# Coverage Strategy for Small-Cell UAV-Based Networks in IoT Environment
## 3 Problem Formulation  
Unmanned aerial vehicles (UAVs) have emerged as a promising technology for wireless communication in Internet of Things (IoT) environments. In such environments, UAVs can act as mobile base stations to provide network coverage in areas with poor or no connectivity. However, the efficient deployment of UAV-based wireless networks faces several challenges, such as limited UAV battery life, interference with ground-based networks, and dynamically changing network topologies. The primary objective of this paper is to propose a novel flying access strategy for UAV-based wireless networks in IoT environments that can optimize UAV movements and communication parameters to provide seamless network coverage while conserving energy and mitigating interference. To achieve this goal, this paper will investigate the following research questions:  
1. What are the key challenges and requirements for the efficient deployment of UAV-based wireless networks in IoT environments? This paper will identify and analyze the challenges and requirements for the successful deployment of UAV-based wireless networks in IoT environments. This will include analyzing the impact of network topology changes, interference, and battery life on network performance. 2. How can UAV movements and communication parameters be optimized to provide seamless network coverage while conserving energy and mitigating interference? This paper will propose a novel flying access strategy that optimizes UAV movements and communication parameters to provide seamless network coverage while conserving \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**References** & **Year** & **AI Category** & **Big Data Analytics** & **Key Enabling Technologies** & **Application Domains** \\ \hline
[31] & 2022 & No. & No. & No. & Yes; unmanned aerial vehicles (UAVs) as edge servers in mobile edge networks. & Yes; Quality-of-Service (QoS) in cluster-based UAV-assisted edge networks, specifically targeting the Internet of Things (IoT) domain. \\ \hline
[32] & 2023 & Yes; deep reinforcement learning (DRL) algorithm, deep deterministic policy gradient (DDFG). & No. & Yes; UAVs, wirelessly powered communication terminals, solar-powered UAVs. & Yes; Quality of Service (QoS). \\ \hline
[33] & 2021 & No. & No. & Yes; mobile edge computing, wireless power transmission, and UAVs for IoT networks. & Yes; UAV-based wireless power transmission and collaborative MEC to optimize the performance of IoT devices. \\ \hline
[34] & 2022 & No. & No. & Yes; non-orthogonal multiple access (NOMA), unmanned aerial vehicles (UAVs), and mobile edge computing (MEC) in the context of secure communication. & Yes; security aspects of NOMA-based communication in the presence of a flying eavesdropper. \\ \hline
[35] & 2021 & No. & No. & Yes; UAVs, multi-access edge computing & Yes; wireless networks, UAV-BSs. \\ \hline
[36] & 2022 & Yes; industrial knowledge graph-based relation mining federated learning-based service prediction, and globally optimized resource resource reservation. & Yes; processing, analysis, and interpretation of the collected data & Yes; federated learning in Industrial IoT & Yes; Industrial IoT. \\ \hline
[37] & 2021 & Yes; distributed aggregation-based dispersed federated learning. & No. & Yes; federated learning for edge networks & Yes; strict latency Internet of Things (IoT) applications. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: \(Cont.\)energy and mitigating interference. The strategy will consider the network topology, UAV battery life, and interference with ground-based networks. * What is the optimal trajectory for UAVs to follow, and how can UAVs efficiently navigate in a three-dimensional space to provide maximum network coverage? This paper will propose an optimal trajectory for UAVs to follow to provide maximum network coverage. The trajectory will consider UAVs' altitude, speed, and communication range.