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Troubleshooting Wireless Coexistence Problems in the Industrial Internet of Things

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The ever-growing proliferation of wireless devices and technologies used for Internet of Things (IoT) applications, such as patient monitoring, military surveillance, and industrial automation and control, has created an increasing need for methods and tools for connectivity prediction, information flow monitoring, and failure analysis to increase the dependability of the wireless network. Indeed, in a safety-critical Industrial IoT (IIoT) setting, such as a smart factory, harsh signal propagation conditions combined with interference from coexisting radio technologies operating in the same frequency band may lead to poor network performance or even application failures despite precautionary measures. Analyzing and troubleshooting such failures on a large scale is often difficult and time-consuming. In this paper, we share our experience in troubleshooting coexistence problems in operational IIoT networks by reporting on examples that show the possible hurdles in carrying out failure analysis. Our experience motivates the need for a user-friendly, automated failure analysis system, and we outline an architecture of such system that allows to observe multiple communication standards and unknown sources of interference.
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Troubleshooting Wireless Coexistence Problems
in the Industrial Internet of Things
Ulf Wetzker∗‡ Ingmar SplittMarco ZimmerlingCarlo Alberto BoanoKay R ¨
omer
Fraunhofer Institute for Integrated Circuits, Division Engineering of Adaptive Systems, Dresden, Germany
Center for Advancing Electronics Dresden, Dresden University of Technology, Germany
Institute for Technical Informatics, Graz University of Technology, Austria
{ulf.wetzker, ingmar.splitt}@eas.iis.fraunhofer.de marco.zimmerling@tu-dresden.de {cboano, roemer}@tugraz.at
Abstract—The ever-growing proliferation of wireless devices
and technologies used for Internet of Things (IoT) applications,
such as patient monitoring, military surveillance, and industrial
automation and control, has created an increasing need for
methods and tools for connectivity prediction, information flow
monitoring, and failure analysis to increase the dependability of
the wireless network. Indeed, in a safety-critical Industrial IoT
(IIoT) setting, such as a smart factory, harsh signal propagation
conditions combined with interference from coexisting radio
technologies operating in the same frequency band may lead to
poor network performance or even application failures despite
precautionary measures. Analyzing and troubleshooting such
failures on a large scale is often difficult and time-consuming.
In this paper, we share our experience in troubleshooting
coexistence problems in operational IIoT networks by reporting
on examples that show the possible hurdles in carrying out
failure analysis. Our experience motivates the need for a user-
friendly, automated failure analysis system, and we outline an
architecture of such system that allows to observe multiple com-
munication standards and unknown sources of interference.
I. INTRODUCTION
AComputerworld article from March 8, 1993 reported on
the vision of General Magic, a former Apple Inc. spin-off
developing the ancestor of the modern smartphone [1]:
“... given another generation,
wireless will be everywhere.
This vision has become a reality. In 2015, the total number
of mobile subscriptions surpassed the world’s population
of 7.3 billion [2], and even conservative predictions by
Ericsson, McKinsey, and Garnter estimate that the Internet of
Things (IoT) will consist of 20–30 billion connected devices
by 2020 [3], the vast majority of which will be wireless.
The range of IoT applications is huge. Applications such
as monitoring of parking spaces and waste containers are
already being deployed today. In the future, the IoT will
increasingly embrace smart sensors and actuators to directly
control the physical world, including the machines, factories,
and infrastructure that define our modern society. Referred to
as Cyber-Physical Systems (CPS) or the Industrial IoT (IIoT),
the corresponding applications such as industrial automation
and process control are safety-critical in nature and require
that the underlying wireless networks operate dependably [4].
In addition to the notorious unreliability and unpredictabil-
ity of wireless communications, one of the major challenges
for a dependable IIoT is cross-technology interference (CTI).
This is because the ever-growing number of wireless IoT
devices and technologies makes the unlicensed industrial,
scientific, and medical (ISM) frequency bands increasingly
crowded. Imagine, for example, a smart factory scenario,
where thousands of devices using heterogeneous wireless
standards, such as Bluetooth Low Energy (BLE), ZigBee,
and
Wi-Fi
, operate in the same frequency band together
with cordless phones and certain radio-frequency identifica-
tion (RFID) systems. How can we ensure that these networks
coexist without degrading each other’s performance?
To this end, wireless standards have passive coexistence
mechanisms built-in. For instance, carrier sense multiple
access with collision avoidance (CSMA/CA) lets a device send
only if it has sensed the channel to be free, and using adaptive
frequency hopping (AFH) a device continuously switches
its carrier frequency while trying to avoid frequencies that
experience a high packet error rate. Improving the robustness
of wireless networks against CTI is also an active area of
research, exploring the use of, for example, forward error
correction [5] and MIMO capabilities [6]. The goal of all
these mechanisms is to prevent performance degradation and
failures due to CTI. But it is hard to foresee all future changes
and potential problems that come along with them, especially
given that new wireless standards emerge frequently and
that changing environmental conditions have a significant
impact on wireless networks [7]. So how can we troubleshoot
wireless co-existence problems once they have surfaced as a
partial or complete failure of an IIoT application?
Our experience in troubleshooting coexistence problems
in operational IIoT installations shows that this is often a
labor-intensive and time-consuming task. The main reasons
for this are that the process is largely manual, involves the
use of many different tools, and requires expert knowledge
in diverse fields. To illustrate the steps, pitfalls, and surprises
one may encounter during troubleshooting, we share in this
paper our experiences with the research community.
After giving some background on wireless coexistence
in Section II, we report in Section III on our experience in
troubleshooting coexistence problems in a prototypical smart
factory and in an open-pit mine. Based on the lessons we
learned from these real-world cases and the methodology we
developed, discussed in Section IV, we describe in Section V
Figure 1. Popular technologies operating in the 2.4 GHz ISM band.
a novel architectural concept, which represents a first step
towards a more systematic and (semi-)automated approach to
troubleshooting wireless coexistence problems. We conclude
this paper by discussing related work in Section VI and by
providing guidelines for future research in Section VII.
