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Location-based Trustworthiness of Wireless Sensor Nodes using Optical Localization

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A continually growing number of sensors is required for monitoring industrial processes and for continuous data acquisition from industrial plants and devices. The cabling of sensors represent a considerable effort and potential source of error, which can be avoided by using wireless sensor nodes. These wireless sensor nodes form a wireless sensor network (WSN) to efficiently transmit data to the destination. For the acceptance of WSNs in industry, it is important to build up networks with high trustworthiness. The trustworthiness of the WSN depends not only on a secure wireless communication but also on the ability to detect modifications at the wireless sensor nodes itself. This paper presents the enhancement of the WSN's trustworthiness using an optical localization system. It can be used for the preparation phase of the WSN and also during operation to track the positions of the wireless sensor nodes and detect spatial modification. The location information of the sensor nodes can also be used to rate their trustworthiness.
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Leander B. Hörmann, Markus Pichler-Scheder, Christian Kastl, Hans-Peter Bernhard, Peter
Priller and Andreas Springer, "Location-based Trustworthiness of Wireless Sensor Nodes
using Optical Localization," 2020 IEEE MTT-S International Conference on Microwaves for
Intelligent Mobility (ICMIM), Linz, Austria, 2020, publication pending.
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Location-based Trustworthiness of Wireless Sensor
Nodes using Optical Localization
Leander B. H¨
ormann
Sensors and Communication
Linz Center of Mechatronics GmbH
Linz, Austria
leander.hoermann@lcm.at
Markus Pichler-Scheder
Sensors and Communication
Linz Center of Mechatronics GmbH
Linz, Austria
markus.pichler-scheder@lcm.at
Christian Kastl
Sensors and Communication
Linz Center of Mechatronics GmbH
Linz, Austria
christian.kastl@lcm.at
Hans-Peter Bernhard
Silicon Austria Labs GmbH and
Institute for Communicatons and RF-Systems
Johannes Kepler University
Linz, Austria
h.p.bernhard@ieee.org
Peter Priller
ITS Global Research & Technology
AVL List GmbH
Graz, Austria
peter.priller@avl.com
Andreas Springer
Institute for Communications
Engineering and RF-Systems
Johannes Kepler University Linz
Linz, Austria
andreas.springer@jku.at
Abstract—A continually growing number of sensors is required
for monitoring industrial processes and for continuous data
acquisition from industrial plants and devices. The cabling of
sensors represent a considerable effort and potential source of
error, which can be avoided by using wireless sensor nodes. These
wireless sensor nodes form a wireless sensor network (WSN) to
efficiently transmit data to the destination. For the acceptance of
WSNs in industry, it is important to build up networks with high
trustworthiness. The trustworthiness of the WSN depends not
only on a secure wireless communication but also on the ability to
detect modifications at the wireless sensor nodes itself. This paper
presents the enhancement of the WSN’s trustworthiness using
an optical localization system. It can be used for the preparation
phase of the WSN and also during operation to track the positions
of the wireless sensor nodes and detect spatial modification. The
location information of the sensor nodes can also be used to rate
their trustworthiness.
Index Terms—Wireless sensor network, security, optical local-
ization
I. INTRODUCTION
Wireless Sensor Networks (WSNs) are often used to mea-
sure physical quantities of industrial plants or processes and
transmit them towards a gateway using a certain communi-
cation protocol [1]. These WSNs consist typically of many
sensor nodes. The need for large numbers is caused by
the overall goal to improve the knowledge of the industrial
plants’ operating status or to further optimize the industrial
processes. Connecting all sensors with wires would be too
This work has been supported in part by the COMET-K2 Center of
the Linz Center of Mechatronics (LCM) funded by the Austrian federal
government and the federal state of Upper Austria, by the Austrian Research
Promotion Agency (FFG) under grant number 853456 (FASAN: Flexible
Autonome Sensorik in industriellen ANwendungen) and the SCOTT project.
