JoWUA

Abstract

The ever-growing development of the technology of Internet of Things (IoT) is augmenting the frequency of producing data on a range of spaces. This is encompassed of the sectors of health care, smart cities, industrial, automation and transport systems. The incessant problem in real-time management of streams of data is the latency growth, bandwidth optimization and scalability. The research article explains a hybrid system of IoT involving the combination of Edge and Cloud Computing to process the Big Data. The empirical study conducted is applied on the performance metrics of a performance assessment of redundant edge nodes and a centralized Cloud throughput, latency and energy resource consumption. The Hybrid Edge–Cloud model reduced latency by ~68% vs Cloud-only (380 → 120 ms) and ~25% vs Edge-only (160 → 120 ms); lowered energy per node by ~31% vs Edge (14.6 → 10.1 W) and ~18% vs Cloud (12.3 → 10.1 W); and cut bandwidth utilization by ~45% vs Cloud-only (85% → 47%). Throughout was about 11% of Cloud and about 50% more than Edge. This shows how Edge Local Computing which is scalable and cheap due to Cloud memory makes models efficient and responsive. It is also economical and provides huge value for money here. The integrative policy is innovatively valuable because decreasing traffic congestion through local data processing delivers a higher degree of intelligent decision-making.