Configuring The Internet of Things (IoT): A Review and Implications for Big Data Analytics

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DOI: 10.24251/HICSS.2019.706 ·
Conference: Hawaii International Conference on Systems Sciences 52
Cite this publication
Big data analytics is emerging as a key initiative in the IoT field as data grows at unprecedented scale and depth. However, considerable uncertainty remains about how organizations are using big data analytics to capitalize on IoT. In this paper we argue that there is a need for a more refined depiction of the relationship between IoT and big data analytics as it tends to be linked by technological and economic viewpoints. Three principal claims are made. Firstly, there is a pressing need to clarify the characteristics configuring and shaping the discourses around IoT. We find that IoT is characterized as a complex, (more than) technological, multi-scale and multi-level information infrastructure that is emergent and uncertain. Secondly, the unique characteristics of IoT are challenging governance capabilities in big data analytics. Thirdly, the impact of IoT through big data analytics for building 'sustainable futures' raises questions about responsible research and innovation.
Configuring The Internet of Things (IoT): A Review and Implications for Big
Data Analytics
Susan P. Williams
University of Koblenz-Landau
Catherine A. Hardy
University of Sydney
Patrick Nitschke
University of Koblenz-Landau
Big data analytics is emerging as a key initiative
in the IoT field as data grows at unprecedented scale
and depth. However, considerable uncertainty
remains about how organizations are using big data
analytics to capitalize on IoT. In this paper we argue
that there is a need for a more refined depiction of
the relationship between IoT and big data analytics
as it tends to be linked by technological and
economic viewpoints. Three principal claims are
made. Firstly, there is a pressing need to clarify the
characteristics configuring and shaping the
discourses around IoT. We find that IoT is
characterized as a complex, (more than)
technological, multi-scale and multi-level
information infrastructure that is emergent and
uncertain. Secondly, the unique characteristics of IoT
are challenging governance capabilities in big data
analytics. Thirdly, the impact of IoT through big data
analytics for building ‘sustainable futures’ raises
questions about responsible research and innovation.
1. Introduction
The Internet of Things (IoT) has been identified
as one of the key drivers in a new technological
revolution that is “fundamentally changing the way
we live, work and relate to one another” [68:1]. The
“seamless integration of the physical and digital
worlds through networked sensors, actuators,
embedded hardware and software will change
industrial models” [77:4] enabling the delivery of
new products and services in domains as diverse as
urban design [54, 80], manufacturing [29], health
[61], agriculture [10] and government [11]. Ongoing
developments in the Internet of Nano-Things (IoNT)
and Industrial Internet of Things (IIoT), as an
example, are accelerating the number of connected
devices [18]. It is estimated that by 2025, IoT will
create an annual economic impact of USD 2.7 trillion
to USD 6.2 trillion [42:51] and that by 2030 8
billion people and maybe 25 billion active smart”
devices will be interconnected and interwoven by one
single huge information network” [51:240]. Further,
the “seamless integration of the physical and digital
worlds” [77:4] and the increase in embedded
technologies brought about through IoT is also
accelerating the convergence between operational
technologies (OT) that work in real-time on physical
systems such as manufacturing and control systems,
and information technologies (IT) that support
information processing, communication and decision-
making to improve the management of business
Big data analytics is rapidly emerging as a key
initiative in the IoT field as data grows at an
unprecedented scale and depth with the proliferation
of smart and sensor devices [8, 16, 40, 45, 53]. Some
commentators go as far as to say that big data
analytics is driving the “next wave of IoT
innovation” [53:64] and making IoT “pertinent to the
world” [2:vii] by offering more effective ways for
managing and analyzing “notoriously messy” IoT
data [45]. Others view IoT initiatives as a disruptor to
data and analytics [22], influencing the adoption and
implementation of “new and different types of data
and analytics technologies and techniques” [20].
Whilst the potential transformational effects of IoT
and big data analytics are widely acknowledged,
considerable uncertainty remains about these
concepts and how organizations are using big data
analytics to “capitalize” on IoT [62] to deliver social
and economic value [25].
