IoT use cases: AI/Machine Learning

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AI and Machine Learning in IoT

By gathering data, analyzing trends, detecting anomalies, and learning what actions or conditions are most suitable, machine learning in IoT systems can help users automate and make the best choices while saving resources from manual work. First, users identify a goal for the system to reach and develop algorithms to predict data patterns. The system’s predictions are then qualified by the user, approving it as a basis for decisions within the business’ operations.

Applications for AI/ML in IoT are many and broad. For example, the technology is deployed within agriculture, weather prediction models, and driverless transport systems, to name a few areas. In addition, users lay the foundation for the future of true AI in IoT through the systems working with machine learning today.

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Sensative, together with Örebroporten, wants to explore and verify how much we can reduce a property's CO2 emissions and energy consumption with the help of our IoT platform, AI and a relatively low investment in 48 volts micro-grid networks with solar cells, batteries and 48 V lighting, heating systems, and more.
DAIS approach is to develop intelligent, secure and trustworthy systems for industrial applications to provide comprehensive cost and energy-efficient solutions of intelligent, end-to-end secure, trustworthy connectivity and interoperability to bring the Internet of Things and Artificial Intelligence together.
The project will, with the help opf Machine Learning and AI algorithms, identify common sensor problems for the most interesting sensors in agriculture, including sensors for temperature, humidity, precipitation and wind in weather stations, sensors for soil temperature and humidity, etc. 

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