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Remote Environmental Monitoring Systems

Remote environmental monitoring systems, such as the Tramex TREMS, are wireless networks of sensors that collect data on environmental conditions like temperature, humidity, and moisture content. This data is transmitted to a central hub or cloud-based platform, allowing users to remotely monitor and analyse conditions in real-time.

(5 Products)

Tramex Wireless Ambient Temperature and Humidity Sensor CS-RHTA

Tramex Wireless Ambient Temperature and Humidity Sensor CS-RHTA

£160.00 (ex VAT)

£192.00 (inc VAT)

Please contact for lead time

Tramex Cloud station CST-4G

Tramex Cloud station CST-4G

£500.00 (ex VAT)

£600.00 (inc VAT)

Typically ships in 3-5 working days

Tramex CS-RHTA Cloud Sensor for Tramex Remote Environmental Monitoring Systems (TREMS)

Tramex AP-TREMS-5 (5 Additional Ambient Sensors for TREM System)

£945.00 (ex VAT)

£1,134.00 (inc VAT)

Please contact for lead time

Tramex AP-TREMS-10 contents

Tramex AP-TREMS-10 (10 Additional Ambient Sensors for TREM System)

£1,745.00 (ex VAT)

£2,094.00 (inc VAT)

Please contact for lead time

Tramex AP-TREMS-Xtra (5 Additional Ambient Sensors for TREM-Xtra System)

Tramex AP-TREMS-Xtra (5 Additional Ambient Sensors for TREM-Xtra System)

£1,755.00 (ex VAT)

£2,106.00 (inc VAT)

Please contact for lead time

About Remote Environmental Monitoring Systems

Remote environmental monitoring systems, such as the Tramex TREMS, are wireless networks of sensors that collect data on environmental conditions like temperature, humidity, and moisture content. This data is transmitted to a central hub or cloud-based platform, allowing users to remotely monitor and analyse conditions in real-time. TREMS, for example, is specifically designed for building moisture management, enabling users to track drying progress, identify potential moisture issues, and ensure compliance with building codes and industry standards. These systems offer significant advantages over traditional manual monitoring methods, including increased efficiency, reduced labor costs, and improved data accuracy and reliability.