Artificial Intelligence Techniques for IoT and Wireless Sensor Network-Based Three-Tier Architecture for Continuous Cardiac Health Monitoring and Alert System Using Spatio-Temporal Graph Convolutional Neural Network: Trends and Challenges

Abstract

The rapid advancement of Artificial Intelligence (AI) techniques in
healthcare has enabled the development of intelligent systems for
continuous cardiac health monitoring. The integration of Internet of
Things (IoT) and Wireless Sensor Networks (WSNs) facilitates real-time
acquisition and transmission of physiological signals such as ECG, heart
rate, and blood pressure. This paper presents a comprehensive review
of AI-driven approaches for cardiac monitoring using a three-tier
architecture consisting of edge, fog, and cloud layers. Spatio-Temporal
Graph Convolutional Neural Networks (STGCNs) have emerged as a
powerful technique for analysing complex cardiac data by capturing
both spatial dependencies among sensor nodes and temporal variations
in physiological signals. These models provide improved accuracy and
computational efficiency compared to traditional deep learning
approaches, making them suitable for healthcare applications.
Furthermore, optimization techniques such as model compression,
edge computing, and energy-aware routing enhance system efficiency
and enable real-time alert generation. The three-tier architecture
improves scalability and reduces latency by distributing computation
across multiple layers. Despite these advancements, challenges such as
data heterogeneity, privacy concerns, and computational complexity
persist. This review highlights recent trends, identifies research gaps,
and discusses challenges in developing efficient, scalable, and reliable
AI-based cardiac monitoring systems.