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Related Experiment Video

Updated: May 23, 2026

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
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RINAsense: An open-source, low-power IoT sensor node for Recursive InterNetwork Architecture (RINA) Experimentation.

David Sarabia-Jácome1, Eduard Grasa1, Marisa Catalán1

  • 1Internet of Things Research Group, Fundació i2CAT, 08034 Barcelona, Spain.

Hardwarex
|December 30, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces RINAsense, an open-source hardware platform for the Recursive InterNetwork Architecture (RINA). It enables low-cost, low-power Internet of Things (IoT) devices, enhancing RINA adoption in smart environments.

Keywords:
ESP32Environmental sensorIoTLow-powerNetwork architectureOpen-source hardwareRINA

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Area of Science:

  • Computer Science
  • Networking
  • Embedded Systems

Background:

  • Conventional network architectures face scalability, security, and management challenges with the rise of the Internet of Things (IoT).
  • The Recursive InterNetwork Architecture (RINA) offers a novel approach to networking, but its adoption is hindered by a lack of suitable hardware for resource-constrained environments.

Purpose of the Study:

  • To introduce an open-source hardware platform, the RINAsense node, specifically designed for RINA in IoT applications.
  • To address the need for accessible, low-cost hardware for practical RINA research and experimentation.

Main Methods:

  • Development of the RINAsense node based on a low-power ESP32 microcontroller.
  • Integration of environmental sensors (temperature, humidity, CO) and custom RINAsense firmware for embedded systems.
  • Deployment and validation of the hardware in a smart building testbed.

Main Results:

  • Successful operation of the RINAsense node as a reproducible scientific instrument.
  • Achieved low-latency communication with an average round-trip delay of 2.05 ms.
  • Demonstrated an 82% reduction in energy consumption compared to non-optimized operation.

Conclusions:

  • The RINAsense node effectively bridges the gap between RINA and resource-constrained IoT environments.
  • The platform lowers the barrier for practical RINA research, promoting open science and reproducibility.
  • The hardware's performance validates its suitability for energy-efficient, low-latency IoT applications.