In this paper, we argue that there is a need for a
more refined depiction of the relationship between
IoT and big data analytics. The paper is not intended
to be a comprehensive literature review, but an
exploratory essay drawing selectively on literatures
in computer science, IT and information systems (IS).
Three principal claims are made. Firstly, the two
fields of IoT and big data analytics tend to be “linked
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and bound together” primarily by technological and
economic viewpoints [66]. Calls have been made for
broadening the scope and diversity of research to
assist in bringing greater understanding to issues of a
non-technical nature that are related to the emergence
of IoT [62]. The big data analytics field is already
making advances in developing characteristics and
research agendas that include behavioral, design and
an economic focus [1, 25]. It is argued that there is a
pressing need to clarify the characteristics
configuring and shaping the discourses around IoT as
the field is still in its infancy and is technology
focused [79]. Further, conceptual clarity is a
precondition for effectively integrating knowledge
between fields [60]. The term ‘field’ is used here in a
conceptual sense rather than to represent professional
fields of practice [38] as IoT and big data analytics
encompasses a variety of emerging professions and
industry groups.
The second claim is that the unique characteristics
of IoT are challenging existing governance
capabilities in the field of big data analytics [3, 21]
and more widely [78]. In a recent survey of CIOs and
CTOs, security, privacy, implementation/integration
complexity, cost/funding concerns and potential risks
and liabilities were cited as the top five barriers to
IoT success [22]. IoT presents greater risks due to the
complexity and distribution of IoT systems [21]. As
IoT is a relatively young and still evolving
technology infrastructure, the consequences it brings
for individuals, organizations, industries and nations
and, in particular, the future implications and
requirements for effectively managing and governing
IoT are still being shaped. Further, the “synecdoche
use” of the digital governance term across the fields
of IoT, big data analytics and digital development
more broadly requires clarification [19]. Floridi [19]
argues that in using the digital governance term care
must be taken to ensure that other normative matters,
namely digital ethics and digital regulation, are
understood as separate and overlapping to avoid
confusion. Based on the characterization of IoT we
examine the implications for governance and in doing
so: identify areas requiring further attention for
research and practice; and bring further clarity in how
the governance term is used.
The final claim is that the shifts in IoT discourses
towards more complex systems, the inclusion of
social, cultural, political and economic issues and the
consequent involvement of multiple stakeholders, are
opening the field to a much wider landscape of social
and environmental concerns, broadly classified as
sustainability. To date research has largely been
focused on developing sustainable technology
solutions [50] and IoT as a disruptive technology and
new source of digital data with the potential to
transform business models and industries [14, 34, 49,
63]. The impact of IoT through big data analytics for
building “sustainable futures” and “sustainable
lifestyles” in areas such as energy management,
healthcare, manufacturing, emergency management
[62], the environment [39] and smart cities [27] is
uncertain and dynamic. Yet, in following Floridi’s
[19] argument we should “resist” the “distracting
narrative” of disruption not because it “is wrong” but
“it is superficially right.” The pace of technological
innovation is no doubt impacting business and
society. However, there is a more fundamental
question in need of answering in terms of the “kind
of mature information societies we want to build”
directing attention from “digital innovation” to the
“governance of the digital” [19]. The imperative for
expanding the boundaries of IS research beyond
organizational and managerial impacts is not new
[75]. However, more recently social inclusion
researchers have posited that IS scholars have a
moral obligation to investigate digital platforms to
“reveal biases coded in their designs that promote
exclusionary practices and prevent equitable work
opportunities” and in doing so “propose new and
creative designs solutions” that are “sensitive to the
values that foster social inclusion and deter
exclusion” [75]. This raises questions about
responsible research and innovation, which we
explore in the context of our characterization of IoT.
The argument is presented in three parts. First, we
clarify how big data analytics is viewed in the context
of this paper. Second, we examine critical discourses
in IoT. Our review is necessarily partial and limited.