SCOTT(www.scott-project.eu) has received funding from the Electronic Com-
ponent Systems for European Leadership Joint Undertaking under grant agree-
ment No 737422. This Joint Undertaking receives support from the European
Union’s Horizon 2020 research and innovation programme and Austria, Spain,
Finland, Ireland, Sweden, Germany, Poland, Portugal, Netherlands, Belgium,
Norway.
costly or restrict flexibility. Therefore, industrial Internet-of-
Things (IIoT) tries to integrate and connect the increasing
number of sensors wirelessly. All measured data is typically
either gathered at a dedicated data storage solution for a
following data analysis or directly used for control tasks at
the plant. Thus, it is vital that only genuine data is used for
analysis or control. The complete data path must be secured
from a broad range of attacks [2].
However, the sensor nodes are exposed to various attacks
ranging from manual modifications or removal of the sensor
nodes itself to remote attacks e.g. eavesdropping. This is
because nodes are often placed at locations with limited acces-
sibility and their small size. Industrial applications typically
use lots of sensor nodes, with each typically mounted at
fixed positions. The information of the current location of
each wireless sensor node can be used to improve the overall
trustworthiness of the applied WSN. This is not only for the
preparation phase of the WSN but also during operation to
track the positions of the wireless sensor nodes and detect
modifications. The location information of the sensor nodes
can also be used to rate their trustworthiness.
Therefore, this paper describes the security enhancement of
a WSN using an optical localization system to locate wireless
sensor nodes by detecting a light source at the nodes. The
application scenario for the wireless sensor network focuses
on the instrumentation of automotive testbeds [3]. The location
information is integrated into a rating of the current overall
trustworthiness of the WSN as well as of the single measure-
ment values of the sensor nodes. The rest of the paper is orga-
nized as follows: Section II describes the application scenario
and the system overview. Section III introduces the optical
localization system and presents evaluation results. Section IV
discusses the trustworthiness enhancement during preparation
and operational phase. Finally, section V concludes the paper
and outlines directions for future work.
Fig. 1. Wireless sensor node with transparent part of the housing for optical
localization.
II. APPLI CATI ON SC ENA RI O AND SYSTEM OV ERVIEW
Testing of prototypes is an essential part in development
and optimization of automotive powertrain systems. Prototypes
are assembled and instrumented as unit-under-test (UUT) in
an appropriate laboratory environment, such as a powertrain
test bed. This allows observing properties in a realistically
simulated environment. Such measurements help to character-
ize the UUTs to support further development. Wireless sensor
nodes offer several advantages: First, a faster and simpler
instrumentation due to elimination of cabling. Second, avoid-
ance of plug contact problems and confusion that may occur
with conventional instrumentation. And third, improvement of
signal quality, because the analog signal is digitized directly
at the node and all measuring points are electrically isolated
from each other and from the automation system.
Energy supply of the wireless sensor nodes should not
cause interruptions to the usual workflow. Any maintenance
needs of nodes should be avoided during a measurement
campaign (typically a few hours to several weeks). The wire-
less sensor nodes are therefore typically powered by batteries
or energy harvesting systems (EHSs) [4] and thus severely
energy constrained. They have either a limited amount of
energy (batteries) or are prone to a low available power
(EHSs) [5]. This is why the light source on the nodes for
optical localization has to be as energy efficient as possible.
For this application, the wireless sensor nodes are equipped
with LEDs as shown in Fig. 1. These LEDs are only activated
during the localization process. Each sensor node activates the
LED according to a unique flashing sequence to identify the
sensor nodes during the localization process.
A. System Overview
For three dimensional optical localization, cameras (C) are
installed around the measuring area as shown in Fig. 2, being
placed in such a way that each wireless sensor node (S) is
within the field of vision of at least two cameras. In order to
be able to cover areas in the UUT that would otherwise be
blocked by parts of the test setup, cables, etc., the number of
cameras can be increased according to the requirements of the
respective scenario.
