Towards Continuous Sensor Operation: Modelling a Secured Smart

Sensor in a Sparse Network Operated by Energy Harvesting

Thomas W. Pieber, Benjamin M

oßlang, Thomas Ulz and Christian Steger

Institute for Technical Informatics, Graz University of Technology, Inffeldgasse 16/I, Graz, Austria

Keywords:

Energy Harvesting, NFC, Robotics, Smart Factory, Smart Home, Smart Sensors, Sparse Networks.

Abstract:

In modern society sensors are omnipresent. They gather information about their environment in order to

optimize production ﬂows, minimize energy usage, learn about the environment, or maximize the owner’s

comfort. To achieve the desired goal in already existing buildings, sensors are introduced afterwards. These

sensors might not be able to connect to a sensor network because of obstacles or user policies. If this happens,

other mechanisms to create a network to gather the data need to be found. Additionally, these sensors should

last for a long period and are therefore probably powered using energy harvesting methods. In this paper we

present an approach for simulating the charging process of such sensors and connecting them to a network

using mobile communication partners.

1 INTRODUCTION

In our society we use sensors to automatically gather

data for almost every aspect of our environment. In

some applications the sensors cannot connect to a net-

work, either it is not feasible to build infrastructure to

connect the sensors (1) or they are not allowed to join

an existing network (2).

The ﬁrst use case is most likely to arise in a sparse

sensor network such as when monitoring a wide area

or if obstacles such as buildings inﬂuence the com-

munication channel. Furthermore, this can arise in

an Industry 4.0 setting where sensors can be added at

any time and due to insufﬁcient wireless coverage, in-

terferences, obstacles, or policies the sensors cannot

connect to the local network.

In the second case the sensors can collect data that

needs to be handled conﬁdentially and are therefore

not allowed to be transmitted over a long-range wire-

less communication channel. This can also happen in

an Industry 4.0 scenario.

For these use cases mobile communication part-

ners (nodes) with additional computational power and

further capabilities can be introduced to connect the

sensor nodes to the data sink. These mobile partners

can be a worker or robot in a factory, a home owner

in his house, or an employee of the city. These com-

munication partners then need to estimate the urgency

of the collected data, the memory usage of the sensor,

and the energy level of the sensor nodes they should

connect to the network. This results in periodical vis-

its from the mobile node at any sensor. These visits

can furthermore be used to prolong the sensor’s oper-

ational time without much effort.

Additionally, when the nodes are visited and their

data is collected by the mobile node it may also be

possible to conﬁgure the sensors behaviour to account

for changing needs of the owner. To mitigate possible

threats that come from unauthorized personnel chang-

ing the conﬁgurations, the sensors need to be secured.

There are many proposed solutions that connect

the sensors to mobile nodes (eg. (Marta and Cardei,

2009; Ye et al., 2002; Kim et al., 2003)). Most

of these use traditional radio frequency communica-

tion to communicate the distance to the mobile node.

Transporting energy in addition to the data requires

other communication technologies such as Near Field

Communication (NFC). In this work we examine the

possibility of using NFC-enabled robots, or NFC-

enabled smartphones to charge, read out, and con-

ﬁgure sensor nodes which cannot be connected in a

traditional way. This should happen while the data is

gathered reliably and secured.

The design of such sensor nodes poses many com-

plex questions such as: “Where is the energy needed

the most?”, “How much energy can be saved by re-

ducing the sample frequency?”, or “How can the en-

ergy be used more effectively?”. To answer these

questions, simulations can be used to gain insights in

this complex topic. These simulations focus on the

Pieber, T., Mößlang, B., Ulz, T. and Steger, C.

Towards Continuous Sensor Operation: Modelling a Secured Smart Sensor in a Sparse Network Operated by Energy Harvesting.

DOI: 10.5220/0008053000570064

In Proceedings of the 9th International Conference on Pervasive and Embedded Computing and Communication Systems (PECCS 2019), pages 57-64

ISBN: 978-989-758-385-8

detailed description of the sensor system and try to

give detailed answers. This however neglects other

major questions such as: “How does the designed en-

ergy harvesting system perform?” or “How does the

environment affect the sensor communication?”.

This publication focuses on the creation of a sim-

ulation that is also able to answer questions like these.

