# OADC: An Obstacle-Avoidance Data Collection Scheme Using Multiple Unmanned Aerial Vehicles
## 2 Related Works  
UAV-aided data collection without considering obstacles: Recently, there have been many studies on UAV-aided data collection in WSNs focusing on different scenarios and objectives. Some of them used a single UAV for data collection. For example, one study [8] focused on reducing unwanted data transmissions among sensor nodes and the UAV and increasing the throughput of their considered system. Another study [9] determined the best trajectory of a UAV to gather data from devices while guaranteeing that each device uploads the bare minimum of data. In [10], the authors proposed an energy-efficient data-gathering approach for WSNs to minimize the total transmission power with the minimal UAV trajectory length. In [11], the UAV sends mobile agents (MAs) to the area of interest, and each MA collects the data from sensor nodes by moving into this area. The UAV uses MAs with data to obtain the information it needs, ensuring the system is more time- and energy-efficient. Another group of studies on data collection considered the use of multiple UAVs. For instance, the work [2] focused on solving a TSP problem using a modified genetic algorithm while considering the clusters of sensors with the objective of fast data gathering and reducing the total energy consumption of UAVs. [PERSON] et al. [12] studied a cloud-aided data collection technique for UAVs in the WSN area. They aimed to minimize the energy utilization in the network and improve the data-collection duration. [PERSON] et al. [13] focused on minimizing average data collection time with the minimal cost in the system between the UAVs and the cluster heads. The authors of [14] aimed to find the shortest path for UAVs to collect data from various roadside sensors and minimize the total UAV traveling time and total length of UAV paths in the network. Furthermore, reference [15] focused on tackling the flight-selection problem as a Hamiltonian path problem that requires visiting all the cluster heads in a given wireless sensor network. However, none of the above studies considered the obstacles in their environment. By contrast, the proposed study considered the existence of obstacles in the environment, where the UAV data collection tour will finish within a given flight time with a minimum traveling distance while avoiding obstacles. Thus, the proposed OADC scheme cannot use these preceding approaches directly. UAV data collection considering obstacles: There are some studies on UAV-aided data collection in WSNs where the researchers considered obstacles when planning the UAV path. For example, in [16], the authors considered a single UAV to collect data while avoiding obstacles. They combined various evolutionary procedures to produce a non-collision path for the UAV. They concentrated on lowering the path length and angle, while minimizing delay and energy consumption. The limitation of this work is that they have used only one UAV for the data collection operation, which can cause more time and energy consumption for the UAV. One study [17] considered trajectory optimization of UAVs to maximize the data collected with task completion deadlines, kinematic restrictions, and collision escape mechanisms. They considered source-to-destination-based data collection using UAVs. Another study [18] designed UAV path planning based on emergency demands. They examined UAV flight time and energy consumption minimization. [PERSON] et al. [19] focused on lowering energy use to gather the most data in a network of IoT devices. They considered that UAVs have sensors that enable obstacle avoidance at a specific safe distance when the UAVs fly at their top speed. In [20], the authors suggested a data collection method for WSNs that can tolerate delays. They used a UAV, which was self-trained, to gather data from sensors within a time frame. The goal of this work was data-collection-time minimization and finding the optimal route in an obstacle-containing system. [PERSON] et al.