II. BACKGROU ND ON WIRELESS COEXISTENCE
The growing proliferation of wireless devices causes an
increasing congestion in the radio spectrum, turning it into
an expensive resource [8]. Indeed, several standardized radio
technologies operate in increasingly crowded ISM frequency
bands, that is, freely-available portions of the radio spectrum
reserved for industrial, scientific, and medical purposes [9].
The 2.4 GHz ISM band is a notable example of how
crowded a portion of radio spectrum can be. Its worldwide
availability made this band one of the most popular choices
for wireless personal and local area networks. Figure 1 shows
the four most pervasive technologies operating in the 2.4GHz
band: IEEE 802.11 (better known as Wi-Fi), IEEE 802.15.4
(the basis of the physical and media access control layer for
low-rate wireless personal area and industrial networks such
as ZigBee, ISA100.11a, and WirelessHART), IEEE 802.15.1
(commercialized as Bluetooth) and its evolution Bluetooth
Smart or Bluetooth Low Energy (BLE). These standards
specify different signal management functions, modulation
schemes, power levels, data rates, channel bandwidths and
separations. However, as visible in Figure 1, they all use over-
lapping frequencies. As a result, standard-compliant devices
need to compete for medium access and may experience cross-
technology interference (CTI) from surrounding appliances.
CTI may cause packet loss, unpredictable medium access
delays, and high end-to-end latencies. Moreover, especially
for low-power wireless devices with constrained energy
budgets typically employed in IoT applications, CTI may
also lead to reduced energy efficiency due to longer listening
and contention times, packet re-transmissions, and loss of
time synchronization. This is an important observation for
low-power wireless sensor and actuator networks used in
safety-critical IIoT scenarios, such as industrial control [10]
and automation [11], health care [12], and high-confidence
transportation systems [13], where guaranteeing high packet
reception rates, bounds on end-to-end packet latency, and
continuous system availability are of utmost importance.
The coexistence problem is further exacerbated by the fact
that also common domestic appliances and other everyday
devices can be a source of interference for wireless networks
operating in the same frequency band. For instance, several
works have shown that also the radio-frequency (RF) noise
emitted by microwave ovens [14], electrodeless lamps [15],
and other domestic appliances such as cordless phones, baby
monitors, game controllers, presenters, and video capture
devices [16] is harmful to low-power wireless technologies
operating in the 2.4 GHz ISM band.
Also other ISM bands suffer from increasing congestion,
although to a lower degree [17]. Telemetry networks [18] and
cellular phones [19] can cause CTI in sub-GHz bands; for
example, Global System for Mobile Communications (GSM)
can impact low-power wireless transmissions during the first
seconds of an incoming call [9]. The increasing popularity
of long-range IoT technologies operating in sub-GHz bands
(e.g., IEEE 802.15.4g, LoRa, and SIGFOX) will soon saturate
this portion of radio spectrum, further aggravating the prob-
lem of wireless coexistence. Similarly, industrial networks
and localization systems based on ultra-wide band (UWB)
technology powered by IEEE 802.15.4a devices begin to
populate the 5.8 GHz band and hence need to coexist with
Wi-Fi
(IEEE 802.11n/ac) and a set of emerging technologies
and solutions, such as Long-Term Evolution in unlicensed
spectrum (LTE-U), radar systems and Google Loon1.
It is clear that wireless networks enabling safety-critical
IIoT applications urgently need solutions for connectivity
prediction and validation. Connectivity prediction tools are
important when using wireless technology to connect critical
equipment (e.g., accident prevention systems or cooling and
corrosion detection units) and must account for the possible
presence of heterogeneous radio-access technologies as well
as allow for post-deployment network validation [21].
Such tools alone, however, are not sufficient. Even when
following best-practice principles with precise connectivity
information [22], it is hard to predict how the environment
changes over time and therefore very difficult to ensure cor-
rect operation on a large scale for prolonged periods of time,
even for technology experts. Debugging and troubleshooting
tools are hence needed to analyze failures in depth and to
provide hints on how to fix them. Such tools should accurately
capture the deployment site’s electromagnetic environment
in the frequency, time, and spatial domain, as well as allow
both an online analysis for on-sight troubleshooting and an
off-line examination in case of more complex problems.
1
Google Loon is a project aiming to provide Internet access in rural and
remote areas using high-altitude balloons that are placed in the stratosphere
and operate in the 2.4 and 5.8 GHz ISM bands [20].
To date, there is a clear lack of tools for troubleshooting
coexistence problems in industrial networks [23], which
is a major hurdle for engineers debugging cyber-physical
and IIoT systems deployed on a large scale in remote
locations. This lack of tools makes troubleshooting industrial
wireless networks an exasperating, labor-intensive, and time-
consuming task – sometimes a real agony.
III. TROU BLE SHO OTING INDUSTRIAL WIRELESS
NET WORKS: OUR HAN DS-O N EXPERIENCE
This section details two exemplary cases from our experi-
ence in troubleshooting coexistence problems in operational
IIoT installations, providing evidence of this agony.
A. Real-world Case 1: Gantry Robot
Developing an emulation platform for coexistence analysis
in wireless automation systems is a viable approach to enable
incremental deployment of new wireless communication com-
ponents within an existing industrial wireless communication
network [24]. In particular, the emulation of the production
network at a test site enables test-driven development and
integration under controlled, yet realistic conditions, while
avoiding downtimes of the original production network.
To test wireless components under realistic coexistence
conditions, one of the key ingredients of such an emulation
platform is a parametrizable packet generator that mimics,
for example, the cyclic traffic patterns of a programmable
logic controller (PLC). As part of a research project to create
such an emulation platform, we strove to develop statistical
traffic models of typical wireless communication protocols
in different IIoT application scenarios and environments.
Setting.
To obtain such models, we chose the Experimental
and Digital Factory (EDF) at Chemnitz University of Technol-
ogy for a measurement campaign. The EDF is well-suited for
experiments with industrial wireless communication systems,
as it features all components of a future smart factory while
accurately representing the environment of production lines
with interconnected building blocks for adaptive factory
systems. The EDF includes components for:
machining (e.g., manufacturing, assembly, test);
material flow (e.g., transportation, material handling);
information flow (e.g., control, interfacing);
energy (e.g., supply, management).
