Special Issue Article
Journal of Intelligent Material Systems
and Structures
24(18) 2245–2254
Ó The Author(s) 2013
Reprints and permissions:
DOI: 10.1177/1045389X13488248
Tool chain for harvesting, simulation
and management of energy in
Sensorial Materials
Thomas Behrmann
, Christoph Budelmann
, Stefan Bosse
and Marc C Lemmel
The continuing decrease in size and energy demand of electronic sensor circuits allows endowing engineering structures
and, to an increasing degree, materials with integrated sensing and data processing capabilities. Materials that adhere to
this description are designated as Sensorial Materials. Their development is multidisciplinary and requires knowledge
beyond materials science in fields like sensor science, computer science, energy harvesting, microsystems technology,
low-power electronics, energy management, and communication. Development of such materials will benefit from sys-
tematic support for bridging research area boundaries. The present article introduces the backbone of an easy-to-use
toolbox for layout of the energy supply of smart sensor nodes within a sensorial material. The fundamental approach is
transferred from rapid control development, where a comparable MATLAB/Simulink tool chain is already in use. The
main goal is to manage limited power resources without unacceptably compromising functionality in a given application
scenario. The toolbox allows analysis of the modeled system in terms of energy and power and allows analyzing factors
such as energy harvesting, use of predictive power estimation, power saving (e.g. sleep modes), model-based cognitive
data reduction methods, and energy aware algorithm switching. It is linked to a simulation environment allowing analysis
of energy demand and production in a specific application scenario. Its initial version presented here supports single self-
powered sensor nodes. A broad set of application cases is used to develop scenario-dependent solutions with minimum
energy needs and thus demonstrate the use of the toolbox and the associated development process. The initial test case
is a large-scale sensor network with optical fiber–based data and energy transmission, for which optimization of energy
consumption is attempted. The toolbox can be used to improve the power-aware design of sensor nodes on digital hard-
ware level using advanced high-level synthesis approaches and provides input for sensor node and sensor network level.
Self-powered sensor nodes, energy harvesting, energy simulation, toolbox, smart energy management, sensor network,
optical sensor network, Sensorial Materials
In recent years, microsystems technology and micro-
electronics have continuously shrunk the size of sensor
network components. This development has led to con-
cepts like smart dust, a vision of dust particle–sized
autonomous sensor nodes or motes incorporating com-
munication devices and able to organize themselves in
sensor networks when distributed in the environment
effectively, cubic millimeter size was reached in 2001
(Warneke et al., 2001). Our own vision is about similar
motes embedded in materials, thus endowing them with
the ability to ‘feel’’: We define a sensorial material
as the combination of host material and motes (Lang
et al., 2011a, 2011b). There are multiple application
scenarios for such materials, ranging from ambient
intelligence to structural monitoring, the latter having
been discussed, for example, by Renton (2001) in the
same year Warneke et al. (2001) published their work.
Bremen Institute for Metrology, Automation and Quality Science
(BIMAQ), University of Bremen, Bremen, Germany
ISIS Sensorial Materials Scientific Centre, University of Bremen, Bremen,
Working Group Cyber-Physical Systems, German Research Center for
Artificial Intelligence (DFKI), Bremen, Germany
Working Group Robotics, Department of Computer Science, University
of Bremen, Bremen, Germany
Corresponding author:
Thomas Behrmann, Bremen Institute for Metrology, Automation and
Quality Science (BIMAQ), University of Bremen, Linzer Str. 13, 28213
Bremen, Germany.
Email: RT@bimaq.de
One of the central issues that need to be solved for both
smart dust and Sensorial Materials is energy and power
supply. In a way, this challenge is more complex for the
latter, since for smart dust, each mote usually has to be
capable of coping with lack or excess of energy indivi-
dually. In contrast, for Sensorial Materials, the prob-
lem can be addressed on different levels, for example,
in terms of local energy management, on the one hand,
and network-wide energy management and distribu-
tion, on the other hand. The example already illustrates
the many similarities to sensor network energy supply
in general. However, there are also major discrepancies.
