Leveraging Edge Computing for Real-Time Analytics in Smart City Healthcare Systems

Abstract

Smart city healthcare systems increasingly rely on real-time analytics to provide timely and efficient medical services. Edge computing has emerged as a pivotal technology enabling rapid data processing at the network's edge, minimizing latency and enhancing decision-making capabilities. This paper explores the transformative role of edge computing in facilitating real-time analytics for smart city healthcare applications. By processing data closer to its source, edge computing ensures faster response times, reduced reliance on centralized cloud infrastructure, and improved data security. Key use cases, including emergency response, remote patient monitoring, and predictive healthcare analytics, are examined. The paper also addresses challenges such as resource constraints, scalability, and interoperability. Recommendations are provided to optimize edge computing architectures for robust, scalable, and secure integration in urban healthcare systems.