To do this we created a simulation that focuses on

the gathering of time insensitive information using

sensor nodes in an environment that does not allow

long range wireless communication. Such scenario

might arise when measuring the temperature in an au-

tomated warehouse, the air humidity of a basement,

or the air quality in a city. In these scenarios, some

agents (robots or humans) constantly move through

the environment performing operations. We propose

that they can also collect the information from the

sensor nodes, keep them operational and adapt the

sensor nodes behaviour while these agents are per-

forming their jobs.

To effectively use such a method requires the prior

knowledge of the sensors energy consumption. This

is necessary as the sensors operational lifetime is lim-

ited by the stored energy. The periodic visits should

happen shortly before the internal memory is full or

the energy level drops below a certain level to opti-

mally use the mobile agent’s time. Thus, this publi-

cation explores a method to estimate the energy con-

sumption of a smart sensor and the possibilities that

energy harvesting offers to prolong the sensors life-

time.

The remainder of this paper is structured as fol-

lows: In Section II related work is described. This

section is split to emphasize the energy harvesting, the

energy usage estimation and the mobile data collec-

tion. The theory and approach of our experiments can

be found in Section III. Here we describe the hard-

ware prototypes, experiments and the performed sim-

ulations. Section IV is dedicated to the measurements

taken with the prototypes and compares it to the re-

sults of the simulations. Section V contains ideas on

how to improve on the ﬁndings of this paper. This

paper concludes in Section VI.

2 RELATED WORK

2.1 Energy Harvesting

Prolonging a battery powered sensor’s lifetime is the

goal of many publications. Most of the publications

include mechanisms for harvesting energy from the

surrounding. These harvesting methods include the

use of solar cells (Chen et al., 2010) or antennas

(Pinuela et al., 2013) to use electromagnetic radia-

tion, devices to convert heat gradients (Dziurdzia and

Stepien, 2011) to electrical energy, and mechanisms

to utilize mechanical energy (Choi et al., 2006).

All those methods are built to support the sensor

with a steady (or at least calculable) amount of en-

ergy. Thus, the sensors need only small energy stor-

age capacities to dampen energy ﬂuctuations. (Kansal

and Srivastava, 2003; Kansal et al., 2007; Chen et al.,

2010; Tan and Panda, 2011).

J. Gummeson et al. (Gummeson et al., 2014) de-

veloped a small worn sensory device. This device is

embedded inside a ring which limits the size available

for energy storage. In their solution to this problem

they used NFC to recharge the internal storage when-

ever the user reaches for an NFC-enabled smartphone.

A similar approach can be used to operate larger de-

vices. In the case of a smart sensor the energy storage

can be made signiﬁcantly larger, allowing for longer

operation between the recharging occurs. At the same

time larger antennas and more powerful NFC-readers

can reduce the time needed for recharging the sensor.

We also plan to use NFC to charge the sensor. In

contrast to the work of Gummeson et al. we want

to power a system that requires more energy.

A study from M. Rahimi et al. (Rahimi et al.,

2003) investigates the feasibility of mobile nodes in

order to provide energy to a sensor network. In this

study the authors show promising results of their pro-

totypical tests. This work focuses on the mechanisms

for searching for energy in an environment and how

the robots split the servicing (charging) of the sensor

nodes. They focus their research on quantifying the

power consumption of the network and specifying if

the network is sustainable.

In contrast to this research we focus on the com-

bination of energy transfer to the sensor nodes and

the simultaneous data collection performed by mobile

nodes.

Chen et al. (Chen et al., 2010) proposed and

demonstrated a nearly self sustaining micro sen-

sor that uses solar cells to generate the needed en-

ergy for sensing capacitance and temperature . This

approach to build a self-sustaining sensor network

seems promising but induces the need that the sensors

are subject to a sufﬁcient light source which might not

be given in many scenarios. The work of Kansal et al.

(Kansal et al., 2007) described power management

techniques that can be used and they described how

such mechanism can be implemented with respect to

a known model of the desired energy source. That

can be used to improve the work of Chen et al. (Chen

et al., 2010). Additionally, such a model can be used

in the proposed model to optimize the sensor usage.

PECCS 2019 - 9th International Conference on Pervasive and Embedded Computing and Communication Systems

In this paper we try to use NFC as communica-

tion technique to transmit data between a sensor and

a mobile node. In this approach a robot is acting as

the mobile node. This robot creates the connection to

the infrastructure, thus acting as a slow and random

link. The use of NFC furthermore allows us to trans-

mit power to the sensor supporting it for high power

operations and keeping it operational.