For example, material-embedded sensor networks
imply that there is little room to remedy a faulty design,
as the system will not be accessible for addition of, say,
the extra battery later found out to be necessary. Thus,
Sensorial Materials require comprehensive develop-
ment tools to analyze their energetic situation through-
out their life cycle and under all conceivable service
conditions. It is the main concern of this study to sketch
the development and evaluation of a dedicated toolbox,
which combines all aspects of energy supply as back-
bone of such a development and at the same time as an
optimization environment. This integrative approach
responds to a major need, as is underlined, for example,
by the IDTechEx report on energy harvesting markets
2011, which predicts a considerable market growth,
while at the same time stating that ‘there is exciting
enabling technology but many component suppliers
sell horizontally when users want solutions, not compo-
nents’ (Das, 2011). Our approach is to adapt rapid
control prototyping methodologies established in mea-
surement system layout for this purpose.
Every designer of measureme nt systems dealing with
battery-powered or self-powered systems has to consider
the energy behavior. Most classic measuring devices were
designed to work on constant power supply. Future
methods for Sensorial Materials are varying from low-
power design, adapting power consumption, and energy
management to self-organization, self-localization, fault
tolerance, cognition, and grid intelligence. These options,
however, are very often discussed on an individual basis
(Mathuna et al., 2008), while the basis for a comprehen-
sive methodology including all of them and specifically
also their interdependencies in relation to a realistic
application scenario is completely missing.
Known approaches for system simulation require deep
knowledge of the component’s physics and use domain-
specific simulation tool s like SPICE (Simulation Program
with Integrated Circuit Emphasis) or finite element (FE)
methods (Elvin and Elvin, 2009). Due to the complexity,
these models are not capable of analyzing long-term beha-
vior, and the components must be defined on early stage.
Other multiphysics analysis techniques develop their calcu-
lation formulas from scratch and get accurate results, but
have to invest high efforts (Liang et al., 1997; Liao and
Sodano, 2009; Sirohi and Mahadik, 2011).
A typical approach in system development is to cal-
culate the system’s power behavior and to assure that
the supply will never drop below the needed power of
the loads. This is often done by evaluating worst case
scenarios, like a dark cloudy day for solar-powered
devices. Adding safety factors will ensure that there is
always a surplus of power, but at the cost of greatly
oversizing the system for the specific application. In
borderline cases, feasibility is often judged negatively
by such calculations. In these cases, adapting the mea-
surement task to available power using energy-based
scheduling, adjustment of sample rate, or more sophis-
ticated adaptive calculation algorithms for leveled data
processing is preferable. Simulations of energy flows
then have to show that the systems will not fail in a rea-
listic environment. These simulation results are often
used to adjust the layout parameters of the system.
Experienced system designers will claim that an opti-
mal solution depends on the special circumstances
of the individual measurement task. Thus, for self-
powered measurement nodes, the system design cannot
be transferred directly from one application to another.
Thus, the toolbox represents a major support to the
layout of Sensorial Materials. It will consist of a simu-
lation toolkit for energy flows and a tool for designing
modular sensor systems with an emphasis on self-
powered systems.
The aim is to support the layout of interconnected
and interacting energy supply, conversion, storage, and
consumer components (including data processing). To
this end, the toolbox contains generic component
blocks implemented in MATLAB/Simulink. They
should be easy to use for a measurement systems
designer to layout single sensor nodes as well as sensor
meshes and networks of autonomous sensor nodes.
At a later stage, advanced methods and technologies
will be added like adaptive data processing, energy
management, and generation of specific hardware
design, completing the features of the toolbox. The
basic algorithms are developed on state-of-the-art low-
power microprocessor architectures. The development
road map of the toolbox ranges from microcontrollers
to programmable logic (field-programmable gate array
(FPGA)) and later on to special custom chips (applica-
tion-specific integrated circuit (ASIC)) (see Figure 1).
