# Coverage Strategy for Small-Cell UAV-Based Networks in IoT Environment
###### Abstract  
In wireless communication, small cells are low-powered cellular base stations that can be used to enhance the coverage and capacity of wireless networks in areas where traditional cell towers may not be practical or cost-effective. Unmanned aerial vehicles (UAVs) can be used to quickly deploy and position small cells in areas that are difficult to access or where traditional infrastructure is not feasible. UAVs are deployed by telecommunication service providers to provide aerial network access in remote rural areas, disaster-affected areas, or massive-attendance events. In this paper, we focus on the scheduling of beaconing periods as an efficient means of energy consumption optimization. The conducted study provides a sub-modular game perspective of the problem and investigates its structural properties. We also provide a learning algorithm that ensures convergence of the considered UAV network to a Nash equilibrium operating point. Finally, we conduct extensive numerical investigations to assist our claims about the energy and data rate efficiency of the strategic beaconing policy (at Nash equilibrium). U  
Footnote †: email: email: [[https://doi.org/10.3390/s23218771](https://doi.org/10.3390/s23218771)]([https://doi.org/10.3390/s23218771](https://doi.org/10.3390/s23218771))
mission adaptability, play a crucial role in realizing the IoT vision and offer a wide array of solutions and services to IoT partners [9]. Despite the numerous advantages of deploying drones as flying base stations, several economic and technical challenges must be addressed. These challenges encompass technical and physical hurdles, pricing strategies, and the availability of management features. To tackle these pressing issues, the scheduling of UAV availability or beaconing becomes a crucial but relatively unexplored aspect. Consequently, there is a growing need for comprehensive modeling and performance analysis of UAV setups to address these challenges effectively [10]. A novel approach that combines long short-term memory (LSTM) and deep reinforcement learning (DRL) techniques to tackle the multi-objective optimization problem is used. By leveraging LSTM-based DRL, the authors aim to achieve efficient resource allocation, maximize energy transfer efficiency, minimize latency, and enhance the overall performance of UAV-enabled wirelessly powered IoT networks. The proposed approach contributes to the field by providing a promising solution that considers multiple objectives and optimizes resource allocation dynamically, taking into account the changing network conditions [11; 12; 13]. This paper explores the potential of integrating UAVs with IoT networks and examines the unique characteristics and capabilities that UAVs bring to the IoT ecosystem. It highlights the opportunities presented by UAV-enabled IoT networks in various application domains, such as disaster management, precision agriculture, environmental monitoring, and smart cities. Additionally, this paper discusses the challenges and issues that need to be addressed for the successful deployment and operation of UAV-enabled IoT networks, including energy efficiency, connectivity, security, privacy, and regulatory considerations. The contribution of this paper lies in providing valuable insights into the architecture, opportunities, and challenges associated with UAV-enabled IoT networks, paving the way for future research and development in this emerging field [14; 15; 16]. This paper focuses on a practical energy consumption model for UAVs and aims to optimize the energy allocation and transmission policies to maximize the overall network performance. By considering the energy limitations of a UAV, this paper aims to find efficient solutions that ensure reliable wireless power transfer to IoT devices while maximizing the UAV's flight time and minimizing the energy consumption.