The wireless sensor nodes are based on an nRF52840
system-on-chip (SoC) which integrates a processing unit and a
Fig. 2. Schematic structure of the optical localization with four cameras (C1
to C4) and three wireless sensor nodes (S1 to S3) and the corresponding
flashing sequence.
wireless transceiver for 2.4GHz radio frequency (RF) commu-
nication. Furthermore, the sensor node contains an analogue-
to-digital converter with an associated analogue circuit. The
analogue circuitry is designed to support Pt100, Pt1000 and
thermocouple temperature sensors as described in [6].
For wireless communication and a structured and efficient
information exchange the communication protocol EPhESOS
is used [3]. This protocol is especially designed for wireless
sensor networks that require low power consumption on av-
erage and low latency. The average power consumption is
minimized by using a time division multiple access (TDMA)
protocol and tightly synchronized wireless sensor nodes. Thus,
the transmission and reception times of the individual sensor
nodes can be minimized. The frame structure of the EPh-
ESOS communication protocol defines time slots within a
superframe (SF). The communication protocol supports two
different modes, which are briefly explained below: First,
the sporadic transmission mode, EPhESOS-S, is responsible
for network commissioning, integration of new nodes, initial
synchronization, node configuration and maintenance. Second,
the continuous transmission mode, EPhESOS-C, is used to
transmit measurement data from the nodes towards the wire-
less network processor (WNP) in their own time slots. A
sensor node does not have to send data in every SF but can also
aggregate data and pack them into an UL packet in an energy-
saving manner. If a packet loss is detected, the incorrectly
received or missing packet is sent again in the following SF.
The sensor nodes remain in sleep mode between receiving and
transmitting times.
III. OPT ICAL LOCALIZATION SYSTEM
For localization, each camera records a sequence of images
first. The cameras itself are connected to special wireless
sensor nodes which synchronizes the recording based on
the master clock of the sensor network. Synchronously to
the recording times, each node starts an individual flashing
sequence on their LED. The flashing sequences are uniquely
coded with respect to the nodes to be identified. The number
of possible flashes in the sequence determines the range of the
code space and thus the maximum number of locatable sensor
nodes. Consequently, each extension of the flashing sequence
also increases the energy consumption for localization. An
analysis of the optimal flashing sequence is shown in a later
section.
Each individual wireless sensors node is first detected based
on the known flashing sequence and then localized by using
the image coordinates of the node’s LED recorded by each
camera having the node in view. To identify each node,
a detection image is calculated from the individual images
of each camera by addition or subtraction according to the
code of each sensor node. After detection, the coordinates
of all visible sensor nodes are available for each camera
(image coordinates). Using the position and orientation of the
cameras, the detection image of each detected LED can now
be converted into a ray in space. The rays leave the cameras at
an angle given by the previously determined image coordinates
of the LEDs. A detailed description is given in [7].
A. Optimal Flashing Sequence
The total energy consumption of a wireless sensor node dur-
ing a localization process was selected as the main optimiza-
tion criterion. Since a localization process is only performed
from time to time, the total duration is not critical and is not
considered here. The total energy consumption is composed of
the energy consumption of the LED for signaling the flashing
sequence, the energy consumption for ongoing communication
and synchronization and the energy consumption during idle
times. The values used to calculate the energy consumption
are listed in Table I. Since the LED has a very high power
consumption compared to the other components, it is important
to activate it as rarely as possible. In the simplest case,
each flashing sequence (or code) contains only a single LED
activation. Of course, the length of the codes increases linearly
with the number of participating wireless sensor nodes. But
from a certain number on, the energy consumption of the LED
becomes lower compared to the energy consumption of other
components. Therefore, it is more energy efficient to activate
the LED twice or more per code depending on the number of
sensor nodes. This behavior is shown in Fig. 3. It shows the
total energy consumption of a localization process of a sensor
node for one to four LED activations per code depending on
the maximum number of sensor nodes.