The industrial wireless network inside the EDF is based on
IEEE 802.11n and the proprietary Siemens IWLAN protocol.
During our measurements, we noticed two single-hop wireless
transport systems connected to an access point (AP) acting
as a gateway to the wired industrial communication system,
as depicted in Figure 2: an Automatic Guided Vehicle (AGV)
that receives mission data and sends position and collision
avoidance information to a control module, and a gantry robot
that connects wirelessly to a Human Machine Interface (HMI)
to control the movement of the robot.
Figure 2. Floor plan of the Experimental and Digital Factory (EDF).
During the design phase of the IEEE 802.11n network in
the EDF, a wireless network planning procedure was carried
out as described in [25]. To mitigate interference factors, the
guide recommends four remedies:
increase the quality of radio coverage by increasing the
number of APs in the network;
use of directional antennas to extend and optimize the
wireless connections;
disable or re-position all coexisting wireless devices that
operate on the same frequency;
avoid the use of the wireless network for data transmis-
sions if there are no effective countermeasures.
Following these guidelines, the position of the AP (see
Figure 2) allowed for an adequate coverage of the operation
area. To avoid interference with coexisting IEEE 802.11
networks, an unused frequency channel was assigned to the
industrial wireless communication system. In addition, the
guide recommends a simulation-based approach using the
Siemens SINEMA E software for planning, simulating, and
configuring a
Wi-Fi
network. In general, industrial plants
are hard to model since they include large metal objects and
moving components. Thus, a static simulation of complicated
radio propagation environments can be misleading and was
not performed during the planning of the wireless network.
Without the many restrictions of an industrial plant running
in production, the EDF allowed us to reconfigure the network
and deliberately interfere with the IEEE 802.11n network.
To analyze possible changes in the traffic patterns caused
by coexistence with other communication standards in the
2.4 GHz ISM band, we deliberately introduced interfering
IEEE 802.15.4 and IEEE 802.11 networks. To determine
the maximum interference level that the application could
tolerate, we increased the data rate of the two interfering
networks in a step-wise fashion until the transport system
encountered an emergency stop and had to be reset.
Problem.
At first, we successfully observed the communica-
tion pattern of the AVG driving in the hallway between the
manufacturing and assembly modules. We recorded multiple
traffic patterns from different driving maneuvers over a
measurement period of 30 minutes. However, following the
same procedure with the HMI-controlled gantry robot, the
application faced emergency stops after a random period of
operation. A local technician had observed the same behavior
before, which could only be resolved by running the wireless
network within the 5 GHz rather than in the 2.4 GHz band. We
describe next the search for the cause of the emergency stops,
which we assumed to be related to wireless coexistence.
Failure analysis.
Initially, we inspected the diagnostic menu
of the HMI to rule out obvious problems related to some
misconfiguration or strong variations in the signal strength
(e.g., dead spots). Then, we used the network analysis tool
Wireshark to observe the IEEE 802.11n traffic. A manual
in-depth inspection of the captured packet traces provided
a complete picture of the network structure, including a
large number of previously unknown devices in the wired
network behind the AP. This discrepancy was due to an
unintended configuration of a managed switch, which resulted
in broadcast and multicast messages from within the wired
network being transmitted by the AP. Resolving this issue,
however, did not fix the problem of emergency stops.
Capturing packets on overlapping
Wi-Fi
channels, we also
observed multiple smartphones and a wireless VoIP telephone.
Finding all coexisting devices of the same standard within
reception range can be an exhausting task, especially if the
cause of the system failure remains undetected even after
disabling all observed devices in the surroundings.
Alternatively, the cause of a failure may be identified by
reconstructing the technical background of the failure. We
observed the packet stream between the HMI and the PLC,
tracked down the communication scheme, and determined
real-time critical heartbeat packets. Each emergency stop
was preceded by at least one re-transmission of a heartbeat
packet. Based on all these indications, we set up a simplified
spectrum analyzer using an IEEE 802.15.4 node to search
for other coexisting systems within the 2.4 GHz ISM band.
Compared to professional measurement equipment, however,
sampling the received signal strength (RSSI) using an IEEE
802.15.4 radio has low accuracy, low sampling rate, and
includes spectral artifacts of the local oscillator harmonics.
Nevertheless, our RSSI traces revealed another communi-
cation system with a bandwidth of 1 MHz that employed
frequency hopping, as shown in Figure 3.
Bluetooth is the most widely used wireless communication
system that matches these observations. After an extensive
search for an active Bluetooth system, we noticed that the
observed frequency hopping-based communication system
constantly used the full 2.4 GHz ISM band even when we
deliberately introduced strong interference using a coexist-
Figure 3. The 2.4 GHz spectrum captured at the EDF using an IEEE
802.15.4 node. Our measurements revealed a coexisting communication
system with a bandwidth of 1 MHz employing frequency hopping.
ing
Wi-Fi
network. Most Bluetooth systems try to avoid
interference with other wireless networks using the adaptive
frequency hopping (AFH) algorithm
2
. In the presence of
coexisting networks, the AFH algorithm dynamically changes
the frequency hopping sequence of the devices, restricting
the number of channels used by all Bluetooth nodes within
the piconet and allowing other wireless systems to use the
remaining frequency channels. As this was not the case, we
ruled out Bluetooth as the cause of the emergency stops.
Besides Bluetooth certain RFID systems operating accord-
ing to the
ISO 18000-4
standard can also use frequency
hopping and a bandwidth of 1 MHz. Having this in mind,
we started to narrow down the location where an emergency
stop occurred after the shortest runtime of the system. By
seeking the datasheets of all industrial devices in the area, we
finally found a Siemens MOBY U active RFID transponder.
After disabling the transponder, the HMI worked flawlessly.
A high-resolution real-time spectrum analyzer might have
helped to reveal the spectral differences between Bluetooth
and RFID much earlier, but expert knowledge in wireless
communications would have been required nevertheless.
