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Abstract
Introduction: The rapid growth of data-generating devices powered by the Internet of Things (IoT), 5G, and smart technologies has created an urgent need for faster, more intelligent data processing. Traditional cloud computing, while powerful, often struggles with latency, bandwidth, and real-time responsiveness. Important by processing data closer to its source. When integrated with artificial intelligence (AI), this edge-cloud synergy enables real-time insights, predictive analytics, and autonomous decision-making. This integration not only improves performance and scalability, but also supports mission-critical applications in healthcare, manufacturing, transportation, and beyond. This research paper explores how the convergence of edge, cloud, and AI is reshaping the future of intelligent computing.
Research significance: The integration of edge and cloud computing with artificial intelligence (AI) is transforming how data is processed, analyzed, and acted upon in real time. This research is significant because it addresses the growing need for low-latency, high-performance computing architectures capable of supporting intelligent automation in critical sectors. By exploring the synergy between edge proximity, cloud scalability, and AI-driven intelligence, this study contributes to the development of robust, scalable, and adaptive systems.
These findings have practical implications for industries such as healthcare, smart cities, industrial automation, and transportation, as real-time intelligence is essential for safety, efficiency, and competitiveness. Furthermore, this research supports ongoing advances in sustainable and decentralized digital infrastructure.
Methodology: analytical to explore integration with to enable intelligence. A comprehensive literature review was conducted to examine existing architectures, frameworks, and use cases across various industries. Case studies from healthcare, smart manufacturing, and autonomous systems were analyzed to identify current trends, challenges, and best practices. In addition, a comparative analysis was conducted to evaluate system performance in terms of latency, scalability, and data processing capacity. Key, deep learning, federated were also reviewed understand their role in distributed environments. The aim of the methodology is to provide actionable insights into the development of intelligent, hybrid computing infrastructures.
Alternative: NINJA UAV, NETRA V4 UAV, SWITCH UAV
Evaluation preference: Flight Time, Operational Range, Target Detection Range, Optical Zoom Results: Hash Table It occupies the first place in the table. Graph is getting last place of the table
Keywords: NINJA UAV, NETRA V4 UAV, Flight Time, Operational Range