TABLE I
PARAMETER FOR CALCULATING THE ENERGY CONSUMPTION DURING
TH E LOC ALI ZATIO N PRO CES S.
Parameter Value
Power consumption LED 42 mW
Avg. power consumption for comm. and sync. 400 µW
Power consumption sleep 30 µW
Time for comm. and sync. per second 20 ms
Time for LED active (1 flash) 100 ms
Time between two flashes 100 ms
Fig. 3. Energy consumption of a localization process per sensor node over
the maximum number of sensor nodes for one to four LED activations.
Fig. 4. View of the measurement setup from 3D laser scan with cameras (C1
to C6) and some visible sensors (S).
B. Evaluation of Optical Localization
The localization has been tested in an engine test bed under
real conditions. Fig. 4 shows a 3D laser scan of the test
environment, which was also used to determine the reference
positions of sensors and cameras. The DUT equipped with the
wireless sensor node is mounted at the center of the test setup
on a metal frame with the associated inlets and outlets for
fuel, lubricants, exhaust gases, and so on. Ten sensor nodes
(S1 to S11, without S7) were used at characteristic positions.
On both sides of the engine block, three cameras (C1 to
C6) were placed at two different heights so that each sensor
node was visible from at least three cameras. The spatial
distances of the estimates to the reference positions determined
with the laser scanner are shown in Fig. 5. The spatial
distance is calculated as Euclidean distance and represents the
absolute localization error. The average absolute error of all
sensor nodes’ localization results is 12.45 mm with a standard
deviation of 6.38 mm. More detailed results can be found also
in [7].
IV. TRUSTW ORT HI NESS
The previous section has shown that the current position
of the sensor nodes can be determined accurately using the
optical localization system. This information can be used
in the preparation phase for installation verification as well
as during the operational phase for online security checks.
Fig. 5. Absolute localization error of ten sensor nodes as Euclidean distance
of the estimate from the reference position determined with the laser scanner.
The information of the current position can increase the
trustworthiness in both cases as described in the following
sections. In both cases, the optical localization represents an
out-of-band security measure since it is not using the main RF
communication channel.
A. Verification of Installation
After applying the wireless sensor nodes at the UUT,
they must be assigned to their corresponding quantity to be
measured. The manual mapping is an error-prone process
which may lead to a mixing-up of sensors nodes measuring
the same quantity in a similar range, for example a coolant
temperature at different positions. If the measured values are
very similar, it will be hard to detect such a miss-configuration
by the operators. The manual mapping can be improved by
an automated verification or an automated assignment of the
senor nodes’ positions to the quantities to be measured. They
can be mapped using a CAD model of the UUT and the
localization results of them. By a positive verification of
the assigned measuring points or an automated mapping of
them, the trustworthiness regarding the correct assignment of
sensor nodes and the corresponding physical quantities can
automatically be confirmed. Since the preparation is typically
done by trustworthy staff and the setup may be checked by
several people, an intentional malicious modification is not to
be expected during the preparation phase.
B. Online Security Checks
The optical localization system can be also used to track the
position of the wireless sensor nodes during the operational
phase. In this application, it can be used to check if a
sensor node’s position has changed. This could be the case
if sensor nodes have been exchanged due to malfunction, if
sensor nodes were badly mounted and had dropped, or if
sensor nodes have been intentionally modified to manipulate
sensor data. A detection of all of these cases is a requirement
for a trustworthy system. Especially, as the detection of
intentional malicious modifications is very important during
this phase, the tests are sometimes running for weeks and
lot of personnel may have access. However, the localization
system may also be a target for attacks to circumvent its
security functions. Thus, the system itself must be secured
using appropriate countermeasures. One approach is to change
the unique flashing sequence according to a certain pattern, or
to define it by a central entity. Thus, attackers are not be able
to perform replay attacks. Also, the change of the localization
interval would enhance security. The attackers will not know
when the next localization procedure will start and cannot be
prepared. However, each localization procedure consumes a lot
of energy at each sensor node. Therefore, the repeated optical
localization during the operational phase may only be used for
applications with (rechargeable) battery powered sensor nodes.