Effort.
The failure analysis was performed in 3 hours of
intense work by two research engineers working in the field
of industrial wireless communications and one technician
of the EDF that supported the process with his detailed
knowledge of the local installation setup. The time and
effort we spent on this sudden troubleshooting process was
relatively low considering that we had to improvise parts of
our measurement equipment. Nevertheless, the work of three
additional engineers was delayed by the faulty industrial
system whereby the losses summed up to 18 person hours.
B. Real-worl Case 2: Surface Mining
Besides mobile machinery components, one of the most
important application areas for industrial wireless communi-
cation systems is large-scale, inaccessible, or remote areas.
2
The vast majority of Bluetooth devices uses adaptive frequency hopping,
as this feature was introduced already in Bluetooth Revision 1.2 (2003).
Figure 4. The open-pit mine where the wireless network was installed.
Setting up a wired network in a mining scenario is particularly
challenging. This is, for example, because most of the mining
equipment is mobile and there are often sections with no
fixed cable installations. Also, the environmental conditions
characterized by dust, humidity, undamped vibrations, and
even explosive or abrasive substances are highly demanding.
Thus, mining scenarios often rely on wireless communication
to implement the required control loops. At the same time,
however, the harsh environmental and extreme radio prop-
agation conditions represent additional sources for failures
of the wireless network. We describe next how we analyzed,
together with a troubleshooting contractor for industrial
communication systems, a failing wireless control network
deployed in a kaolinite open-pit mine in Saxony, Germany.
Setting.
As shown in Figure 4, the open-pit mine was about
0.25 km
2
in size and incorporated four TAKRAF SRs bucket-
wheel excavators (SRs1–SRs4). Each excavator featured a
flexible conveyor belt to carry the material to one of two fixed
conveyor belts. These conveyor belts were placed between the
working areas of the bucket-wheel excavators and converged
into the main conveyor belt, as shown in Figure 5. With a total
length of about 3 km, the conveyor belt system connected
this smaller open-pit mine with the major mining complex,
including the processing area and the main buildings.
A control mechanism prevented congestion in the hierar-
chical conveyor belt system. To this end, a wireless network
based on industry-strength IEEE 802.11 equipment was used
to interconnect the PLC of the conveyor belt system with
the four SRs. A star topology connected the wireless stations
on the bucket-wheel excavators (SRs1–SRs4) to an AP. As
shown in Figure 5, all four wireless stations were within 220
meters from the AP with a maximum height difference of
15 meters with respect to the AP. The setup included two
antenna types with omnidirectional radiation pattern: a 10
dBi collinear antenna (A) and a 3 dBi monopole antenna (B).
The IEEE 802.11 wireless network was designed according
to the “Coexistence of Wireless Systems in Automation
Figure 5. Floor plan of the open-pit mine, including the locations of the
Wi-Fi AP and the four bucket-wheel excavators (SRs1–SRs4).
Technology” [22] guidelines of the German Electrical and
Electronic Manufacturers’ Association (ZVEI). The planning
procedure was carried out by radio experts as recommended
in the VDI/VDE 2185 guideline [26]. In addition, a site survey
was performed using the Ekahau Site Survey (ESS) system to
estimate the radio coverage in the open-pit mine. The latter
consists of temporarily installing an AP, taking reception
strength measurements at equally distributed locations across
the area of interest, and deriving the resulting coverage as a
heat map. The results revealed a weak signal strength only
at a few locations behind the bucket-wheel excavators.
In addition, a spectrum analysis was performed to manually
search for possible coexisting wireless devices. To this end,
commercially available systems such as the Metageek
Wi-Spy
and the corresponding Channelizer software were used. As
the mine is located far away from residential buildings, no
coexisting device was detected at deployment time, and the
IEEE 802.11 network was installed without precautionary
measures or a coexistence management scheme.
Problem.
Once the installation in the mine was finalized, the
wireless network remained operational for about two years.
Then the first connectivity problems occurred, and parts of the
system had to be switched back to full manual control. These
connectivity problems persisted over the following days, so
a thorough failure analysis of the system was performed.
Failure analysis.
A troubleshooting contractor for industrial
communication systems started with an in-depth investigation
to detect misconfigurations and hardware defects in the wired
network. As no evidence for the cause of the connectivity
problem was found, the wireless network was inspected next.
To this end, we used a distributed measurement system
based on Raspberry Pi devices connected to a consumer-
grade IEEE 802.11 transceiver. At the time of the failure
analysis, the wireless station at SRs1 was switched off, the
connection between SRs4 and the AP was very unreliable,
whilst SRs2 and SRs3 did not show connectivity issues. We
therefore enclosed three Raspberry Pi nodes in a waterproof
housing that included a power bank module and strapped
them on each enabled bucket-wheel excavator. In order to
get synchronized measurements from all nodes, the on-board
crystal of the Raspberry Pi providing an accuracy of 140 ppm
was backed up by a NXP PCF2127AT real-time clock with
an accuracy of 3 ppm. The measurement procedure started
with an
NTP-based
time synchronization by connecting the
three Raspberry Pi nodes via Ethernet to a notebook. After
this initial synchronization, the nodes were disconnected and
started to capture wireless packets in monitor mode.
In addition to the distributed packet capture system, we also
set up a simple spectrum analyzer using an IEEE 802.15.4
node (similar to the one described in Section
III-A
) to search
for other coexisting systems within the 2.4 GHz ISM band.
After observing the spectrum for more than three hours, we
only saw
Wi-Fi
traffic in the surroundings. Most of this traffic
used the same channel as the AP, and we could not detect
any additional source of interference within the mining area.
The observations made during the network planning phase
described a similar coexistence scenario, but did not mention
occasional IEEE 802.11 transmissions on other frequencies.
