Energy-efficient Operation of GSM-connected Infrared Rodent Sensor
abor Paller and G
echenyi Istv
an University, Information Society Research & Education Group, Egyetem t
er 1., Gy
or, Hungary
Agriculture, Infrared Camera, Power Efficiency.
Camera sensors have been deployed in the agriculture for various use cases. Most of the applications tried to
infer the health and development of the plants based on image data in different wavelength domains. In this
paper, we present our research of rodent population estimation with infrared camera sensors.
The usual camera sensor applications in the agricultural domain are quite simple from the sensor architecture
point of view as the environment rarely changes. Image capture/transmission at preconfigured moments is
usually enough. Rodents move quickly so the sensor must be able to capture images with low capture time
interval. As the data link to the server backend is relatively slow, this fast capture rate may require image
processing capability in the sensor.
The paper analyzes the effects of such an image processing capability, in particular the power consumption
trade-offs. Inadequate power management support of the selected embedded Linux platforms is identified as
a problem and proposals are made for improvement.
Data captured by agricultural sensors may be scalar
(Adamchuk et al., 2004), (L
opez et al., 2009) (Arm-
strong et al., 1993) or of more complex data types, e.g.
spectrogram. Among the latter, 2D imaging sensors
(”cameras”) have been found to be efficient in detect-
ing effects of drought (Grant et al., 2006), (Alderfasi
and Nielsen, 2001), plant phenotype (Li et al., 2014)
or diseases (Moshou et al., 2004). is an ongoing research project funded
by the Government of Hungary (Paller et al., 2015).
In the first phase the project concentrated on sen-
sors providing scalar value like soil temperature, soil
moisture, concentration of salts in groundwater and
concentration in the ground, air temperature, hu-
midity, rainfall, wind speed and direction, solar radi-
ation intensity and leaf wetness. The project decided
to use GSM/GPRS network as its data carrier.
Due to the fact that one type of our sensor
poles has only underground parts, solar cells as en-
ergy source could not be employed hence energy-
efficiency was an important concern from the begin-
ning. These decisions made in the first phase when the
project deployed only sensors providing scalar time-
series data influenced the second phase when cameras
are added. In particular, we wanted to reuse the phys-
ical sensor pole structure and the GSM/GPRS data
carrier layer. This led us into researching energy ef-
ficiency in case of the data capture and transmission
patterns of 2D image structures.
Our intention was to create a flexible framework for
camera sensors therefore we looked for a more de-
manding use case. Simple use cases of agricultural
camera sensors normally involve taking a picture 1-4
times a day about a specific feature of the environ-
ment, e.g. the selected part of the foliage. From the
communication pattern and sensor framework points
of view, these use cases are just slightly different from
the scalar value use case. The sole difference is the
larger data size but as these larger data packets are in-
frequently sent, the conclusions are not significantly
different from the ones presented in (Paller et al.,
One of the more challenging use cases we iden-
tified is animal monitoring, specifically rodent track-
ing. Population outbreaks of certain rodent species
can cause significant damage in crop production.
Grain Producers Association-Hungary (GPAH) esti-
mated that common vole (Microtus arvalis, Figure 1)
caused 500000 tons of damage in winter wheat alone
in 2014. More agressive rodenticides are applied ac-
cording to population estimation hence this estima-
Paller, G. and Élõ, G.
Energy-efficient Operation of GSM-connected Infrared Rodent Sensor.
DOI: 10.5220/0005628500370044
In Proceedings of the 5th International Confererence on Sensor Networks (SENSORNETS 2016), pages 37-44
ISBN: 978-989-758-169-4
2016 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
Figure 1: Common vole (Microtus arvalis) (source: Na-
tional Forestry Association, Hungary).
tion is an economically important task.
Common vole population estimation was reported
using radio collars (Jacob and Hempel, 2003) but ob-
viously this method is not suitable to detect animals
that have not been trapped previously. Detection of
itinerant animals which just try to settle on a certain
field is best accomplished with cameras but the noc-
turnal nature of the animal and the excellent camou-
flage of their fur make them difficult to spot in visible
light. (Ou-Yanga et al., 2011) reports about the ob-
servation of laboratory animals with infrared camera.