IoT is notable for its ubiquity across domains and
applications and is a dynamic and contested space.
Hence, framing the boundaries of the field is a
challenge as no singular discourse can define the
field. However, there are certain powerful discourses
operating in the field that are explored to develop a
characterization of IoT. Third, we examine the
implications for governance based on this
characterization. Finally, we explore questions about
responsible research and innovation in IoT and big
data analytics followed by the conclusion.
2. Representing big data analytics: An
Definitions and labels related to big data,
analytics, big data and analytics and big data
analytics (to name a few) have evolved over time
with technological changes and from different
vantage points [1, 14, 16]. For example, Chen et al
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[14] classified (big) data analytics as one of five
“critical technical areas” contributing to business
intelligence and analytics, the others being text
analytics, web analytics, network analytics and
mobile analytics. Others have used the labels big data
& analytics or big data analytics to describe the
platform that captures and processes large volumes
and varieties of data at high velocity and the methods
or techniques for revealing patterns, trends and
insights to improve business decisions [16, 25]. Much
of the literature in this space may be characterized by
“its many speculations and opinions” as well as its
emphasis on “opportunities afforded by big data
technologies” [25]. Whilst still in its nascent stages
there have been responses to calls more recently for a
more “socio-technical characterization” directing
attention towards how organizations are deriving
value from big data analytics [25]. In the context of
this essay, we adopt the broader socio-technical
characterization of big data analytics.
In the IoT field big data and analytics is identified
as a function and requirement for IoT [3] providing
‘large’ scale data management and computational
technologies for analysis [66]. In addition, it is also
viewed as a challenge of IoT [40, 56] and has similar
challenges relating to matters such as privacy and
security, data quality, interoperability and business
value concerns [3, 56]. A number of reviews of big
IoT data analytics have been conducted recently in
technology related fields such as computer science,
engineering and IT [see for e.g. 9, 16, 40, 45, 53].
Whilst useful, these reviews tend to present a
mechanistic view and technological focus, with
limited attention to the human, organizational and
social aspects.
3. Internet of Things (IoT) Configurations
The field of IoT has a strong base in technology-
related fields such as computer science, engineering
and IT. Bibliometric [46], and scientometric studies
[64] and other extensive reviews [43, 48, 74] of the
IoT literature have been conducted. In this section,
we examine definitions and discourses surrounding
IoT. Several studies have presented in-depth analyses
and definitions of IoT [6, 30, 32, 44]. It is not our
intention in this article to derive a universal definition
of IoT. Rather, we examine the characteristics
configuring and shaping the discourses around IoT in
keeping with the first claim of our argument.
3.1 IoT – complex (more than) technology
Efforts to define IoT frequently begin with a
technology focus to describe what IoT is. IoT is
typically defined as “a world-wide network of
interconnected objects uniquely addressable, based
on standard communication protocols” [31:6] and as
“a network of items-each embedded with sensors-
which are connected to the Internet” [30]. The focus
in these definitions is on the software and hardware
that enables the embedding of sensors and other
technologies into physical things and the protocols,
standards and platforms that enable the connection
and coordination of “smart” things.
Atzori et al [6] reviewed the various definitions
presented in the academic literature and identify three
different groupings of definitions of IoT
technologies: a “things-oriented” vision, an “Internet-
oriented vision” and a semantic-oriented vision”.
