Laith S. Ismail

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.

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 : Artificial Intelligence (AI) has recently emerged as a transformative innovation within the software industry, disrupting conventional approaches to application development by automating tasks, refining code, and enhancing resource efficiency. Prior research indicates the effectiveness of AI-powered tools across various domains. However, contemporary studies lack a detailed analysis of the diverse sectors utilizing AI tools for software development. Objective : This article aims to identify the potential benefits and impacts of AI in software development, specifically regarding time-to-market, productivity, code quality, bug-fixing rates, resource flexibility, and developer satisfaction. The goal is to present fact-based information about AI’s impact on multiple industries and scopes of work. Methods : A mixed-methods research design was employed to analyze quantitative data from 40 projects across healthcare, financial services, retail, technology, and e-commerce industries. Data were collected using various project management tools, automated testing environments, and online questionnaires addressed to developers. The study incorporated a comparative evaluation of AI-based projects and traditional projects, with statistical analysis. Results : AI-driven software development projects demonstrated a mean reduction in time-to-market by 34.6%, an improvement in code quality by 70%, and a mean reduction in bug-fixing time by 57.7%. Productivity per sprint increased by over 70%, resource flexibility was higher (90.2% in AI projects vs. 67.8% in traditional projects), and developers reported higher satisfaction levels. These findings reinforce the concept that AI significantly enhances workflow and the achievement of optimal results. Conclusion : AI substantially improves both the speed and quality of software development. Further research should expand to explore the experiences of different sectors, the application of AI-driven tools, their differentiation, and usage, as well as the ethical considerations to promote sustainable and innovative software engineering solutions.

Background: Neuromorphic computing is a newly developed technology that is based on data-flow architectures similar to the brain, which has the potential to power energy-constrained, latency-sensitive, and large-scale applications. The lack of flexibility in energy consumption and response time of traditional systems is a problem where neuromorphic platforms shine in real-time applications like robotics, IoT and autonomous systems. Objective: The article aims to assess the capabilities of neuromorphic computing platforms with respect to conventional schemes, both quantitatively and qualitatively, in terms of energy consumption, response time, modularity, and application-dependent adaptability, and to determine the drawbacks and application prospects for its further development. Methods: The study uses a comparative analysis approach to compare the identified factors and make statistical comparisons of the performance measures. The performance of the neuromorphic platforms as compared to non-neuromorphic platforms like Intel Loihi, IBM TrueNorth, NVIDIA Tesla V100, and Google TPU is compared based on its applications in robotics, IoT, and especially in healthcare. Data is derived from the experimental assessments of knowledge and theoretical paradigms encountered in prior research studies. Results: Neuromorphic systems showed better energy consumption, system size, and delay characteristics. Nevertheless, that the algorithm so excellently solves particular tasks does not mean that it can successfully be used regardless of its purpose, or can be adapted freely to new, further-reaching trends, such as quantum computing. Regression results demonstrate a high degree of dependency between these measures as well as their potential for real time data processing. Conclusion: Neuromorphic computing can be regarded as a new paradigm of energy-efficient and scalable AI and is especially promising for latency-sensitive deployment. Their shortcomings have been discussed earlier, yet it is worth stating that extension of these approaches by hybrid systems and more sophisticated integration frameworks might open new opportunities and eventually promote them as a foundation for new-generation computation models.

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: 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.