The monitored area was rather small - about 50x100
cm - and they employed Kinect sensor. Kinect op-
erates in the near-infrared range (830nm) therefore it
requires external illumination of the target which lim-
its its range.
Figure 2 shows a small rodent (Phodopus sun-
gorus) similar in size to the common vole in near-
infrared image. The picture was taken in complete
darkness, the target was illuminated by near-infared
light. Note that even though the animal is recogniz-
able, it would be hard to create a computer algorithm
that spots it. In this image the animal is close to the
camera but as the distance grows, illuminating the tar-
get becomes more and more complicated.
Long-wavelength infrared (LWIR) cameras detect
the infrared radiation emitted by the object in the pic-
ture hence they do not require infrared illumination
of the target. LWIR cameras has existed for a long
time but their price and other restrictions (e.g. export
control) confined them to specialist use cases. Rela-
tively low cost LWIR cameras appeared just recently.
We experimented with FLIR Lepton camera module
whether small rodents can be detected reliably. This
camera operates in the 8000-14000 nm wavelength
range and has a resolution of 80x60 pixels. An animal
similar to the common vole (Phodopus sungorus) was
Figure 2: Small rodent similar to a common vole (Phodopus
sungorus) in near-infrared image. The animal is marked by
the red ellipse.
placed in a cage and images were captured with dif-
ferent distances between the camera and the animal.
The background was lawn and other common foliage.
The images were made in the night.
Figure 3 shows the result. Minimum and maxi-
mum intensity values were calculated from raw image
(automatic gain control (AGC) switched off) and the
0-255 pixel intensity in the resulting greyscale image
was mapped into this minimum-maximum range so
= 255
where p
is the intensity value of the raw image
pixel produced by the Lepton camera, p
are the maximum and minimum intensities
detected in the raw image, p
is the pixel in-
tensity value produced for the greyscale image.
When the animal is farther from the camera, its
observed infrared radiation decreases as the size of the
animal gets closer to the size of a pixel in the image.
The advantage of the dynamic calculation of the pixel
intensity range is that the animal remains bright, even
if it is farther from the camera. Disadvantage is that
this method makes features in the background with
higher infared radiation (trees, bushes, etc.) to be-
come more pronounced (”brighter”) in case of larger
distances to the target which makes the identification
of the animal in the image more challenging. The an-
imal is clearly visible up to 4 meters of distance, in
fortunate situation the distance can be as large as 5
The experiments made with the FLIR Lepton
LWIR camera convinced us that small animals can be
reliably recognized with infrared camera, even with
such a limited resolution as the FLIR Lepton has. Due
to the non-trivial infared signature of the background,
however, we had to develop an image processing al-
gorithm to identify the pictures which are suitable for
population estimation.
SENSORNETS 2016 - 5th International Conference on Sensor Networks
Figure 3: Small rodent similar to a common vole (Phodopus
sungorus) in long-wavelength infrared image.
According to the architectural goal of the
project, data processing happens on the server side.
The sensor unit, however, is responsible of sending
data that can be meaningfully used to extract relevant
information, in our case the population estimate of the
small rodents in the area. These animals move rela-
tively quickly so for meaningful observation, the bait
area needs to be monitored with an interval of some
seconds. Sending an image with this frequency is in-
feasible for mobile bandwidth and power consump-
tion reasons. The sensor itself must make the first fil-
tering and upload only pictures which has high prob-
ability of containing relevant information.
The requirements of the image processing algo-
rithm are the following.
Remove large non-moving elements from the im-
age. These are supposed to be background ele-
Look for relatively small objects that are moving.
These are potentially the rodents we are looking
Our implementation is based on OpenCV image
processing framework
and comprises the following
A greyscale image is produced from the raw Lep-
ton output image according to the transformation
described in section 2.
The greyscale image is transformed into a binary
image with a fixed threshold of 204 (0.8*255). As
the greyscale intensity values are calculated dy-
namically, taking into account the minimum and
maximum intensity values in the raw image, this
step is really dynamic thresholding with respect to
the original raw infrared image. The high thresh-
olding limit is due to the assumption that our ro-
dents are among the warmest things in the night
scene in the agricultural field.
