HiCH

TLDR

The Internet of Things promises remote health monitoring, but centralized cloud systems lack reliability and edge nodes lack accuracy, creating a need for a hybrid solution that balances availability and precision. This paper proposes HiCH, a hierarchical computing architecture for IoT‑based health monitoring systems, to address these trade‑offs. HiCH comprises a novel hierarchical architecture that partitions and executes machine‑learning analytics across fog and cloud layers, coupled with a closed‑loop management technique that autonomously adjusts resources based on patient condition. Performance evaluation on a continuous remote health monitoring case study for arrhythmia detection in cardiovascular disease patients demonstrates HiCH’s efficacy.

Abstract

The Internet of Things (IoT) paradigm holds significant promises for remote health monitoring systems. Due to their life- or mission-critical nature, these systems need to provide a high level of availability and accuracy. On the one hand, centralized cloud-based IoT systems lack reliability, punctuality and availability (e.g., in case of slow or unreliable Internet connection), and on the other hand, fully outsourcing data analytics to the edge of the network can result in diminished level of accuracy and adaptability due to the limited computational capacity in edge nodes. In this paper, we tackle these issues by proposing a hierarchical computing architecture, HiCH, for IoT-based health monitoring systems. The core components of the proposed system are 1) a novel computing architecture suitable for hierarchical partitioning and execution of machine learning based data analytics, 2) a closed-loop management technique capable of autonomous system adjustments with respect to patient’s condition. HiCH benefits from the features offered by both fog and cloud computing and introduces a tailored management methodology for healthcare IoT systems. We demonstrate the efficacy of HiCH via a comprehensive performance assessment and evaluation on a continuous remote health monitoring case study focusing on arrhythmia detection for patients suffering from CardioVascular Diseases (CVDs).