Remote monitoring technologies are expanding across healthcare, environmental observation and animal health, creating opportunities to analyse interconnected data streams. Sensors embedded in homes, cities and natural environments now generate continuous information about physiological conditions and environmental changes. Integrating these heterogeneous data sources requires infrastructure capable of secure processing, interoperability and continuous operation. LinkAll is a cloud–edge reference architecture designed to support monitoring across intertwined digital health domains while remaining compatible with existing information systems. Two implementations, one in an urban greenery monitoring context and another in elderly telerehabilitation at home, demonstrate how distributed sensing, edge processing and cloud coordination can support cross-domain monitoring aligned with Findability, Accessibility, Interoperability and Reusability principles.
Cloud–Edge Integration for Interoperable Monitoring
LinkAll is organised into three layers that distribute sensing, processing and orchestration functions across the system. The Edge Devices Layer contains low-power sensors designed for long-term deployment without frequent maintenance. These devices operate within Personal Area Networks and transmit measurements through gateways that coordinate communication and manage network load. The use of gateways allows monitoring systems to remain responsive while adapting behaviour according to stakeholder-defined policies.
Between field devices and central infrastructure, the Edge Node Layer manages communication routing and intermediate processing. This layer performs operations without identifying monitored individuals or storing sensitive data, reducing risks associated with information leakage. Consumer hardware and open-source software support these functions, enabling flexible deployment without specialised infrastructure. Communication with the cloud environment relies on web-based interfaces that allow measurements to be transmitted for further processing.
The Cloud Layer hosts the Cloud Node, which performs cross-referential analysis and system orchestration. Deploying this component at stakeholder premises supports data protection requirements while enabling large-scale processing and storage. The architecture is designed to be domain-agnostic and policy-driven, allowing integration of monitoring systems from different digital health domains without requiring extensive redesign of existing information systems.
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Environmental Monitoring in Urban Greenery
One implementation of the architecture focused on monitoring urban greenery within a smart city environment. Collaboration with a municipal administration supported development of a system capable of detecting environmental stress affecting plants. Sensors collected measurements such as wind speed, humidity, soil temperature and plant movement. These data were transmitted through the edge infrastructure to a cloud-based eGovernment platform for analysis.
Outdoor deployment required sensors that could operate reliably in exposed conditions while maintaining low energy consumption. Protective casings resistant to water and dust enabled long-term operation. Bluetooth Low Energy communication supported extended battery life, and adjustable signal amplification allowed balancing of transmission range and power use. A gateway built on an ESP-32-WROVER module with GSM connectivity managed sensor configuration, data aggregation and communication with the rest of the system. The gateway also collected local weather information to provide environmental context for measurements.
At the Edge Node Layer, measurements were processed to derive time-series indicators describing plant behaviour, such as structural inclination. These processed data were then transmitted to the Cloud Node, where cross-referential analysis could be performed alongside other monitoring information. The deployment demonstrated how low-power sensing and edge processing can support environmental monitoring across large outdoor areas while maintaining stable communication with central systems.
Telerehabilitation Monitoring in Home Care
A second implementation applied the architecture to elderly home care within a telerehabilitation prototype developed with a university hospital. Participants performed assisted physical activity at home while medical-grade Internet of Things devices recorded physiological measurements. These data were securely stored, analysed and incorporated into Electronic Medical Records managed by the hospital’s cloud infrastructure, with the deployment approved by an ethics committee.
Monitoring devices included both passive sensors that operated continuously and interactive devices requiring patients to initiate measurements. Examples included thermometers, pulse oximeters, blood pressure monitors and body-weight scales. Gateways simplified device operation by managing connectivity and configuration automatically once a measurement began. Android-based gateway devices were selected because manufacturers provided compatible software interfaces. An in-house application handled Bluetooth Low Energy communication and monitored device connectivity events.
Behavioural policies transmitted from the Edge Node Layer allowed gateways to update configuration during operation without requiring patient involvement. Data flowed from sensors through the edge infrastructure to the hospital systems, where they became part of the Electronic Medical Record environment. Installation relied on simple power and local network connections to minimise technical complexity for patients. The deployment showed how distributed monitoring infrastructure can support home-based rehabilitation while maintaining secure integration with clinical information systems.
LinkAll demonstrates how a cloud–edge architecture can support monitoring across environmental and healthcare domains using interoperable sensing, distributed processing and policy-driven orchestration. Deployments in urban greenery monitoring and elderly telerehabilitation illustrate how low-power sensors, gateway coordination and cloud-based analysis can operate together to enable continuous observation without invasive intervention. Reported system performance showed latency measured in hundreds of milliseconds, reliability above 98% and stable throughput across both deployments. The architecture also highlights practical considerations, including connectivity interruptions, wireless congestion and regulatory requirements related to privacy and device certification. The approach provides a framework for integrating heterogeneous monitoring systems while preserving compatibility with existing digital infrastructure and supporting cross-domain data analysis in One Digital Health environments.
Source: Health Informatics Journal
Image Credit: iStock