The things-oriented vision focuses first on the things
themselves at the atomic level of sensors, actuators
and smart objects, and gives attention to the methods
for registering, tracing and awareness (in terms of
locations, status etc.) of these objects [6, 7]. The
Internet-oriented vision focuses on the networking
aspects of IoT that enable vast numbers of
heterogeneous, constrained objects to be connected
together. Enabling them to communicate with each
other and with other systems, and to function in low-
power and low bandwidth environments [6, 7]. The
semantic-oriented vision focuses on the ways that
vast networks of heterogeneous objects and the data
that they are creating can be controlled and managed
from a technical viewpoint. Semantic technologies
and information-centric networking architectures are
required to simplify and handle the scale and scope of
these vast networks of things and the processes to
organize and coordinate search, retrieval, storage and
analysis of the vast volumes of data being generated,
transported and consumed [6, 7]. The three visions
identified by Atzori et al. point to a deep, complex
and evolving ecosystem involving many different
technologies and stakeholders, including sensor and
device manufacturers, network, telecommunications
and middleware providers, IoT platforms and service
providers, end-users and consumers of IoT services.
IoT technology architectures are still evolving [7], as
are the standards and protocols for ensuring IoT
quality of service delivery [71]. For example,
standards and protocols for object naming,
authentication, operation of low-power wireless
networks, security and privacy [6].
Other definitions of IoT look beyond technology
and ask not only, what is IoT? but also, what does
IoT enable and for whom? The International
Telecommunication Union adopts a broader view of
IoT that includes its purpose, defining it as a “global
infrastructure for the information society, enabling
advanced services by interconnecting (physical and
Page 5850
virtual) things based on existing and evolving
interoperable information and communication
technologies” [32:1]. IoT infrastructure is more than
just the technology that it is built upon, it is a vast,
complex and evolving system, comprising many
millions of nodes, connected across multiple
ecosystems with diverse standards and protocols that
is enabling the development of a wide range of
operations (sensing, actuating, monitoring,
controlling, data capture), within and between
multiple domains of application and situations of use.
It is in essence, a new information infrastructure [26,
47]. An information infrastructure (II) has been
characterized by openness to number and types of
users (no fixed notion of ‘user’), interconnections of
numerous modules/systems (i.e. multiplicity of
purposes, agendas, strategies), dynamically evolving
portfolios of (an ecosystem of) systems and shaped
by an installed base of existing systems and practices
(thus restricting the scope of design, as traditionally
conceived)” [47]. This characterization of an II can
clearly be applied to the emerging IoT information
infrastructure (IoT-II) and raises interesting
challenges about the nature of, and requirements for
IoT governance in such a diverse ecosystem of
technologies, users, purposes and practices. Monteiro
et al. [47:576] also reason that II's are “typically
stretched across space and time: they are shaped and
used across many different locales and endure over
long periods (decades rather than years)” a
characteristic of IoT-II to which our attention now
3.2 IoT – multi-scale and multi-level
In addition to crossing multiple domains and
industries, IoT is also visible across multiple
dimensions or scales (e.g. spatial, temporal, data,
technology architecture, jurisdictional, governance
etc.) and at differing levels of abstraction (e.g.
atomic, local, regional, global). The IoT-II can be
examined at the micro-, atomic level of a single
sensor through to the macro-level of a massively
connected and integrated, global network of smart
things and at all levels in between. In a world where
physical and digital things come together, these
digitally material artefacts take up a defined position
in Cartesian space; a single sensor is attached to a
specific tree in a given forest, physical location
matters. The data that is captured by a single sensor,
may subsequently be shared or aggregated across
multiple spatial, temporal and jurisdictional levels
and applied to different uses or purposes, increasing
the scale and level complexity of IoT and
amalgamating data to a point where the exact
physical location of the sensor is less important to the
purpose at hand. In the scenario presented below
(illustrated in Figure 1) we examine the complex and
interconnected nature of IoT Information
Infrastructures. We begin with a single scale, the data
scale and follow the scenario of a homeowner who
installs a home weather station provided by a
provider of smart home solutions. We follow a single
point of data across various levels in the IoT-II from
the micro-level of the homeowner with her single
weather station, through the meso-level and macro-
levels, where other stakeholders, with different data
needs and intentions appear. Our aim is to use the
scenario to consider the different scale and cross-
scale issues that are shaping the IoT-II.
Despite being a very small point in the overall
dataset, the data gathered by the single home weather
station at a single location and point in time, has a
potential impact on a global scale and for a much
longer period of time.
