Integrating Big Data and Edge Computing for Enhancing AI Efficiency in Real-Time Applications - Susatyono, Jarot Dian; Suasana, Iman Saufik; Rozikin, Khoirur (University of Science and Computer Technology, 2024)
Integrating Big Data and Edge Computing is revolutionizing the efficiency of artificial intelligence (AI) systems, particularly in applications requiring real-time responses. This study explores the synergistic role of these technologies in two critical sectors: autonomous vehicles and healthcare. Using a case study approach, real-world datasets and simulation platforms were employed to evaluate improvements in latency, prediction accuracy, and system efficiency. Key findings reveal that Edge Computing reduces latency by 30%, with response times dropping from 150 ms to 105 ms in autonomous vehicles and from 200 ms to 140 ms in healthcare applications. Additionally, leveraging Big Data for AI training enhanced prediction accuracy by 15% for traffic pattern recognition and 12% for patient condition monitoring. Despite these advancements, challenges such as scalability, data security, and interoperability persist, necessitating robust infrastructure and end-to-end encryption solutions. This research highlights the transformative potential of combining Big Data and Edge Computing to optimize AI systems for real-time applications, offering insights into improving operational efficiency and predictive accuracy. The findings are expected to guide future developments in AI technologies, particularly in the context of expanding 5G networks and growing demand for real-time data processing.