Humanizing the Internet of Things
Toward a Human-centered Internet-and-web of Things
Antonio Pintus, Davide Carboni, Alberto Serra and Andrea Manchinu
CRS4, Loc. Piscina Manna Ed.1, Pula, Sardinia, Italy
Keywords: Internet of Things, Web of Things, IoT, WoT, People, IoP, Web Platforms, Social.
Abstract: This paper envisions how the Internet of Things (IoT) complements the Internet of People to build a human-
centered Internet-and-Web of Things. The Internet of Things should go beyond the Machine-to-Machine
paradigm and must include people in its foundation, resulting in a “Humanized Internet of Things (H-IoT)”.
Starting from a relevant work of Fiske, this paper defines how the Human-centred Internet of Things can
embed the Fiske patterns in this particular domain. An analysis of some of existing IoT platforms and
projects is also presented with the aim to analyse how real implementations are in the same direction of such
social patterns.
1 INTRODUCTION
Today the Internet of Things (IoT) is one of the main
topics of discussion in the ICT world; it can be
defined as the interconnection of uniquely
identifiable embedded computing devices within the
existing Internet network. The IoT evolved to a
convergence of multiple technologies, ranging from
many different fields of application such as
industrial, health, Smart Grid and Smart Cities in
general covering the Machine-to-Machine (M2M)
paradigm. Until nowadays the main effort was to
create applications and platforms hardware oriented,
to improve devices connection and communication,
giving little importance to aspects related to the
user-experience, privacy and security policies. In
other words, giving little importance to the human
side of the Internet (of Things).
The aim of this work is to investigate an
alternative point of view that includes people in the
IoT loop to give a more human perspective to the
technology.
2 RELATED WORKS
Several works stressed the need for the IoT to go
beyond a pure Machine-to-Machine (M2M)
paradigm, in order to also include people. First steps
toward this aim have been connecting things in a
sort of extended social networks, the so called
“Social Internet of Things”, but it was originally a
concept where things were capable of establishing
social relationships with other objects and
autonomously with respect to humans (Atzori et al.,
2014). About this topic, other works focused in
using supernetwork theories (Cheng et al., 2014) to
model relationships between humans, things and
services, resulting in proposing models to create a
social network involving humans and things but with
a user experience and human interaction to improve
and further test. First fusion of traditional social
networks with data coming from sensors remarked a
potential, strict correlation between that world and
the IoT (Schmid and Srivastava, 2007); where other
works (Guinard, Fischer and Trifa, 2010) and
platforms (Paraimpu, 2015) not only extended this
paradigm of socializing things and produced data
through Facebook or Twitter, but also envisioned the
possibility to share these things with people in a
social circle and to use them for their personal aims.
That vision of a social IoT could be seen as a
declination of the Sharing Economy and
Collaborative Consumption (Botsman and Rogers,
2010) paradigms (Pintus, Carboni and Piras, 2011).
3 HUMANIZING THE INTERNET
AND THE WEB OF THINGS
In earlier IoT research, its related definitions,
scientific papers and scenarios remarked the
498
Pintus A., Carboni D., Serra A. and Manchinu A..
Humanizing the Internet of Things - Toward a Human-centered Internet-and-web of Things.
DOI: 10.5220/0005475704980503
In Proceedings of the 11th International Conference on Web Information Systems and Technologies (WEBIST-2015), pages 498-503
ISBN: 978-989-758-106-9
Copyright
c
2015 SCITEPRESS (Science and Technology Publications, Lda.)
property of a new way to automate daily processes
involving “things” with a little or no human
intervention at all.
Nowadays, on the other hand, we believe that the
IoT deeply involves people interaction with things
and devices, at least in every day scenarios, like
training, health, home appliances automation and so
on.
As stated in an interesting work (Wilson, 2014),
in a human-friendly IoT vision, involved things need
to talk to other things we use, be conspicuous and
attractive and go beyond remote controlling them.
These assumptions are a good starting point and
we state that a Humanized Internet of Things (H-
IoT) includes the “classic definition” of IoT
(basically M2M-focused) plus the Social Internet of
Things (S-IoT) and the Internet of People (IoP),
going toward the concept of the Internet and the
WWW as an extraordinary means enabling
interactions between communicating entities: smart
things and people, the physical world and the digital
one.
There is plenty of scientific literature about
technology in IoT, while in this paper we focus on
human and organizational perspective. On the other
hand, despite we believe that a user-centered design
of IoT applications should be taken into
consideration to complete the H-IoT concept, in this
paper we do not face Human-Computer Interaction
aspects, which are accurately analyzed in
(Koreshoff, Leong, and Robertson, 2013).
