Bassam H. Habib

Background: Telecommunication infrastructure requires regular maintenance and upkeep for its networks’ matrices, but existing approaches have been associated with issues such as time consumption and concern costs, as well as safety hazards. Newer developments in drone technology present progressive opportunity through the improvement of current maintenance processes by means of automation, predictability, and real time computation. Objective: The article seeks to assess whether the use of drone in telecommunication maintenance enhances the operational productivity through increasing the efficiency, reducing cost, safety, environmental and scalability and in different terrains. Methods: The methods followed included the conduct of experimental surveys with drone operations in five different telecommunication settings. These areas of interest were inspection efficiency, the accuracy of condition-based maintenance, signal received signal power, delay reduction through edge computing, and energy consumption. Sophisticated numerical computations, like Kalman filters and various frameworks of edge computing, were used in this context to draw analytical insights on the collected data. Results: The methods that used drones lowered the time needed for inspections by ¾ and cut the expenses by 49.3% and increased safety and quality of the coverage. Predictive maintenance was found to have achieved 89.7% accuracy with the system response time being 246ms at different site. The results of energy consumption model depicted the errors under 2% confirming this approach’s suitability for operational planning. Conclusion: By evaluating the applicability of drones in telecoms maintenance, the paper shows that the notion of drones in this context is promising both now and in the future. These results signal existing and potential applications of drones is to incorporate drone technology into infrastructural management solutions to address emerging needs in the industry.

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.