Contour tracing algorithm from the OpenCV li-
brary is applied, then the resulting contours’ con-
vex hull is filled. This step gets rid of spurious
noise in the image resulting from the thresholding
Elements in the image are dilated by a kernel of
6x6, then again contour traced. This step merges
features that are separated by just a gap of up to 6
The encloding circle of each resulting contour is
The enclosing circles of this iteration and the pre-
vious iteration are compared. In order for two en-
closing circles to be considered the same, their
overlapping area must be at least 70% of the
smaller circle’s area. Circles present in both the
current and the previous picture are removed from
the set of circles in the image. This filtering step
ensures that the feature we are looking for needs
to move.
If there are circles remaining that have not been
considered the same as any circle in the previous
image and the any of the remaining circle’s radius
is smaller than 5 pixels then we have a candidate
image for uploading to the server.
Figure 4 demonstrates the steps of the image pro-
cessing algorithm. The image labelled as ”eq” is the
input greyscale image. ”th” is the result of the thresh-
olding, note the large amount of unconnected dots.
”c1” is the result of the first contour tracing-convex
hull filling step. ”c2” is the output of the dilation-
second contour tracing. At the end, ”circle” shows
the resulting object circles identified in the picture.
Of the 5 circles identified, only one corresponds to
Energy-efficient Operation of GSM-connected Infrared Rodent Sensor
Figure 4: Demonstration of image processing steps in the
sensor. Image labels are detailed in section 3.
the test rodent. The rodent is identified when it starts
to move. Then its small circle will not overlap with
the circle on the previous picture, triggering an image
The algorithm above is presented as a demonstra-
tion that the image processing expected from the sen-
sor is not trivial and efficient implementation is heav-
ily based on a publicly available library (OpenCV).
As we will see, these conclusions will have impor-
tant consequences when the power consumption of
the sensor is analyzed.
As the first version of the sensor network
will target corn, typical cornfield locations were con-
sidered when designing the communication architec-
ture. Due to large field sizes and the production area
often located far from existing infrastructure, only a
radio technology with large coverage area was accept-
able. There are a number of alternative radio tech-
nologies with this characteristic (e.g. WiMax or cus-
tom VHF/UHF system) but due to its wide availabil-
ity, low cost and well-established regulatory frame-
work, we decided to use the GSM mobile network.
The first version of the sensor network collects
data that change slowly (e.g. soil temperature, soil
moisture) and the data representation requires only
short data packets (with our coding format it means
200-400 bytes of data). This means that the sensor
communicates on the mobile network relatively rarely
(1-3 times a day) and even then only low amount
Figure 5: Conceptual communication architecture of the sensor network.
of data is sent. Part of the sensor stations is only
equipped with underground sensors and minimally
protrude above ground level therefore solar cell-based
power supply was not possible. Energy efficiency
was a key requirement when designing the sensor sta-
tion. Due to the low amount of data transfer in the
first version and the energy efficiency requirement,
the prorotype was designed using the low-bandwidth
services of the GSM network. This may mean GPRS
or SMS-based data transfer.
The second iteration added image and video trans-
fer to the requirement set. This new requirement
changes the amount of data to transmit. One infrared
image is typically in 3-4 kByte of size (80x60 pixel
resolution, 16 bit greyscale, PNG format). The sensor
may also send short videos (20-30 sec) created from
consecutive infared images. In MP4 format, these
videos have the typical size of 30-50 kBytes. Im-
ages captured by ordinary web cameras are up to 60
kBytes in PNG or JPEG format. This higher payload
size may require the usage of more recent telecommu-
nication technology, e.g. 3G or 4G. Our experience,
however, is that in the deployment areas of our sen-
sors, even basic GSM coverage may be problematic,
particularly with an antenna mounted so close to the
ground that some of our sensors have. For the exper-
iments reported in this paper, we did not change the
GSM/GPRS communication technology but analyz-
ing the possibility of 3G/4G is definitely a work to be
Figure 5 shows the architecture used in the sec-
ond iteration of the sensor network. The
GPRS/HTTP is used to send bulk data to the server.
