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6 IEEE INDUSTRIAL ELECTRONICS MAGAZINE ■ DECEMBER 2018
I
ndustrial wireless has great poten-
tial to improve the monitoring and
control of various processes and
equipment in distributed automa-
tion systems because of advances
in wireless networks and instal-
lation flexibility. However, harsh
industrial environments and interfer-
ence from the crowded electromagnetic
frequency spectrum make it challenging
for wireless to achieve the required per-
formance. Thus, it is important to under-
stand the benefits of wireless in certain
industrial settings, the approach for reli-
able operations, the technologies to be
used, and the best practices for success-
ful deployment.
With the increase in the use of exist-
ing wireless technologies in various ap-
plications in industrial environments,
Industrial Wireless
Systems Guidelines
Practical Considerations and Deployment Life Cycle
RICHARD CANDELL,
MOHAMED KASHEF,
YONGKANG LIU,
KANG B. LEE, and
SEBTI FOUFOU
Digital O bject Identif ier 10.1109/MIE .2018.2873820
Date of pu blication: 19 Decemb er 2018
©istockphoto.com/metamorworks
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DECEMBER 2018 ■ IEEE INDUSTRIAL ELECTRONICS MAGAZINE 7
manufacturers need help in making
confident decisions when installing ap-
propriate wireless technologies, based
on their operating requirements. Hence,
the purpose of this article is to present
our approach for industrial wireless
system implementations by discussing
a number of phases of the deployment
life cycle. This approach considers vari-
ous industrial settings, including manu-
facturing, oil and gas refineries, chemical
production, and product assembly. We
then examine the problems and tech-
nology spaces for industrial wireless.
Furthermore, we discuss various objec-
tives and success criteria in deploying
this technology.
The Need for Industrial
Wireless Systems
Along with the advances in wireless
devices for the Internet of Things (IoT)
and cyberphysical systems, the use of
industrial wireless has continued to
grow at a rapid pace. It has come to of-
fer great possibilities for deployment
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8 IEEE INDUSTRIAL ELECTRONICS MAGAZINE ■ DECEMBER 2018
in industrial automation, including pro-
cess control, discrete manufacturing,
and safety systems. Among the advan-
tages of applying wireless technologies
in industrial systems for monitoring
and control of equipment and process-
es are the enabling of configuration
flexibility, supporting mobility, and
eliminating costly cabling. Moreover,
industrial wireless allows for easier net-
work expansion to improve productiv-
ity and efficiency.
Wireless technology has been used
in industry for many years in the li-
censed spectrum bands for interfer-
ence-free wireless transmissions, which
ensures flexible networking and reliable
over-the-air data communications for
plant process monitoring and control.
In recent years, license-free spectrum
bands, such as the industrial, scientific,
and medical (ISM) band, have pro-
liferated and provided industry with
more options for building wireless sys-
tems, such as wireless networking with
mobile workers and wireless sensor
net
works for process optimization
and asset management. However, the
explosive growth of such technolo-
gies has encountered many challenges,
such as interference, congestion, and
spectrum planning, especially in large
factories with dense deployments and
harsh settings.
The adoption and use of wireless
technologies has often been hampered
by the perceived lack of reliability, in-
tegrity, and security in wireless links.
The factory is usually rich in metallic
surfaces and obstructions, which re-
sult in a harsh radio-wave propagation
environment [1]. Given these concerns,
many questions a re often asked, such as
which wireless technology is best suited
for an industrial application and what
approach should be taken to ensure re-
liable operation. Having an appropriate
understanding of the capabilities and
applications of wireless technologies
allows potential users to recognize the
benefits of wireless while avoiding the
problems of misapplication in challeng-
ing industrial situations in which many
potential physical obstructions and
sources of interference exist.
Industrial wireless practitioners and
researchers recognize the need for
guidelines that will help manufacturers,
users, and their suppliers to effectively
design, assess, select, and deploy wire-
less systems. Manufacturing organiza-
tions often use ad hoc methodologies to
install and manage their wireless net-
works without first understanding their
own problem space, selecting appropri-
ate candidate technologies, and devel-
oping a plan for spectrum management
and growth. Ad hoc methods are usually
suitable for smaller organizations, where
engineering resources are limited. We
believe that industrial wireless systems
are best deployed and managed when
organizations apply an incremental,
systematic, and vendor-independent ap-
proach to selecting and implementing
their wireless networks, using engineer-
ing best practices and policies.
The approach described in this ar-
ticle for choosing and deploying wire-
less systems in the factory reflects best
practices developed by the Industrial
Wireless Systems Technical Working
Group (IWSTWG), managed by the Na-
tional Institute of Standards and Tech-
nology (NIST). The IWSTWG was estab-
lished in March 2017 at the IEEE Sensors
Applications Symposium Workshop on
Industrial Wireless sponsored by NIST
and technically supported by the IEEE
Industrial Electronics Society and IEEE
Instrumentation and Measurement So-
ciety [2], [3]. A guidelines document
was released in May 2018, and details
of our approach were discussed in [4].
The Industrial Wireless
Problem Space
Having a comprehensive understanding
of the problems and potential solutions
of wireless networks in manufacturing
industries significantly eases the chal-
lenges of selecting and deploying wire-
less solutions. Identifying industrial use
cases not addressed by existing wire-
less technologies can lead to targeted
growth through technological innova-
tion using new or existing wireless stan-
dards. Motivated by industry’s need for
independent, best-practice guidelines
and solutions to difficult wireless con-
trol problems, we begin by providing a
taxonomy of industrial problem catego-
ries where wireless networking tech-
nologies may be deployed, followed by
our perspective on the state of the art
of relevant wireless standards.