There are many possible scenarios for self-powered
sensor applications (Bartholmai and Ko
ppe, 2010;
Budelmann and Krieg-Bru
ckner, 2011; Moser, 2009).
One potential scenario that could be analyzed and
parameterized by the use of the proposed toolbox is
discussed in the following sections.
Sensor nodes
In the context of this article, a sensor node could realize
one or more measurements and could be supplied by
2246 Journal of Intelligent Material Systems and Structures 24(18)
single or multiple sources. Sensorial abilities should be
integrated in a single sensor node.
Figure 2 shows an example of implementing sensor
nodes. The elements of the model of a sensor node can
be identified on one side as the parts for data processing
like acquisition, computation, and communication. On
the other side, there is energy distribution like harvest-
ing, storage, and consumer. Though the research activi-
ties also focus on the optimization of data processing
and communication with respect to energy consump-
tion, this report concentrates on modeling, simulation,
and analysis of the energy branch.
For most self-powered applications, the energy pro-
vided by the energy harvesting device has to be
converted. In Figure 3, a more detailed view of a power
system for a sensor node is displayed. Power flow from
the triggered harvester is converted to higher voltage
levels and normally charges a small capacitor. When a
certain voltage level is reached, the following circuit
activates the main functions of the sensor node. A vol-
tage regulator is used to provide constant conditions
for the parts of the node like measurement unit, proces-
sor/microcontroller and, if needed, a radio transmitter.
The structure of the toolbox is in accordance with the
node scheme depicted in Figure 2 and ordered from the
energy’s point of view (Figure 4). For each element,
there are generic blocks in the structure to cover the
main functionality. Special parts can be derived and
added to the library.
A mask in Simulink can be provided as a graphical
user interface (GUI) for setting parameter values
Figure 1. Development plan of the tool chain.
COTS: components of the shelf; OS: operating system; HDL: Hardware description language; IP: intellectual property; FPGA: field-programmable gate
array; ASIC: application-specific integrated circuit.
Energy supply
Energy storage
Energy distribution
Sensor with
energy harvesting capability
Receiver +
Sensorial material
Power / Energy flow Information
Figure 2. Elements of a sensor node as a part of a sensorial
material with energy distribution.
Rectifier and
Storage Capacitor
Sensing and
AC/DC or
DC/DC converter
Power / Energy flow Information
Figure 3. Example for a radio-based sensor node with devices
for energy harvesting and sensing.
AC: alternating current; DC: direct current.
Behrmann et al. 2247
comfortably, which is then connected to constant vari-
ables inside the block model implemented in Simulink.
All blocks of this toolbox are equipped with an input
mask for convenience, for example, to parameterize the
block according to the data sheet (see Figure 6). In the
following sections, some exemplary implementations
are described to show the basic structure of the
Energy sources
The energy sources group covers the different possibili-
ties for providing energy to the sensor system. In conven-
tional design, there is a constant source always providing
enough power. This tool enables the user to tailor the
ratings for average and maximum use. For the possibil-
ity of environment-dependent energy sources, the blocks
have the ability to be controlled by environmental condi-
tions, for example, by solar radiation in the case of a
photovoltaic cell. The scenario in section ‘ ‘Tool chain
application scenario’ uses a solar cell generator (see
Figure 5), which converts light pulses into electrical
power P
using the photoelectric effect.
is calculated as follows (see Figure 6)
= h A E = FF I
= I
where h is the efficiency, E is the irradiance, A is the
area of the cell, FF is the fill factor, I
is the short-
circuit current, U
is the open-circuit voltage, I
is the
optimum operating current, and U
is the optimum
operating voltage.
Energy converters
Components for converting and regulating energy flows
are technically mandatory. Focusing on the energy, the
most important fact is that these devices have electric
losses and so the loss of power has to be calculated.