2.2 Energy Usage Estimation

The estimation of the energy consumption of sensors

is an ongoing research. There are approaches to min-

imize the energy consumption of sensor nodes based

on their speciﬁc energy levels and that take into ac-

count the energy levels of the surrounding nodes (Yan

et al., 2013).

Other researchers such as Halgamuge et al. (Hal-

gamuge et al., 2009) generated a model of a sensor’s

behaviour and try to estimate the energy consump-

tion of the sensor based on the information of the be-

haviour.

To get better results than pure estimation of the

energy consumption, we decided to create a research

prototype of a low-power sensor on which the energy

consumption can be measured.

2.3 Mobile Communication Partners

The idea of using mobile nodes in order to connect a

wide spread sensor network has been explored widely

in the existing literature. Most of these solutions use

erratic moving partners (such as animals in their habi-

tat who are equipped with a sensor node) to try to con-

nect all of the stationary nodes(Shah et al., 2003; Ulz

et al., 2017a; Rahimi et al., 2003).

The approach developed by Ulz et al. (Ulz et al.,

2017a) to connect industrial machines using robots as

links can be used to calculate the sensor node that

should be visited next.

In the work of Shah et. al. (Shah et al., 2003)

a multi-layer network with mobile nodes to connect

sensors with each other and with the data sinks was

proposed and explained. One of their main goals was

to minimize the sensors memory to decrease energy

demand. In their studies they proposed to mount the

mobile nodes on animals, roaming through their habi-

tat. In their assumption the mobile nodes (MULEs)

are performing a random walk and stumble upon the

sensors is not applicable to our use case. With that

we can simplify many of the calculations done in or-

der to get a reasonable memory size. Furthermore, as

the mobile partner can directly communicate with the

sensor, the sensor can suggest a return time for the

mobile partner to improve memory usage and sensor

lifetime.

Rahimi et. al. (Rahimi et al., 2003) describe an

approach for energy harvesting and distributing the

energy in a wireless sensor network (WSN) with the

help of mobile autonomous robots. In their approach

the robot moves through the observed area and ﬁnds

a spot with enough available solar energy to charge

the battery. The robot then moves towards the sensor

nodes which need the energy and charges them. With

this approach also sensors that can not harvest enough

energy to sustain themselves can be operated using

the delivered energy. This approach furthermore in-

creases the lifespan of the rest of the sensors as they

are provided with more energy than they could harvest

on their own. In our approach the sensors are not only

sustained by the mobile robot, but also their data is

collected. This cuts the energy demand for transmit-

ting the gathered data, allowing the sensors to operate

longer.

In our presented approach we use controllable

means of transportation in order to efﬁciently collect

all gathered data, update conﬁgurations of the sensors,

and charge the batteries. This controlled data collec-

tion can also provide means to predict the arrival of

new data and the possibility to have information on

the timeliness of the data.

3 APPROACH

Many systems, designed to be used as a sensor for

a sensor network, use batteries, wired electrical con-

nections, or continuous energy harvesting methods as

their main power source. To utilize short bursts of

energy as power source, the energy must be received

and the excess must be stored in a usable manner. To

do this, the energy is stored using accumulators or ca-

pacities. As we try to combine the transport of data

and the delivery of energy, NFC technology, and thus

energy bursts, are examined.

To use NFC as means of energy transport, the cir-

cuitry of the smart sensor must allow the extraction

of excess energy of the NFC ﬁeld. Furthermore, the

excess energy must be directed to charge either a ca-

pacitor or an accumulator. This is done by extract-

ing electrical energy from the electromagnetic ﬁeld.

The generated AC (alternating current) voltage is then

rectiﬁed and the voltage and current are controlled to

protect the sensor. To be more efﬁcient, the circuitry

can include means of distinguishing between operat-

ing the sensor from the stored energy and operating

it from the NFC ﬁeld and storing the excess energy.

This switching of operation mode can be performed

Towards Continuous Sensor Operation: Modelling a Secured Smart Sensor in a Sparse Network Operated by Energy Harvesting

by the sensor’s main controller.

To store the energy different solutions can be used.

