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7.2 SMART THINGS

It has been argued that IoT has a PR problem (see Eberle, 2016). Eberle
argues that rather than talking about IoT, we should be talking about smart
things, such as smart cars or smart cities, which are powered by IoT. We agree
with this assessment and so do others (Bassi et al., 2013; Willems, 2016). At
the most basic, IoT is about connecting all sorts of things to the internet.
Those things, whether washing machines, cars, our bodies, or our food,
produce data, in particular real-time data (see Heikell, 2016). Often this data
is useful on its own; however, we are interested in what we can do when
those devices interact.
In addition to producing, processing, and reporting data from internal
sensors, IoT devices may also receive input from entities external to them.
Consider Google’s “Nest” thermostat, which may receive weather informa-
tion from a website in addition to data from internal sensors. As such people
consider Nest to be a smart thermostat. Taking several devices inside the
home and programming them so that they communicate with each other
leads to a smart home.
While often data collected and processed by a smart device is useful on its
own, and while connecting smart devices together is useful too, more value
can be generated by building models of the data available to them. At the
most basic, a model of a sensor may be used to interpolate missing data or
determine whether data is out of an expected range and as such may be
faulty. At a higher level, models of data can be used to produce considerable
value. Cummins Engines, the largest independent manufactures of diesel
engines, uses telematics, i.e., real-time engine data to build real-time models
of how their engines actually perform. These models are then used by
Cummins in several ways. By running live engine data against the model, they
canascertainthegeneralhealthofaparticular engine.Byusing predictiveanal-
ysis, Cummins is able to predict various scenarios ruinous to an engine and as
such is able to alert fleet operators, in real time, about fault-codes and their
significance on the continued operation of the engine (see Cummins, 2016).
Moving a step further, one can authorize a model to act. While the model
of a Cummins engine alerts an operator at Cummins, consider the Nest ther-
mostat; it builds a model of the comfort preferences throughout a week and
then enforces the preferences by turning on and off the air conditioner
and heater.
We consider Google’s Nest to be the state-of-the-art with regard to cur-
rent practice for IoT, in the sense that robust and repeatable solutions in this
mold exist. This state-of-the-art is captured in Fig. 7.1.