Although the example follows just one data
stream captured in one IoT domain, it illustrates that
moving between scales and levels the data
requirements, data consumers, data uses, spatial and
temporal reach and scope are changing; raising the
potential for challenges to the contextual integrity of
the data and its use. The complexity of interactions
between different dimensions and levels brings
potential challenges for the management and
governance of IoT; as the interests and intentionality
of stakeholders at one level may not easily be
translated or interpreted at other levels. Useful
theoretical and analytical insights into ways of
accounting for the multi-scale complexity of IoT and
for governance in a multi-level world may be drawn
from research into scale and cross-scale dynamics
conducted in the field of human-environment systems
[cf. 12, 13, 23, 72]. Cash et al [12:1] argue that
understanding scale and cross-scale dynamics is
increasingly important in complex worlds and that
failure to take transboundary problems into account
has led to many examples of policy failure in human-
environment systems. They identify common scale
challenges where cross-scale and cross-level
interactions threaten to undermine the resilience of a
human-environment system. These challenges are
equally likely to occur in the multi-level, multi-scale
IoT-II illustrated above, where different communities
of interest, technological ecosystems and vested
interests may overlap, conform and conflict.
Formulating approaches to IoT governance may thus
require transdisciplinary research approaches that
focus attention on the interplay between scales and
across levels and represent the theoretical and
practical imperatives of different communities.
Page 5851
Figure 1. IoT information infrastructure: multi-scale, multi-level
Scenario of IoT Use and Data Requirements
A homeowner installed a home weather station on her
balcony to make her home smarter a nd to c ontrol her
heating costs. The we ather station measures weather data
(air temperature, humidity, rainfall etc.), which is used to
trigger actions in other sensors and actuators she has
installed i n her apartment. For example, w hen outside
temperatures reach a pre-specified minimum value a signal
is sent to an actuator that automatically closes the windows
to save energy. The weather data is captured at the local
level of her apartment, a relatively small and clearly bounded
‘patch’ of the IoT-II. For the required actions to be
meaningful, the data being created and acted upon must be
available in real-tim e and the data structures pre-specified.
Temperature sensor values and alert levels are defined in
degrees Celsius and trans ferred in JSON format as specified
by the smart home solutions provider. The sensor data can
then be consumed by the smart window actuator and viewed
by the hom eowner on her smart phone through a dashboard
provided by the smart home s olution provider.
The smart home solutions provider aggregates readings
from the weather stations of many individual customers in
the region. This aggregated spatial data is managed and
owned by th e smart home solutions provider who sells it to
third parties who have very different uses for the data and
different data req uirements. For examp le, one use of the
data is made by a local weather serv ice, which provides
weather warnings to farmers and local councils in the region,
such as impending frost events that could damage cr ops or
require roads to be gritted to prevent ice forming. The
weather service requires readings from many weather
sensors in a region. The data must be in a structured format,
individual data points still matter although data coverage
may be uneven depending on where individual sensors are
located. The region and the boundaries of the region may be
imprecise. Whereas the owner of a single weather station
only has access to the single readings of her weather
station, the weather service combines multiple data-streams
along with their geo-locati ons to visualize weather events
(e.g. as frost maps) in near real-time. Another use of the
regional data is made by the local energy provider who uses
the data in their energy supply prediction models. For
example, a sudden cold snap might increase energy
consumption by households; by predicting these weather
events the energy provider can buy or generate additional
energy to manage peak demand or participate in the energy
commodity market. The energy provider is interested in data
at an aggregate level (for example, city block or the area
covered by a specific electricity sub-station) and uses
retrospective data to train the predictive models, and real-
time data for taking decisions about immediate energy
supply. The weather service, the energy supplier and the
manufacturer each have a use for the data but with different
motivations and end goals and varying data requirements.
