Energy- and resource-efficient artificial intelligence for modern IoT applications


The idea

The rapid growth of the Internet of Things fueled the design of devices that are based on microcontrollers, equipped with sensors, and capable of exchanging data. These devices - used, e.g., in smart home applications or to build environmental monitoring stations - enable the collection and analysis of large amounts of data and the development of potentially powerful applications. However, applications are currently limited by the need to exchange collected data via cloud services to use state-of-the-art AI processes, which consumes significant resources in the form of energy, material, and bandwidth. The aim of the TinyAIoT project is to reduce these resource requirements by developing efficient and tiny AI models that can be used on the microcontrollers themselves. This not only extends the range of possible use cases to more powerful applications, but also reduces the required bandwidth of applications, enabling microcontrollers to operate autonomously for several weeks to years.

Network partners

The project will be carried out as a joint project between the University of Münster and Reedu GmbH & Co. KG (Dr. Thomas Bartoschek).


re:edu is a start-up and spin-off from the Institute of Geoinformatics. Since 2018, re:edu is the producer of the senseBox and offers a wide range of services around the senseBox and the fields of Digital Education, Citizen Science and Smart Cities.


The University of Münster participates with the Institute for Geoinformatics(Prof. Dr. Angela Schwering) and the Institute for Business Informatics(Prof. Dr. Fabian Gieseke).

Associated partners

In addition, various application scenarios are to be realized together with four associated partners, Stadtwerke Emsdetten GmbH, Stabsstelle Smart City of the City of Münster, Naturschutzzentrum Kreis Coesfeld e.V. and Hof Homann eG. Additionally, subcontracts are to be awarded to two further companies (opensenselabgGmbH and Budelmann Elektronik GmbH).


Stadtwerke Emsdetten GmbH


Stabstelle Smart City der Stadt Münster


Naturschutzzentrum Kreis Coesfeld e.V.


Hof Homann eG




HANZA Tech Solutions GmbH

Resource efficiency

The main goal of the TinyAIoT project is to further reduce the resource requirements of existing implementations and to adapt further AI models accordingly. In particular, the resource and energy requirements are to be reduced to such an extent that the underlying microcontrollers can be operated autonomously by means of batteries over a longer period of time. A special focus shall be on the special combination of microcontrollers of the Arduino family and the LoRaWAN network protocol (e.g. very small main memory and limited bandwidth of LoRaWAN). The results will eventually be used to adapt and extend the senseBox and associated sensor networks, leading to a 'smart' version of the senseBox -the TinyAI-senseBox- that can be operated autonomously for longer periods of time Combination
The market for IoT applications has already grown rapidly, and the number of microcontrollers is expected to increase sharply in the future. More efficient implementation of the processes and the associated energy savings thus offer the potential to reduce energy consumption for a large proportion of such devices and thus make a significant contribution to saving CO2 emissions. In addition, intelligent microcontrollers will enable numerous applications relevant to the environment and nature, such as in the fields of Agriculture 4.0 and Smart Grids, or in the field of ecosystems.

Use cases

The TinyAIoT project was partly inspired by the existing Birdiary project.

Detecting dangerously close takeover manoeuvres with ToF imaging technology

Measuring and sending fill height of public trashcans

Counting people based on step vibrations