Hussain Kassim Ahmad

Background: Satellite systems which orbit in Low Earth Orbit (LEO) are one of the innovative solutions to enable the connectivity in segregated geophysical parts of the world which are left out of the digital society, as they provide almost instantaneous, high-speed connections. Despite progress made there are barriers to deployment including lack of scalability, high costs, traffic management, and environmental vulnerability. Objective: The aim of the present study is enhancing the overall throughput and concurrently – steaking the LEO satellite networks reliable operation in various applications with usage of the effective traffic control and adaptive routing techniques meanwhile taking into account the costs and other factors. Methods: Quantitative and qualitative research was used in this study in which both theoretical and simulations of LEO satellite networks were used. The traffic engineering was done using Software-defined networking (SDN), whereas bio-inspired routing, including bee colony optimization algorithms, were evaluated for adaptive routing. Parameters like latency, throughout, packet loss, and costs that are exhibited to change with conditions like atmospheric interferences were also considered. Results: The results proved that the current latency can be cut by up to 60%, packet lost by up to 90%, with operating expenses slashed beyond 85% and resource utilization beyond 85%. Improved routing techniques improved transmission reliability over dynamic network loads; simulations have validated the environmental suitability of LEO networks. Conclusion: The article offers a coherent framework for the appropriate design of LEO satellite networks and discusses their ability to address the digital divide and guarantee economic, effective, and highly accessible computing access globally.

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.