At an even higher level the data from multiple regions is
aggregated and used by a national weather bureau for
weather forecasting and for informing decisions about water
saving measures in extended hot and dry periods. Over a
longer timeframe a global climate research institute
aggregates multiple data sets to feed large scale weather
and long-term climate monitoring models and inform climate
change policymakers. These activities cover larger areas,
and require data from multiple data suppliers, each with
potentially different data formats, bound by diverse data
sharing agreements and subject to different jurisdictional
requirements. The volume and variety of data is much higher
at this level as it is aggregated from potentially millions of
sensors in different sensor networks. Now the interest of the
data consumer is on longer time series of data for guiding
future climate change policy, rather than the original purpose
of usi ng real-time sensor data at a single point in space to
trigger an action to close an apartment window.
Medium volume & variety, low velocity
Higher volume due to multiple data
sources (e.g. weather stations)
Higher variety due to different data
structures from d ifferent types
of devices / vendors
Lower velocity due to transformation and
aggregation requirements
Low volume, velocity & variety
Low volume due to limited data sources
Low variety
Velo cit y can be hig h wh en r eal time dat a is use d
Highly aggregated and transformed data from various
Tran sfo rme d a nd ag gre gate d dat a s tre ams fr om
different timeframes
Highest volume & variety, lowest velocity
Large size and increased heterogeneity of data sets
(e.g. structure), but low velocity due to transformation
and aggregation requirements
Single intended, well defined use
e.g. the data is used to trigger an action
according to defined parameters
Generally, data is mainly used to control or
react to events in a known location
Multiple, well defined uses
e.g. short term local weather
forecast s and war nings by local
weather service
e.g. optimizing energy grid
utilization by local energy
Data mostly used to make
Various potential uses
Data can be used in many
different ways, which are not
obvious/ known during the
collection of data
Data is likely to be used to
make predictions or as
supporting variables in
simulations or models
Other sources of data
Page 5852
3.3. IoT – emergent and uncertain
IoT is frequently characterized as a disruptive
technology with the potential to transform
organizations [58] and industries [52]; to re-shape
value chains [57] and having impact locally,
nationally and globally [33, 52, 73]. Porter and
Heppelmann [57:67] argue that IoT has the potential
to “drive yet another wave of value-chain based
productivity improvement” that will “reshape
industry structure” and redefine industry
boundaries”. At a national level the US National
Intelligence Council has identified IoT as one of the
six most disruptive civil technologies likely to impact
US national power in coming years [73]. The OECD
has similarly identified IoT as having “profound
implications for all aspects and sectors of the
economy, the largest impacts are expected in the
healthcare sector, the manufacturing sector, network
industries and local government” [52:80].
However, there is considerable uncertainty about
these future scenarios and the scale, nature, timing
and impact of potential disruptions. The US National
Intelligence Council considered the likely impact of
IoT on aspects of national power along two major
axes: timing of development, that is, whether
disruption would occur slowly or rapidly; and depth
of penetration, whether IoT would be restricted to
niche applications or be ubiquitous in effect. From
this analysis, they developed four scenarios for how
IoT might evolve towards 2025, along with the
potential opportunities and risks that could arise
[73:27–28]. The outcome shows multiple possible
trajectories and futures for IoT and many unknown
factors. The International Telecommunications Union
(ITU) also examined the future impact of IoT and
envisages high complexity and diversity. Whilst
identifying IoT's potential to address various global
challenges, such as delivering power, water and
sanitation services and managing megacities and
natural hazards, ITU concludes that IoT
opportunities are not equally distributed between and
within countries” and unlocking the potential of IoT
requires significant cooperation between a wide
range of stakeholders from different industries and
levels of government [33]. Similarly, the OECD
identifies potential interoperability issues due to
persisting technology uncertainties relating to
competing technology standards, technology
platforms and applications and has further concerns
regarding the costs of IoT, the skills and knowledge
required and the potential for social inequality to
widen for those nations that cannot keep up [52:82].
In this section, we have identified three different
ways that IoT is being characterized; the implications
of these characterizations are examined in the
following sections.
