# Coverage Strategy for Small-Cell UAV-Based Networks in IoT Environment
###### Abstract
The proposed optimization framework and energy consumption model contribute to the field by providing insights into the design and operation of wirelessly powered IoT networks with energy-limited UAVs, facilitating the deployment of efficient and sustainable IoT applications [17; 18; 19]. This paper focuses on optimizing the data collection process by proposing a cluster-based approach that leverages UAVs for efficient communication and data gathering. The goal is to minimize energy consumption, maximize network lifetime, and improve data collection efficiency. This paper presents an optimization study that explores different parameters and factors affecting the performance of UAV-assisted cluster-based Wireless Sensors Network (WSNs). By identifying the optimal configuration and parameters, the proposed approach contributes to the field by providing insights into the efficient data collection in WSNs with UAV assistance in 3D environments, facilitating the deployment of reliable and energy-efficient sensor networks for various applications, such as environmental monitoring and surveillance [20]. This paper explores two different approaches to optimizing the AoI: off-policy and on-policy. Off-policy optimization refers to the optimization of the AoI based on historical data and past experiences, while on-policy optimization involves making real-time decisions to minimize the AoI. This paper compares these two approaches and investigates their effectiveness in UAV-RIS-assisted IoT networks. By examining the trade-offs between off-policy and on-policy optimization techniques, this paper contributes to the field by providing insights into the optimal strategies for minimizing the AoI in UAV-RIS-assisted IoT networks. The findings of this study can assist in the design and implementation of efficient and real-time data transmission protocols in IoT networks, improving the freshness and reliability of information [21]. In this paper, we delve into the intricate challenge of optimizing pricing and availability for UAVs, taking into account their limited battery capacity. Our primary focus is on the scheduling of availability periods, a pivotal factor that holds the potential to enhance energy efficiency, extend UAV operational lifetime, and ensure a satisfactory quality of service. Specifically, our research centers on addressing the joint pricing-availability problem within UAV-based networks. We introduce a novel approach that simultaneously tackles the issues of UAV pricing and energy efficiency, as documented in [22]. To grapple with these complex challenges, we investigate the intricate interplay between availability and pricing among service providers within the UAV ecosystem, treating it as a non-cooperative game. Our study adopts a duopoly framework, where a finite network of mobile unmanned aerial vehicles is deployed based on a homogeneous Poisson point process (PPP) to serve ground-based IoT devices. These UAVs navigate according to a random waypoint (RWP) model, as described in [23]. As an initial step, we derive expressions for the coverage probability and service probability of each UAV in the current scenario. To optimize the system's overall performance in terms of pricing policies and energy efficiency, our approach incorporates a Nash equilibrium analysis. Additionally, we propose the use of learning automata as a strategic learning strategy to establish a joint price-availability equilibrium. Finally, we present extensive numerical simulations that underscore the significance of considering price and availability as interconnected decision parameters, offering valuable insights and heuristics for setting them optimally. The remainder of this article is structured as follows: In Section 2, we provide an overview of the proposed UAV duopoly system model and our strategic approach to pricing and availability. Section 3 delves into the analysis of the availability game, addressing the existence and uniqueness of the Nash equilibrium solution and defining the game's sub-modularity characteristics.