Disaster recovery

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: 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.