A Fog Computing Framework for Latency-Aware IoT-Based Healthcare Monitoring

Abstract

Internet-of-Things-based healthcare monitoring continuously streams patient vital signs, and the timeliness of processing these signals can be critical when an emergency must be detected. Sending all data to a distant cloud introduces latency that is undesirable for time-sensitive alerts. This paper presents a fog computing framework that places lightweight processing on local gateways close to the patient, reserving the cloud for long-term storage and heavy analytics. The framework filters and analyses vital-sign data at the fog layer, raising emergency alerts locally while forwarding summarised data to the cloud. It was evaluated against a cloud-only architecture under varying numbers of connected devices, with end-to-end response time as the principal metric. The fog-assisted framework reduced response time by between fifty-nine and sixty-seven percent across the workloads tested, with the largest gains under heavy load, while also lowering the volume of data transmitted to the cloud. The results show that fog computing is an effective architecture for latency-aware healthcare monitoring, enabling faster emergency response without sacrificing the cloud's storage and analytical capabilities.

KEYWORDS:

fog computing, Internet of Things, healthcare monitoring, latency, edge processing, cloud computing