Industrial Applications
Applications for industrial wireless are
quite diverse, and we begin with a short
survey of the application areas of indus-
trial networks. These areas have been
discussed previously in various articles
[5]–[10]. However, most of these works
do not comprehensively cover the en-
tire industrial wireless problem space.
In [5]–[7], industrial wireless networks
were divided into various levels, to in-
clude field equipment and instrumenta-
tion, control and automation systems,
and supervisory and management
systems. In [8] and [9], the focus for ex-
amining wireless technologies was on
factory instrumentation. In [10], the au-
thors provided a comprehensive analy-
sis of the problem space and a mapping
between the industrial problem space
and the existing wireless solutions.
To map a specific wireless technol-
ogy to an application area, we first
divide the problem space into a man-
ageable set of classes based on the
functions performed in a factory. Here,
the term factory refers to both the plant
that is often identified with discrete
job-based manufacturing and the en-
tity that is more often characterized by
flow-based processes. We refer readers
to the Purdue Enterprise Reference Ar-
chitecture (ANSI/ISA-95) [11] for more
information on the functions of the
factory. For the sake of our scope, we
limit our perspective to the operational
aspect of the plant from the physical
ground of the factory up to the enter-
prise interface. Our classification of
the industrial problem and technology
spaces is shown in Figure 1. The pos-
sible industrial wireless applications
Industrial wireless allows for easier network
expansion to improve productivity and efficiency.
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DECEMBER 2018 ■ IEEE INDUSTRIAL ELECTRONICS MAGAZINE 9
within the plant can then be described
as follows:
■Manufacturing instrumentation: This
class includes the transmission of
measured variables by sensors and
manipulated variables to actuators.
This functional area often requires
deterministic latency and reliabil-
ity as well as interface compatibility
with legacy industrial communica-
tions protocols. The latency and
reliability requirements will vary
depending on the consumers of fac-
tory information, such as the auto-
mation system and optimization ap-
plications. Table 1 lists the latency,
loss, and scale requirements of typi-
cal classes of manufacturing instru-
mentation applications. Latency is
defined as t he application layer delay
in one direction, such as between a
sensor and a controller. Reliability is
defined as the likelihood that a block
of application layer information is
lost either from unacceptable delay
or noise. Our guidelines adopted the
IEC 62264 (ANSI/ISA 95) definitions
of flow-based and job-based manu-
facturing systems [11]. Flow-based
systems include those in which raw
materials are in continuous motion,
such as oil refineries, water treat-
ment plants, and chemical reactors.
Job-based systems are those that
manufacture or assemble products
in discrete steps, such as an automo-
tive assembly line. Because indus-
trial applications are all unique and
as such have unique latency and reli-
ability requirements, it can be diffi-
cult to validate these specifications.
Often, the requirements presented
by industrial device manufacturers
can appear daunting and, therefore,
should be viewed as a range or up-
per bounds on performance. Sourc-
es for performance specifications of
industrial networks may be found in
[2] and [12]–[14]. The performance
requirements provided in [15] are
based on experimental results of rep-
resentative use cases employing test
beds. While experimental outcomes
based on test beds are useful and
necessary, each use case is not sta-
tistically representative of an entire
class of industrial systems. The data
in Table 1 are, therefore, a product
of academic sources, collaborations
within industry through our technical
working group, and the professional
experience of the authors.
■Personnel safety: This area includes
all applications with the sole purpose
of preventing accidents or injury. All
types of accidents are included, such
as slips, trips, and falls of workers;
air-quality monitoring; gas leakage de-
tection and control; and robot-related
accidents. Safety applications can be
further divided into safety monitoring
and safety integration systems (SISs).
Safety monitoring systems, such as
those that check for gas leakage, can
tolerate appreciable delay and loss;
however, communications within
SISs must achieve very low latency
and ultrahigh reliability and resilience
to be trusted. Wireless is rarely used
as a primary communication channel
for SISs mostly because of the impact
of interference, so this presents an op-
portunity for research.
■Backhaul connectivity: Backhaul com-
munications include data trans-
missions between factory floors
and data centers, between various
buildings in a plant, and remotely
to headquarters in different cities
or countries. This class is typically
characterized by the large amounts
Industrial Wireless
Problem Space
• Manufacturing
Instrumentation
• Personnel Safety
• Backhaul Connectivity
• Tracking
• Security and Surveillance
• Remote Assets
• Maintenance Support
Industrial Wireless
Technology Space
• Home and Office
• Instrumentation
• Wide-Area Sensing
• Esoteric
FIGURE 1 – The industrial wireless problem and technology spaces.
TABLE 1 – A LIST OF TYPICAL RELIABILITY AND LATENCY REQUIREMENTS FOR INDUSTRIAL SYSTEMS.
APPLICATION CLASS LATENCY, lLOSS PROBABILITY, r DEVICES, S
Monitoring
l1s
1r10 5
1-
s10,0001
Supervisor y control
Flow based
l100 ms1
r10
6
1-
s301
Job based
l100 ms1
r10
7
1-
s101
Feedback control
Flow based
l1s0
1
r10 6
1-
s1, 0001
Job based
l10 ms1
r10 7
1-
s101
Safety alarming
l1s
1r10 7
1-
s101
Wireless is rarely used as a primary communication
channel for SISs mostly because of the impact
of interference, so this presents an opportunity
for research.
