Instrumentation & Measurement Magazine 23-3 - 5

distribution. Various sensors and instruments are used to measure stability, power flow, and power quality. This data must
then be securely transmitted and used in conjunction with
models and algorithms to make adjustments in order to respond to changes or failures, with the goal of maintaining high
quality service to users [31]-[33]. When incorporating IoT into
smart grids, smart grids allow full duplex communication between utilities and customers. Smart meters, as part of the
smart grid structure, can communicate energy usage to customers and energy needs to utilities. This interactivity can be
leveraged to allow more efficient power distribution, and reduce unnecessary energy consumption, while maintaining a
more reliable power delivery system [34], [35]. These smart
grid ideas can also be expanded to other utilities like water for
creating intelligent monitoring and distribution systems [36].
IoT SHM, smart grids, intelligent utility monitoring, and
connected transportation systems can all come together to
create smart cities. In the case of all of these smart city technologies, these monitoring capabilities come at a price: the need for
more sensors that are energy efficient, secure, and robust, and
the need for a network architecture that can support this level
of communication with low latency and reliability.
In terms of challenges with respect to security and robustness, if a sensor in the system were to malfunction or if
communications between devices are not reliable, inaccurate
information could be fed into decision making models, leading
to incorrect commands being sent to city system management
devices. This could have catastrophic consequences [37]. The
same effect could be realized if devices in the system were to be
hacked. Thus, security has been a major concern as the prevalence of IoT grows [38], [39]. Creating more security for IoT is
difficult, mainly due to the lack of standardization of IoT devices [40]. Beyond IoT, security and by association privacy
are concerns in many application realms. Thus, security/privacy is considered its own Trend of Consequence and discussed
more in depth later in this article. Another consequence of the
security of IoT is the need to be able to detect faults and have
systems that are more fault tolerant [32], [41].
While connectivity is expanding, network capacity must
also expand and adapt to meet the communication demands-
more devices requiring more speed and producing more data
to be handled. Dynamic spectrum sharing and 5G have been
proposed to address this need, but spectrum crowding is a continually developing challenge [30], [42]-[44].
Another challenge that arises from the trend of connectivity
is managing the huge amount of data that can be amalgamated
through the monitoring of so many systems. Cisco predicts
that by 2020 there will be over 50 billion connected devices,
that each possess multiple sensors, that by nature, make measurements and provide data. This data needs to be processed
in a meaningful way [45]. This is the Big Data paradigm [46],
[47]. As Big Data is a challenge across many application realms
for I&M and is a result of more than just connectivity, it is considered a Trend of Consequence and is discussed later.
A theme across the previously-mentioned trends is accessibility. In making instruments smaller, less expensive,
May 2020

more portable, and more connected, the ability to make measurements and monitor in a broad range of applications
becomes more achievable and available to more people. This
is propelled by the development of open source systems, 3D
printing, and inexpensive, user-friendly microcontrollers,
which have also allowed for greater customization in instrumentation systems [48], [49].
3D printing provides developers with the capability to
quickly prototype designs and can be used to manufacture the
final design. The 3D printing community has also made itself
more accessible by creating ways to share models for printing in an open source manner and this has been furthered by
the reducing costs of 3D printers. While 3D printing is most
commonly done with plastics, it can also be done with metals, glass, nylon, carbon fiber, and other materials including
concrete, allowing a wide variety of parts, even photonics, antennas, and surgical instruments, to be made in this manner
[50]-[54]. Another version of additive manufacturing (AM)
that has gained popularity is using inkjet-printing with conductive inks to create electronics. This allows for inexpensive
and flexible sensors, antennas, and circuit boards to be prototyped and manufactured quickly [55]-[59]. In this area, there
has also been work done to create printed electronic components like capacitors, diodes, and interconnects [60], [61].
While open source systems have allowed for more reproducible instrumentation, open source software has also
allowed researchers to more easily take advantage of automation for a variety of measurement systems and processes [62].
In this sense, accessibility can also be thought of as a Trend of
Choice.

Trends of Consequence
While automation, miniaturization, connectivity, and accessibility are trends propelling the field of I&M forward, there
are also byproducts of these trends that are garnering attention as they create new challenges that need to be addressed.
One of these byproducts is increased complexity. While complexity could be considered a Trend of Consequence, as a concept
complexity is very broad and also permeates through the other
identified Trends of Consequence. Additionally, growing complexity is a natural result of advancing technology. Therefore,
it will not be further discussed in this article.
As instrumentation systems become more capable and accessible, monitoring abilities grow through automation, and
connectivity allows information to be more readily exchanged,
the amount of data that needs to be managed has also increased. This Trend of Consequence is the Big Data paradigm. Big
Data is described by volume, velocity, variety, veracity, and
variability, and it is a growing challenge within the I&M community across many different application realms [46], [47].
In attempting to manage Big Data, the goal is to efficiently
aggregate and extract meaning from the data that has been
gathered. In doing this, AI and data visualization techniques
have proven helpful [63], [64]. However, since this poses a
broad challenge that affects so many application realms and
technology fields, there is not a single management solution

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