UAVs

Background: UAV-assisted communication networks have emerged as vital tools for disaster recovery, offering rapid deployment and scalability in dynamic environments. However, challenges such as regulatory compliance, data security, energy efficiency, and real-time adaptability limit their widespread implementation. Objective: This study aims to develop a multi-objective optimization framework for UAV-assisted networks that enhances coverage efficiency, reduces latency, and optimizes energy consumption while addressing regulatory and data security challenges. Methods: The proposed framework integrates k-means clustering, genetic algorithms, and real-time adaptation mechanisms. Key metrics: coverage, latency, energy efficiency, and regulatory compliance, were evaluated across urban, suburban, and rural disaster scenarios. Dynamic geofencing, end-to-end encryption, and anomaly detection were incorporated to ensure compliance and secure operations. Results: The framework achieved significant improvements: coverage efficiency increased by 8%, latency reduced by 43%, and battery life extended by 33%. Regulatory compliance rose from 75% to 95%, and data security was enhanced with a 50% improvement in threat detection. The framework demonstrated robust scalability, maintaining high performance across diverse user densities. Conclusion: The study presents a scalable and adaptable UAV-assisted communication framework that addresses operational, regulatory, and security challenges. Its results validate its potential for real-world disaster recovery, paving the way for further innovations in this critical domain.

  Background: Unmanned Aerial Vehicles (UAVs) utilizing and active interface with 5G networks has become the new frontier to tackling problems of latency and energy efficiency, interference, and resource management. Although prior researches explained the benefits of UAV integrated networks; overall assessment of various parameters and cases is still scarce. Objective: The article seeks to assess the performance of UAV integrated 5G network in terms of latency, power, signal quality, task coordination and coverage optimization and to ascertain the efficiency of optimization algorithms in the improvement of the integrated 5G network. Methods: Emulations were done in MATLAB and NS3 platforms in urban / suburban / emergency call settings. Latency, power consumption, SINR, and completion time were the performance indicator chosen in the paper. Optimization algorithms: Particle Swarm Optimization (PSO), and Genetic Algorithm (GA), and the Multi-Objective Evolutionary Algorithm (MOEA) is evaluated in terms of Convergence time and Solution quality. Results : UAV-aided networks showed 36.7% and 29.2 % improvement in latency and energy consumption, while 33.6 % enhancement in SINR. MOEA offered the best results with 98.3% solution quality, and the PSO being the most convergence oriented. Minor deviations between simulation and real results highlight the need for adaptive mechanisms. Conclusion: The results presented focus on the enough potential of UAV-assisted 5G networks and their potential influence on improving performances in case of different criteria. Further research should focus on successfully implementing and deploying the proposed solutions and broadening the context of study to include 6G technologies.

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.

Background: Failures of communication usually occur during natural disasters, therefore signaling the importance of flexible networks. Unmanned Aerial Vehicles (UAVs) are anticipated to solve this problem by acting as on-the-move networks in the disaster-stricken regions. However, barriers that include challenges in UAV control coordination, resources allocation as well as security of the data being drawn are still pushing the technology backward. Objective: The article seeks to design and analyze enhanced heuristics for employing UAVs in disaster communications to enhance performance, availability, and security. Methods: Both primary, semi-structured interview and survey and post-disaster reports as well as secondary, computational analysis based on MATLAB and NS - 3 simulations were used as the data collection technique. Five algorithms: Multi-UAV Coordination, Dynamic Resource Allocation with Security, Hybrid Communication Framework, AI-Driven Path Optimization, Privacy-Preserving Data Sharing were implemented and incorporated. Theoretical models built on the basis of multi- objective optimization and the theory of games confirmed work ability to scale. Results: The introduced algorithms increased coverage by 75%, decreased latency by 27 percent, and also introduced 30 percent energy efficiency. Average privacy compliance levels floated above 90%, and an advanced resource allocation model achieved equal distribution. All these enhancements were affirmed in urban, rural and mountainous regions further proving versatility and stability. Conclusion: The article proposes a framework for UAV-enabled disaster communication system that can incorporate advanced algorithm and theoretical models to overcome the coordination challenge while ensuring the efficiency and security of the system. The presented results can be considered to be the reliable base for the UAVs usage in disaster situations.

Background: The advances in use of resilient telecommunication networks have shown the possible use of autonomous drones to support connectivity in unpredictable and complex terrains. Current network infrastructures have limitations in delivering optimized service in areas like traffic congestion, area of sparseness, disasters etc., which requires some form of innovation. Objective: The article is meant to propose a framework for using autonomous drones in practical telecommunication systems, with emphasis on the energy consumption, scalability, dependability, and flexibility of the solution for various situations. Methods: The study also uses other state-of-the-art approaches such as trajectory optimization, swarm coordination, dynamic spectrum management, and machine learning based resource allocation. Various slips were used on urban, rural, and disaster-sensitive scenarios to assess performance indices including energy input, network connectivity, signal strength, and lag time. The simulation results were supported by field experiments providing insights into various circumstances. Results: The simulation results of the actually proposed framework show network scalability enhancements, where coverage area involves up to 50 km² and power saving higher than 15%. The performance improvement included near perfect trajectory anticipation at a rate of 98%, while the utilization of resources was also optimized. Dynamic spectrum management was useful in reducing interference and increasing efficiency especially in areas of high density. Conclusion: The article promotes the use of UAV based telecommunication networks where challenging questions on scalability and reliability are raised and solved. Through the work presented, strong theoretical and empirical assumptions are made to foster concepts that will solidify next generation communication network.