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10 IEEE INDUSTRIAL ELECTRONICS MAGAZINE ■ DECEMBER 2018
of transferred data. Various wire-
less technologies for backhaul con-
nectivity are discussed in [16].
■Track in g: In this class of application,
wireless is used for tracking, local-
ization, and identification of materi-
als, inventory, personnel, and tools.
The characteristics and applica-
tions of various tracking, localiza-
tion, and identification technologies
are discussed in [17].
■Security and surveillance: Tran sm is-
sions of voice, video, and identification
information related to factory floor
security are included here. Commer-
cial technologies, such as Wi-Fi, are
often used when wires are cost pro-
hibited; however, their coexistence
with other industrial wireless trans-
missions, such as with factory instru-
mentation, needs careful spectrum
planning before deployment.
■Remote assets: The use of wireless
communications in remote moni-
toring and control significantly re-
duces cost. The goal of remote mon-
itoring and control, especially in
flow-based industries, is to improve
the operation of remote sites while
reducing labor, transportation, and
installation costs. Security is often
one of the major concerns for this
class of application because of the
geographical remoteness.
■Maintenance support: This class in-
cludes the communication of ma-
chine health and status; environment
control, such as heating and air con-
ditioning; and software and firmware
updates of various components. De-
pending on the amount of transferred
data and the risk level, various wire-
less technologies can be used. Re-
cently, augmented reality has been
utilized for maintenance supports
that require significant bandwidth
and high reliability, but it provides a
valuable resource for on-the-job train-
ing and maintenance support [18].
State of the Art of Industrial
Wireless Networks
A similar classification can then be ap-
plied to the types of industrial wireless
networks. This segmentation of the in-
dustrial wireless space can be helpful
in determining the appropriateness of
specific wireless products for an app-
lication. The adoption, development,
and application of wireless communica-
tion technologies in industry have been
discussed recently in the literature [2],
[19]–[25]. In [2] and [19], applications
relevant for industrial wireless systems
in the Industrial IoT (IIoT) were covered,
and a survey of a variety of wireless
technologies for industrial implementa-
tions was introduced. In [20], the use of
wireless communications for automa-
tion areas was briefly discussed, where
the corresponding special section in-
cluded papers dealing with new appli-
cation scenarios and new technologies
for wireless-critical communications.
In [21], a survey of industrial wireless
system requirements was presented,
and possible matches between indus-
trial applications and existing wireless
technologies were explored. In [22]–
[25], opportunities brought in by intro-
ducing IIoT and the challenges for its
realization were highlighted. Moreover,
technological trends were reviewed and
the standardization of IIoT connectivity
solutions was examined.
As a result, we have identified pat-
terns of the industrial wireless net-
work technology space (Figure 1), and
they are as follows:
■Home and office: This domain in-
cludes standards-based communi-
cations systems typically found in
office environments but that may
be useful in factories. They include
IEEE 802.11 variants and Wi-Fi–
compliant devices.
■Instrumentation: These are systems
specifically designed for building
automation, factory instrumen-
tation, and the IoT. Standards, such
as ISA 100.11a, WirelessH ART, ZigBee,
and Bluetooth Mesh Networking, fall
into this category. International stan-
dards include the Wireless Networks
for Industrial Automation-Factory
Automation (WIA-FA) and Process
Automation (WIA-PA), or IEC 62948
and IEC 62601, respectively. Many ex-
ceptional proprietary options based
on familiar low-cost chipsets exist for
industrial practices, including strong
encryption, frequency hopping, and
coexistence management.
■Wide-area sensing: Some applica-
tions require the ability to transmit
over long distances with minimal
power to conserve battery life for
sensing and control. These technol-
ogies vary in bandwidth and trans-
mit power but share the common el-
ement of a low information bit rate,
e.g., LoRaWAN and SigFox.
■Esoteric: This area includes such tech-
nologies as radiating coaxial cable
and troposcatter solutions that are
designed for applications that have
unique requirements and mandate a
solution that cannot be met with off-
the-shelf alternatives. This category
may encompass such systems as sat-
ellite, cellular, optical (visible light),
and land-mobile radio networks.
In Table 2, we present a mapping be-
tween major industrial applications
and current or emerging wireless tech-
nologies. This table is an abbreviated
version of the one presented in [10].
Practical Guidelines for the
Wireless Life Cycle
Based on the wireless problem space,
applications, and availability of the in-
dustrial wireless networks discussed,
let us now examine how we developed
the guidelines for the entire wireless
life cycle.
Success Criteria
System availability is paramount for
any operational systems, such as a
factory. Prior to embarking on any
industrial/plant upgrade or enhance-
ment, the objectives and success cri-
teria should be clearly defined. The
success criteria involve developing
one or more enterprise objectives and
Prior to embarking on any industrial/plant upgrade
or enhancement, the objectives and success criteria
should be clearly defined.
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DECEMBER 2018 ■ IEEE INDUSTRIAL ELECTRONICS MAGAZINE 11
should not be viewed as a threshold
of technical performance. The objec-
tives of an industrial wireless deploy-
ment should be discussed in the early
stage of the deployment life cycle and
be reviewed throughout. The success
criteria relate to the operational per-
formance of the factory rather than
the network itself.
The economics, risks, and physics
of a factory can play important roles
in the selection of the technologies
to be applied. For example, wireless
is typically preferred for applications
with moving objects, areas with lim-
ited physical access, situations where
wires can cause physical hazards, or
site layouts that change frequently.
