A Scalable Hybrid Edge-Cloud Approach to Minimizing Latency in IoT Applications

Abstract

The increasing reliance on IoT applications demands efficient, scalable solutions to address latency, a critical factor in time-sensitive operations. Hybrid Edge-Cloud approaches leverage the strengths of both edge and cloud computing to optimize performance and ensure seamless connectivity. However, existing methods often struggle with excessive latency due to resource allocation inefficiencies, limited edge device capabilities, and network congestion. This study proposes a Hybrid model based on Scalable Hybrid Edge-Cloud Approach (SHECA) framework, designed to mitigate these challenges in IoT applications. SHECA integrates edge computing for real-time data processing and cloud computing for storage, advanced analytics, and long-term decision-making. By dynamically distributing computational loads and leveraging intelligent resource allocation, the framework significantly reduces latency and enhances system responsiveness. The findings demonstrate that SHECA reduces average latency by 35% compared to traditional cloud-only methods, ensuring faster response times, scalability, and improved user experience in IoT applications. This hybrid solution offers a robust approach for latency minimization in diverse IoT scenarios.