Ali Alsaray

Background: The rapid development of fifth-generation (5G) networks highlights challenges in extending coverage to remote and underserved areas due to infrastructure limitations and cost constraints. UAVs (drones) equipped with 5G base stations emerge as an innovative solution to this problem. Objective: This study aims to analyze the potential of drones as mobile 5G base stations to enhance connectivity in remote regions, addressing challenges like optimal deployment, energy efficiency, and user coverage. Methods: The research utilizes algorithms like Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) for placement and energy management of drone-based 5G stations. Simulation models were employed to test these algorithms, with key metrics including coverage efficiency and energy consumption. Results: The study shows that drone-based stations can significantly improve coverage in remote areas, achieving up to 95% user coverage with optimized algorithms. Tethered drones and advanced energy management strategies were instrumental in enhancing endurance. Conclusion: Drones as mobile 5G base stations present a feasible and scalable approach to bridging the digital divide in remote regions. However, energy and regulatory challenges remain critical areas for future research.

Background: Smart city technology solutions have recently ramped up the utilization of drones with Internet of Things (IoT) technologies for improving smart city systems. IoT sensors combined with real-time communication ad hoc network drones are also another area with great potential including traffic monitoring, environment management, disaster management, etc. Nevertheless, issues regarding energy consumption and density, the number of nodes that can be incorporated into the network, as well as the issue of avoiding collisions between the signal sent by one node with the signals that may be transmitted by other nodes are still observed as essential impediments to the wide application of WSNs. Objective: The article seeks to propose and assess algorithms for operating drone-IoT systems whilst dealing with issues like energy efficiency, real-time data communication, avoiding mid-air collisions, and dealing with the increasing number of systems in crowded urban areas. Methods: This study utilizes a two-time algorithm technique that was adopted from the prior study. The first algorithm provides a method for speed and position control of drones, ensuring that the distance between the drones is sufficient and not violable. The second algorithm is centered on energy reduction, which selects the precise energy usage by employing path planning in real time. The effectiveness of these algorithms was determined using simulation models with respect to metrics including latency, energy consumption, and scalability. Results: The proposed system revealed the systems’ improvements in energy efficiency, fewer collisions, and strong scalability of drone management. Main conclusions possible to conclude during the experiment reveal the system’s generic aptitude to the different urban situations and its stability in changing traffic conditions. Conclusion: The article presents a scalable and efficient solution for extending drone applications to smart cities using IoT platforms. In this way, the results can serve as the further theoretical and experimental base for investigating the trends of management and the infrastructure of cities.