Wireless can also be used to handle
operational regulatory compliance for
remote installations, e.g., addressing
safety regulatory requirements for
distant wellheads, where wired solu-
tions could be cost prohibitive. Before
beginning a factory improvement in-
volving wireless technology, we sug-
gest that the upgrade answer at least
one of the criteria illustrated in Fig-
ure 2. These success criteria include
the following:
■Reliability: Will the introduction of
wireless improve the ability of the
plant to conform to the required
function or mission under the spec-
ified production conditions?
■Safety: Are people or equipment
made safer? Will wireless improve
the ability of employees to perform
their jobs free from recognized
hazards, including falls, hazardous
energy, confined spaces, ergonom-
ics, or dangerous materials?
■Efficiency: Will the technology im-
prove the ability to lower or meet
target operating and production costs
or reduce maintenance costs?
■Quality: Will wireless be used to im-
prove the ability of the factory to
produce products within specified
design tolerances or to be able to
demonstrate design conformity?
■Environment: Will the introduction
of the technology improve environ-
mental stewardship by better mon-
itoring the industrial process and
preventing industrial accidents or
minimizing pollution?
TABLE 2 – THE APPLICABILITY OF WIRELESS TECHNOLOGIES.
PROC ESS
MONITORING
SUPERVISORY
CONTROL
FEEDBACK CONTROL
FACTORY
MONITORING
ASSEMBLY: SENSING
AND ACTUATION
QUALITY
INSPECTION
FALL PREVENTION
CONFINED SPACES
NEARBY OR INDOOR
DISTANT
MATE RIALS
TOOLS
PERSONNEL
VOICE AND VIDEO
GROUNDS CONTROL
SPECTRUM
MONITORING DATA
MONITORING
MACHINE HEALTH
MONITORING
BUILDING
AUTO MATION
AUGMENTED
REALITY
FLOW BASED JOB BASED SAFETY BACKHAUL TRACKING SECURIT Y REMOTE MAINTENANCE
Home and office IEEE 802.11
◐◐◐◐
777
◐ ◐
IEEE 8 02.15 .1
◐◐ ◐ ◐◐ ◐
Instrumentation IEEE 802.15.4 TDMA
◐¹◐¹◐
7 7
◐◐
IEEE 802.15.4 CSMA
◐ ◐ ¹◐¹◐
7 7
◐◐◐
IEEE 802.11 TDMA
ÛÛÛÛÛÛÛÛ
------
Û
-
Û Û Û
-
Wide-area
sensing
2G/3G
◐ ◐ ¹ ¹ ◐ ◐ ◐ ◐
4G
◐ ◐ ¹ ¹ ◐ ◐ ◐
5G
ÛÛÛÛÛÛÛÛÛÛÛÛÛÛ Û Û Û Û
VLBR WAN
¹¹¹ ◐◐◐¹ ◐ ◐
Esoteric Geostationary
◐ ◐ ◐ ◐ ◐
Low-earth orbit
◐ ◐ ◐◐ ◐ ◐ ◐ ◐ ◐
Free-space optics
◐◐◐◐ ◐ ◐
: technology fully supports problem domain;
◐
: suppor ts problem domain with practicality, throughput, latency, reliability, or energy limitations;
7
: energy requirements of assumed battery-powered devices prevent
applicability;
¹
: latency prevents applicability;
: throughput prevents applicability;
Û
: emerging technology or evolution may support problem domain;
: not recommended; -: not considered by authors.
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12 IEEE INDUSTRIAL ELECTRONICS MAGAZINE ■ DECEMBER 2018
■Government regulation: Will
wireless technology enable
demonstration of compliance
with applicable safety and en-
vironmental laws?
Wireless Deployment Life Cycle
Here, we discuss the various stag-
es of the installation life cycle
and the application-specific de-
tails that may be mentioned at
each of these stages. Also, we ex-
plore the general steps to be per-
formed at each phase, along with
expected outputs, and highlight
the importance of performing
these operations. It is good to un-
derstand that we do not recom-
mend that organizations modify
their engineering procedures to
strictly follow our recommen-
dations. The guidance is meant to in-
form and improve the engineering
practices within an organization where
enhancement is needed. The process
is not expected to be serialized but
can and should overlap and iterate to
include feedback and discussion by
stakeholders. The key takeaway from
our guidelines is the education of fac-
tory engineers in wireless principles
and best practices as applied to their
own enterprise objectives.
A general project life cycle includes
multiple stages to achieve the f inal goal
of having successfully deployed indus-
trial wireless networks satisfying the
functional requirements defined. Our
life cycle is shown in Figure 3. The first
step in the process is to define the objec-
tives and success criteria and consider
whether a wireless system is requir ed.
Next is the fact ory s ur vey, in w hich
factory operations and surrounding en-
vironments are analyzed comprehen-
sively to determine the requirements for
candidate solutions. Once the sur vey has
been performed and its data are readily
available, a set of candidate solutions
should be identified based on a com-
plete set of requirements, and one or
more such solutions should be selected
for deployment through a merit-based
selection process. A solution should then
be developed, perhaps simulated, and
tested. Performance should be validat-
ed against the original requirements
and design intent, and an installa-
tion plan should then be devised.
The result is a deployed solution
that meets the technical require-
ments and effectively addresses
the original business goals.
