Performance Optimization of Real-Time IoT Applications Using Edge and Cloud Hybrid Architecture

An era of massive data generation and real-time application demands has been ushered in by the Internet of Things (IoT). Strong compute and storage capabilities are offered by traditional cloud-only solutions, but they frequently fall short of the stringent latency requirements of mission-critical IoT applications. Edge computing reduces latency by bringing processing closer to the data source, but it also adds computational and storage limitations. Using a Smart Traffic Monitoring System as a case study, this paper suggests and assesses a hybrid edge-cloud architecture intended for real-time IoT applications. A thorough review of the literature, the problem statement, the suggested architecture, the methodology, a comparison of cloud, edge, and hybrid performance, and deployment recommendations are all presented in this study. The findings show that a hybrid strategy can significantly lower latency while controlling bandwidth and cost