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  • Sensative Strips Multi-Sensor +Oil for LoRaWAN

    This item is currently out of stock. Please contact us for ordering.

    SV1301002194

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    Receive Alerts When an Oil Spill is Detected

    Make your smart garage and home safer from oil spills and water leaks with the Sensative Strips Multi-Sensor +Oil for LoRaWAN. This innovative, ultra-thin sensor is designed to monitor and report the presence of oil or water, as well as ambient air temperature. The sensor is capable of distinguishing whether oil or water is touching the sensor and sends a corresponding alert to a paired receiver or gateway. The Multi-Sensor can be programmed to trigger automation or security tasks when its leak or temperature sensors are triggered. An ultra-thin 3mm design allows for universal installation to fit into hard to reach locations where larger sensors may not fit well.

    As a LoRaWAN compatible device, the Strips Multi-Sensor +Oil can communicate with other LoRaWAN devices, plus Internet of Things (IoT) compatible devices, up to 6 miles (10 km) away, making it perfect for both indoor and outdoor applications. It can integrate with Internet of Things (IoT) platforms like Yggio to participate in smart automation functions. The Strips +Oil can trigger a smart water valve to shut off your water supply to minimize potential damage or, trigger an alarm when spilled oil is detected, or order your HVAC system to turn On/Off when a certain temperature setpoint is reached. Through the vast interoperability of Internet of Things networks, the Strips Multi-Sensor +Oil can be a valuable addition to any smart automation system.

    Works with SmartThings Badge. Easily setup your device throught the SmartThings App interface. Following remaining instructions and your device is ready for use within the SmartThings App.

    • Ultra-thin multi-sensor monitors and reports the presence of oil or water, plus measures temperature
    • 3mm thin design allows for universal installation anywhere within and around your garage or home
    • LoRaWAN wireless connectivity for long-range (6 mi/ 10km), low-power communication, and integration with Internet of Things (IoT) networks
    • Built-in temperature sensor accuracy measures within ±1 °C/ 1.8 °F
    • IP67 rated for waterproof and dust resistant performance
    • Long-lasting battery with up to 10 years of operation

    Installation Details

    The Sensative Strips Multi-Sensor +Oil is built to fit into virtually any setting thanks to its ultra-thin design and universal mounting application. All that is required for installation is a piece of double sided tape to secure the sensor and its paired magnet into your preferred position. The sensor's IP67 rating indicates it is water and dust resistant, plus the Sensor can operate within extreme heat and below freezing temperatures, making it ideal for both indoor and outdoor installations.

    Contents

    • Strips Multi-Sensor +Oil for LoRaWAN
    • Magnet
    • Manual
    • Quick Start Guide

    NOTE: Compatible controller or hub sold separately.

    Sensative: Empowered By Data

    Since 2015, Sensative has been an innovator in the smart home market, designing unique and elegant products to make life at home easier, safer, and smarter. With an ultra thin design, Sensative Strip Sensors can be installed discreetly and seamlessly without being obtrusive to the eye.

    • Manufacturer Part #: 1301002_194
    • EAN: N/A
    • Application: Water leak detection, Oil leak detection
    • Sensor: Oil leak, Temperature, Water leak, Open/Close
    • Sensor Type: Magnetic contact
    • Benefits: Blend-in design, No maintenance (10 years), Long-range (10 km), Simple installation & mounting, Low-lifecycle cost (multi-purpose multi-sensor)
    • In/Outdoor Usage: Indoor & sheltered outdoor
    • Firmware Update Over the Air (FOTA): No
    • Frequency: EU868 – Europe 863-870 MHz or US915 – US 902 – 928 MHz or AU915 – Australia 915 – 928 MHz or AS923-1 – Asia 902 – 928 MHz or IN865 – India
    • Temperature Accuracy: ±1 °C/ 1.8 °F @ 0-30 °C/32-86°F
    • Humidity Accuracy: 1.8%