www.dbecore.com

CaféIoT

One mainframe of the project is the Café IoT, personnel cafeteria at LUT School of Business and Management. About fifty people is visiting this cafeteria daily. Actually, we don’t know yet how many people visit this cafeteria daily or what the quiet hours actually are, but after this project we have measured data and we can show you the facts.

There are many ideas and plans what to measure and monitor in this cafeteria ecosystem. One of the wild ideas was to identify with machine vision people to avoid or meet some specific person on purpose. Or predict the spread of influenza epidemics from the acoustic sensors catching sneezing and coughing…

Before that, one of our first goals in Café IoT testbed is to monitor material flow (coffee, water, milk, tea…), crowded hours, and energy and water consumption. Then, with (big) data analytics, we can automatize and adjust many processes in the cafeteria. Maybe our coffee room can even autonomously order and pay coffee to fill the stocks.

Subjects to be measured and monitored in CaféIoT during 2017:

  • Energy consumption
  • Weight of the coffee stock
  • presence of people by IR motion sensors
  • Optical image/infra red image of the coffee machine
  • Air quality
  • Temperature(s)
  • Air pressure
  • Humidity

We also collect open data, such as Nord Pool spot electricity stock price and weather information from the nearest weather station.

Basically, one part of the LUT Café IoT is camera monitoring of coffee pot, which was also the subject of the very first webcam at Cambridge University 1991.

However, pictures, setting up sensors, information or data is not the point of this testbed. Data analysis and clever use of data in addition to the business and logistics are subjects to be explored.

Goals of this project include:

  • test new models and architectures in digital supply chain using IoT data
  • implement and test scalable IoT system architecture for digital supply chain and predictive maintenance
  • test machine to machine interface with smart contracts, such as blockchain
  • understand better limitations, challenges and possibilities in data exchange between different organizations
  • find ways to combine different type of data and measurement in analysis (e.g. voice, product datasheet, open data, time series…)
  • gain knowledge in energy efficient and sustainable use of cafeteria