Minimizing Latency in IoT Through Edge and Cloud Collaboration

Abstract

The exponential growth of the Internet of Things (IoT) has introduced significant challenges in meeting the stringent latency requirements of real-time applications. This paper proposes a hybrid architecture that leverages collaborative edge and cloud computing to minimize data transmission delays and enhance responsiveness in IoT systems. The methodology involves deploying distributed edge nodes close to IoT devices for local data processing, while the cloud is utilized for centralized storage, historical analytics, and orchestration. A latency-aware data routing algorithm is introduced to dynamically prioritize time-sensitive workloads at the edge and offload less critical data to the cloud. Experimental validation was carried out in a smart manufacturing testbed with over 500 connected devices. The proposed architecture achieved an average latency reduction of 62% compared to cloud-only processing. Moreover, the system demonstrated adaptive load balancing, reducing network congestion and ensuring uninterrupted operation during intermittent connectivity. The results confirm that a synergistic edge-cloud model not only enhances real-time performance but also improves scalability, reliability, and overall Quality of Service (QoS) in massive IoT deployments.