Instrumentation & Measurement Magazine 23-3 - 22

works that focus on getting continuous and thorough measurement and through such measurements obtain insights on
the changes in the measurand.

Simultaneous Wide Area Measurements
IoT can enable cost effective simultaneous wide area measurement, by deploying many low-cost embedded devices with
ubiquitous connectivity, which can simultaneously gather
measurements over a large area more cost effectively compared to the conventional approaches such as coverage by a
satellite. The changes of the measurand at the spatial domain
can also be captured, for example, crowd size and movement
of the crowd in public space over a specific duration can be
captured and visualized with IoT.

In order to reduce the effect of network latency on the measurement, we might employ a store-and-forward approach
where the local latency is lower between the sensor node
and the IoT-gateway. The collected measurements are then
transmitted to the cloud in batches. Fig. 2 shows the same
measurement with an average latency of 15 ± 7 ms, which is
achievable via WiFi and BLE.
In order to further reduce the effect of network latency, it is
possible to store the measurements locally in the sensor node
with a timestamp and subsequently send the measurements
to the cloud. However, such implementation will make it difficult to perform causality analysis from the measurements
from two different sensor nodes due to the high likelihood that
the clocks of the two sensors have some drift and are not completely synchronized.

Real-time Measurement Analysis
In a conventional measurement process, the measurement
and the measurement analysis are two distinct stages, where
measurements have to be collected and transferred to a computer for processing in batches. By incorporating IoT into the
measurement process, the measurement collection and analysis are streamlined as one process. Such stream processing
enables actions to be taken as soon as an event arises. The processing for analysis can be done in the smart instrument itself,
or at a nearby edge node, or a combination of the two.

Enhance the Integrity of Measurements
Data captured by sensors can be blockchained to ensure the
integrity of the measurements, for instance, a government
agency's monitoring of emission of gases by factories. The
data coming from the sensors at the factory can be recorded
and blockchained to avoid being tampered with by any party.
As such, the integrity of the measurements can be maintained
without any centralized party.

The Caveats in Deploying IoT in I&M
Unfortunately, incorporating IoT into measurement systems and instruments is not without consequences. While
there are many existing research works to push the limits of
IoT technology, it is important for us to be aware that by incorporating IoT into I&M, the measuring process inherits
additional uncertainties from the IoT system itself. These are
discussed next.

Causality Analysis
As mentioned before, IoT has the ability to provide simultaneous measurements from a large area, and it is interesting to
draw the correlation from the measurements of different sensors nodes, for instance, to identify heat sources in a server
room by interpolating the temperature data collected via multiple sensors with known locations distributed in the server
room. The challenge of such a feat is to ensure that the clock
of all the sensor nodes are synchronized for every measurement taken. Such synchronization is not trivial, and unless we
use additional solutions, such as those based on IEEE standard
1588 which allows clock synchronization with a sub-microsecond accuracy [6], it is very challenging to ensure all of the clocks
of the sensors are synchronized. Drifted clocks of these sensor
nodes will affect the causality analysis among the sensors. Fig.
3 illustrates measurements taken from two sensor nodes in sequential order at their respective clocks. Unfortunately, due
to drifted clock and network latency, the measurements may
arrive at the cloud in different orders. Therefore, causality
analysis of the measurements might not necessarily represent
the causality of the actual scenario without knowing the consistency model of the measurement instruments. For example,
it is possible for the scenario shown in Fig. 4 to happen if the
underlying protocol used for transmitting the measurements
is not TCP but UDP, in which datagrams might arrive at the
destination out of order.

Aging and Faulty Sensors
Network and Operating System Latencies
IoT can provide continuous and thorough measurements
where the sensor nodes are designed to perform measurement,
continuously using a wireless, low power and embedded microcontroller. However, due to the transmission latency, the
read intervals between successive measurements may not be
consistent. A measurement from a sensor node will experience
the latency of both the IoT-gateway and the cloud. Therefore,
analysis that requires frequency domain transformation may
not be able to represent the original measurement accurately.
Fig. 1 illustrates the impact of measurement experiencing an
average delay of 150 ms with a standard deviation of 70 ms.
22	

As is well known in the I&M community, conventional uncertainty evaluation focuses on type A and type B, where type A
estimates uncertainty using repetitive readings and statistic
approaches, and type B estimates uncertainty based on information such as manufacturer's specification [7]. Typically, for
type A uncertainties, we calculate the arithmetic mean, standard deviation, standard uncertainty and degrees of freedom.
Unfortunately, these evaluations may not be able to reveal aging or intermittent failing of the sensors. In IoT, sensors are
often deployed to run for a long period. As the sensors age,
they might produce inaccurate readings. IoT measurement
instruments must take such uncertainties into consideration.

IEEE Instrumentation & Measurement Magazine	

May 2020



Instrumentation & Measurement Magazine 23-3

Table of Contents for the Digital Edition of Instrumentation & Measurement Magazine 23-3

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