=Paper=
{{Paper
|id=Vol-2101/paper10
|storemode=property
|title=An IoT Model for Coping with Trade-offs in Designing Smart Environments
|pdfUrl=https://ceur-ws.org/Vol-2101/paper10.pdf
|volume=Vol-2101
|authors=Fabio Cassano,Antonio Piccinno
|dblpUrl=https://dblp.org/rec/conf/avi/CassanoP18
}}
==An IoT Model for Coping with Trade-offs in Designing Smart Environments==
https://ceur-ws.org/Vol-2101/paper10.pdf
An IoT Model for Coping with Trade-offs in
Designing Smart Environments
Fabio Cassano, Antonio Piccinno
Dipartimento di Informatica, Universitá di Bari Aldo Moro, Bari, Italy
{fabio.cassano1, antonio.piccinno}@uniba.it
Abstract. The Internet of Things (IoT) world is composed by a huge
number of different so called “smart devices” and every year new and
different models are released on the mass market. Most of those devices
are intended to be used by professional people or by companies. Thanks
to the constant growth of the “smart objects”, end users and people with
low or no-knowledge of the IT-world get in touch with these pieces of
technology. Those people are expected to use the smart devices “out of
the box” and in a very simple and easy way so, the human-device inter-
action needs to be as easiest as possible. Despite of this need, end users
are commonly faced with thousand of different technological standards
which are hard to evaluate without a solid IT background. Thus, the com-
parison to understand which IoT device performs better in a particular
situation become complicated. In this paper we propose a comparison
of two different IoT solutions using an IoT model. The model assesses
the different technical specifications of the devices and then extracts a
“score” for each technological aspect. The end user can use the score
to better understand the points of strength and the weaknesses of the
devices.
Keywords: IoT, Smart Device, IoT model
1 Introduction
The mass market offers to expert and non-expert users thousands of different
Internet of Things devices. Those devices are generally composed by sensors, elec-
tronics boards and antennas that allows them to communicate with the “outside”
world. Due to the enormous number of combinations of those three components,
the number of the available devices gets higher and higher. Hardware, program-
ming, security, protocols etc. are terms that often are not clear to the end user
that has to face the choice among multiple IoT devices. Indeed, the complexity
of the choice increases when multiple objects can be used in multiple application
contexts. Often, specific devices that are built to work in certain domains, thanks
to their characteristics, can be also used in more and different contexts. For ex-
ample, a temperature monitoring device for the home environment can be also
used in a pharmacy store to check and control the temperature of the medicine
room, using a “sink” node to transmit the data over the Internet. To correctly
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configure an ecosystem made by multiple different devices, end users need to un-
derstand most of the device’s characteristics [3,15]. This is not an easy task even
for experienced people, due to the high number of different technical variables
that need to be considered during this process. Thus, finding the right “trade
off” between the technological aspect of the ecosystem and the different devices
may be long and challenging. In literature, examples of comparing physical de-
vices that require skills in selecting the right devices, preparing the environment
settings, selecting users, identifying the tasks, etc. are reported [2,3].
In this paper we first propose a model to assess smart devices in order to give
a way to assess the suitability of a specific smart device to a certain application
domain. Each device characteristic can be assessed and compared in order to
choose the one that best satisfies the end user needs. We also report a first
validation of the model by comparing two different smart devices.
2 The IoT Device Model
The proposed model has been developed by analyzing the characteristics of a
number of different devices. It is shaped as a star diagram (Figure 1). A first
version has been proposed in [14,12]. The model is composed by four “macro”
elements each describing a specific technological perspective of the device: Com-
munication, Target, Data Manipulation and Development.
The first one represents the device communication element and concerns how
the data are transmitted by the device. The “Target” element considers the con-
text of use of the device, and it is associated to the device hardware implemen-
tation. The “Data Manipulation” element covers all the characteristics related
to the data generated and managed by the device. Finally, the “Development”
element analyses the programming characteristics of the device. “Target” and
“Development” are detailed with a group of more sub-elements that help the
assessment of the device.
2.1 Communication
The Communication element represents how the communication between the
device and the external world occurs. It analyses the technical aspect of many
device’s characteristics, such as: the Security, the communication Protocol, and
the Destination of the communication.
The Cryptography is an aspect related to the security of the communication.
Cypher data allow them to be sent and received over the net, keeping the content
safe from unauthorized users [22]. For example the elliptic cryptography is always
preferred to a dictionary-based one, as reported in [18].
The Protocol is related to the application protocol in the communication.
