## 2 Related Work
Finally, simulation findings revealed that the proposed technique maximizes the data delivery performance and can maintain the access probability, low block probability, low and average access time, and low collision. Authors in [13] discussed the pollution issues and related these issues to newly proposed UAV technologies. Air pollution is one of the challenging areas which directly affect human life and health. To save the community from this toxic air pollution, this problem should be solved. There are multiple ways of monitoring air pollution through fixed sensor monitoring units, but these monitoring units have a limited range. There is also a height issue in this scenario. We cannot properly have fixed sensors at a certain height from the Earth's surface. In this paper, the authors solved this problem by using the IoT network along with UAV nodes. The proposed system monitors the atmosphere and calculates the air pollutants' data. This data is collected by drone and sent to the ground station. The ground station further processes the information and sends the data to the servers for further data analysis. The experiment results revealed the better performance of the proposed solution. The authors in [14], investigated the UAV-assisted IoT communications system and addressed the coverage optimization issues with partial channel information. To improve the coverage issues for UAVs, the nodes assisted the IoT networks. The authors optimized the resource allocation technique and UAV altitude optimization. The authors also optimized the UAV's altitude and maximized the minimal average rate. The large and small-scale channel fading method was used to characterize the practical propagation environment. Large-scale channel fading was used for gradual variation. The issue as stated did not convex. Block coordinate descent and consecutive convex optimization techniques were used to create an efficient iterative method. The performance of the altitude optimization greatly beat the fixed altitude, according to numerical data. Furthermore, the findings suggested the UAV-assisted node's performance for IoT data communications systems was highly influenced by the urban environment. Authors in [15] highlighted the importance of UAV networks, especially those designed for IoT networks. The authors proposed a UAV deployment solution by using Delay Tolerant Network (DTN) protocol. In this approach, the flight path is determined and examined by using a path planning method called a Hilbert Curve. The UAV's nodes collect the sensed data by using infrastructure or collect the data without infrastructure. The authors also discussed the four different issues related to the data collection process in IoT networks including large data management, infrastructure-free areas handling, the scalability of IoT networks, and resource allocation. The proposed solution was implemented after a detailed quantitative evaluation process and estimation-based strategy. The results indicated the better performance of the proposed solution in terms of data collection. Authors in [16] proposed a data collection scheme for UAV networks integrated with IoT networks. The authors also explored the gateway nodes deployment and energy factor for UAV networks for large Wireless Sensor Networks (WSNs). Because UAVs require smooth pathways, they framed the problem and examined a cost function to determine each UAV's trajectory. The shortest path was then calculated by using a Travelling Sales Problem (TSP). In the TSP problem, the authors used the pattern search technique to find the distances between cities. The routes from a given TSP tour were then smoothed using the Bezier algorithm. The TSP was used to plan the shortest path and then convert the path by using Bezier curves. The controller function was also utilized to install the UAV locally which was enabled with orbital motion. The findings demonstrated that the suggested technique may collect the data quickly and productively.