A new heuristic algorithm for 3D UAV deployment was introduced in Reference [17], which minimizes the number of required UAVs to keep a specific level of service quality. To mitigate interference, the authors suggest lowering the altitude, but there is an obvious tradeoff between this and coverage. Similarly, the authors in Reference [18] analyze the tradeoff between delay and coverage, as far as the number of UAV stop points is concerned.  
As far as path planning is concerned, it is directly related with trajectory optimization. In general, finding the optimal flight path for a UAV is considered a challenging goal, since it is affected by multiple factors, such as energy limitations, flight time, and obstacle avoidance. Hence, as explainedin Reference [10], path planning is usually approached as an optimization problem with various objectives depending on the criterion of interest.  
Energy consumption, in particular, constitutes a critical issue for the deployment and mobility of UAVs. Because of their limited battery capacity, UAVs are not typically able of providing for long continuous wireless coverage in scenarios such as IoT networking. Their energy autonomy is highly affected by the UAV role and flight path, weather conditions, etc., and actually constitutes the main constraint for UAV adoption in many cases. There are several recent research endeavors toward improving UAV energy efficiency, focusing on various aspects, such as trajectory optimization [19], co-operative communications [20], energy harvesting [21], and resource allocation [22], et al.