Sensor networks can be comprised of dozens, hundreds, or thousands of sensors, often measuring different parameters under different environmental conditions. In industry, they can be used to ensure conformity with Directive 2010/75/EU on emissions, control processes, or monitor quality control in advanced manufacturing. Sensor networks are also used to measure pollution in cities to demonstrate compliance to 2008/50/EC on ambient air quality and cleaner air for Europe. They are also integral to the Internet of Things.

However, developments in sensor types and the introduction of artificial intelligence software systems have meant that these networks are struggling with data quality to varying degrees. In addition, many networks have unknown measurement uncertainties and lack traceability to the SI.

This project will investigate the metrological aspects of sensor networks and develop reliable and accurate methods for assessing data quality and measurement uncertainty in real-world environments.

Sensors in distributed networks will also be metrologically assessed, covering a wide variety of sensor types and geographical distributions. Semantic technologies will be applied to develop novel approaches for sensors in large, transient networks, such as those used for air quality measurements in urban environments.

Results obtained will be validated in at least three real-world case studies covering areas such as industrial processing, environmental monitoring, and building and utility sectors. The data obtained will then be published as guidelines for end-users.

At the end of the project information on how to lower the measurement uncertainty and improve SI-traceability for static and mobile sensor networks will positively impact environmental monitoring and help support European Directives.