Real-Time Collaborative Computing for Multi-Domain Digital Twins in Edge-Cloud Ecosystems

Soham Sunil Kulkarni¹ & Dr. Lalit Kumar² 

¹University of California

Irvine, CA 92697, United States

grepsoham@gmail.com

² Dept. of Computer Application

IILM University

Greater Noida, U.P., India

lalit4386@gmail.com

Abstract

In the burgeoning field of digital twin technology, the convergence of edge computing and cloud infrastructure presents transformative opportunities for multi-domain applications. This paper introduces a novel framework for real-time collaborative computing tailored to the demands of digital twins across various domains, including manufacturing, healthcare, and urban planning, within edge-cloud ecosystems. Our approach leverages the low-latency capabilities of edge computing and the scalable processing power of cloud systems to deliver synchronized and efficient real-time operations.

The core of our proposed framework is a distributed architecture that facilitates seamless data flow and computational tasks between the edge and cloud layers. By situating initial data processing and analysis at the edge, we drastically reduce the latency typically associated with cloud computing. This setup not only speeds up the reaction times for digital twins but also minimizes bandwidth usage, which is crucial for systems where real-time data processing is critical, such as autonomous vehicles and real-time patient monitoring systems.

Moreover, our framework employs advanced synchronization algorithms to maintain the consistency of digital twins across the edge and cloud. These algorithms are designed to handle intermittent connectivity and varying network conditions, ensuring that digital twins can operate reliably and accurately in dynamic environments. We also incorporate machine learning techniques to predict changes in the system’s state, thereby enhancing the proactive capabilities of digital twins.

To validate our framework, we conducted a series of experiments involving digital twins in a simulated smart factory environment. The results demonstrate significant improvements in operational efficiency and data processing times compared to traditional cloud-only solutions. Additionally, the adoption of our framework in a healthcare setting for monitoring patients in real-time showed a marked enhancement in the accuracy and timeliness of medical interventions.

Our study also explores the challenges of implementing real-time collaborative computing in digital twin systems, including data security and privacy concerns. We propose a set of security protocols tailored to the edge-cloud architecture that address these issues without compromising system performance.

In conclusion, the integration of edge computing with cloud infrastructure offers a robust platform for developing digital twins that can operate in real-time with high efficiency and reliability. Our proposed framework not only addresses current technological hurdles but also sets the stage for future advancements in the field of collaborative computing for digital twins.

Keywords

Real-time computing, digital twins, edge computing, cloud ecosystems, multi-domain applications, data

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