3.1 The Social Internet of Things and
the Internet of People
In this paper we define a new domain for social
interactions pattern as introduced by Fiske (Fiske,
1992). In his relevant psychological work, Fiske
identified four common forms or models of sociality
that people use in their relations. Each model is
distinct in the rules and values of how people
interact. These patterns are: Communal Sharing
(CS), Authority Ranking (AR), Equality Matching
(EM) and Market Pricing (MP).
Figure 1 shows how Fiske’s model can be
mapped to the social aspects of a H-IoT, remarking
the main features of each pattern and how them
relate to equivalent ones in our specific domain.
A deep analysis of Fiske’s forms goes beyond
the aims of this work; they are applicable to many
domains, but what we want to shape here is if and
how they can be shifted and projected toward our
idea of a H-IoT, stressing where these model of
sociality can naturally include Things and People.
Figure 1: Fiske’s Four Elementary Forms of sociality
projected to a Humanized Internet of Things.
In our view, a social H-IoT exposes all the
elementary forms of sociality between people but it
adds a new layer: Things over the Internet/Web.
People interact with things and devices; people use
Fiske’s similar patterns to establish social actions
and group goals with other people through things. In
this domain, Fiske’s Communal Sharing is adapted
to a totally trusted sharing of things, where a person
let all persons in the community to use his/her smart
things, such permission is not revocable in principle
and the other persons have the same level of control
of the owner because of mutual trust. So, for
example, a person could share its connected smart-
TV with his/her friends and family members because
of the strict level of trust; or he/she can create
groups of social equivalence in sharing home things;
for example, some devices can be used only by
family members, whereas others also by hosts.
In the domain of the IoT, the Authority Ranking
pattern is built around an authority, maybe
hierarchical: things and related Internet resources
can be shared but not the authority over them.
Things owners can set restrictions and/or revoke the
social interaction between a thing and another
person. Thus, most of the authority is in the hand of
a single actor while the others have least-authority.
Again, in this IoT domain, the Fiske’s Equality
HumanizingtheInternetofThings-TowardaHuman-centeredInternet-and-webofThings
499
Matching form is adapted to shape a collaboration of
people sharing smart things for a specific goal. For
example, adopting a weighted one for one
correspondence people could use environmental data
observed by shared sensors to build together a new
distributed application for pollution alerts in a city,
with no authority over these relations and with a
good balance between benefits and contributions.
Finally, adapting the Fiske’s Market Pricing
pattern requires a transaction-based model over
exchanges and things sharing, providing a definition
of rational cost-benefits calculation over things
usage; For example, we can think about it as smart
devices renting, or an IoT platform sold as-a-
Service, where also contracts and specific terms-of-
service could rule this type of relationship.
A H-IoT tool should expose one or more of these
features, theoretically tracing an equivalent form of
natural social relationship patterns between people.
A good definition of IoP we like can be found in
(Vermesan et al., n.d), where the IoP is defined as
the interconnects growing population of users while
promoting their continuous empowerment,
preserving their control over their online activities
and sustaining free exchanges of ideas. The IoP also
provides means to facilitate everyday people’s life,
communities and organizations, allowing at the same
time the creation of any type of business and
breaking the barriers between an information
producer and an information consumer (the
emergence of prosumers).
Another definition of IoP is proposed in
(Hernández-Muñoz et al., 2011) and it is envisaged
as people becoming part of ubiquitous intelligent
networks having the potential to seamlessly connect,
interact and exchange information about themselves
and their social context and environment.
The IoP and the S-IoT are strictly related in
overtaking the original M2M-related definitions of
the IoT: adopting a H-IoT people interconnect,
interact, socialize, create, communicate, make and
become prosumers (both producers and consumers)
through the Internet/Web of connected things and
people, implicitly using the IoT equivalent of the
Fiske’s four elementary forms of sociality. That’s
the new era of the Internet: a Humanized Internet (of
Things).
But, to go toward a real H-IoT it is mandatory to
take into consideration how the four H-IoT patterns
can be managed. From a technical point of view,
using some form of digital contract or policy could
shape the patterns. The policies should be flexible
enough to cover the four aspects described above
and, of course, they involve a balance between
identities and privacy, authorities and trust, rules and
permissions.
3.2 Implementing the Social Patterns in
the H-IoT Domain
This section addresses some more detailed
descriptions of the Internet of Things domain as a
new one in the Fiske classification. For each social
pattern the issues and possible high-level technical
solutions are broadly described. In the next sections
some existing Internet platforms for smart Things
are then evaluated against these features.