4. Implications: governance and
Our characterization shows IoT as (more than)
technology, multi-scale and multi-level, and
emergent and uncertain. The discussion that follows
examines how these characteristics are materially
implicated in big data analytics in terms of
governance and responsible research and innovation.
4.1 Implications for Governance
Data governance is at the centre of IoT and big
data analytics governance. Challenges relating to the
effective governance of transactional data, master
data and analytical data are further amplified in the
IoT-II, at the physical edge, platform and enterprise
level. The number and distributed nature of sensor
and smart devices, the related volumes of data
generated, and the multiple formats and standards
that need to be supported present additional
challenges, including decisions as to whether existing
information infrastructure capabilities may be
leveraged or new investments are required to ensure
that platforms can scale with need and integrate with
business applications at multiple levels [59].
The distributed nature of the IoT-II also presents
vulnerabilities and governance challenges in a
number of areas relating to security, privacy, data
quality, data retention, standards and policy. The
nature of these risks and vulnerabilities are not
necessarily new to the big data analytics field or the
IS community more broadly, with common security
principles centered on confidentiality, integrity and
availability. However, the “surface for attacks” in
IoT-II increases security risks due to the broad
external ecosystem in which it is embedded and
presenting governance challenges as it is outside of
the IT organization’s control [59]. Further,
organizations operating in the industrial IoT (IIoT)
and converging IT and OT settings are faced with
additional challenges since “most industries have
developed and managed OT and IT as two different
domains, maintaining separate technology stacks,
protocols, standards, governance models and
organizational units” [5]. In the OT domain, safety
awareness rather than security awareness has
traditionally been the focus [4]. The principle of
availability is shared between the IT/OT domains.
However, facilitating an integrated approach will also
require integrity and confidentiality matters to be
Page 5853
considered each presenting unique circumstances in
The scale and pace at which IoT technologies are
generating, collecting and streaming data also
introduces data integrity and availability challenges
brought about with sporadic connectivity of things
and network reliability issues [21]. Assuring the
integrity of data of every event generated in IoT is
not practical. IoT data collected from multiple
sources and their synchronization may present data
inconsistency problems. For example, reading event
data from a sensor done independently of other data
is different to the serial consistency required when
comparing it with data from previous readings or the
full consistency required when combining data from
multiple streams and needing the full context of that
data within each stream [24]. Further, data generated
from IoT structured, for example, to minimize
resource consumption may not be consistent with
formats, terminologies or have the metadata of
‘traditional’ data types with business applications or
what is referred to as ‘semantic inconsistencies’ [21].
While some loss of data in the pipeline may be
tolerated from a data consistency perspective [24],
this may not be the case in a security context. For
example, the operation of critical infrastructures such
as power plants, energy grids or transportation could
lead to costly downtimes or an environmental
catastrophe if critical data was unavailable [21].
Further, the loss of data generated and analyzed about
individuals through for example wearable devices
may result in reputational damage and liabilities due
to commercial confidentiality agreements and privacy
regulations relating to the protection of personal
identifiable information (PII) [21]. Finally, with
constant streams of data the decision to keep
everything to satisfy regulations and policies relating
to data retention may not be sustainable. Against this
backdrop is the question of who owns IoT data, the
person who created it, the manufacturer of the
sensors collecting the data and/or the owners of the
platforms aggregating and analyzing the data [69].
As seen in the above discussion governance
processes are “complexly constituted” [19]. Whilst it
is widely recognized that innovative and
collaborative approaches to governance will be
required, there is currently limited guidance as to
how this can be achieved. Structures, processes and
mechanisms that are well established in the fields of
IT and data governance may still provide useful
guidance as they are underpinned by similar
principles of integrity, confidentiality and
availability. However, we argue that as IoT is
transboundary, engagement with multiple
stakeholders outside of (inter)organizational
structures is required. Further meanings and
interpretations of values and principles may differ
across the multiple scales and levels of IoT. What
does stakeholder engagement mean in these contexts?
