The Rise of Edge Computing Impact on Data Processing and IoT Systems

Abstract

Edge computing has rapidly evolved from a conceptual extension of cloud services into a pivotal architectural paradigm that relocates data storage, analytics, and decision‑making closer to the devices that generate information. By minimizing the physical and logical distance between data sources and compute resources, edge architectures promise lower latency, bandwidth efficiency, enhanced privacy, and improved resilience capabilities that are crucial for the explosive growth of the Internet of Things (IoT). This paper investigates how edge computing is reshaping data‑processing pipelines and influencing the design and operation of IoT systems. We combine a systematic review of recent scholarship with an empirical mixed‑methods study: (a) quantitative benchmarking of edge‑versus‑cloud processing on a smart‑factory testbed and (b) qualitative interviews with senior engineers and product managers across manufacturing, healthcare, and smart‑city domains. Our findings show that migrating analytics workloads to the edge reduces end‑to‑end latency by an average of 63 % and cuts upstream bandwidth consumption by 48 %, while maintaining comparable inference accuracy for real‑time anomaly detection tasks. At the same time, practitioners report challenges related to heterogeneous hardware, distributed orchestration, and the widening security perimeter.