# A Collaborative UAV-WSN Network for Monitoring Large Areas
## 1 Introduction
The former allows to guarantee the feasibility of the trajectory (in the sense that it respects the dynamics of the UAV) and the latter allows various algebraic and geometric guarantees (e.g., continuity up to a predefined degree, inclusion in to a given convex region). The UAV network can assume the role of mobile base stations when needed, in order to regroup the sensor nodes placed within the coverage area, into WSN clusters. By means of such subnets measuring the surrounding physical world becomes more focused, more relevant and control oriented over the particular analyzed area, thus ensuring that potentially critical data is not lost. Moreover, smarter measurement systems powered by UAVs embed more processing at the point of acquisition, capturing valuable critical data that in the hands of the right people or proper software solution, can translate to better and faster data-driven decisions. WSNs that incorporate UAVs can be transformed into applications that offer specific features and benefits such as: (1) On-site data collection and examination: UAVs can be implemented as data agents, capable both to upload data to the network and/or send the data back to base stations [7]; (2) Clustering support: UAVs can support ground WSN clustering mechanism by electing the cluster heads; (3) Sensor data 3D modeling: UAVs can provide mapping information (three-dimensional sampling) about the area of interest. UAVs can be also used to support ground WSN deployment [8]. This brings the flexibility of flying UAVs to any location in the area of interest. In [9] authors discussed the opportunities of optimized WSN architectures at different design levels, to meet application requirements. The paper is particularly focused on WSN dynamic optimization and proposes a Markov Decision Process (MDP)-based dynamic optimization technique for WSNs to meet application requirements considering the presence of changing environmental stimuli. More articles on WSN had as main theme the study of long-term monitoring [10; 11; 12]. The maintenance cost and implementation of the ground WSN are of great importance for long-term environment monitoring. A solution to this problem is proposed in [10] by integrating WSN platform in IoTapplications. The same author used a low-cost primary cell of WSN to design and implement a long-term, low maintenance wildfire monitoring WSN platform [12]. Arguably, the main factor in extending network life is energy efficiency [4; 13]. A poor management of energy consumption can lead to shorter network life, especially when the sensor nodes are powered by batteries. Depending of the sensor types, a balance between the number of acquisitions and the number of transmitted messages is necessary [13]. To ensure the network efficiency, a UAV trajectory optimization for sensor data gathering is proposed in [4; 14]. In case of sparse ground WSN [14] the solution is the minimum path length. In our paper we proposed the minimum path length with obstacle avoidance restriction. The trajectory design in [4] is based on a mixed-integer, non-convex optimization problem. Moreover, the surface features are taken into account for efficient transmission between the sensors and cluster heads at ground level [15; 16]. More recently, multiple UAVs-multiple WSNs systems were investigated [17; 18]. For data collection with multiple UAVs, the authors in [17] present three trajectory planners: based on genetics algorithm, rapidly-exploring random trees, and optimal rapidly-exploring random trees. A new and promising application of multiple UAVs-multiple WSNs systems is in disaster management and rescue operations [18]. For example, a cooperative heterogeneous framework for UAV-WSN is presented in [2]. The system presented in [19] is composed of a hierarchical, three-layer network architecture: sensor nodes, fixed-group leaders, and an UAV sink node. A ground WSN clustering method based on communication performance, position and other indicators is firstly introduced.