=Paper=
{{Paper
|id=Vol-2815/CERC2020_short01
|storemode=property
|title=Possibilities for Software Development for Energy-Limited Constrained Devices
|pdfUrl=https://ceur-ws.org/Vol-2815/CERC2020_short01.pdf
|volume=Vol-2815
|authors=Mario Hoss,Jens-Peter Akelbein
|dblpUrl=https://dblp.org/rec/conf/cerc/HossA20
}}
==Possibilities for Software Development for Energy-Limited Constrained Devices==
https://ceur-ws.org/Vol-2815/CERC2020_short01.pdf
Internet of Things, Networks and Robotics
Possibilities for software development for
energy-limited constrained devices
Mario Hoss and Jens-Peter Akelbein
Hochschule Darmstadt, University of Applied Sciences
{mario.hoss, jens-peter.akelbein} @h-da.de
Abstract. With the spread and rising complexity of IoT scenarios there
are also new challenges emerging in planing and predicting the lifetime of
application specific energy-limited resource constrained devices. To allow
for a state of the art software development process, energy consumption
as well available energy input will need to be considered during early
stages of development. Depending on what information are available or
predictable during which stage of the product lifecycle, adaptive behavior
could also be used to supplemented or compensated for predictions. This
paper presents an early stage work into a new research topic. It gives
a review of the possibilities and challenges of predicting and determin-
ing energy consumption and input information throughout development
and deployment of constrained devices. Contrasting existing approaches
from related fields, the paper concludes in the outlook on the upcoming
research questions.
Keywords: Energy Models · Adaptive Systems · IoT
1 Introduction
According to forecasts of exponential growth in the Internet of Things market [1],
the demand for application-specific connected devices is expected to reach bil-
lions. This growth is driven by the emergence of a variety of new application
scenarios, like Industrial IoT (IIOT) and Smart Cities. Many of these scenarios
rely on masses of energy-limited constrained devices [2] that wirelessly transmit
collected information for up to a decade or more without being connected to
a permanent power supply. This poses new challenges in the design and devel-
opment phases as it is critical for both battery operated and energy harvesting
powered devices to optimize the power consumption of hard- and software to
ensure that they can reach their targeted lifespan.
With more efficient power-saving modes for a System on a Chip (SoC), the
potential complexity of the application has increased drastically. Until recently,
commonly only class 0 and class 1 devices [2] have been used in energy-limited
scenarios with such long lifespans. Both classes are not intended to use standard
internet protocols. By now, class 2 devices are also a realistic option. Since com-
mon internet protocols are often used, state-of-the-art security is also required.
Providing the necessary functionalities further increases software complexity, as
Copyright © 2020 for this paper by its authors.
CERC 2020 78 Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0).
Internet of Things, Networks and Robotics
2 Hoss et al.
well as resource- and power-consumption. This increased complexity is also a
major driver in the increased use of operating systems for the IoT, which should
accelerate further.
This shift will likely be accompanied by a change in requirements for the
developer. While writing bare-metal applications requires a more in-depth un-
derstanding of the underlying hardware, using operating systems providing ab-
straction layers and reusable libraries is suitable for more general software de-
velopers.
In light of these developments, there is a need to provide software developers
with tools and methods to accurately predict the life expectancy of a constrained
device in the development phase. Optimally, developers should be able to select
combinations of hardware, software and energy-source and immediately receive
a life expectancy prognosis. Such a forecast supports early development phases
and allows for an ”energy by design” approach in product development. Devel-
opers are able to test and customize their software for different energy-sources,
for example different illuminance levels for solar powered devices. Using such
forecasts in test and verification allows automated detection of energy bugs so
software defects that cause abnormal power consumption.
Whilst mobile device applications face similar problems, solutions in this
area are generally not applicable to the field of energy-limited constrained de-
vices due to vastly different hardware as well as energy consumption pattern.
Current lifetime prediction research for constrained devices originates in the
Wireless Sensor Network (WSN) field with assumptions for less complex soft-
ware, stronger hardware bindings, and more complex network communication.
Like in the are of mobile devices, research for constrained devices also focuses on
adaptive in-situ behavior rather than using a predictive approach during devel-
opment. Existing approaches also utilize simplified battery models or additional
hardware for measurements in the field.
Verifying the functionality and life expectancy by measuring power consump-
tion during HIL testing is also not a realistic option. In addition to the to be
discussed practical problems, achieving a sufficient test coverage is problematic.
With a life expectancy of a decade, there is just not enough time during a normal
development phase to sufficiently test the devices behavior over this time span.
This paper explores the possibilities for energy predictions of energy-limited
application specific constrained devices in product development and its synergies
with adaptive in-situ functionalities. As such it will be a first look into early
research and a first step towards sufficient tooling for developers of energy-limited
constrained devices.
