A cloud-edge reference architecture for intertwining health digital domains.

Objective: In the present work, LinkAll is introduced as a novel architectural model designed to enable real-time monitoring and cross-referential data analysis in remote monitoring systems across human, animal, and environmental health domains. LinkAll leverages Edge-Computing and Internet of Things principles to handle data collection, processing, and presentation from various sources. Methods: Two sibling systems were implemented to demonstrate its capability, one for monitoring urban greenery and the other for elderly home care. These systems were evaluated based on their ability to integrate with existing information systems, collect biophysical parameters, and ensure data cross-referencing. Results: Both systems demonstrate effective pluggability and cross-referenceability performances, meeting the stakeholders' requirements. LinkAll's ability to integrate diverse sensors and devices into existing infrastructures while providing real-time, machine-actionable insights, is also underscored. Conclusion: Pluggability, cross-referenceability, and compliance with FAIR principles make the architectural model introduced a robust solution for integrating human, animal, and environmental health monitoring systems, enhancing decision-making and contributing to One (Digital) Health's strategic goals.

Duke Scholars

Author Arriel Benis Biomedical Engineering