For this domain the most useful solutions are accumu-

lators or capacitors. The energy density of capacitors

is lower in comparison to accumulators. This means

that capacitors can hold less energy. In contrast to that

the power density of capacitors is larger. This allows

capacitors to store and draw energy faster than equally

sized accumulators (Zhang et al., 2013).

We decided to use a super-capacitor based devel-

opment board as basis for the research prototype mea-

suring the energy provisioning system (henceforth

Prototype A).In addition to the prototype to measure

the energy provisioning system, we created a research

prototype on which the energy consumption of the

sensor components can be measured (henceforth Pro-

totype B).

Using these research prototypes, we gather data to

set up simulations that can represent the interactions

between the sensors and the mobile agents, as well as

help in gathering data to optimize the agent’s visiting

schedule. Here a simulation approach by Pieber et al.

(Pieber et al., 2017a; Pieber et al., 2017b) can be used

to connect the simulation of a smart sensor system to

a simulation that is more capable of simulating the

interaction between an agent and the sensor.

3.1 Research Prototypes

3.1.1 Prototype Overview

An MSP430FR5969 development board (TI, 2014) is

chosen as a basis for Prototype A. This board includes

a super-capacitor as energy source, a microcontroller

tailored for low power use, and a temperature sen-

sor. It is then extended with a custom made PCB to

connect an NFC interface (ams AG, 2006), capable

of handing energy to the host system. The extension

PCB can be conﬁgured using jumpers to allow differ-

ent communication channels. This PCB furthermore

features a simple power management system that lim-

its the power that can reach the super-capacitor and

can switch between the available power sources such

that the capacitor can be charged when an NFC ﬁeld

is present. A circuit plan of the prototypical PCB is

shown in Figure 1.

Prototype B consists of three parts. The Energy

Measurement Unit (EMU, Testbench), a control com-

puter, and the Smart Sensor itself. As basis for the

EMU another PCB was designed. This is ﬁtted to

an MSP340FR5969 board that gathers the data and

acts as a bridge between the measurement unit and

the control computer.

The smart sensor consists of a microcontroller, inter-

face ports, additional memory, an NFC interface, and

a security co-processor. Each of these components is

supplied by an energy channel coming from the mea-

suring testbench. Additionally, most components can

be cut from the power supply to reduce the energy de-

mand of the sensor.

The testbench is controlled by a LabVIEW computer

simulating the energy provisioning system. This is

done by reading the energy demand of the sensor and

setting the supply voltage according to the capabili-

ties of the simulated provisioning system. In addition

to the supply voltage the LabVIEW script also sets

the digital potentiometers to adapt the gain of the cur-

rent sensors. Using the information about the current

drawn by the sensor components and the voltage of

the system, an energy proﬁle can be created that can

be used to create simulations describing similar sen-

sors. The design for Prototype B is shown in Figure

3.1.2 Prototype Details

When the NFC antenna of Prototype A is subjected to

an NFC ﬁeld, a DC (direct current) voltage is gener-

ated. This voltage is represented as NFC DC in Fig-

ure 1. If this voltage is larger than the voltage at the

capacitor C connected to CHARGE+ the controller

switches the analog-switch connected to GPIO such

that the capacitor is connected to the voltage source

via the resistor (723 Ω). This controls the current

that can pass through the capacitor. If the voltage

difference between CHARGE+ and VCC is smaller

than a threshold, the switch is ﬂipped and the resis-

tor is short-circuited. This leaves the capacitor con-

nected to VCC via the Schottky diode D. With this

the current can not ﬂow to the capacitor and it will

not be charged any more. If the voltage of NFC DC is

smaller than the voltage of the capacitor at VCC, the

controller is powered from the capacitor. Should the

voltage at VCC drop below a threshold, the controller

switches in a low-power mode and waits for a voltage

increase at VCC to start the charging again.

The voltages and currents that are present in the sys-

tem can be measured at the jumper pins on the PCB.

The most interesting values are the voltage at the ca-

pacitor, the voltage that reaches the sensor and the

current that is drawn by the sensor.

The three parts of Prototype B are shown in Fig-

ure 2. The Smart Sensor, the Testbench, and the Com-

puter/Control are represented as the boxes that com-

bine the necessary elements.

The Control is located at a LabVIEW computer. This

computer receives the measurements of the testbench

and sets the control signals for the testbench such that

a speciﬁed energy provisioning behaviour is reached.