2 State of the art
2.1 Energy consumption
In order to predict energy consumption, it is generally necessary to determine
both the CPU power consumption of individual instructions as well as hardware
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Possibilities for software development for energy-limited constrained devices 3
components. Because of the differences in usage scenarios and underlying hard-
ware, energy consumption at an instruction level is much more relevant on mobile
devices [3] then on constrained devices. Energy-limited constrained devices can
also easily accumulate small inaccuracies, because unlike mobile devices, they
are not regularly recharged. However, they do spend most of their lifetime in
deep sleep modes, interrupted by repeating usage patterns, which reduces the
overall complexity of the required models. Furthermore, due to significant differ-
ence in available resources, mobile devices can track the various hardware states
in the field and are also able to measure the remaining battery charge [4], which
due to per device cost is rarely an option for constrained devices. For instruc-
tion level models, determining data dependent dynamic power consumption of
the worst case energy consumption was recently shown to result in a NP-hard
problem, where an approximation cannot be made to a usable degree [5]. The
examined class 2 constrained devices showed a variation in the data dependent
power consumption of nearly half of a cores power dissipation. Another study
also documented SoCs with a similar ratio [6]. More relevant for constrained
devices are finite state machines (FSM) where the target hardware is modeled
by different power states and their connecting transitions, determined through
a measurement cycle. The parameters that influence the power consumption of
a state and their behavior can then be identified by using regression analysis to
create approximation functions for the parameter-dependent energy consump-
tion of peripheral devices [7]. Transition triggers are identified by power bursts,
since power states often do not line up with the utilization in software [8]. Au-
tomatically creating and refining such state machines is still an open research
question [9]. A more detailed overview for both types of models can be found in
earlier work [10].
There is also the established practice of using power consumption measure-
ments during Hardware in the Loop (HIL) testing [11]. Woehrle et al. [12] vali-
dated WSN nodes utilizing HIL tests and the testbed Flocklab [13] also supports
automated power tests for WSN nodes. This however puts practical limits on
the test coverage, since the constrained devices are designed to run for years,
making the coverage minimal in a normal development time frame. ”Wearables”
face this problem to a lesser degree. ”Rocketlogger” is designed for in-situ mea-
surements of the energy consumption through a normal wearable lifecycle of a
few days [14].
One aspect that complicated past work on energy consumption models is the
consideration of production irregularities and a variance in energy consumption
in different power modes [15, 16]. These variances are usually considered in en-
ergy consumption models by factoring in an error margin during the creation.
The variance has to be considered for any measurements on the actual hardware,
be it HIL tests or in the creation of energy consumption models.
Energy-aware software engineering is used for mobile devices. This includes
the identification of energy bugs and hotspots [17] and static analyses [18], which
can also be used for general software development [19]. Energy consumption
models and measurements are also utilized for compiler optimization [20].
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4 Hoss et al.
2.2 Energy input
There is ample work on models for the remaining battery capacity [21], but
the ambient temperature needs to be considered for more accurate results [22].
Battery capacity approximation also exists for energy harvesting powered con-
strained devices [23]. To predict the energy input of solar cells, both static and
dynamic factors have to be considered. The static input consists of the efficiency
of the cell for the different light sources and their respective wavelength. Informa-
tion about the cells efficiency is readily available from the manufacturer and can
be verified by confirmation measurements published in the biannual solar cell ef-
ficiency tables [24]. The dynamic input is dependent on environmental conditions
during runtime, for example on the available illuminance level. A list of expected
influencing factors indoors is listed in figure 1. A list for factors influencing the
efficiency of photovoltaic systems in general can be found in [25]. Weather re-
ports have also been used for short-term [26] and long-term [27] predictions of
the energy input of solar powered constrained devices operating outdoors.
Positioning Positioning Hardware
Hardware properties
(direct) (indirect) condition
Light Ambient Efficiency of the solar cell at
Cell age
composition temperature different wavelength
Efficiency of Dust and
Light level Partially shaded cells
energy-harvesting IC dirt
Weather and user in-
Cell orientation Availability of maximum Hardware
teraction
and angle power point tracking failure
(e.g. shades)
Fig. 1. Factors influencing the energy input of solar powered constrained devices
Sufficient accurate data-sets for the prediction of indoor light levels in living-
and functional buildings are currently lacking. Existing architectural informa-
tion can not be used, as the field focuses on the indoor light level relative to the
outdoor light level [28]. Consumer protection studies [29] can be useful but gen-
erally focus on living spaces. This information gap will be filled over the course
of the LOEWE3 project ”LONG MOVE” with continuous measurements over
18 month in a wide variety of functional buildings. Important aspects are con-
sideration of both incoming sunlight over different seasons, as well as artificial
light sources and information about the placement of the devices.
2.3 Energy management and adaptive functionality
Energy management functionalities are typically used to track and manage hard
to predict power consumption in the field of mobile devices. For mobile devices,
this is largely caused by the impact user interaction and usage pattern have on
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Possibilities for software development for energy-limited constrained devices 5
the device power consumption. Such an approach was adopted by Tamkittikhun
et al. [30] for solar powered embedded devices. Different to mobile devices, direct
user interaction is rarely possible for these devices. However, since user interven-
tion is not an option to achieve lifetime goals through regular charging, adaptive
behavior to reduce functionality or increase sleep durations is often necessary.
