# Coverage Strategy for Small-Cell UAV-Based Networks in IoT Environment
## 2 Related Works
The proposed solution involves formulating an optimization problem that aims to maximize the minimum achievable data rate of all users, subject to constraints on the UAV's maximum transmit power, energy harvesting efficiency, and collision avoidance. The authors use a successive convex approximation (SCA) algorithm to solve the optimization problem and obtain the optimal power allocation and 3D trajectory of the UAV. The SCA algorithm involves iteratively solving a series of convex subproblems that approximate the original non-convex problem. The authors also propose a heuristic algorithm for collision avoidance that ensures that the UAV does not collide with obstacles while moving along the optimized trajectory. In Table 1, we present an inclusive overview of paramount research works across distinct dimensions in the realm of coverage strategy for small-cell UAV-based networks. The table is segmented into four distinct sections, each contributing to a holistic understanding of the field's progression and multi-disciplinary nature:  
* AI category: This section compiles seminal papers delving into the application of Artificial Intelligence (AI) within the context of our research area. By cataloging authors, publication dates, and central AI themes, this segment elucidates how AI methodologies have been harnessed to optimize coverage strategies for UAV-based networks in IoT environments. * Big Data analytics: In this section, we assemble key contributions that explore the role of Big Data analytics within our research domain. By highlighting influential authors and pivotal concepts in the realm of data analysis, this section underscores the significance of data-driven insights in shaping effective coverage strategies. * Key enabling technologies: This segment encapsulates pivotal works focusing on the foundational technologies that empower coverage strategies in small-cell UAV-based networks. With emphasis on authors and essential technological facets, this section portrays the intricate tapestry of technologies contributing to the operational efficacy of our research scope. * Application domains: Lastly, we curate a selection of papers that elucidate the diverse application domains where our research finds relevance. This section unveils authors, publication chronology, and specific application contexts, illustrating how the proposed coverage strategies align with and enhance various IoT environments. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**References** & **Year** & **AI Category** & **Big Data Analytics** & **Key Enabling Technologies** & **Application Domains** \\ \hline
[29] & 2020 & Yes; fuzzy logic-based approach for efficient UAV positioning. & Yes; processing, analysis, and interpretation of the collected data. & Yes; flying base stations (FBS) in an IoT environment. & Yes; optimization of the placement of drones as base stations to ensure full coverage sensors and actuators. \\ \hline
[30] & 2023 & Yes; optimization in UAV-enabled wireless powered communication networks. & No. & Yes; UAVs, wirelessly powered communication networks, and optimization techniques. & Yes; disaster management, surveillance, or remote sensing. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Qualitative comparison of related works. By structuring the analysis of related work in these meticulously crafted sections, we aim to offer readers a nuanced perspective on the multiple facets that collectively constitute the landscape of coverage strategies for small-cell UAV-based networks. This comprehensive tableau serves not only to contextualize our research but also to map the progression of ideas and innovations that have paved the way for our contributions.