PECCS 2019 - 9th International Conference on Pervasive and Embedded Computing and Communication Systems

Figure 1: Circuit of the energy provisioning system of Pro-

totype A.

The Testbench receives these signals and controls the

variable voltage source and the digital potentiometers

according to the control signals. It furthermore relays

the measurement values of the current sensors to the

Control computer. The Testbench has seven variable

gain current sensors that can be used to observe the

behaviour of the device under test.

The device under test - in this case the Smart Sensor

- consists of six components, and therefore uses six

measurement channels. The control unit of the sensor

is an Ambiq Micro Appollo 2 MCU. This controller

is connected via an I

C bus to an Optiga Trust X Se-

curity Co-Processor. Another separate I

C bus con-

nects to an additional FRAM module as well as to an

NFC Interface. Additionally, two External Ports are

connected via IO pins. At these interfaces, different

expansion modules can be connected. Using the IO

pins, I

C or SPI buses can be simulated to communi-

cate with sensors, actuators, or other controllers.

Using Load Switches, the controller is able to cut dif-

ferent components off the energy supply to reduce the

energy demand of the smart sensor as far as possible.

This setup allows the measurement of the energy de-

mand of each component of a smart sensor in a ﬂexi-

ble way.

3.2 Simulation

The data generated from the prototypes is fed into

simulations describing similar sensor systems. These

simulations are necessary, as we are interested in the

behaviour of the sensor in combination with different

systems.

To get a more abstract system description of a smart

sensor the gathered data needs to be generalized.

Thus deviations in the results of the simulations from

the measurements are expected.

To describe the sensor system electrically, a simula-

tion using a SPICE program is created that represents

the smart sensor. Here simpliﬁed but usable param-

eters can be extracted that are used in the calcula-

tions for the energy consumption of the smart sensor.

The gathered parameters are then used to describe the

electrical behaviour of the sensor is subsequent simu-

Figure 2: Design of Prototype B.

lations.

The simulation of the sensor itself is written in

SystemC as it allows the description of the entire sys-

tem at different levels of abstraction. This is espe-

cially useful as the accurate simulation of a complex

system impedes the simulation performance. This is

counteracted by performing simulations on a more ab-

stract level.

The environment is simulated using the Gazebo sim-

ulator, a simulation tool commonly used for robotic

purposes. These two simulations are connected us-

ing a method developed by Pieber et al. (Pieber et al.,

2017a). In Figure 3 such a simulation run can be seen.

The robot interacts with the sensor, charging it, col-

lecting data, and possibly reconﬁguring it to alter the

sensors behaviour. Using this simulation technique,

the sensor simulation gets the stimuli from the envi-

ronment and reacts according to it. This allows the

quick creation of new test cases and stimuli, as well

as the automatic evaluation of sensor responses.

Within the Gazebo environment, it is possible to

align the antennas in various orientations to each

other. Additionally, it is possible to introduce extra

noise (such as noise from nearby communications or

multi-path signal propagation) to the communication.

This allows to ﬁnd the answers to the questions asked

in Section 1.

Summarized, this means that the measurements of

the prototypes are used to generate a SPICE simula-

tion. Parameters are extracted from this simulation

that can be used to describe the electrical behaviour

of the sensor. This description is done in SystemC. To

subject the SystemC simulation to stimuli, the Gazebo

simulator is used. This is done in order to quickly

change the alignment of the NFC antennas and to in-

Towards Continuous Sensor Operation: Modelling a Secured Smart Sensor in a Sparse Network Operated by Energy Harvesting

Figure 3: Simulation of an interaction between robot and

sensor.

troduce additional noise to the system.

This simulation yields data about the interaction

of the smart sensor with the environment in differ-

ent situations. Furthermore, information about the

energy usage during operation as well as information

about the memory usage of the stored values are cre-

ated. This information, in connection with informa-

tion about the type of generated data, can then be used

by a mobile node to calculate the need to visit the sen-

sor node to collect data and recharge it.

If multiple sensors are connected in this way, a sen-

sor network is created using the robots as links be-

tween themselves and the infrastructure. This method

was described by Ulz et al. (Ulz et al., 2017a) as a

“Sneakernet on Wheels”.

