## 2 Applications of U-WSN
### Functionalities of UAV as a Communication Node
#### 2.3.3 Localization  
Location-based services in variety of applications, such as weather forecasting, traffic monitoring, smart home [33] and rescue application. Global Positioning System (GPS) [34] is a solution in such applications. GPS works well on localization when the applications are implemented outdoor. It is better for the GPS to be used far enough from buildings or obstacles otherwise GPS signals become unreliable. However, GPS has high power consumption, especially in large scale networks, and poor performance when applied indoors. Thus, a large number of works have been done to optimize the location-aware performance.  
Various types of categories of existing localization methods have been introduced according to different standards, range-based and range-free, coarse-grained and fine-grained, cooperative and cooperative-free, networking centric positioning and self-positioning. Among these classifications, the Range-Based and Range-free algorithms are the typical one according to whether to use range information. Typical Range-Based algorithms such as Time of Arrival (ToA), Time Difference of Arrival (TDoA) and Received Signal Strength (RSS). Range-free methods calculate the location information from the connectivity information.  
There is a huge scope of application offline training for localization. Thus, it is critical for the localization mechanisms to implement the training step for taxonomic hierarchy in the infrastructure of internet of things. The localization algorithms are classified into two categories, self determining and training dependent methods, if we take into account the internet of things scenario.  
Localization methods with mobile nodes are the best solutions regarding these issues. Vehicles moving in the interesting area and broadcasting 'beacons' messages, through which vehicles can self-localize via combining with appropriate methods. Nodes that have received the 'beacon' messages are within the transmission range of the mobile vehicles. Through combining with proposed technologies, the sensor nodes can provide an estimation about their locations after enough 'beacons' messages are received. The vehicle-aided localization algorithms are classified into two categories, static vehicle-aided localization and mobile vehicle-aided localization. In static vehicle-aided localization algorithms, e.g., in [35], the localization accuracy depends on many factors, the number of vehicles, the deployment and the trajectory of the vehicles, etc. It can be predicted that uniform deployment in high densely will bring a high localization accuracy. However, it also leads to high hardware cost and energy consumption. The UAV, which moves at a high speed and has high flexibility, works as a mobile vehicle which is the best choice in such applications.  
In the context of U-WSN, the authors in [28] address the issue of how to achieve three-dimensional localization using a UAV. In this work, sensors are deployed in the monitoring area without equipped a GPS while UAV is equipped with a GPS. UAV flies over the area and broadcasts 'beacon' messages which include the geographical information of the UAV. The nodes that received the 'beacon' messages are able to estimate their geographical positions through combing with appropriate technologies. However, it is not a real-time algorithm. The authors in [29] proposed a real-time localization algorithm using Extended Kalman Filter which is based on time difference of arrivals. The proposed algorithm works well on the estimation of sensors positions. In Section 4.2, we focus on the dynamic topology wireless network and provide a definition on the relationship between the UAV and the mobile nodes, based on which the nodes positions could be well estimated. In [36], the authors provide a summary on mobility-assisted localization algorithms in wireless networks.  
In Table 5, we summarizes the use of UAV for localization in the existing applications. Thereafter, the functionalities of UAV as a communication node are detailed in Table 5.