# Deep Reinforcement Learning for UAV-Based SDWSN Data Collection
## 2 Related Work
The UAV's flight path is optimized by utilizing the potential dynamics in WSN orchestrations as suggested in our approach. We used the Contiki-Cooja simulator to establish communication between the UAV and the ground WSN, with the communication planning method based on the distance between the sensor nodes and the UAV, as indicated by RSSI measurements. Other researchers, [PERSON] et al. [26], have introduced an innovative virtual design for UAV-assisted vehicular networks, which gathers and processes new information before sending it to the UAV. Current methods for UAV data capture during flights have not accounted for the dynamic topology and the adaptability of a virtual network structure along the UAV's flight path. Enhancing the UAV data collection method can be achieved by analyzing the UAV's travel route and integrating it with software-defined networking and network virtualization. One way to reduce UAV energy consumption during travel is by designing a UAV path framework using topological sorting and optimizing the UAV path shape to enhance energy efficiency and communication fairness. In our previous work [25], the concept of a fuzzy route is explored, where the proposed method considers geographical grouping with one or more nodes acting as ground data collection representatives. This results in more efficient and smoother UAV travel planning. The scheme's performance is evaluated based on UAV energy consumption while visiting points, showing that the proposed network communication analysis and smooth UAV flight path lead to a higher percentage of served SNs and improved ground SN power usage. From the UAV path planning point of view, numerous studies have focused on data collection applications using drones within a distributed wireless network, where the drone visits each data point individually. To maximize the number of served SNs within the operational connectivity during a limited flight period, identifying the shortest route is crucial. The domain of UAV trajectory design encompasses a variety of evolving methods, including geometric-based path planning and heuristic trajectory planning. Therefore, choosing the appropriate UAV movement pattern can result in a more scalable and energy-efficient flight route design. One approach involves applying geometric constraints to the UAV path, such as circular, spiral, strip-based, and zig-zag patterns, to heuristically enhance UAV energy efficiency [27]. Additionally, various protocols and algorithms, such as iterative genetic algorithms (GA) and ant colony optimization (ACO), have been proposed to address the UAV path problem heuristically. In [28], solutions to the Traveling Salesman Problem (TSP) and the Pickup-and-Delivery Problem (PDP) are utilized for preliminary UAV trajectory design. The authors of [20] also address the challenge of optimizing wireless sensor network coverage using UAV platforms. They formulate this as an optimization problem, employing a heuristic approach based on the TSP to determine the optimal UAVrouting for data collection while minimizing the energy required for data transmission from the SNs. It has been demonstrated that the proposed TSP algorithm outperforms particle swarm optimization (PSO) across various flying heights, speeds, and network sizes. When using the TSP algorithm, the results for the tested configuration in this paper show less than half the total energy consumption at the gateway level. Conventional algorithms struggle to meet the growing coverage demands due to the high energy consumption of UAV movements and the negative impact of slow navigation on coverage performance. Recently, leveraging machine learning in wireless communication has emerged as a promising alternative for optimizing UAV navigation. In [29], a deep learning-based method for UAV navigation is proposed, which does not require sensing data to provide mapping information. However, this method faces challenges with slow convergence, making it unsuitable for real-time navigation scenarios.