Nada Abdulkareem Hameed

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: Enabling Edge Artificial Intelligence (Edge AI) to be implemented in autonomous systems and telecommunications can offer for improved real-time data, non-recurring latency, enhanced operational proficiency. Some empirical research suggests that Edge AI minimizes latency by 70%, enhances computing speed by 50%, and cuts bandwidth consumption by 30% in the most demanding cases. Objective: The purpose of this article is to investigate how Edge AI can serve as an enabling technology for the future of self-sustaining environments such as autonomous mobility and telecommunications in terms of measured utility and differentiation. Methods: Screening 120 refereed articles and 25 case studies connected to Edge AI application in telecoms and self-governing systems, this systematic looked-for patterns in the proximal research and promising agendas. The review encompassed research works concerned with latency minimization, bandwidth enhancement and enhancement in the processing capacity. Focus was made on application areas like self-driving cars, industrial IoT, and smart city platforms and performance analysis was made in these areas. Results: The current study prove that when employed in autonomous systems, Edge AI enhances decision making reaction time by 40-60%, while enhancing data traffic throughput within telecommunications networks by 35%. Further, Edge AI makes the overall energy consumption lower in IoT-based applications by cutting down the average usage by a quarter thus creating a sustainable network. Conclusion: Edge AI becomes a central tool in the development of self-driving cars and telecommunications, increased performance and ability to handle mass amount of data at a low latency. These developments place Edge AI at the base of the evolution of future intelligent systems as the basis for smarter and more responsive technological landscapes.