(Paller et al., 2015) demonstrated that the use of pro-
tocols more optimized than HTTP like CoAP does not
yield a significant power consumption saving over the
GPRS bearer. The SMS infrastructure is employed
as the push bearer for server-initiated operations like
SENSORNETS 2016 - 5th International Conference on Sensor Networks
management operations. (Paller et al., 2015) ana-
lyzed the energy-efficiency of two ”push” solutions
and found that SMS is significantly more energy-
efficient than long-lived TCP connections. The man-
agement server initiates management operation by
sending SMs to the sensor. Some of these commands
like status query, changing a single configuration vari-
able, reset request fit into one SM and the entire op-
eration is carried out over the SM service. In case
of other commands like sensor log download or soft-
ware update, the SM sent by the management server
is used to trigger a GPRS connection to the manage-
ment server and the data transfer is carried out over
the GPRS connection. The main change compared to
the soil sensor is the sensor control component. In the
first iteration, this component controlled a set of low-
bandwidth sensors with scalar value output therefore
an Atmel ATxmega128A4 microcontroller performed
the sensor control task. As argued in section 3, for
the camera sensor case we have a set of requirements
that demands more powerful sensor control. These
requirements are the following.
In case of the cameras operating in visible light
frequency domain, we want a platform that fa-
cilitates the interfacing of popular cameras, e.g.
USB-connected webcams.
We need a platform that is powerful enough to
execute the relatively complex image processing
tasks detailed in section 3.
In order to implement the image processing logic
efficiently, we need a platform that is able to de-
ploy popular image processing frameworks like
Considering these requirements led us to the con-
clusion that the sensor control component in our cam-
era sensor should be implemented based on an em-
bedded ARM-based computer (we used BeagleBone
Black, based on the TI Sitara AM335x ARM Cortex-
A8 system-on-chip (SoC)) and an embedded Linux
operating system. BeagleBone Black is supported by
a number of Linux distributions. The measurements
were made with 2 of them: Ubuntu Snappy
is a variant of the Ubuntu distribution particularly
targeted to Internet of Things (IoT) applications and
Texas Instrument’s own version of Linux specifically
targeted to the Sitara family of SoCs, called Linux
EZ Software Development Kit
. Snappy is attrac-
tive due to the large software base that Canonical, the
developer of the Ubuntu distribution constantly up-
dates. For example OpenCV and the libraries it de-
pends on are available as software packages for this
Figure 6: Image processing-image sending cycle in case of
communication logic running on the Sitara CPU.
platform. TI EZSDK on the other hand provides the
best available integration with the Sitara SoC’s hard-
ware features of which the power management was
particularly important for us.
It has been already pointed out in the literature
(Margi et al., 2006) that the power consump-
tion of a camera sensor node can be chatego-
rized as idle, processing-intensive, storage-intensive,
communication-intensive and visual sensing. In this
section we try to optimize the power consumption by
allocating idle consumption to processing units and
by finding a balance between processing-intensive
and communication-intesive tasks.
In the first version of the sensors the
main control logic of the sensor was executed by the
Telit GL865 module which combines the GSM mo-
dem with a low-power microcontroller. As Snappy
Ubuntu is positioned as a platform with secure up-
date option, the first sensor architecture we evaluated
was that both the image processing and the commu-
nication logic was executed by the main Sitara CPU
running Snappy Ubuntu Linux, the Telit GL865 was
used only as a modem. Figure 6 shows the power con-
sumption of the entire system (BeagleBone Black and
the GL865 GSM modem) when running the image
processing-image sending cycle. The image process-
ing cycle happens between the timestamps of 435-
440 seconds while the image sending is between the
timestamps of 440-540 seconds. The image size to be
sent to the server with a HTTP POST request was 4
Kbytes, over GPRS bearer.
The resulting power consumption for the image
sending cycle is 10.755 mAh which is much higher
than the expected consumption of about 1 mAh in
(Paller et al., 2015). The source of this significant dif-
ference is the Sitara CPU’s base non-idle consump-
tion of 250 mA. This alone results in 250 mA*100
Energy-efficient Operation of GSM-connected Infrared Rodent Sensor
Figure 7: Image sending cycle in case of communication
logic running on the GL865 module.
seconds=25000 mAs (about 7 mAh) consumption.