3.2.1 Communal Sharing
In this pattern the level of trust is the highest as the
smart things are in principle controllable and
shareable by everyone in the community. Building a
community of trusted peers is then the point to face
here, and the balance between disclosing the
identities of peers and keeping their privacy is
important, too. Given the level of control on the
smart things, this management is similar to the
sharing of credential among a group of sys admin in
a computer system. In such a case there exists at
least one authority over the community, which
knows the identities of each peer, but inside the
community the hierarchy is flat and every participant
is equally entitled to manage the resources. The
community manager is commonly a trusted entity
with a known identity. The community manager is
not required in cases like a community is
spontaneously formed by means of contextual fact.
For example, people and device inside a given place
forms a “community” because their mere presence is
a proof of trust in that context.
3.2.2 Authority Ranking
In this pattern a person with a particular authority
(e.g., ownership) shares things with other people but
he/she doesn’t share the authority over them. Thus,
the set of defined rules in resulting digital
environments development must ensure that
authority can not be changed by people down on the
established hierarchy; for example, by people who
are not owners of a shared thing. Authority must
have the choice to change policies, too. In this case,
social circles or groups are formed because people
follow an acknowledged leader: identified by social
influence, value of shared things and resources, level
of influence in a specific community or by technical
skills.
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3.2.3 Equality Matching
To extend this pattern to the domain of the Internet
of Things we could start from the definition in the
domains of work and contribution in (Fiske, 1992).
The idea is that every peer contributes in a balanced
way eventually to reach common goals. As example
we could imagine a use case in which an actor
contributes with a temperature sensor, while another
with a noise sensor, and a third one contributes
developing a software application that elaborates
data from the sensors and provides new information
with value for the local environment. The three
actors are not establishing a hierarchy but they are
pursuing a common goal where each one is equally
contributing and equally getting some benefits.
When speaking of Internet-related resources, the
point is how to measure the “equality” of each
contributor in order to keep the balance among
peers. What if one of the contributions is
quantitatively much bigger or much lower than the
others? To manage the above points a distributing
approval process could be deployed. In other words
the individual feeling that some of the other
contributions are unfair should be able to choose
either to withdraw the group or to promote an action
to ban the participant who are not providing enough
value by starting a remote voting.
3.2.4 Market Pricing
This pattern is based on the existing infrastructure of
a market. Products pages, shopping carts and back-
office processes are the key elements of this pattern
in order to serve orders from the purchase to the
final delivery. In the field of the Internet of Things it
should be defined what is finally sold. At one end
there are manufactures that sell physical objects, in
other words they sell the smart things and then
publish on an app store like Apple Store or Google
play some free-apps. Often, the real value is not in
the hardware but in the software, even if this is given
for free. As example, we mention the case of iRig™,
a special purpose cable which allows to add real
time effects to an electric guitar. The valuable part is
the software, which is given for free from the App
store, but the revenues come from cables shipping.
At the opposite end, there are markets, which do not
sell the physical stuff, but sell some form of digital
asset for one or more hardware platform. Glue.things
is an example of this concept. In this platform users
can register their smart objects, design applications
and event management processes, and finally share
and trade their applications in a dedicated market.
3.3 Comparison of Existing IoT Web
Tools and Platforms
To analyse the impact of the Fiske’s model and to
discover its application, we seek the four patterns in
a number of tools and platforms already available on
the Web. Here follows a short analysis of major
platforms. The analysis is aimed at classify if a
platform is more inclined to one or more Fiske
patterns as we have previously informally defined
for the domain of IoT.
IFTTT (If This Than That) (IFTTT, 2015) is a Web
platform that allows users to automatize tasks on the
Internet. For instance, the user can define a rule
(called “recipe”) to manage an event coming from
one device and under a given condition to perform
an action on another system (a device or a web
service). Its main advantages are easiness of use,
recipe sharing between users and a large set of
available services/devices.
In IFTTT it is not possible to share things, so a
Communal Sharing seems not applicable, however it
is possible to share recipes as templates to define
personalized actions for a specific goal, thus recipes
goes toward an incomplete Equality Matching
pattern because only goals are shared but not things
as means to fulfil them.
Paraimpu (Paraimpu, 2015) provides a personal
workspace where users can register devices
providing a basic level of virtualization. An
integrated transformation engine allows composing
things managing their heterogeneity. It has a good
balance between simplicity of use and flexibility,
social-ability and things sharing (Pintus, Carboni
and Piras, 2012). Paraimpu partially supports the
Communal Sharing pattern because it is not possible
to create fine-grained social circles; however, when
things are shared as “public” they are available to
use to all the people belonging to a person’s social
circle. Paraimpu implements the Fiske’s Authority
Ranking model: authority over things is enforced
and cannot be changed by people whose not own a
particular thing. The platform enables Equality
Matching pattern because people can share things
and data with other users to build “cooperative”
applications to fulfil a particular shared goal.
