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Related Experiment Video

Updated: May 28, 2026

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem

Published on: February 3, 2021

FRESH: An Autonomous IoT Platform for Multi-Parameter Environmental Sensing and Short-Term Forecasting.

Feiling Pan¹, James A Covington¹

¹BioSensors Laboratory, School of Engineering, University of Warwick, Library Road, Coventry CV4 7AL, UK.

Sensors (Basel, Switzerland)

|May 27, 2026

Summary

This summary is machine-generated.

This study introduces FRESH, an autonomous Internet of Things (IoT) platform for environmental monitoring and forecasting. The system offers portable, multi-parameter sensing and short-term predictions for air quality and weather.

Keywords:

IoT air quality environmental monitoring machine learning multi-parameter sensing short-term forecasting

Related Experiment Videos

Last Updated: May 28, 2026

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem

Published on: February 3, 2021

Area of Science:

Environmental Science
Sensor Technology
Internet of Things (IoT)

Background:

Traditional environmental monitoring systems face limitations in cost, portability, and pollutant coverage.
Fixed infrastructure restricts the suitability of current systems for distributed, real-time sensing applications.

Purpose of the Study:

To present FRESH, an autonomous IoT-based platform for multi-parameter environmental monitoring and short-term forecasting.
To evaluate the practical performance of the FRESH system over a 10-month deployment period.

Main Methods:

Integration of sensors for air quality, thermal conditions, light, acoustics, and weather.
Utilized GSM for remote data transmission, onboard data logging, and hybrid battery-solar power management.
Developed machine learning models for short-term environmental variable prediction.

Main Results:

FRESH demonstrated practical performance across diverse indoor and outdoor conditions over 10 months.
Machine learning models achieved high predictive accuracy for particulate matter (R² = 0.93), volatile organic compounds (R² = 0.92), and ozone (R² = 0.98).

Conclusions:

The FRESH platform shows potential for portable, multi-parameter environmental monitoring.
The system's integrated forecasting capabilities can support distributed sensing and localized early-warning applications.