This example shows that the high-performance Sitara
performs very poorly from the power consumption
point of view if the computation pattern is of action-
and-wait type which is very common in case of
telecommunication state machines. In this case the
event the application code waits for happens too
quickly therefore the CPU cannot be put into an idle
state while the overall length of the execution is quite
long. This level of power consumption overhead was
unacceptable for us, hence we decided to rearchitect
the sensor so that the code is executed on a CPU that is
most suitable for the task with regards to power con-
In the rearchitected model we returned to the orig-
inal sensor control model where the main control
logic is in the GL865 module. The microcontroller
in the GL865 is very efficient in low-power, low-
performance execution and can sleep with very low
power consumption, using its internal real-time clock.
The experience also applies to different communica-
tion modules where the controller and the modem are
not integrated. In this case a separate low-power mi-
crocontroller can take care of the main control. Due
to its high active power consumption, the Sitara CPU
is kept in inactive state as long as possible.
Figure 7 shows the power consumption of the
GL865 sending the image of 4 Kbytes. The sending
cycle takes much longer (600 seconds vs. the 100 sec-
onds when the Sitara CPU executed the same logic),
this is due to the slowness of the GL865 Python en-
gine. The power consumption for the sending cycle is
much lower, however, about 3 mAh while the power
needed to send over the image from the Sitara CPU to
the GL865 over the serial line is negligible.
In case of the rearchitected sensor control, the
Sitara CPU waits for wakeup signal, acquires and pro-
cesses images and if suitable image is available, up-
loads the images to the GL865 over the serial connec-
tion between the two units. Then the Sitara CPU goes
to sleep. Currently we see the following use cases for
the combinations of timed picture acquisition vs. au-
tomatic identification of relevant pictures.
Figure 8: Image processing power consumption, 5 itera-
tions, 500 msec inter-image delay.
Images or video (infrared and/or visible-light) are
acquired at preconfigured moments of time. In
this case the advantage of the embedded Linux in
the sensor is its wide support of different cameras.
Image processing is not done.
At preconfigured moments of time, automatic im-
age acquisition is triggered and if image process-
ing finds a relevant feature, the images/videos
(visible light/infrared) are uploaded to the server.
Image acquisition/image processing is executed
continuously and images are uploaded to the
server if relevant feature is found by the image
processing pipeline. There are sub-requirements
about the acquisition time interval between the
images (from zero to up to 60 seconds) but the
images are not sent to the server at preconfigured
moments, only when the image processing algo-
rithm identifies something important.
Depending on the use case, there are different
power consumption balance between the image pro-
cessing and image sending activities. For the first
use case that sends images only at preconfigured mo-
ments, the use of high-performance SoC platform
with embedded Linux is not justified from the power
consumption point of view. There is some marginal
software engineering advantage as embedded Linux
comes with a large number of camera libraries and
image acquisition tools (e.g. V4L). Regarding the
third use case, image processing runs continuously
therefore this function consumes the most power,
there is no point in talking about power consumption
balance. Power consumption balance becomes rele-
vant in the second use case where images taken may
not be sent if there is no relevant feature on them. The
image processing performed to figure out if the image
is worth sending may be justified by lower amount
of data to transfer and lower power consumption as
GSM communication is an expensive operation from
the power consumption point of view. Prior literature
about camera sensor power consumption has already
pointed out that tasks with lower energy consumption
should trigger tasks with higher energy consumption
SENSORNETS 2016 - 5th International Conference on Sensor Networks
(Kulkarni et al., 2005) and in this case the image pro-
cessing corresponds to the task in with lower hierar-
chy level to trigger the GPRS transfer.
Figure 8 shows the power consumption of an im-
age acquisition/processing activity on Ubuntu Snappy
15.04. One iteration comprises image acquisition
from the infrared camera, running the algorithm de-
scribed in section 3 and waiting 500 msec if no
relevant feature was found. The image acquisi-
tion/processing was repeated 5 times, consuming
about 0.62 mAh. It can be observed that the power
consumption increased for the duration of the activ-
ity as clock scaling option was active (cpufreq with
”ondemand” governor). This automatically increased
the clock speed to the maximum of 720 MHz from
the base of 275 MHz when the CPU was active. Even
though the Sitara SoC contains a GPU, this time it was
not used because the small size (80x60) of the infrared
images would not make the GPU usage efficient.
Considering strictly the image acquisition/image
processing step, the 0.62 mAh consumption of this
5-iteration activity compares favorably to the 3 mAh
consumption of sending the image with the GL865.