Most modern devices like direct current DC-to-DC
converters and so on use switched power operation and
smoothing capacitors. A generic approach for calculat-
ing losses without implementing the real operation is
sufficient at the level of system definition and could be
recalculated when the hardware layout is fixed. This
approach also speeds up the simulation for, for exam-
ple, life-time assessment (Figure 7).
Energy Sources
+ Batteries
+ Ideal Sources
+ Inductive/Magnetic
+ Mechanical
+ Piezo Electric
+ Photo Electric
+ Thermal Electric
+ Transformators
Energy Converters
+ Voltage Regulators
+ Amplifier
Energy Storage
+ Accumulators
+ Capacitors
Energy Consumers
+ Actuators
+ Microcontrollers
+ Sensors
+ Radio Modules
+ Illuminants
Figure 4. Structure of toolbox.
AC: alternating current; DC: direct current; PWM: pulse width
Optical Fibre
Energy harvesting
Figure 5. Photo generator block.
Figure 6. Block parameters solar cell block.
2248 Journal of Intelligent Material Systems and Structures 24(18)
Energy storage
The generic capacitor block contains a simple model of
a capacitor. The equivalent circuit is shown in Figure 8.
An initial voltage across the capacitor can be entered in
the mask.
The values R, L, C, and R
have to be entered into
the block’s input mask. Alternatively, the dissipation
factor tan d and the corresponding frequency can be
entered instead, which can be measured easily. The
series resistance R represents effects of dielectric losses
and conductor resistance. The capacitor inductance is
represented by the series inductance L. The parallel
resistance R
models leakage current flow and self-
Energy consumers
The consumer block provides generic constant power
consumption. It is a good way to estimate the compo-
nent’s energy demand using datasheet’s values. It is
useful for modeling components, which have not been
modeled in detail yet. The cyclic wake-up consumer
block (application examples in Figure 9) models the
power consumption due to the electrical resistance of
the switch when in ‘on’ position and switching losses.
The power consumed is calculated as
= t
U I + I
where t
is the time needed to switch between on
and off, f
is the switching frequency, U is the voltage
of the power source, I is the current at maximum vol-
I is the average current, and R
is the ohmic resis-
tance of the switch when in ‘on’ position (Graovac and
rschel, 2009). The output port provides information
on power consumption.
Tool chain application scenario
A first field test is aimed at optimizing the energy man-
agement of small sensor nodes within the SMaLL proj-
ect developed by the DFKI and other partners at the
University of Bremen (Budelmann and Krieg-Bru
2011). The electronic sensor nodes are interconnected
only by an optical fiber, which is used for data exchange
and energy transfer. Local energy supply or storage is
not necessary, making the sensor nodes completely
maintenance free and ideal for the integration into
Figure 10 shows the first prototype of the SMaLL
sensor node, which has several sensor units onboard to
explore the range of applications. It already offers
reduced power consumption using low-power hardware
and advanced energy saving sleep modes.
Most of the modeled components are dominated by
a sleep schedule executing a cyclic wake-up reducing
energy demand by an estimated duty cycle of 1:100
(Figure 11).
When in operation, the sensor node’s power balance
is strictly negative. In order to drive the system directly,
a 17 times (see Figure 12 at graph minimum at t = 0.1
s) stronger light harvester would be needed to match
the short-term power demand.
When introducing an energy storage device, the
overall energy trend is positive, proving that the origi-
nal power supply is sufficient (Figure 13).
P_in P_out
DC-to-DC converter
Figure 7. DC/DC step-up converter.
DC: direct current.
Figure 8. Equivalent circuit of a generic capacitor.
Cyclic wakeup Sensor
Figure 9. Microcontroller and cyclic wake-up sensor block as
examples for cyclic wake-up consumers.
Figure 10. Prototype of the SMaLL sensor node (Budelmann
and Krieg-Bru¨ckner, 2011).
Behrmann et al. 2249