Viking Analytics and iioote announce AI-based tool for early detection of black mould
Viking Analytics, a Swedish provider of advanced analytics solution for predictive operations, and iioote, an IoT solution provider and LoRA Alliance member, presented today a new solution that uses Artificial Intelligence (AI) to detect the early onset of black mould (Stachybotrys chartarum) in buildings.
Integrated into iioote’s technology agnostic platform WebIoT, the algorithm developed by Viking Analytics uses data collected by sensors to detect variations in temperature and humidity that indicate the risk of fungus growth, known for causing damage in construction materials such as fibre-board and drywall.
Humidity, temperature, and leakage sensors are installed in places where there is a high risk of water damage, as bathrooms, kitchens, water distribution pipes, and drain systems, where they constantly register and transmit measurements. The information is then sent to a platform, in which an anomaly detection algorithm constantly analyses it and detects levels that could favour the development of black mould. The analysis is displayed in a user-friendly interface and triggers a notification when the risk level rises, so experts can decide which actions to take based on the data. SMS and e-mail alarms can also be configured.
Viking Analytics’ anomaly detection algorithms are normally used by intensive manufacturing industries, where many sensors have been installed in machines and collected data for years. According to Stefan Lagerkvist, CEO of Viking Analytics, the new offering shows that “sectors that do not rely on heavy machinery, like insurance and property management, benefit from the adoption of Internet of Things (IoT) and our data analytics. We are now taking an important step to include smart cities,” he explains.
The new offer is a result of a partnership between both companies that started in September 2019, to address a necessity from water and sewage treatment and insurance companies for a way to detect the early onset of black mould and act on it. The use of Artificial Intelligence saves time spent in manually going through the data and allows even professionals without a background in data science to analyse and make decisions based on it.
Robert Spertina, CEO and Head of IoT of iioote, explains that monitoring and being able to detect moisture before it can cause extensive damage is extremely important to construction companies, landlords, property owners, and insurance companies. “It is estimated that water damages cost more than SEK 6 billion (around US$ 619,4 million) every year to homeowners in Sweden, which directly affects compensation paid by insurance companies. One of our clients calculated that the avoided cost of one water leakage would pay off the investment in over 100 sensors, so it is really a very cost-effective solution,” he adds.
The sensors are wireless with several kilometres of range, battery powered with many years of operation, and can be connected to public or private LoRaWAN radio network built for IoT.
