# A Collaborative UAV-WSN Network for Monitoring Large Areas
## 1 Introduction
Further discussions evaluate the performance of a proposed optimal flight trajectory algorithm from a comparative standpoint, considering other related approaches. Starting from the same idea of heterogeneous layered architecture, and exploring WSN ground clustering methods, the authors in [19] evaluated the performance of two algorithms for optimal selection of cluster heads. Selected cluster heads became waypoints along the UAV trajectory. The Particle Swarm Optimization (PSO) is used to decide the network's topology in order to reduce the energy consumption, improve communication quality and reduce the travelling time. All the results for proposed algorithm evaluations are obtained through comparative simulations. UAV support for heterogeneous sensor network monitoring was also discussed in [20]. The UAV is mainly used as mobile gateway for the ground sensor layer. The main objective was to design effective clustering mechanisms for resource balancing given the constraints introduced by battery operated devices. WSN constraints are thoroughly discussed in [21], with respect to the main optimization problems. In [22] the authors analyzed a data collection strategy based on three-layer architecture, using mobile data collectors. The proposed modeling and simulation provide clear guidelines for deployment for sparse WSN. A UAV relay based system for hybrid wireless sensors networks, in the context of emerging 5G telecommunication networks architectures, was presented in [23]. Simulation based modelling validates the optimization approach of UAV path planning with respect to node power and radio channel constraints. An event detection framework for cloud WSN with UAV support is discussed in [24]. The proposed algorithm, with cloud backend support, enables on-demand optimal data collection with respect to both flying parameters and ground network deployment. UAV as a mobile node sink within an existing WSN is illustrated in [25] for IPv6 over Low-Power Wireless Personal Area Networks (6 lowpan) communication protocols and dynamic clustering. Communication performance of a dual-stack single radio implementation for downstream and upstream links is assessed thus eliminating the need for the UAV to use separate radio interfaces for sensor network and ground control station communication. From an application perspective, the authors in [26] described the implementation of a solar powered UAV in conjunction with a ground WSN for environmental monitoring. Also, UAV-supported WSN have been deployed in coastal areas by placing the sensing nodes on floating buoys [27]. In [28] is approached the opportunity for in-network pre-processing of sensory data and also for the planning of the UAV's local tasks, aiming at increasing the efficiency of the data collection process. A gradient scheme to support decision-making tasks planning was introduced. Authors proposed and evaluated a general architecture for a wide area monitoring system and the use of decentralized sensor fusion mechanisms for efficient data mining, promising to overcome WSN constraints. In this paper we proposed a hybrid UAV-WSN system [29] to improve the large area monitoring. We first describe a decentralised multilevel architecture, with an UAV fitted with a sink node, serving as data collector, used to enhance the connection between ground WSNs and central gateway (base station). Considering the random deployment WSN nature and therefore the clustered structure, we focus on describing an effective mechanism for ground WSN discovery, while ensuring certain support for the election of the most appropriate communication links and cluster grouping. Trajectory-wise, we consider two distinct stages. First, we plan a discovery trajectory, based on a photogrammetry algorithm, during which the sensor position is estimated and information about cluster structure is gathered. Second, we plan a trajectory for data acquisition, which passes through the neighbourhoods of the cluster heads, with guaranteed communication time and guaranteed obstacle avoidance.