Lara Mwafaq

Background: The growth of the number of connected devices and the extent of Internet of Things (IoT) integration has led to new and emerging needs such as the management of big data, real-time reaction, efficient bandwidth utilization, and security considerations. Due to the intrinsic latency, network load and argue of scalability, standard cloud computing models do not suffice these requirements. In response to this, edge computing the function of analyzing data closer to its source hence leading to performance gains. Objective: This article explores the impact of incorporating edge computing in the optimization of IoT systems specifically in aspects like latency minimization, bandwidth utilization, security, processing capability, flexibility in expansion, and data reliability. Methods: A combined computational model was used to mimic edge and cloud platforms. Performance metrics were evaluated under three primary IoT scenarios: traffic management of smart cities, industrial applications, and health care management applications. Regression models and confidence intervals also provided general support to the findings. Results: The findings showed edge computing to be a more effective substitute for cloud-based systems; proving that latency can be reduced by 82%, and data bandwidth by 65-68%. Perennial threats including interception of data were cut by 50-66% while processing was done at 73% higher efficiency. Other criteria such as scalability and data consistency also pointed out the application of edge computing for resilience in more extensive IoT environment. Conclusion: Essentially, edge computing helps overcome limitations of cloud-based IoT systems, and is therefore imperative to real-time, secure, and scalable IoT. Future work should consider the integration of hybrid edge-cloud models, self-healing schemes, and more robust rigorous security solutions in order to fine-tune its applicability.

Background: The cloud-native architectures have reinvented the original strategies of the companies’ IT infrastructure approach and became popular due to the concepts of modularity, scalability, and resilience. These architectures respond to the shortcomings of the monolithic architectures to meet the new business challenges and workloads, including embracing innovation technologies like Artificial Intelligence and big data processing solutions. Objective: This study was designed with the objective of assessing the performance and business viability of cloud-native systems, based on critical indicators such as availability, resilience to failure, resource use, and compatibility with innovative technologies. The objective was to define the barriers and possibilities for improving cloud native architectures in various enterprises. Methods: A cross-sectional research, consideration, experiment test and case study and performance analysis. Response time, CPU and memory consumption and recovery time were compared across the range of throughput from 1000 to 12000 requests per second. To enhance the interpretational framework, key usage scenarios in the three sectors of healthcare, retail and finance were collected and compared with the results. Results: Cloud-native systems proved to provide high availability rates (> 99.9%), resource scalability, and component resource efficiency. With the use of AI in combination with big data analytics, improvement in performance was realized. But some of the problems that were seen include vendor lock, integration issues, and fluctuating peak load issues. Conclusion: All identified improvements signify the potential of cloud-native architectures for improving enterprise IT functioning. It is thus possible to continue perfecting the identified challenges to enhance their effectiveness, optimal for the current dynamic digital environment.

: Background: Unmanned aerial vehicle (UAV) networks as an important part of the modern telecommunication are gaining importance in various situations, including rural coverage, urban settings and emergency situations. However, Interference, scalability and energy efficiency still pose problems to the advancement of wireless networks. Objective: The aim of current study is to integrate and assess an adaptive frequency management technique for improving the performance of communication networks involving UAVs in terms of interference, transmission rates, and reliability within different deployment settings. Methods: Experimental and simulated studies were performed to evaluate the effectiveness of the algorithm in this combination. Performance measurements in terms of latency, throughput, packet loss, energy consumption and signal strength were made under rural, urban and emergency conditions. The adaptable algorithm used certain working frequencies depending on the interferences present and the network performance parameters recorded. Results: The algorithm showed very distinct enhancements in all models and positions, decreasing latency by 20.5%, enhancing throughput by 14.5%, and decreasing the packet loss by 57.6% in the urban site settings. Other executed experiments documented improved energy efficiency and communication reliability in rural and emergency situations. Conclusion: The adaptive frequency management algorithm proposed by the authors effectively solves significant issues of critical concern in UAV networks while offering robust scalability for next-generation telecommunications infrastructure. The future research recommendation incorporates the combination of the proposed optimization with other complementary approaches and/or the testing of the developed system at more severe actual conditions.