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Improving Health Monitoring With Adaptive Data Movement in Fog Computing.

Cinzia Cappiello¹, Giovanni Meroni¹, Barbara Pernici¹

¹Dip. Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy.

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|January 27, 2021

Summary

This summary is machine-generated.

This study introduces a data utility model for pervasive sensing, addressing data quality and performance issues in Fog environments to ensure reliable patient monitoring and analytics. It enables context-dependent quality assessment and suggests actions to maintain data utility.

Keywords:

data analytics data movement data quality data utility fog computing quality of service

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Area of Science:

Health Informatics
Computer Science
Data Management

Background:

Pervasive sensing generates vast patient data for monitoring and analytics.
Data quality and system performance issues can render this data unusable.
Effective data management is crucial for reliable healthcare applications.

Purpose of the Study:

To propose a data utility model for pervasive sensing in Fog environments.
To address data quality and performance challenges impacting data usability.
To enable utility-driven data management for improved analytics and patient monitoring.

Main Methods:

Developed a context-dependent data utility model considering data quality (accuracy, completeness, consistency, timeliness) and Quality of Service (QoS) (availability, response time, latency).
Proposed a goal-model-based approach to identify and address data quality and QoS violations.
Evaluated the approach using a real-world dataset from a sensor-based physical activity monitoring project.

Main Results:

The proposed model effectively assesses data utility based on application-specific requirements.
The goal-model approach successfully identifies violations and suggests corrective actions.
Demonstrated the practical applicability of the data utility model in a pervasive sensing scenario.

Conclusions:

A novel data utility model enhances data management in Fog environments for pervasive sensing.
Context-dependent quality assessment and QoS integration are key to ensuring data usability.
The approach provides a framework for improving the reliability of data-driven healthcare applications.