Each of these life-cycle phases
may include a variety of stake-
holders. Specifically, defining ob-
jectives may include management
and engineering staff; candidate
comparisons and design may draw
in design and implementation
engineers; and deployment may
involve both communications and
factory floor engineers, operators,
and technicians. Then, operators
deal with networks in the later
stages, during monitoring and
maintenance. To be practical, the
guidelines must be suitable for
the educated factory technician or
engineer, and they must be relatable to
managers, who need to perceive their
value to the enterprise. Overall, they
must include required details without
burdensome mathematics or techni-
cal jargon.
Defining Objectives
In this stage, success criteria are con-
sidered by the stakeholders to justify
the need for industrial wireless de-
ployment. The objectives should be
clearly enumerated and communica -
ted to all of the stakeholders. Re-
al istic technological expectations are
defined for the wireless ut ilization
and communicated to stakeholders.
Success
Criteria
Reliability
Safety
Efficiency
Quality
Environment
Regulations
FIGURE 2 – The success criteria for industrial wireless systems.
Define Objectives
• Define Purpose
• Manage Risk
• Control
Expectations
• Involve the
Stakeholders
Factory Survey
• Spectrum
Utilization
• Interference
• Factory Model
• Software
Select Candidates
• Vendor Survey
• Validation
• Band Selection
• Training
• Regulatory
Design a Solution
• RF Survey
• Spectrum
Allocation
• Simulation
• Testing
Deploy and Monitor
• Iterative
Deployment
• Spare Parts
• Monitoring
FIGURE 3 – The life-cycle components of wireless systems deployment.
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DECEMBER 2018 ■ IEEE INDUSTRIAL ELECTRONICS MAGAZINE 13
Thus, it should be clear that wireless
technologies cannot serve all appli-
cations. Objectives based on the suc-
cess criteria should be measurable,
and measurement methods should be
selected and approved at this early
stage. We recommend that an iterative
approach to deployment be adopted
within an organization new to wire-
less, which is shown in Figure 4. This
will be helpful in managing expec-
tations and giving the stakeholders
time to adapt to the new technology
within their operation.
Two important factors should be
included in the objectives study: risk
assessment and future growth. The
mention of wireless is usually coupled
with concerns over lack of security and
resilience, which can lead to factory
downtime and economic loss. Security
and resilience may impact a number of
the success criteria, depending on the
way they affect t he industrial operation.
As an example, if a machine in a produc-
tion line goes down, both reliability and
efficiency are impacted, while having a
machine that is not operating within de-
sign tolerances may impact the quality
of the manufactured product. Hence, a
risk assessment should be performed
to assure a secure and resilient wireless
system implementation. Future growth
plans should be considered in paral-
lel with wireless deployment, evaluat-
ing the scalability of current wireless
technologies to achieve future business
goals. A general reference for wireless
applicability is given in Table 3.
Factory Survey
Many organizations lack a complete
awareness of their own factory opera-
tions. This is primarily due to obsolete
tools and technologies, employee turn-
over, retirement, and general complex-
ity. More often, operations are cap-
tured in paper-based blueprints, which
are difficult to analyze without human
knowledge that may have been lost.
Therefore, it is important to assess the
factory operation early in the deploy-
ment life cycle to understand the opera-
tion, identify process points of interest,
and, if safety or control of any kind is
to be implemented, what systems may
be affected. The factory survey should
identify all machines, processes, design
artifacts, programs, and applications to
be affected by the deployment.
The physical environment and de-
tailed layout of the deployment site
should be reviewed, including the ma-
terials used in the construction of its
components. The measurement and
control points are those between which
the data will be transferred and hence
need to be determined accurately. Data
collection systems and management
locations should be identified. Further-
more, after defining the data points,
the process variable specifications are
needed to determine the deployed net-
work requirements. These specifica-
tions should include the amount of data
transferred, process variable change
rates, delay tolerances, and criticality
to the site operations.
The results of the survey must also
include the operational models and safe-
ty requirements, especially when wire-
less is to be used for safety or control.
Operational models define all abstract
models, such as equations, simulations,
and statistics, that help in predicting the
industrial operation behaviors. As a re-
sult, the wireless network performance
can be validated later through simula-
tion, using these models. The safety
requirements are identified to limit the
selection of wireless devices to those
that satisfy the safety requirements,
including the intrinsic safety of the de-
vices themselves.
Candidate Selection
Once the objectives are defined and a
factory survey has been conducted,
candidate solutions can be pursued. It
is possible to consider candidates ear-
lier in the process by consulting indus-
trial wireless vendors for possibilities.
Technology vendors and system inte-
grators are a valuable resource for fac-
tory engineers to learn about options
for technology and factory improve-
ment. Relationships will form between
factory representatives and the ven-
dors themselves, often to the benefit
of the factory enterprise. Vendors will
sometimes provide systems engineer-
ing guidance that can be quite valuable
to factory engineers. Despite these ben-
efits, it is important that vendors not
too heavily influence the requirements
analysis phase. Candidate selection is
better conducted based on an objec-
tive assessment of the available solu-
tions to satisfy the requirements. The
requirements used for evaluation will
Objectives
Factory
Study
Candidate
Selection
Solution
Design
Deployment
FIGURE 4 – An iterative wireless deployment
life cycle.
TABLE 3 – THE GENERAL APPROPRIATENESS
FOR INDUSTRIAL WIRELESS APPLICATIONS.
APPLICATION
GENERAL
RECOMMENDATION
Factory and building
monitoring, IIoT
Yes
Condition alarming Ye s
Supervisor y control Yes
Feedback control
backup to wired
Yes
Feedback control
primary
Possible
Safety monitoring and
alarming
Possible
Personnel safety Possible
Safety integrated
systems
Possible
The economics, risks, and physics of a factory
can play important roles in the selection of the
technologies to be applied.