Xively (Xively, 2015) provides a platform, services
and support needed to create and manage connected
products and services on the IoT. It provides a basic
workspace and it’s more developer-oriented than the
other tools, thanks to a good, consistent, set of
libraries. The set of available tools are really
oriented toward a company-to-product-to-customer
model, so things sharing and related patterns could
HumanizingtheInternetofThings-TowardaHuman-centeredInternet-and-webofThings
501
be applied in an intra-company environment, where
people belonging to a company (in this case: the
social circle) use Fiske patterns to reach a particular
goal, that is: to produce and to sell products.
Equality Matching can be not directly implemented
through provided API and credentials.
SocIoTal (SocIoTal, 2015) aims to design and
provide key enablers for a reliable, secure and
trusted IoT environment. It will enable the creation
of a socially aware citizen-centric Internet of Things
by encouraging people to contribute with their IoT
devices and information flows.
By providing communities with secure and trusted
tools that increase user confidence in IoT
environment, SocIoTal will enable their transition to
smart neighbourhood, communities and cities.
SocIoTal supports Communal Sharing pattern
because each person has a number of different trust
zones or communities. A trust zone represents a
group of people or objects that can access the
resources in the community. Participants can decide
at any time to leave the community revoking any
previous access to the other participants.
SocIoTal presents also a form of Authority Ranking
model: information sharing and data access have the
primary role to limit and control the access to data or
resources.
This platform also enables the Equality Matching
pattern. That’s because the entire project is finalized
to share things, create a community of trust and
reach a common result. An example of this pattern is
represented by the description of the project use
cases like “Car Pooling” (N. Gligoric et al., 2014).
Glue.things (Glue.things, 2015) is a Platform-as-a-
Service (PaaS) designed for applications and
services for the Internet of Everything. It sells some
form of digital assets for one or more hardware
platforms. In this platform users can register their
physical smart objects and connect them with other
virtual objects, can design apps and event
management processes, and finally share and trade
their apps in a dedicated market.
There is a smart objects marketplace that gives to the
user the chance to distribute and share the output
data of his/her devices and his/her applications with
the community. This market provides flexible
revenue models, which clearly focus on the means of
your target groups. Glue.things supports Fiske’s
Market Pricing pattern allowing enterprises and
innovators to introduce new Internet of Things
enabled services and apps in a short time and with
limited upfront investment.
The platform also partially supports Communal
Sharing pattern, sharing smart objects data and apps
to the developer community through the
marketplace. There is no implementation of the
Equality Matching because people cannot share,
contributing in balanced way, smart objects to reach
a common goal.
Authority Ranking pattern is partially implemented
because people can share apps and data selling it
through the market and sharing the authority over
them.
The following Table 1 summarize the comparison
between the selected tools/platforms described and if
they match the four patterns of Fiske in the H-IoT:
Table 1: Comparison of some existing Web tools and
platforms with respect to the envisioned H-IoT properties.
CS AR EM MP
IFTTT No No Partially No
Paraimpu Partially Yes Yes No
Xively Partially Yes Not directly No
SocIoTal Yes Yes Yes No
Glue.things Partially Partially No Yes
The indications on the Table could suggest which
one of these common IoT platforms to date is more
ready for the H-IoT than the others.
4 FUTURE WORKS AND
CONCLUSIONS
In this paper we describe a socially-centered model
where Internet of Things applications are be
confined to Machine-to-Machine technology issues.
In this respect, people and sociality patterns are a
key factor for the emergence of a Humanized
Internet of Things (H-IoT).
These considerations led us to examine not only
a user-centered design, which is not covered in this
paper, but also to dissect an implementation of the
common basic patterns of human sociality defined
by Fiske in his famous work. We have extended
Fiske work introducing the IoT domain as a new one
in the Fiske classification. For each social pattern,
the issues and possible high-level technical solutions
are broadly described together with some
suggestions about their implementation in a real IoT
application or platform.
Fiske’s model projection to the IoT domain is
interesting because all processes involving people
sociality could build a conceptual framework to
better envision and design the IoT of the future.
Trying to find an actual implementation of
Fiske’s models toward a real H-IoT guided us to a
basic set of lesson learned and recommendations
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about open issues in IoT platforms. Then, we
examined some of the existing platforms, checking
their adherence to the envisioned H-IoT model and,
as remarked, none of them fully support the four
transposed Fiske patterns, maybe due to specific
business models or market targets. Anyway, some of
them seem very promising toward a better H-IoT
adherence. Future works will refine the explored H-
IoT concepts to provide a full conceptual framework
and recommendation to build socially-aware and
Fiske-complete systems.
ACKNOWLEDGEMENTS
This paper describes work undertaken in the context
of the SOCIOTAL project (www.sociotal.eu).
SOCIOTAL is a Collaborative Project supported by
the European 7th. Framework Programme, contract
number: 609112.
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