Next, we tried to rule out hardware failures. An investiga-
tion of the wireless equipment revealed that the fiberglass
housing of all antennas was affected by environmental influ-
ences like UV degradation and that none of the transceiver
inputs of the wireless stations had a lightning protection. The
10 dBi collinear antenna of the AP had a small crack inside
its plastic cap, causing water to leak inside the fiberglass
housing and changing the electromagnetic properties of the
antenna. After replacing the antenna of the AP, however, the
link between SRs4 and the AP was still unreliable. We also
ruled out a hardware failure due to lightning, because this
would have caused all stations to be affected by the same
problem and is also more likely to compromise the wireless
interface completely rather than causing partial packet loss.
As a next step, we analyzed the wireless traffic captured
by the distributed Raspberry Pi nodes to further study the
unreliable link between SRs4 and AP. As the spectral analysis
revealed additional
Wi-Fi
traffic on other channels, we started
to analyze the recorded traces searching for coexisting
Wi-Fi
networks employing common tools such as Wireshark and
TCPdump. These tools focus on the dissection of the packet
payload and provide the possibility to carry out basic data
processing and filtering. We exploited these features to
derive a snapshot of the role and the connection state of
all detected wireless nodes. In addition to the industrial
wireless network under study, the packet capture observed 29
different IEEE 802.11 stations during the observation period.
Based on the traffic behavior and the manufacturer ID of the
MAC address, we could recognize 22 smartphones, 5 tablet
PCs, and 2 notebooks. However, none of these devices had
an active connection creating a significant amount of traffic.
To confirm that none of these devices was causing the
failure, we developed traffic analysis scripts that revealed the
full network topology, including connections to 7 nodes in
the wired network behind the AP. We found no significant
correlation between the traffic from the 29 coexisting wireless
nodes and the connectivity drops between SRs4 and the AP.
Thus, to our surprise, these results were a clear sign that
coexisting devices could not be the root cause of the failure.
A closer observation of the recorded packet traces revealed
a low reception rate of packets sent by the AP. We found
that the position of the monitoring node had a poor antenna
path alignment with respect to the narrow beam width of
the 10 dBi collinear antenna. Most of the packets received
from SRs4 and the AP were indeed corrupted, given that
the content of the packet did not match the frame check
sequence (FCS). To include also these corrupted packets in
our analysis, we used packet pre-processing algorithms to
check the validity of the header information and to correct
bit failures based on preceding packets. We further exploited
the spatial diversity of the three monitoring nodes and cross-
checked the corrupted information across all packet traces.
The enhanced dataset allowed us to identify a discrepancy
between the received signal strength at SRs4 and the number
of disassociation packets sent by the AP. By analyzing the
management frames of the wireless network, we could notice
that the link between SRs4 and AP experienced on average 6
associations and disassociations within 10 seconds. In other
words, SRs4 continuously tried to initialize a connection, but
the AP suspended the connection shortly afterwards. The
reason code field of the disassociation packet indicated a
high packet loss rate caused by low link quality. Interestingly,
a monitoring node placed right next to the AP could receive
packets from all active nodes, including SRs4, with a signal
strength ranging between -70 dBm and -60 dBm, hinting that
the link quality was good and similar across all connections.
However, consolidating a passively measured parameter
and information extracted from within the wireless links
gave a strong indication for a physical difference among the
receive characteristics of the links. Considering the narrow
beam-width of the 10 dBi collinear antenna, the minor
differences within the orientation of the antennas and the
gradually changing height and position of the four bucket-
wheel excavators, the antenna path alignment might have
changed up to a point where the directional antenna of the
AP could barely receive the signal from SRs4. We therefore
suggested a recalibration of the antenna system to reclaim
full connectivity within the mining area. After restoring
the connectivity between the AP and SRs4, we proposed
as a long-term solution the use of omnidirectional wide-
beam antennas and a denser wireless network with additional
repeater nodes to compensate for the lower antenna gain.
Effort.
The troubleshooting was particularly expensive in
terms of personnel. Building the distributed measurement
system involved two research engineers for 20 hours. The
measurements in the kaolinite open-pit mine were escorted
by a miner, who was responsible for the engineers’ safety.
Altogether the measurements required 14 person hours. The
most labor-intensive part was the development of the offline
analysis tools by two research engineers in about 128 hours.
In total, it took about 4 person weeks to find and correct the
failure within the wireless network of the open-pit mine.
IV. DISCUSSION
The previous section highlighted that troubleshooting the
IIoT can be tedious and time-consuming even for wireless
communication experts. We now summarize the key lessons
we learned and describe the methodology we developed while
troubleshooting several industrial wireless networks.
A. Lessons Learned
Lesson 1: Manual approaches don’t scale.
An important
lesson we learned is that almost every failure scenario requires
a comprehensive black-box analysis of the wireless network,
because the information provided by the network operator
about the structure or state of the network and the surrounding
conditions are often outdated or incomplete. Indeed, manual
network planning and site survey procedures are typically
only done once at deployment time, and manual coexistence
management as proposed in industrial guidelines [22], [26]
is too costly for most network operators. A (semi-)automated
failure analysis to obtain complete, up-to-date information
would help minimizing the overall troubleshooting time.
Lesson 2: Fusing data from diverse sources is key.
To
obtain such detailed view, it is necessary to collect accurate,
high-resolution data from diverse sources, clean the data
(e.g., remove observations whose temporal order cannot be
reliably reconstructed), and correlate them with one another.
For instance, we learned from the open-pit mine case that
passive observations can differ significantly from link-internal
parameters due to differences in the hardware components
(e.g., amplifier, antenna, physical-layer implementation) or in
the position of observation, which led to misconceptions that
set us on the wrong track and delayed the troubleshooting pro-
cess. Thus, collecting both passive (i.e., external) and internal
observations across multiple standards and technologies is
an important prerequisites for purposeful troubleshooting.
Lesson 3: Dynamic changes in the environment matter.
Another important lesson we learned is that the surrounding
environment has a strong influence on the wireless network
and thus should never be underestimated. Environments with
moving equipment introduce a strong temporal impact on
the wireless propagation conditions, whilst environmental
effects such as heat and humidity [27], [28] can degrade the
performance of a wireless system and lead to complicated
failure scenarios. A system able to detect and incorporate
these temporal effects as part of the failure analysis would
simplify the engineer’s work and save both time and resources.