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14 IEEE INDUSTRIAL ELECTRONICS MAGAZINE ■ DECEMBER 2018
encompass the technical and nontech-
nical alike. Technical requirements will
include such items as supported radio-
frequency (RF) bands, data reliability,
latency, throughput, system availabil-
ity, safety, equipment failure rates, cy-
bersecurity, interoperability with other
systems, and costs of ownership.
System options are identif ied through
vendor consultation, Internet searches,
data sheets, trade shows, conferences,
and word of mouth. Candidates’ capa-
bilities are validated against the various
requirements to compare those alterna-
tives that fulfill the requirements. Valida-
tion can be achieved through analysis,
simulation, and testing, depending on
the procedures’ complexity, the avail-
able budget, and the technical expertise
of the engineering staff. Tools, such as
simulation and hardware-in-the-loop
test beds, are valuable resources to le-
verage for assessing highly important or
high-risk requirements.
Finally, candidates are compared
and ranked to select the best one to be
deployed. We recommend that a score-
card selection method be used to create
a hierarchy of the alternatives for making
an informed and unbiased decision. In
this method, each option is qua ntit ively
graded for achieving various technical
requirements. Then, mathematical and/
or logical operations are applied based
on the defined objectives to rank, com-
pare, and choose the most suitable can-
didate. An example of a scorecard can be
found in Appendix A-4 in [4].
Solution Design
By this point, a candidate has been
selected, and now a complete solution
must be designed and implemented.
This phase entails all of the elements
of engineering design, to include de-
vice placement, antenna selection and
positioning, software development for
controllers and noncontroller comput-
ing platforms, databases, interfaces,
design validation, and testing. This list
is not exhaustive. Because the focus of
the design is wireless, frequency plan-
ning and topology selection are again
considered, given data sheets and
design decisions. The enterprise spec-
trum management plan must be up-
dated with new frequency allocations
and spectral-monitoring techniques.
Internal and external regulatory elec-
tromagnetic spectrum approvals must
be obtained at this juncture in the
wireless deployment life cycle.
Quality of Service (QoS) analyses
are performed to determine if the wire-
less design can meet the requirements.
Validation of the design is usually con-
ducted iteratively through best engi-
neering practices. Channel utilization
planning, interference effects, and cov-
erage cells should be planned based
on the various data points of an indus-
trial site. A QoS analysis can then be
conducted to verify through testing or
simulation that coverage and, ultimate-
ly, network QoS can be achieved as ex-
pected. Commercially available modifi-
cation request systems can be used to
track and report the results of various
testing phases and all of the modifica-
tions made to correct the problems.
Verification of the design can be
costly for a large-scale deployment.
Thus, simulation can be a cost-effec-
tive way for experimenting with dif-
ferent topologies, though it cannot
replace testing with real devices. Sim-
ulation requires technical expertise in
modeling, simulation tools, and com-
puter programming often not available
within small or medium-size enterpris-
es. Simulation expertise can be made
available through systems integrators,
device manufacturers, universities,
and research institutions. For small-
er enterprises with more resources,
we advocate a mix of simulation and
controlled field testing with wireless
devices. Network emulation can be
employed to enable hardware-in-the-
loop simulation, such that the impacts
of scale, data loss, and delay can be es-
timated before an operational system
is deployed. Black-box test methods
also can be a valuable tool for verify-
ing expected performance under con-
trolled conditions.
However, for large deployments,
we recommended simulating network
performance using wireless channel
models that include realistic propaga-
tion and interference statistics. Realis-
tic propagation statistics for the 2.4-GHz
and 5-GHz ISM bands can be found
in the industrial wireless propagation
report, NIST Technical Note 1951 [1],
which provides empirical propagation
measurement data and statistics. The
raw data of measurements at three dif-
ferent sites—a factory, machine shop,
and process plant—are available for
download. They are currently being
used to support research and devel-
opment of new low-latency reliable
wireless protocols [26].
Security, which is important for the
success of wireless network implemen-
tation, is merely mentioned in passing
or even often missing or considered
last in a design process. But security
should be treated simultaneously with
the functional design, to include physi-
cal security, data security, and spec-
trum security. We refer readers to sev-
eral industrial security standards and
guidelines that exist, among them NIST
SP 800-82 and IEC 62443 (ISA 99) [27].
Spectrum monitoring should be included
as part of any security risk manage-
ment plan. We recommend incorporat-
ing a security element in a factory’s
spectrum-monitoring system during
the design phase and prior to deploy-
ment. Spectrum-monitoring tools can
be useful in identifying normal versus
anomalous wireless activity, geolocat-
ing interference, and projecting growth
patterns of factory wireless activity
and interference activity [28].
One of the important steps when de-
ploying an industrial wireless system is
managing the spectral resources with-
in an industrial environment. Frequen-
cy harmonization of the RF spectrum
becomes essential with the increase of
industrial wireless solutions occupying
The key takeaway from our guidelines
is the education of factory engineers in wireless
principles and best practices.
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DECEMBER 2018 ■ IEEE INDUSTRIAL ELECTRONICS MAGAZINE 15
the same RF band. Practical consider-
ations of frequency harmonization
include channelization, multiplexing,
matching, and data filtering. Details of
these considerations can be found in
[4, Section 4.4.4].