This leads to a trade-off between the energy consumption and
thus the lifetime of a sensor node and the localization interval
and thus the temporal resolution of modification detection. The
best trade-off depends on various factors and typically changes
from application to application.
V. CONCLUSION
This paper presents an optical localization system for WSNs
in industrial applications. The application scenario and the
system overview are given. The evaluation of the localization
system in a real-world application shows an accuracy in
the centimeter range. The trustworthiness of the WSN and
its measured data is important and must be secured against
intentional malicious modification. The usage of the optical
localization system during the application’s preparation phase
and operational phase is discussed. It shows that overall
trustworthiness of the WSN can be improved in both phases.
Future work will target an improvement of the localization
accuracy by improving the LED geometry at the sensor node
and of the wireless sensor nodes’ energy consumption needed
for the optical localization procedure by an reduction of the
LED flashing periods during the localization procedure.
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Conference Paper
Full-text available
Zusammenfassung Zur Überwachung von industriellen Prozessen und zur laufenden Messdatenerfassungen von Indust-rieanlagen und-Geräten werden immer mehr und mehr Sensoren benötigt. Diese Entwicklung wird vor allem durch industrielle Internet-of-Things (IoT) Anwendungen gleichzeitig ermöglicht und voran-getrieben. Die Verkabelung stellt einen erheblichen Aufwand und potenzielle Fehlerquelle dar. Daher sind autarke Sensorknoten, die ihren Leistungsbedarf mittels sogenanntem Energy Harvesting aus ihrer Umgebung decken und eine drahtlose Kommunikationstechnologie zur Datenübertragung ver-wenden, oft von Vorteil. Die Sensorknoten bilden ein drahtloses Sensornetzwerk, um die Daten effi-zient an den Zielort zu übertragen. Weiters sind autarke Sensorknoten in bestimmten Situationen not-wendig, da dort eine kabelgebundene Messung nicht möglich ist. Für Anwendungen, bei welchen sich das Messobjekt oft ändert und daher die Sensoren neu angebracht werden müssen, verringert man durch die Verwendung von autarken Sensorknoten die Anzahl von Kabeln zu einer zentralen Messda-tenerfassung. Die Zuordnung vom jeweiligen Sensorknoten zu einer bestimmten Messgröße ist jedoch aufwendig und muss meist händisch vorgenommen werden. Um dies zu vereinfachen präsentiert die-ser Beitrag ein optisches Lokalisierungssystem für autarke Sensorknoten in industriellen IoT Anwen-dungen. Die Sensorknoten sind mit einer schaltbaren Lichtquelle ausgestattet, welche eine kodierte Blinksequenz erzeugen. Diese Blinksequenz wird von mindesten zwei Kameras erfasst und zur Identi-fizierung und gleichzeitigen Lokalisierung der Sensorknoten verwendet. Dadurch kann eine teilauto-nome Zuordnung eines Sensorknotens zu einer bestimmten Messgröße erfolgen. Einleitung Autarke Sensorknoten werden immer häufiger zur Erfassung industrieller Messgrößen ver-wendet. Einerseits wird diese Entwicklung durch die technologische Verfügbarkeit und Verbesserung von kostengünstigen und ener-gieeffizienten Transceiver-Schaltungen und Erhöhung der Rechenleistung integrierter Schaltungen ermöglicht. Andererseits sind zur weiteren Optimierung technischer Prozesse zusätzliche Messdaten notwendig und werden daher von der Anwendung selbst gefordert. Um diese zusätzlichen Messdaten kosteneffek-tiv erfassen und meist an eine zentrale Einheit übertragen zu können, werden die Sensorkno-ten zu sogenannte drahtlose Sensornetzwerke m-mengefasst. Die Daten werden drahtlos unter
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