TinyAIoT

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

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The idea

The TinyAIoT project aims to adapt AI methods to the needs of modern Internet of Things (IoT) applications. Nowadays, these are often based on microcontrollers, such as devices of the Arduino family, which can send and receive data via special network protocols - for example, the Long Range Wide Area Network (LoRaWAN) network protocol. The microcontrollers are generally equipped with various sensors, e.g. to measure temperature, fine dust pollution and other parameters [3, 14]. Corresponding sensor networks consist of many such microcontrollers. They form the basis for IoT applications, which can already be found in various fields (e.g., smart cities, agriculture 4.0, . . . ).

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).

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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).

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Stadtwerke Emsdetten GmbH

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Stabstelle Smart City der Stadt Münster

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Naturschutzzentrum Kreis Coesfeld e.V.

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Hof Homann eG

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opensenselabgGmbH

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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

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

Potential for resource efficiency.

  • Microcontroller: Arduino Pro Mini (5 volts) with a power consumption of 22mA under full load and approx. 3.6mA in sleep mode
  • Number of IoT devices: 10 billion microcontrollers are assumed for the estimate; estimates are for about 75 billion IoT devices in 2025
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.

The microcontroller has a yearly energy consumption of roughly (22, 1 · 24 ·365mAh·5V )/1000000 ≈ 0,97 kW/h in normal mode and (3, 6·24·365mAh·5V )/1000000 ≈ 0,16 kW/h in sleep mode. Looking at 10 billion devices one can derive an annual energy consumption of 7 TW/h (normal mode) and 1.6 TW/h (sleep mode). Assuming that more efficient implementations of AI techniques could result in a reduction of approximately 15mA per device (e.g., sleep mode, reduced data collection/transmission, etc.), a savings of approximately 657 kW/h per device per year could be achieved, analogous to the calculations above. For 10 billion devices worldwide, this would lead to a savings of approximately 57 TW/h, which corresponds to approximately 1.3% of Germany's net electricity consumption in the year 2020 (488 TWh).

Relation to potential negative environmental impacts

The number of IoT applications as well as corresponding AI-based microcontrollers will increase dramatically in the future, both for direct applications to protect our environment and nature as well as for numerous economic applications (with possibly also positive environmental balance). However, increasing digitalization and especially more IoT applications will lead to additional energy consumption. In addition, more efficient implementations could lead to additional smart microcontrollers being used. However, it can be assumed that the implementation of corresponding scenarios will be largely determined by the economic benefits and not by energy consumption. In this respect, more efficient implementations are desirable in any case.