4 MEASUREMENTS AND

RESULTS

Prototype A is designed to measure the energy har-

vesting capabilities of the NFC connection. A typi-

cal measurement of this prototype can be seen in Fig-

ure 4. In this measurement the capacitor was charged

for approximately 120 sec. During this time the volt-

age at the capacitor (middle line) reached the nom-

inal voltage. After that the NFC ﬁeld was switched

off and the voltage at the rail (bold line) drops be-

low the voltage at the capacitor. The current through

the sensor (ﬁne line) also drops signiﬁcantly as the

controller switches to a low-power mode. After that

the controller awakes every 60 sec to take a measure-

ment. At this times the current rises and the voltage at

the rail drops as the capacitor and diode pose a resis-

tance to the current. Again at 360 sec the NFC ﬁeld is

switched on brieﬂy.

The operation of the smart sensor is measured us-

ing Prototype B. In Figure 5 a sample measurement is

shown. In this example the controller switches on an

Figure 4: Measurement of the charging of Prototype A.

Figure 5: Current ﬂow during measurements at Prototype

external sensor at port EXT-1. After that, it waits un-

til the sensor has initialized itself and starts the mea-

surement. In this test, the controller ﬁnally saves the

gathered data and shuts down the sensor.

The microcontroller (uC) current rises at the start of

the measurement as the sensor module starts its op-

eration. During the communication with the sensor

and the subsequent storing of the gathered values the

power consumption and therefore the needed current

rises further.

The continuous high current at EXT2 is generated by

other sensor hardware. Furthermore, the current us-

age by the Security Controller (SC), and the FRAM

and NFC modules are increased at the start of the

measurement routine.

Using the available data and the data from the

SPICE simulations, a model can be created that shows

the energy consumption of a generic smart sensor.

Figure 6 shows the results of a simulation using the

sensor model. In this simulation the charging be-

haviour of the sensor was simulated. This behaviour

is comparable to the measurements of Prototype A,

thus a valid simulation has been created this way.

PECCS 2019 - 9th International Conference on Pervasive and Embedded Computing and Communication Systems

Figure 6: Simulation of the sensor charging.

These results help in predicting the time it takes to

charge one sensor using NFC energy harvesting and

can furthermore help in estimating the time it needs

to discharge the smart sensors energy storage.

In summary these results show that the charging

of a smart sensor using NFC is possible. To charge

the 0.5 F capacity approx. 2 minutes are needed. This

also depends on external parameters such as the align-

ment of the antennas. The simulation of the sensor

furthermore shows that the sensors operational time

primarily depends on its duty cycle. While the sensor

can be operated for approximately one day when per-

forming measurements every hour, the lifetime is cut

to about seven hours when measuring every minute.

5 FUTURE WORK

Using the prototypes we can perform measurements

of security relevant operations. As Prototype B also

features a security coprocessor, we want to perform

experiments determining the difference in energy us-

age during cryptographic operations. These experi-

ments can show the requirements smart sensors and

the energy provisioning systems need to fulﬁl to en-

able secured data handling. We are planning to take

measurements of user authentication algorithms on

smart sensors such as the one proposed by Pieber et

al. (Pieber et al., 2017c), and testing data transmis-

sion protocols that secure the transmitted data using

encryption and forward error correction such as the

one proposed by Ulz et al. (Ulz et al., 2017b).

6 CONCLUSION

In this paper we showed the development of secured

smart sensor platforms. Two prototypes are used to

evaluate (1) the energy harvesting possibilities that

arise when using NFC technology, and (2) the energy

consumption of the main components of a smart sen-

sor. These results are then used to implement simula-

tions of the sensors. The simulations are able to not

only simulate the smart sensor but also the environ-

ment. This is then used to answer crucial questions

about the efﬁciency of the energy harvesting possi-

bilities and the inﬂuence the environment has on the

communication.

ACKNOWLEDGEMENTS

This project has received funding from the Electronic

Component Systems for European Leadership Joint

Undertaking under grant agreement No 692480. This

Joint Undertaking receives support from the European

Union’s Horizon 2020 research and innovation pro-

gramme and Germany, Netherlands, Spain, Austria,

Belgium, Slovakia.

IoSense is funded by the Austrian Federal Ministry

of Transport, Innovation and Technology (BMVIT)

under the program ”ICT of the Future” between

May 2016 and May 2019. More information

https://iktderzukunft.at/en/.

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