At the same time, the monitoring equipment must be rugged
enough to sustain the environmental challenges of the
operational area, including dust, heat, frost, moisture, and
mechanical shocks [29].
Lesson 4: Cheap off-the-shelf hardware is viable.
Finally,
we learned that a home-made solution built from inexpensive
off-the-shelf hardware components can be advantageous. For
instance, using three Raspberry Pi devices we were able to
built a distributed traffic capture system that satisfied our
needs in the open-pit mine case, and in both failure cases an
IEEE 802.15.4 node enabled a sufficiently accurate spectrum
analysis. Compared to commercially available measurement
equipment, these home-made solutions are cheaper, easier to
extend, consume less power, and can be deployed in larger
numbers in a distributed fashion.
B. Methodology
While troubleshooting industrial wireless networks includ-
ing those presented in Sections
III-A
and
III-B
, we developed
a specific methodology to find the root cause of the failure.
Phase 1: Preparation.
In our experience, troubleshooting re-
quires about 0.5 person days (of a wireless networking expert)
of preparation, which involves customizing the monitoring
system for the failure scenario at hand. Important decisions
include whether a distributed or centralized monitoring setup
should be used and which wireless standards and technologies
are expected to be present in the area under investigation.
Phase 2: On-site analysis.
This is the most important phase
and typically requires from 0.4 to 1.6 expert person days. It
consists of six subsequent steps, as described in the following,
along with a breakdown of the approximate working time.
2.1
Observation of the network under study to gain in-
depth knowledge of the environment while focusing
on physical defects of the network equipment (
13 %
).
2.2
Deployment of the monitoring system, which includes
identifying appropriate observation position(s) for the
monitoring node(s) and time-synchronizing the nodes
in case of a distributed setup (7 %).
2.3
Structure analysis using a black-box approach, which
includes spectral analysis to detect CTI and revealing
the topology of all networks in reception range (
32%
).
2.4
Passive link quality analysis of all links between nodes
as an indication for possible failure causes (24%).
2.5
Interference analysis based on spectrum measurements
or analyzing the failure pattern in packet traces (
8%
).
2.6
Correlation of all available information to derive the
root cause of the failure (16%).
Phase 3: Off-line analysis.
This phase is needed if the failure
was not found on-site, which is typically due to one of the two
following reasons. First, unsuitable observation position(s)
have been chosen, resulting in incomplete or erroneous data
recorded by the monitoring node(s). Preprocessing algorithms
may partially solve this problem, thereby improving all sub-
sequent failure analysis algorithms. If preprocessing does not
help, the complete on-site analysis must be redone. Second,
the failure scenario and/or the environmental conditions are
extremely challenging, demanding the development of new
analysis algorithms. For example, developing new packet
analysis scripts typically takes from 0.5 to 2 expert person
days in our experience. Afterwards, the failure analysis may
proceed with any of the steps 2.3–2.6 above.
V. TROUB LES HOOT ING SY STE M FOR INDUSTRIAL
WIRELESS NETWORKS: REQUIREMENTS AND DESIGN
We now derive from the lessons we learned the require-
ments for a failure analysis system that can implement our
methodology, and present a corresponding system architecture
that is tailored to troubleshooting IIoT coexistence problems.
We also describe a modular hardware platform we designed
and currently use for a prototypical implementation of our
proposed failure analysis system architecture.
A. Requirements of Failure Analysis System
A failure analysis system must satisfy 5 key requirements:
Automated:
Steps 2.3–2.6 above can and should be au-
tomated by implementing a blackbox approach without
requiring manual interventions. As a result, the system
reduces the amount of expert knowledge required and
speeds up the troubleshooting process considerably.
Interactive:
On the other hand, failure analysis may
require interacting with the environment, such as repo-
sitioning an object or reconfiguring the network. Thus,
online processing and concurrent analysis algorithms
are needed to allow for an interactive observation of the
continuous data stream from all monitoring devices.
Comprehensive:
The system should detect all coexist-
ing standards and technologies. This includes estimating
the location of all signal sources and recovering the
full network topology, including the role of each node.
Cross-technology observations and analysis should be
performed on multiple network layers. Temporal effects
on wireless propagation (e.g., due to moving parts and
mobile equipment) should also be taken into account.
Flexible:
New standards and technologies appear fre-
quently, leading to a constantly increasing fault tree that
needs to be investigated. Thus, a flexible and extensible
hardware and software design based on a generic multi-
stage analysis structure is essential. The possibility to
reconfigure the monitoring hardware and to modify the
analysis pipeline is a plus. The system should support
a single-node and a multi-node (i.e., distributed) setup.
User-friendly:
To allow use by non-experts, the system
should be easy to set up and configure, and should
present the results of the analysis in an intuitive way.
Packet Preprocessing
Interference Analysis
Structure Analysis
Link Quality Analysis
Causal Analysis
Spectrum
Analyzer
IEEE 802.11
Bluetooth
IEEE 802.15.4
Capture Modules
time
sync.
Mixed NodeMonitoring Node Computation/
Storage Node
Distributed AnalysisCentralized Analysis
Mapping
Figure 6. Architecture of the proposed failure analysis system for IIoT
coexistence problems, and mapping onto centralized and distributed setup.
B. Architecture of Failure Analysis System
Based on these requirements, we detail a concrete hardware
and software architecture for an IIoT failure analysis system.
Global structure.
Figure 6 shows the system architecture.
We partition the data flow into multiple horizontal planes.
Each horizontal plane represents one communication standard
(e.g., IEEE 802.11, Bluetooth, IEEE 802.15.4) or observation
parameter (e.g., spectrum analysis) and hence one class of
transceiver module. The vertical planes are derived from the
system requirements and the analysis stages in Section
IV-B
.
Analysis stages.
The first stage is the Capture Module, fea-
turing a high sampling rate and a source-specific data format.
A high-performance implementation of an information filter
is the main prerequisite to tailor the analysis system to an
embedded hardware platform. Moreover, the capture module
enforces a unified data structure across all horizontal planes.
