Adaptive Edge-Cloud Architectures for Real-Time Internet of Things (Iot) Systems: Enabling Efficient and Scalable Data Processing in Dynamic Environments

Abstract

The rapid expansion of Internet of Things (IoT) systems has created unprecedented opportunities and challenges for real-time data processing and decision making. Traditional cloud-based solutions often suffer from latency, bandwidth constraints, and centralized bottlenecks, making them less suitable for time-sensitive IoT applications. This paper explores adaptive edge-cloud architectures that combine the strengths of edge computing and cloud platforms to meet the demands of real-time IoT systems. The study discusses architectural designs, adaptive mechanisms, and integration strategies to optimize latency, reliability, and resource allocation. Furthermore, the paper highlights the challenges, future research directions, and potential applications of these hybrid architectures in dynamic IoT environments.

KEYWORDS: Adaptive edge-cloud architecture, Internet of Things (IoT), realtime systems, latency optimization, resource management, distributed computing, scalability.