They are also faced with hard to predict power consumption in the form of pro-
cessing incoming transmissions. For this Tamkittikhun et al. combined power
measurements of individual functionalities before deployment with a functional-
ity counter during runtime to estimate energy consumption on the device. This
allows for lifetime predictions, as well as adaptation of the device functional-
ity to meet lifetime goals. Since energy-limited constrained devices achieve their
lifetime goal by remaining in sleep states, the transceiver is also powered off. As
such energy consumption caused by processing incoming transmissions rarely
has to be considered.
For energy-limited constrained devices there are also research activities into
adapting the device behavior depending on the available energy [16,31,32]. Here
the prediction problems often stem from hardware variances as well as the de-
pendence of the energy-input on the environment.
Lachenmann et al. [31] proposed a programming abstraction for constrained
devices, associating different sets of functionalities with different energy levels.
For that the energy consumption of different functionalities is measured dur-
ing development and associated with these levels. Depending on the remaining
charge, the devices then adapt their behavior in the field to a functionality level
with a lower energy consumption. For this a battery monitor hardware and a
battery model mapping the voltage to the remaining battery capacity was used.
Sieber [16] followed a similar approach but focused on more dynamic perfor-
mance adjustments in the field based on an energy bucket concept in addition to
the energy consumption model. For that application and system functionalities
are defined with an energy priority and individual energy consumption limits.
Instead of the use of measurement hardware on the device, the remaining bat-
tery charge is estimated based on a model of linearized remaining charge values
based on measurements taken of the battery before deployment. This approach
is motivated by profiting off and coping with production variances. Geissdoerfer
et al. [32] utilized local energy input predictions on energy harvesting powered
devices during run time, simultaneously taking into account the batteries cur-
rent state of charge. The underlying model gets adjusted through state of charge
feedback to the predictions.
In general, it is possible to identify several different approaches to adaptive
energy management. There are those solutions that collect additional informa-
tion about their environment, be it light intensity, temperature or state of charge,
and act on that information. This often requires additional hardware, resulting
in increased cost and software overhead for each device. Other approaches [16,30]
do not collect environmental information at runtime, and are instead shipped
with, and act on, energy-consumption- and -input-models created before deploy-
ment. These models can come in a wide variety of forms, from battery-models to
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6 Hoss et al.
simply measuring the power consumption of specific functions or features. While
these approaches do not require additional hardware, they still produce software
overhead on each device. However, if this approach is applied to constrained de-
vices rather than to more complex embedded devices, then it should be possible
to move large parts of the computation into the development phase, reducing the
required overhead or even making the solution completely application-neutral.
Lastly, there are managed devices that can communicate with a management
server. In addition to the possibility to report environmental conditions and of-
fload costly model based computations, it is also possible to provide a device with
additional information, like weather forecasts to predict the energy input of solar
powered devices [26]. This is however achieved by additional overhead for the
management of the device as well as running costs for the management infras-
tructure. If however predictions about the necessity of ”over the air” firmware
updates come true, the additionally required overhead on the device would be
minimal. Current management options for constrained devices, like ”Lightweight
Machine to Machine” (LWM2M) [33], have not found wide acceptance, likely due
to the introduced overhead [34]. However, the current IETF draft for ”Software
Updates for the Internet of Things” (SUIT) [35] could provide such management
options in the long term future, though likely first for devices with a permanent
power supply. Another advantage of this approach is, that with the energy con-
sumption model centralized in one location, the model could be adapted and
refined over the lifetime. The categories listed are not mutually exclusive. Hy-
brid approaches like Lachenmann et al. [31] rely on both energy consumption
models and additional measurement hardware on the device.
3 Outlook
This paper summarizes the literature review giving a comparison of current
approaches. Based on this review, three research questions are derived to be an-
swered in the next steps of research: Q1: How can energy consumption models be
utilized for simulating power consumption of energy-limited constrained device?
Q2: How can energy-input predictions be utilized for simulating the energy-input
variability of energy-limited constrained devices? Q3: How can energy-input- and
-consumption-information be utilized for lifetime prediction during the software
development process for application specific constrained devices?
To find answers to these questions the design science research process is
used by creating and evaluating proof of concept prototypes iteratively. These
allow to determine the usability and precision of the individual models as well
as forecasts based on a combination of both models. As a result, it will be
possible to determine the prediction accuracy of each element within the forecast
depending on the phase of the development process. Identifying the limitations
of the approach allows defining where adaptive in-situ behavior can address
deviations found over long time periods. This leads to determining a minimum
requirement profile.
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Possibilities for software development for energy-limited constrained devices 7
Based on these answers, it should become possible to further evaluate how the
presented approach of forecasts can be integrated in todays product development
processes.
This work was supported by the Hessenagentur within the Project ”LONG
MOVE - Leistungsadaption und ortsbezogene Verhaltensregeln fr eine nach-
haltige IoT-Sensorik in der Gebudeausstattung zur modularen Vernetzung von
Einheiten” (HA-Projekt-Nr. 802/19-122).
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