![](bg2.png)
2 RELATED WORK AND
DISRUPTIVE ASPECTS OF IoT
To handle an IoT project, which extends within dif-
ferent departments in an organisation and also across
organisations, a methodology, process or framework
is required to manage and coordinate throughout the
IoT landscape. There are already different methodo-
logies, frameworks, and processes discussed in the
literature that are considered as a candidate for IoT
projects. Few to name are 5S (Osada, 1991), Kai-
zen (Imai, 1986), Six Sigma methodologies (Smith,
1993), Kotter model (Kotter, 1996), SEMMA (sam-
ple, explore, modify, model, and assess) (Azevedo
and Santos, 2008), CRISP-DM (cross-industry stan-
dard process for data mining) (Shearer, 2000), and
KDD processes (Fayyad et al., 1996; Lodhi et al.,
2008).
However, these methodologies have shortcomings,
for example, methodologies and frameworks like
CRISP-DM, KDD, SEMMA are more concerned
with the data mining process and do not include the
implication of business applications, business models
and scarcely describe the effect of one phase on the
other. Others, such as 5S, Kotter model, are very ab-
stract and rather suitable with the view to workplace
organisation or organisation change.
There are numerous works (Swan, 2012; Bonomi
et al., 2014) who address some parts of the IoT lands-
cape. Some of the works discuss only the IT per-
spective and others focus on the analytics perspective
and neglect other parts. There is a considerable
amount of literature which discuss IoT from the ap-
plications and future trends perspective (Curran and
Curran, 2014; Khan and Turowski, 2016b; Crowley
et al., 2014; Khan and Turowski, 2016a). In (Swan,
2012), the author reviews the eco-systems for IoT. It
covers only a part of the whole landscape. In (Bonomi
et al., 2014), authors propose a hierarchical distribu-
ted architecture and use a fog platform for analysis.
We argue that there are other factors which are inter-
related and have effects on other parts of the IoT pro-
ject or landscape. We describe these factors and pha-
ses and elaborate the whole process with the help of a
case study example.
3 AN ANALOGY OF THE IoT
PROCESS MODEL
For our IoT process, we advertise that we can use Six
Sigma methodology. Six Sigma methodology (Smith,
1993) was initially proposed to remove defects and
improve quality in 1986 but is now often used to me-
asure improvement in IT process execution and ser-
vices (George and George, 2003; Antony, 2006). Six
Sigma methodology is the best candidate to remove
defects, and we can use it for IoT project problems
described earlier. We propose to use Six Sigma sub-
methodology DMAIC because of its success in other
industries, and it has an analogy with the phases in our
process model. The sub-methodology DMAIC is an
acronym of the following steps, namely define, mea-
sure, analyse, improve, and control.
The brief description of these steps of DMAIC
methodology is as follows. In the define phase, we
define what is the problem; in the measure phase, we
find the areas of a problem; in the analyis phase, we
analyse the problem; in the improve phase, we take
necessary steps to remove the problem; and in the
control phases we control or check whether the pro-
blem is removed.
Our IoT process model consists of the following pha-
ses: sensors, pre-processing & analysis, business ap-
plications, and business value. The detailed descrip-
tion of these phases is described in Section 4.
The methodology steps of DMAIC have an analogy
with the following IoT process phases.
Define: This phase has an analogy with the business
value phase. In this phase, we define the scope and
objective of the IoT project. General questions in the
phase consider the nature of the problem or the aims
we want to achieve with this project. As this phase is
on the strategical level, we also identify the possible
stakeholder of the project, their roles, resources, and
what are the general requirements.
Measure: The sensors phase from IoT is associated
with this phase of methodology. In this phase, data or
measurements (raw data) are collected from the sen-
sors. We decide what kind of sensors we need and
how frequently measurement should be done.
Analyse: This phase is inline with the analysis phase
in the landscape. In this phase, we perform the ana-
lyses based on the data collected in an earlier phase.
We get insights from this phase which tell about the
behaviour or exhibit specific patterns.
Improve and Control: This is associated with busi-
ness application phase in the landscape. This phase
describes the necessary steps to improve the overall
situation or how we can use the outputs of other pha-
ses to improve business applications or business pro-
cesses of an organisation are. In the control phase, we
take measure to monitor the overall progress or per-
formance of the cycle.
We depict our process model phases and associ-
ated DMAIC steps in Figure 1. The organisation’s
vision serve as an input for the IoT project, and after
A Holistic View of the IoT Process from Sensors to the Business Value
393