Deployment, Monitoring, and Updating
In this phase of the life cycle, the de-
sign of the wireless system is put into
production. Deployment is defined as
positioning the design elements, such
as devices, machines, and software, into
a live operational system. Three stages
are included in this phase, which are
deployment (i.e., implementation and
provisioning), monitoring, and updating.
Deployment includes the setup and con-
figuration of various network devices.
After this, the system is monitored and
analyzed in terms of its wireless net-
work states, traffic statistics, cybersecu-
rity events, and physical environment
changes. Updating is the process of con-
tinuously improving the performance of
the network through software and firm-
ware updates and tuning of the opera-
tional parameters for optimization.
Deployment begins with the instal-
lation of various wireless equipment;
antennas are positioned and oriented
to achieve the best transmission quality.
Antenna alignment in both polarization
and radiation pattern is a key consid-
eration for fixed-position wireless de-
vices. Cables are used to position the
antennas away from obstructions to
improve connection reliability assuring
line-of-sight coverage. Mobile devices
will not have the advantage of a fixed
antenna position. For mobile platforms,
such as unmanned ground vehicles and
mobile robotic platforms, it is essen-
tial to test the system, inclusive of the
physical and control elements, under a
comprehensive set of wireless channel
conditions. Safety systems and wire-
less interlocks should be exercised as
well under a variety of channel condi-
tions, encompassing nominal and worst-
case interferences.
Once the wireless system has been
deployed and is operational, it should
be continuously monitored. This in-
cludes the capture and analysis of
electromagnetic spectrum, network
traffic, and security events and can
be performed in real time or offline
in batch mode. Real-time monitoring
provides troubleshooting functions
to capture the pitfalls of systems that
cause factory downtime, loss of data,
or injury. We recommend that RF
spectrum be checked and analyzed
constantly at various locations. The
use of inexpensive software-defined
radio platforms makes distributed
monitoring of the electromagnetic
spectrum within the factory possible.
Radio activity can be correlated with
the industrial site activities to detect
any abnormal RF transmissions. The
spectrum-monitoring system could
be integrated with the automation
system to make the latter spectrally
aware for reliable sensing and control.
This presents an opportunity for aca-
demia and industry, as the interopera-
tion of spectrum-monitoring tools and
automation systems do not yet exist.
Network upgrades, where new nodes
are added to the wireless network, can
be performed on either a small or large
scale. Network operation after the en-
hancements should be verified by using
various simulation and testing methods
identified during design. Furthermore,
during network operations, updates
and parameter adjustments should
be implemented regularly to maintain
optimized network operations. This
presents another area of opportunity.
Machine-learning techniques can be
applied to optimize network parame-
ters on an ongoing basis to improve and
maintain latency and reliability within
the operation.
Future Directions
In the process of developing our guide-
lines, we have identified areas of re-
search opportunity that include but are
not limited to cyberphysical systems
modeling, wireless channel models,
interoperability between heteroge-
neous wireless systems, wireless for
manufacturing safety, machine learn-
ing for network optimization, and test
methods. In particular, good cyber-
physical models are essential steps
toward improving industrial wireless
deployments [29]. The current theoreti-
cal wireless channel models, including
the one specified in IEEE 802.15.4a [30],
do not capture the industrial channels
closely enough. Industrial channels in
various scenarios may have significant-
ly variable channel models in confined
spaces and in factories with large metal
machines. RF interferences caused by
commercial devices, such as micro-
wave ovens, and electromechanical ef-
fects need also to be incorporated into
the models.
Uncertainty models need to be de -
veloped that characterize wireless
systems comprehensively from the
perspective of instrumentation and
propagate network uncertainty into
the physical world. Existing models for
physical systems are defined for very
specific scenarios. A more generic dis-
crete event model that captures a wide
range of applications is needed to al-
low for better system design, and the
integration between channel models
and physical models must be studied,
especially with different time scales for
these systems.
Another important direction re-
lated to wireless deployment is test-
ing various network components and
parameters, which should be studied in
an industrial setting that should include
production-related criteria. The effects
of wireless channels and uncertain-
ty in factory environments should be
characterized mathematically and incor-
porated into designing industrial wire-
less networks. Measurement methods
must be created to quantify these un-
certainties. Tuning methods based on
machine learning that account for the
wireless network, the physical system,
and the changing factory environment
Realistic models, experimental results, and data
are needed to address the growing need for
reliable wireless communications in industry.
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16 IEEE INDUSTRIAL ELECTRONICS MAGAZINE ■ DECEMBER 2018
could present interesting challenges to
motivated researchers.
A third direction related to both
modeling and testing is that of cyber-
physical system simulations, which
are useful in predicting and evaluat-
ing the impact of industrial wireless
systems’ operational performance.
Some works in the literature, such as
[31]–[33], have considered interactions
between network and operational sys -
tems. Realistic models, experimen-
tal results, and data are needed to
address the growing need for reliable
wireless communications in industry.
More studies are expected in the future
for various use cases and simulations
of the impact of industrial wireless un -
certainties on the performance of op-
erational systems.
Finally, the wireless technology
space is vast, considering the number
of unique industrial applications, es-
pecially those found in discrete manu-
facturing. Assessing existing wireless
technologies and evaluating their abil-
ity to satisfy current and future indus-
trial requirements is an important step
in finding the deficiencies in existing
technologies. Hence, the testing and
comparison of various technologies
must be carried out for various areas
of the problem space to identify the ap-
plicability of wireless technologies for
deployment. It could be of benefit to the
academic and industrial communities
to devise a design challenge to address
this issue. Furthermore, it is important
to define realistic industrial wireless
network requirements, including the
desired latency, reliability, and scale of
the network that needs to be validated
against the physical requirements of spe-
cific classes of industrial applications.