We argue that there is a need for theoretical and
empirical development into the governability of IoT
and big data analytics. The field of interactive
governance (IG) [see for e.g. 37] may offer useful
guidance, examining, broadly speaking, how the
properties of a system to be governed, namely its
diversity, complexity, dynamics and scale, the ability
of actors to participate and the responsiveness of
different governance modes (e.g. hierarchical, self or
co-governance) make, in this case, IoT more or less
Further the IoT and big data analytics governance
terrain is peppered with work that is separate and
related to law and regulations and ethics.
Governance may comprise policies and guidelines
that overlap with regulations. For example, the EU
General Data Protection Regulation (GDPR) means
that data and analytics leaders need to comply with
privacy requirements whilst at the same time meeting
demands for more autonomous access to data [17].
Or, the complexity of developing standards for
multiple ‘things’ at multiple scales and levels
requiring the coordination of different standards
bodies to address the range of concerns from the data
format itself to global infrastructures [55]. Coupled
with this are ethical matters relating to the
generation, recording, processing, distribution,
sharing and use of data, the algorithms that process
and analyze the data and corresponding practices and
infrastructures, including codes, standards and
responsible innovation [19]. As an emerging
technology, the question for IoT is not simply in
terms of attempting to anticipate unforeseen
circumstances, a limitation of top-down risk based
models of governance, but also to become more
responsive to societal needs [36], where our
discussion now turns under the umbrella term of
responsible research and innovation.
4.2 Responsible Research and Innovation
As discussed above, the design, management and
governance of complex and emerging IoT
information infrastructures presents a ‘grand
challenge’ as it traverses different scales, levels and
involves diverse stakeholders, with different,
potentially conflicting intentions, motivations, ethical
frames and data needs.
Further, the emergent and uncertain characteristic
of IoT raises possibilities for unknown or unintended
consequences [73] and uneven or unequitable access
Page 5854
to IoT resources [52]. This presents the “dilemma of
control” [15]; where the negative consequences of
decisions made today become expensive, difficult or
impossible to reverse in the future when they are
embedded into the social and economic fabric of the
global IoT information infrastructure. Researchers,
policy makers and practitioners are required to adopt
responsible research and innovation (RRI)
approaches [35, 67] to ensure that future risks or
negative outcomes of IoT can, as far as possible, be
anticipated and prepared for during the technology
design and policy making processes surrounding IoT
development. RRI is presented as “a meta-
responsibility that aims to shape, maintain, develop,
coordinate and align existing and novel research and
innovation processes, actors and responsibilities with
a view to ensuring desirable and acceptable research
outcomes” [70]. This requires that designers and
researchers consider the future consequences of their
design decisions and place greater emphasis on
technology assessment and foresight studies [28, 41].
By doing so, to incorporate ethics and reflexivity into
the design process [35, 65] and explicitly address
matters of sustainability and equitable access to IoT
systems, products and services [76].
5. Conclusion
In this paper we frame the field of IoT as (more
than) technology, multi-scale and multi-level and
emergent and uncertain. Our purpose was not to
provide a comprehensive literature review of the
field, but rather to examine the multiplicity and
fluidity of views and practices through critical
discourse to begin laying a theoretical and
methodological foundation for advancing research in
big IoT data analytics. In doing so we draw attention
to a number of issues and examine research
implications arising from them by the foregoing
discussion. IoT is not simply constituted by
technologies, but also by the principles of
configuration by which these technologies are
organized. The contours of the IoT field are
necessarily fluid and contingent as it is a developing
field. By providing a more ‘malleable’ framing of the
IoT field rather than a singular conception that
presents a simplified picture, we recognize IoT as a
complex empirical reality. By undertaking this
interdisciplinary review, we have charted a path that
provides concepts and dimensions that does not
exhaustively catalogue all conceptualizations
available but provides an anchor to a research agenda
and stimulates debate in the emerging field of IoT
and big data analytics.
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Page 5857
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