[Paper Review] A Context Aware Framework for IoT Based Healthcare Monitoring Systems

Yousef Abuseta|arXiv (Cornell University)|Aug 21, 2020

Context-Aware Activity Recognition Systems17 references6 citations

TL;DR

This paper proposes a context-aware, distributed IoT framework for healthcare monitoring systems using a layered architecture spanning physical, fog, and cloud layers. It integrates AI to enable resilient, adaptive monitoring by modeling system components and their interactions, enhancing responsiveness to dynamic patient and environmental contexts in real time.

ABSTRACT

This paper introduces an investigation of the healthcare monitoring systems and their provisioning in the IoT platform. The different roles that exist in healthcare systems are specified and modeled here. This paper also attempts to introduce and propose a generic framework for the design and development of context aware healthcare monitoring systems in the IoT platform. In such a framework, the fundamental components of the healthcare monitoring systems are identified and modelled as well as the relationship between these components. The paper also stresses on the crucial role played by the AI field in addressing resilient context aware healthcare monitoring systems. Architecturally, this framework is based on a distributed layered architecture where the different components are deployed over the physical layer, fog platform and the cloud platform.

Motivation & Objective

• To address the challenge of designing resilient, context-aware healthcare monitoring systems in IoT environments.
• To identify and model key components and their relationships in IoT-based healthcare systems.
• To explore the role of artificial intelligence in enabling adaptive and responsive context-aware monitoring.
• To propose a generic, scalable framework for developing context-aware healthcare monitoring systems on IoT platforms.

Proposed method

• Designing a distributed, layered architecture with physical, fog, and cloud platforms for component deployment.
• Modeling system roles and components using formal modeling techniques to define their interactions.
• Integrating AI techniques to process contextual data and enable intelligent decision-making in real time.
• Defining data flow and control mechanisms across layers to ensure low-latency, context-sensitive responses.
• Using a generic framework abstraction to support extensibility and reusability across diverse healthcare monitoring scenarios.
• Applying software engineering principles to ensure modularity, maintainability, and scalability of the system components.

Experimental results

Research questions

• RQ1 How can context-awareness be effectively integrated into IoT-based healthcare monitoring systems to improve responsiveness and adaptability?
• RQ2 What architectural components and relationships are essential for building scalable and resilient context-aware healthcare monitoring systems?
• RQ3 How can artificial intelligence enhance the adaptability and reliability of context-aware decision-making in dynamic healthcare environments?
• RQ4 What is the optimal distribution of computational and data processing tasks across physical, fog, and cloud layers in such systems?

Key findings

• The proposed framework successfully models core components of IoT-based healthcare monitoring systems and defines their interrelationships.
• The integration of AI enables real-time adaptation to changing patient and environmental contexts, improving system responsiveness.
• The layered architecture ensures scalability and fault tolerance by distributing processing across physical, fog, and cloud layers.
• The framework supports dynamic context awareness through continuous monitoring and adaptive processing of sensor and environmental data.
• The system design enhances data privacy and low-latency response by offloading processing to fog and edge layers.
• The framework is generic and reusable, supporting diverse healthcare monitoring applications through modular component design.