The Packet Preprocessing stage is mainly responsible for
cleaning up the dataset and generating a tidy data structure.
This includes the reconstruction of corrupted information, a
high-precision time synchronization of all data sources, and
structuring the datasets to facilitate further analysis.
The aim of the Interference Analysis stage is to extract,
join, and process CTI-related information. It correlates the
time-synchronized data streams of all capture modules to
detect packet collisions and all sources of interference.
The Structural Analysis stage leverages a database of ob-
servations to automatically reconstruct the network topology,
including the role of each node in the network, and estimate
the location of all signal sources. Hence, this stage plays a
major role in our proposed black-box analysis.
The Link Quality Analysis stage utilizes the pre-processed
information to estimate various characteristic link quality
parameters and determines the health status of the network.
The Causal Analysis is the final stage. It takes into account
the output of all prior analysis stages across all horizontal
planes using static decision trees as well as supervised and
unsupervised learning algorithms, which facilitates presenting
the failure analysis results in an intuitive way to the user.
Building blocks.
Each stage of the data flow architecture is
based on slender analysis modules within the horizontal plane
of a transceiver module. These building blocks are tailored to
one specific task and are interconnected in a flexible manner
to enable a fast reconfiguration of the analysis architecture.
In this way, we can customize the troubleshooting system for
the failure scenario at hand, perform on-site upgrades of the
analysis structure, and easily extend the software system with
regards to upcoming wireless communication systems. By
specifying a well-defined interface between all vertical planes
it is possible to decouple the development of analysis modules.
To enable rapid prototyping, suitable software libraries and
programming languages (e.g., Python, R) may be used to
implement a module. Even though the architecture primarily
targets on-line analysis, storage modules may be introduced
to save intermediate results for a subsequent off-line analysis.
Interconnection system.
In addition to the block-based
software structure, we propose a flexible networked inter-
connection system that makes it possible to easily map a
failure analysis to a single monitoring node or to create
a distributed failure analysis system. The latter allows the
presence of multiple monitoring and computing/storage nodes
that collaborate within one analysis task, as shown in Figure 6.
Hardware mapping.
The mapping procedure must incorpo-
rate platform-specific dependencies within a heterogeneous
distributed hardware architecture. Capture and packet pre-
processing modules are directly bound to the monitoring
node that includes the appropriate transceiver module. The
hardware requirements of these monitoring nodes may be
tailored towards filtering and pre-processing data on a per-
packet base, which makes it possible to use commercial off-
the-shelf hardware (e.g., wireless sensor nodes or APs running
OpenWRT). Algorithmic analysis modules may require more
processing and storage resources and hence must be mapped
onto more powerful platforms, such as a multicore single
board computer (SBC) with a mass storage device.
Figure 7. Overview of the proposed modular hardware platform.
C. Modular Hardware Platform
We now sketch a concrete hardware platform we have built
in order to implement the aforementioned system architecture.
The platform enables both an affordable implementation of
mobile battery-powered failure analysis instruments and an
integration into a permanent observation system.
In accordance with the requirements in Section
V-A
, we use
a modular hardware platform that is easy to adapt and extend.
Figure 7 provides an overview of the modular platform. It is
based on affordable off-the-shelf components and consists of
three separate modules: (i) a base board including an SBC
of compact form factor, external interfaces (e.g., Ethernet,
HDMI, USB), a mass storage device to record all intermediate
results for off-line analysis, and an indoor/outdoor location
tracking module; (ii) a power unit with a battery lifetime of
at least 5 hours; and (iii) a capture module to plug in standard
transceiver modules (e.g., via PCI Express Mini Card). The
platform has a hand-held form factor and is enclosed by a
rugged metal casing for use in harsh industrial environments.
D. Prototype
As of today, we have developed and commissioned a first
prototype of the proposed hardware platform including the
operating system and its device drivers. We accomplished an
intensive performance evaluation with satisfactory evaluation
results. Currently, a final revision including minor upgrades
with an industrial-suited enclosure is developed. At the same
time, we implemented the interconnection system for the
modular building block based system architecture. So far, the
communication framework is finished and tested on an
IA-64
system. We implemented an IEEE 802.11 capture module in
C++ and a corresponding packet preprocessing module in
Python. In addition, we created a prototype for the structure
analysis module in Python.
In a next step, we will finalize the remaining stages of the
Wi-Fi
plane. Afterward, we will test the performance of this
plane on our ARM-based hardware platform. We also intend
to extend our analysis framework by adding all required
modules for the other wireless communication standards in
order to enable an improved and user-friendly troubleshooting
process of coexisting wireless IIoT systems.
VI. REL ATE D WORK
Wireless coexistence is a known problem in communi-
cation networks and attracted a large body of work. Many
researchers have studied the coexistence problems in the ISM
frequencies used by common IoT devices [9], especially in the
crowded 2.4 GHz band [17], [30]. Most of these studies have
focused on the coexistence among two wireless technologies,
typically between IEEE 802.11 and IEEE 802.15.4 [31]–
[33] or between IEEE 802.15.1 and IEEE 802.15.4 [34].
Others have analyzed how application-specific technologies
can tolerate interference, e.g., body area networks [35]–[37]
and industrial systems based on Wireless HART [38]. All
these works highlight how strongly co-existing devices may
affect the performance of a wireless networks, but specifically
focus on a few exemplary technologies and do not provide
solutions on how to enable coexistence in general.
Bluetooth was one of the first systems trying to minimize
its interference on coexisting wireless technologies. Adaptive
frequency hopping was introduced to dynamically change
the hopping sequence of Bluetooth devices, thereby allowing
other systems such as
Wi-Fi
networks to use the remain-
ing frequency channels [9]. Although choosing orthogonal
channels is a very simple but effective solution to enable
coexistence, it becomes rather ineffective when multiple
networks in the surroundings cover the whole ISM band.