All the application protocols generate some overhead in the communication, as
they add more “control” information to be sure that the data reach the receiver
uncorrupted. To receive a good assessment in this element, a low-overhead pro-
tocol must be used. For example, COAP (COnstrained Application Protocol) is
81
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User
Engagement
Functionatlities
addition
Firmware /
API Customization
Low level design
Professional Domain
End Users
Developer Experts
Development
Cryptography Data Privacy
Collection
IoT Data
Protocol Communication
device Manipulation
Visualization
Data
Destination
Transformation
Target
Personal Professional Mixed
Hardware Communication type
Microcontroller
Cost Accuracy
Standard Remote
Size
Compliant Application
Fig. 1: The IoT device model
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preferred to HTTP (Hyper Text Transfer Protocol) since it generates a very low
overhead [21].
The Destination is related to “where” the data are sent. This is a critical
aspect as it makes the difference in an IoT ecosystem when multiple similar
device communicate each other at the same time. Sending the data directly to
a remote server may generate excessive traffic and interference on the network,
thus it is important that a coordination (such as the usage of a sink node) is
present.
2.2 Target
The Target element represents the main usage of the device and is related to
the hardware characteristics of the device. We have classified the Target use in
three main area, according its use and/or deploy: Personal, Professional and
Mixed. All those aspects consider, in the assessment process, the hardware char-
acteristics, including the physical communication type. As a general guideline
for the assessment process, an hardware that uses low energy to work, supports
a communication type IoT-compliant (low transfer rate and low power) and is
usually exploited in multiple contexts, performs better than the others [4].
The Personal element considers some technological and commercial aspects
of the device such as the cost and the size, two valuable aspects in the assess-
ment process. The Mixed element considers the cost and the possibility to use
a remote application combined to the device. The Professional element assesses
the accuracy of the sensor used on the device and its “IoT standard compliant”.
Each of the above elements of the target perspective, share one more sub-
element: Hardware communication type, Microcontroller, Cost, Accuracy, Size,
Remote Application and Standard compliant. While Hardware communication
type sub-element refers to the low-level technology used by the device to com-
municate, the Microcontroller assesses the type of the hardware used. The device
cost is particularly important for personal and mixed usages, while for profession-
als, the accuracy in its measures should be considered. Personal devices should
be small in size in order to fit everywhere, while for a mixed usage, the possibility
to have a remote application is an important factor. Finally, for professionals it
is imperative to use standard-compliant devices.
2.3 Data Manipulation
The Data Manipulation element is related to the data, from the Data Collection
and Data Transformation, to the Visualization involving also the Data Privacy.
This part is critical as it is the connection between the device low level sensor
and the “outside world” represented by mobile applications, servers, services etc.
[5].
The Data Collection is an important aspect related to how the data are
temporary stored into the device. There are two different approaches to this
aspect: the buffering (which is the temporary store of multiple data instances)
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or the “immediate send”, which involve the transmission of the data as soon as
it has been produced. The first one is less energy consuming [19].
The Data Transformation involves the conversion or the enrichment of the
data. These kind of operations are supposed to be made on an external server
rather than on the device itself.
The Data Visualization analyses the aspect of the representation of infor-
mation on the device. To save energy, the device should not show anything on
it, but it should send the data to a remote service. An example of visualization
available on a web server is CBP, that shows home energy consumption detected
by IoT devices [6,7]. CBP was also used to show collaboration among distributed
team member that can be detected by IoT devices or software [8,9].
The aspect of the Data Privacy is related to the type of the data collected
by the device [17]. Data integrity (the assurance of accuracy and consistency of
the data) and privacy level of the people using the device are two of the most
critical aspects in this element.
2.4 Development
The Development element involves the possibility of the device to be programmed
by people. It has three target users: the Professional Developers, Domain Expert
and End Users.
Professional Developers are computer-literate people that are familiar with
programming languages, low-level programming and model driven testing [20].
They can read and understand the project specifications and they are able to
modify it according to their needs.
Domain expert users are people familiar with the IT world. They know how
to program, thus they can use the API to develop a small piece of software that
is able to perform the required need.
End users are not familiar with the computer or (in general) with the technol-
ogy. They are not able to program, thus they need to be supported by possibly
visual paradigms (such as ATOOMA, IFTTT, Tasker or EFESTO)[16,10]. They
may also have the need to collaborate each other to reach together a shared
result [1].
This element has multiple sub-elements: Firmware/Low level design, API,
Customization, Functionalities addition and User engagement. The former be-
longs to the world of Professional Developers: as their ability to program with
low level languages, the source of the project and the possibility to edit the
“core” of the system is essential. Domain experts, can use the API to program
their solution to their own needs. End Users need to Customize and add or re-
move some functionalities on their own. The user engagement is important as
well [11].