Conclusions
In this article, we discussed the prob-
lem space and the deployment life cy-
cle of industrial wireless. Because of
the wide range of application areas of
industrial wireless and the differences
among users’ network deployments,
both generic and application-specific
guidelines are needed to clarify vari-
ous phases of decision making as to
whether to deploy industrial wireless
and thereafter to implement the sys-
tems. In addition, we discussed in de-
tail general guidelines for industrial
wireless deployments.
As a result, we concluded t hat there is
a need for the standardization and adop-
tion of systems engineering processes to
effectively apply wireless technologies
in factory systems. Through the adop-
tion of standardized guidelines, the reli-
ability of factory operations can thus be
maintained while reaping the benefits of
deploying wireless technologies in facto -
ry environments. We also showed some
opportunities where research is needed
to gain some better understanding of
industrial wireless uses, practices, and
associated problems in industrial set-
tings, through which we can help speed
up the wide adoption and deployment of
wireless technologies in current indus-
trial scenarios.
Biographies
Richard Candell (richard.candell@
nist.gov) earned his B.S. and M.S. de-
grees in electrical engineering from
the University of Memphis, Tennessee,
in 1993 and 1996, respectively, and
has more than 20 years of experience
in telecommunications systems engi-
neering, with extensive experience in
the design and evaluation of wireless
communications systems. He spent
12years developing, testing, and de-
ploying secure wireless technologies
for commercial and defense applica-
tions and holds patents in successive
interference cancellation and trans-
mission burst detection applied to
spread-spectrum satellite communi-
cations signals. He joined the National
Institute of Standards and Technol-
ogy (NIST), Gaithersburg, Maryland, in
2014. His current research interests
include the performance impacts of
wireless networks on industrial sens-
ing and actuated control applications
for mobile robotic, manufacturing, and
safety applications. He was the primary
contributing author of Guide to Industri-
al Wireless Systems Deployments (NIST
AMS 300-4) and the chair of the NIST
Industrial Wireless Systems Technical
Working Group. He is a Senior Member
of the IEEE.
Mohamed Kashef (mohamed
.kashef@nist.gov) earned his B.Sc. and
M.S. degrees (honors) in electronics
and electrical communications engi-
neering from Cairo University, Egypt,
in 2006 and 2009, respectively, and his
Ph.D. degree in electrical engineering
from the University of Maryland, Col-
lege Park, in 2013. He is a research
associate at the National Institute of
Standards and Technology, Gaithers-
burg, Maryland. His current focus is
on industrial wireless systems, includ-
ing wireless network deployment and
test methods for wireless networks
in industrial scenarios. His research
interests include wireless communi-
cation systems and networks, visible
light communication networks, and
optimization of stochastic systems.
He is a Member of the IEEE.
Yongkang Liu (yongkang.liu@nist
.gov) earned his Ph.D. degree in electri-
cal and computer engineering from the
University of Waterloo, Ontario, Cana-
da, in 2013. He is currently a research
associate at the National Institute
of Standards and Technology (NIST),
Gaithersburg, Maryland. He has been
working in NIST’s Wireless Systems for
Industrial Environments project since
2013. His research interests include
wireless implementations in Industrial
Internet of Things applications, wire-
less network protocol analysis, and
algorithm design for spectrum and
energy-efficient wireless networks. He
is a Member of the IEEE.
Kang B. Lee (kang.lee@nist.gov)
retired after 42 years of U.S. federal
government service and is currently
More studies are expected in the future for various
use cases and simulations of the impact of industrial
wireless uncertainties on the performance of
operational systems.
Authorized licensed use limited to: NIST Virtual Library (NVL). Downloaded on April 14,2022 at 13:06:53 UTC from IEEE Xplore. Restrictions apply.
DECEMBER 2018 ■ IEEE INDUSTRIAL ELECTRONICS MAGAZINE 17
a research associate at the National
Institute of Standards and Technology,
Gaithersburg, Maryland, participating
in research on wireless systems for
factory automation and smart sensors
for smart grids. He has more than 30
years of experience leading research
and working with industry on smart
sensor communication interfaces, pre-
cision time protocol, and wireless sen-
sor networks for industrial systems.
He has authored or coauthored 40
research papers and has given invited
keynotes on smart sensors, radio-fre-
quency identification, the Internet of
Things, and precision time synchroni-
zation. In 2006, EE Times profiled him
as one of 29 innovators in the world.
He is a Life Fellow of the IEEE.
Sebti Foufou (sebti.foufou@
u-bourgogne.fr) earned his Ph.D. de-
gree in computer science in 1997 from
the University Claude Bernard Lyon I,
France, for a dissertation on paramet-
ric surfaces intersections. He has been
with the Computer Science Depart-
ment at the University of Burgundy,
Dijon, France, since 1998. He worked
in 2005 and 2006 as a guest researcher
at the National Institute of Standards
and Technology, Gaithersburg, Mary-
land. He was with the Department of
Computer Science and Engineering at
Qatar University between 2009 and
2017, where he served as head of the
department for three academic years,
from 2012 to 2015. He joined NYUAD
in September 2017. His research inter-
ests include geometric modeling for
shape representations and image pro-
cessing for face recognition. He is also
interested in data models, data repre-
sentation, and processing for product
life-cycle management and smart ma-
chining systems.
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