The Sitara CPU has a significant idle consumption,
however. Our prototype was implemented on the
Ubuntu Snappy distribution which at the moment of
writing this paper, does not offer CPU idling support.
This means that an inactive CPU still consumes about
250 mA (same as active CPU with no load), consum-
ing 3 mAh (the cost of sending one image) in just 43
seconds. The TI EZSDK implements one sleep state,
the suspend-to-RAM (S3) state. TI EZSDK can enter
and exit this state in 3 seconds but the consumption
in this state is still 156 mA, which means 69 seconds
to reach 3 mAh. Ubuntu Snappy 15.04 consumes 120
mA even in shutdown state but TI EZSDK properly
shuts down. Unfortunately, a full shutdown-reboot
consumes 4.78 mAh with TI EZSDK which is more
than the 3 mAh required to send the image. Idling the
Sitara CPU with shutdown is therefore not an option.
Our conclusion is that saving battery power and
cellular data transfer by putting more intelligence into
the sensor and prefiltering image data there is still an
attractive option. Unfortunately the current platforms
are inadequate from the power consumption point of
view, in particular the idle state management needs
more improvement. Until the embedded Linux sytem
can be placed into a state with near-zero consumption
in relatively short time, efficient battery-powered op-
eration is not possible.
We found that a use case exists for applications
with image processing in the sensor. If the require-
ment is to monitor the environment continuously with
short image capture interval and the data link to the
server side is relatively slow, detection of the relevant
features must be done in the sensor. This was the case
for our rodent detection use case. According to our
experiments, if the image processing is implemented
on the BeagleBone Black using high-productivity,
popular software stacks (e.g. Linux/OpenCV), the
power consumption will be very high. It is certainly
possible to decrease this high power consumption
with dedicated hardware (e.g. microcontrollers) but
the software productivity will drop dramatically as
powerful image processing frameworks are not avail-
able for these devices. The outcome is that continuous
monitoring with high-productivity frameworks is an
expensive choice from the power consumption point
of view.
Camera sensors have been deployed in the agricul-
ture for various use cases. Most of the applications
tried to infer the health and development of the plants
based on image data in various wavelength domains.
These applications are simple from the sensor point
of view as capturing/sending images at predetermined
moments is usually enough. The larger data payload
that these sensors generate would justify the usage of
a more recent cellular standard (3G/4G) but coverage
is spotty in the areas of our interest. We intend to
analyze more the question of 3G coverage in areas
relevant for agricultural activity.
In our research, we looked for a use case that re-
quires more sophisticated processing in the sensor and
we found that rodent population estimation is an eco-
nomically relevant application and due to the quick
movement of the target animals, fast capture interval
is required. We also found that a bait area can be
efficiently monitored with a reasonably priced long-
wavelength infrared camera.
It was an attractive proposition that the power
consumption of the sensor system can be efficiently
decreased with image processing because the sen-
sor can filter out non-relevant images. Initial anal-
ysis of power consumption cost of a relatively com-
plex image processing operation was promising. Un-
fortunately the idle state support of the embed-
ded Linux platform of our choice prevented the ex-
ploitation of this possibility. We found that con-
tinuous image capture/monitoring is a use case that
still requires image processing capability in the sen-
sor but energy-efficient implementation is not sup-
ported with the popular software stack we evalu-
ated (Linux/OpenCV). There is a trade-off here be-
tween implementation productivity and energy effi-
Energy-efficient Operation of GSM-connected Infrared Rodent Sensor
ciency with these platforms.
We propose the following directions to resolve
this trade-off. One direction is to improve the idle
state management of embedded Linux systems. This
is not trivial due to the complex interaction between
the peripherals (e.g. network cards) and the tasks run-
ning on the main processor. The expectation here
is that the Linux system may be placed into a state
where it consumes near to zero current. The other di-
rection is to port software libraries necessary for im-
age processing to microcontrollers without operating
systems. This would eliminate Linux and its power
management complexity entirely from the picture but
it would also make impossible the exploitation of se-
curity management and prepackaged software of em-
bedded Linux systems.
I would like to thank the Government of France
for the generosity in granting a scholarship to the
ESEO institute in Angers, France. I thank especially
ebastien Aubin at ESEO for facilitating my stay.
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