3 Discussion
The aim of this model is to give users a way to assess one or more different IoT
devices. By the assessment the end user can detect the right trade off between
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Level 1 Level 2 Level 3Technology Assessment
Protocol HTTP 1
Communication Destination Server 1
Cryptography N/A 0
Hardware Communication Wi-Fi 1
Microcontroller Arduino 1
Personal
Cost 50e 1
Size Small 1
Target
Hardware Communication Wi-Fi 1
Microcontroller Arduino 1
Mixed
Cost 50e 1
Remote Application Mobile App 2
Collection Buffer 2
Data Data Transformation N/A 2
Manipulation Visualization No 2
Data privacy Ambient Data 1
Professional Dev.s Firmware Design N/A 0
Domain Experts API N/A 0
Development Customization N/A 0
End Users Functionalities Addition Graphic Design 2
User Engagement N/A 0
Table 1: The Sonoff SC assessment table
Level 1 Level 2 Level 3Technology Assessment
Protocol HTTP 1
Communication Destination Sink 2
Cryptography None 0
Hardware Communication Bluetooth 4 2
Microcontroller Arduino 1
Personal
Cost 30e 1
Size Small 1
Target
Hardware Communication Bluetooth 4 2
Microcontroller Arduino 1
Mixed
Cost 30e 1
Remote Application N/A 2
Collection Immediate Send 1
Data Data Transformation N/A 2
Manipulation Visualization LED 1
Data privacy Ambient Data 1
Professional Dev.s Firmware Design N/A 0
Domain Experts API N/A 0
Development Customization N/A 0
End Users Functionalities Addition N/A 0
User Engagement N/A 0
Table 2: The Home-made device assessment table
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all the characteristics of the devices and chose which one suits better to his/her
needs. The domain experts take advantage of the model by having under control
all the devices characteristics at a glance, even if they are not technical experts.
This is an evolution of the work of presented in [13,14,12].
In this section we propose a comparison between two devices: the Sonoff SC
ambient analyzer and an home-made air and light analyzer. The first one is an
all-in-one device, which has all the required components inside the plastic case.
It has an external power supply (an USB power plug) and a slot for a memory
card to store all the data collected by the sensors.
The home-made air and light analyzer is composed by two parts: a Rasp-
berry PI 3 and an Arduino board with many sensors (such as humidity, CO2,
light sensors) connected to it. The communication between the Raspberry and
the Arduino uses the Bluetooth 4 technology. The Raspberry can formally be
connected with other Arduino boards and act as an external “sink”, where all
the data get collected and then sent to the external web server.
To assess all the device characteristics, we propose a score (s) from 0 to n
(with n > 0) to assess the elements and the (optional) sub-elements of the model
as follows:
– s = 0: the characteristic is not supported by the device;
– [0 < s < n]: the device partially supports the characteristic (e.g.: it adopts
a technology, but a new and more performing technology is available);
– s = n: the device fully supports the characteristic in terms of both function-
alities and recent technology.
In this assessment, for the sake of simplicity, we set n = 2.
Table 1 shows the assessment for the Sonoff SC ambient analyzer, while
Table shows 2 the assessment of the home-made air and light analyzer has been
shown. For the sake of simplicity, each element and sub-element has been named
according to a “level”, which is the distance from the center of the star. At
a first sight, the two devices are very similar in terms of performances, price
and technology. Both of them can be used in a personal and semi-professional
environmental setting.
The assessment of the Communication element is similar. The home-made
device, supporting the sink node, performs slightly better than the Sonoff and the
assessment of the Target element is almost the same as the previous one. Thanks
to the Bluetooth 4 technology, the home-made device has a better assessment
than the Sonoff.
The Data Manipulation is performed better by the Sonoff device, thanks to
the possibility to bufferize the data and the absence of any LED or display that
shows the data sent. Similarly, the Sonoff device allows a little bit of personal-
ization of the behavior of the IoT device: thanks to the graphical programming
application, it is possible to define rules and send alerts to the users, if needed.
The overall results from the assessment of those two devices shows that they
are almost equivalent in all the fields. The home-made device is more industry
oriented, while the Sonoff is better to be used in an home environment. They
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resides in the End Users element because the Sonoff SC device gives users the
possibility to program alerts and functionalities.
4 Conclusion and Future Works
In this paper we have presented a model to support the assessment of a smart
devices according to different dimensions. The model, has four “macro” elements:
Communication, Target, Data Manipulation and Development. Each of them is
then further specified. By ranking (using a discrete scale) each element, it is
possible to get an overall assessment of a given IoT device. Its suitability for a
specific domain is then evaluated comparing its assessed characteristics to the
domain needs. We provided an example that assesses and compares two similar
IoT devices that can be used at home.
In order to formally evaluate the model we are currently planning a test
study with speech-therapist physicians and IoT devices.
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