# A Collaborative UAV-WSN Network for Monitoring Large Areas
## 1 Introduction  
Wide area monitoring requires a robust and efficient data collection infrastructure, following the latest system design approaches of combining ground sensor nodes and unmanned aerial vehicles (UAVs) [1; 2]. Currently, high level research focuses on and emphasizes intelligent data management for various critical infrastructure applications, such as border surveillance or large-scale crop monitoring for precision farming. In the last years, due to the technological progress and wide spreading of the unmanned aerial vehicles they were introduced into sensor networks thus increasing the performances of large scale monitoring. Many challenges are still open like optimal ground sensor clustering, communication performance, saving energy, and trajectory planning for optimal data acquisition from ground sensors. For example, a cooperative framework for UAVs and ground wireless sensor networks (WSN) taking into account a k-means driven grouping approach, the communication performance, the position, and other factors can lead to higher performances [2]. By enabling the acquisition of site-specific measurements, data processing and local information sharing, WSNs have become one of the most promising technologies for wide area monitoring [3]. However, when WSNs are increasingly being deployed over large geographical areas, the limitations of the individual sensor nodes begin to hinderthe overall performance of the monitoring system. It has been established that wireless sensors nodes can easily become overused due to high power consumption when used in processes involving the continuous exchange of data. In addition to energy management and data transfer issues, data bandwidth reliability and time synchronization are also major downsides of WSNs. In terms of energy management, dynamic power and sleep settings are typically required. These are scheduled based on a process runtime timetable, time of day, weather, service schedule, etc. An iterative algorithm based on successive convex optimization technique was recently proposed in [4] for an energy-efficient data collection by means of UAVs. This approach can prolong the network lifetime. Basically, in order to overcome WSN challenges in large scale monitoring deployments, complementary peripherals can be added. One conventional system architecture uses a fixed sink node surrounded by sensor nodes. This strategy is known as static data collection. Data is uploaded using one-hop or multi-hop routing. High energy consumption within the sensor nodes and shorter life cycle for the sink node must be taken into account. An alternative strategy involves the use of a ground mobile sink node. The mobile sink node exchanges data with the sensor nodes found on its static or dynamically computed navigation path. This method reduces the energy consumption but shows a significant downside by limiting the mobile collector to flat navigable surfaces. To overcome this limitation, the mobile sink node can be carried by an UAV. Recent research has shown that such a UAV-WSN collaboration enables the widespread deployment of WSNs for large scale monitoring applications. Of particular importance in optimizing the energy consumption of the UAV network is the trajectory generation. The reference trajectory generation can be partitioned into a couple of distinct stages. First, the entire area of interest is covered in order to retrieve information about the location and strength of the deployed sensors. Second, based on an intermediary step of aggregation (where the sensors are clustered and sensor heads for each cluster are selected), the UAV has to pass near all of the sensor heads in order to retrieve information. In both cases the reference trajectory is based on the notions of flatness and subsequent B-spline parameterization, extensively discussed in [5; 6].