Another body of works has studied how to actively ensure
coexistence of heterogeneous wireless systems. Gummadi
et al. [39] have proposed a coordination mechanism that
uses multi-technology gateways and an expressive policy
language to ensure coexistence between
Wi-Fi
, Bluetooth
and IEEE 802.15.4 devices. An alternative approach consists
in using indirect coordination by transmitting busy tones
or special carrier signaling pulses for preventing another
wireless technology to interfere [40]–[42] or by modulating
the payload length to encode channel access parameters [43].
FreeBee [44] is an example of indirect cross-technology com-
munication among
Wi-Fi
, ZigBee, and Bluetooth obtained
by shifting the timing of periodic beacon frames without
incurring extra traffic. Similarly, other works have aimed to
establish a communication link between different wireless
technologies to ensure coexistence, especially between
Wi-Fi
and IEEE 802.15.4 devices [45]–[48]. Most of these works,
however, target two or three specific technologies (typically
Wi-Fi
and IEEE 802.15.4), but do not provide a generic
solution to the coexistence problem in a given ISM band.
Nevertheless, all these efforts show an increasing trend to
ensure that the wireless networking standards operating in
the same frequency band contain mechanisms to actively
communicate and prevent causing harmful interference to
each other, an objective shared by the newly-established
IEEE 802.19 coexistence working group [49].
Despite these increasing efforts in ensuring co-existence
between wireless technologies, the vast majority of wireless
devices is deployed in the wild and still interferes with
each other in an “anarchic and arbitrary manner” [39],
causing a large number of network and deployment failures.
For this reason, troubleshooting of wireless networks has
been a hot research topic. Several solutions have been
proposed by the wireless sensor networks community to
simplify network debugging, the majority of them addressing
passive monitoring of packets [50] to verify the health of
the network [51], to reconstruct network dynamics [52], or
to infer complexity bugs [53] and root causes of abnormal
phenomena [54]–[56]. All these systems, however, analyze
local traffic from the deployed sensor networks and lack
information about the influence of co-located devices using
other technologies, practically making it difficult to debug
and study actual coexistence problems.
Similar problems exist outside the wireless sensor networks
community: a number of works has, for example, proposed
monitoring and debugging systems for
Wi-Fi
. Cheng et
al. [57] have proposed Jigsaw, a system using multiple
monitors to provide a unified view of physical, link, network
and transport-layer activity. Sheth et al. [58] have proposed
fine-grained detection algorithms that are capable of distin-
guishing between root causes of wireless anomalies at the
depth of the physical layer of IEEE 802.11 systems. Finally,
RFDump [59] is a software architecture for monitoring
packets on heterogeneous wireless networks running on off-
the-shelf (but expensive) software-defined radios.
In contrast to this body of literature, our work proposes a
low-cost battery-powered system for failure analysis tailored
to an automated and cross-technology troubleshooting of
wireless coexisting systems. Based on our experience and
the lessons we have learned in the time-consuming process
of troubleshooting wireless coexistent networks, we also em-
bedded specific features tailored towards a user-friendly and
simplified observation of multiple communication standards
and unknown sources of interference in the proposed device.
We believe that the proposed system can augment several
of the aforementioned debugging tools, simplifying and
speeding-up troubleshooting in industrial wireless networks.
VII. CONCLUSIONS
Troubleshooting a failure is an almost unavoidable task
during the lifetime of a real-world wireless network. CTI
caused by the increasing number of wireless technologies and
devices within the unlicensed ISM frequency bands increases
the complexity of this task, making it difficult to identify and
eliminate failures, especially in safety-critical environments.
In this paper, we have shared our experiences by reporting
on two exemplary cases and the lessons we learned from
troubleshooting industrial wireless networks. We propose
a generic system architecture and hardware platform that
enables a systematic, automated troubleshooting of wireless
coexistence problems in the IIoT. One of the key features of
the proposed system is its ability to simplify the observation
of heterogeneous wireless communication standards and
unknown sources of radio interference in the surroundings.
This work represents a first step towards simplifying the
troubleshooting of coexistence problems in the IIoT. We ex-
pect the research community to follow up on this work in the
near future and propose novel systems to predict connectivity
and automatically debug safety-critical wireless networks
in the presence of coexisting wireless technologies. At the
same time, we anticipate an increasing effort in designing
strategies aiming to ensure cross-technology coexistence by
means of active/passive communication as well as distributed
coordination among heterogeneous wireless technologies.
Acknowledgments.
This work was supported by the project
“fast automation” and the Federal Ministry of Education and
Research of the Federal Republic of Germany (BMBF) within
the initiative “Region Zwanzig20” under project number
03ZZ0510A, and by the German Research Foundation (DFG)
within the Cluster of Excellence “Center for Advancing
Electronics Dresden” (CFAED).
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Thesis
Wireless technology is a key enabler of the promises of Industry 4.0 (Smart Manufacturing). As such, wireless technology will be adopted as a principal mode of communication within the factory beginning with the factory enterprise and eventually being adopted for use within the factory workcell. Factory workcell communication has particular requirements on latency, reliability, scale, and security that must first be met by the wireless communication technology used. Wireless is considered a non-ideal form of communication in that when compared to its wired counterparts, it is considered less reliable (lossy) and less secure. These possible impairments lead to delay and loss of data in industrial automation system where determinism, security, and safety is considered paramount. This thesis investigates the wireless requirements of the factory workcell and applicability of existing wireless technology, it presents a modeling approach to discovery of architecture and data flows using SysML, it provides a method for the use of graph databases to the organization and analysis of performance data collected from a testbed environment, and finally provides an approach to using machine learning in the evaluation of cyberphysical system performance.
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An important factor contributing to the degradation and variability of the link quality is radio interference. The increasingly crowded radio spectrum has triggered a vast array of research activities on interference mitigation techniques and on enhancing coexistence among electronic devices sharing the same or overlapping frequencies. This chapter gives an overview of the interference problem in low-power wireless sensor networks and provides a comprehensive survey on related literature, which covers experimentation, measurement, modelling, and mitigation of external radio interference. The aim is not to be exhaustive, but rather to accurately group and summarize existing solutions and their limitations, as well as to analyse the yet open challenges.