2016 IEEE 8th International Conference on Intelligent Systems
Smart Learning Environment for
the Development of Smart City Applications
Manuella Kadar
Department of Computer Science
1 Decembrie 1918 University
Alba Iulia, Romania
mkadar@uab.ro; manuellakadar@yahoo.com
and the benefits of learning out of the classroom in
combination with smart classroom activities carried out in a
smart environment. The paper is structured as follows: section
2 presents the key features of smart learning environments and
ubiquitous learning, section 3 presents the concept and
architecture of the SCL computing system, followed by the
functionalities of SCL computing system, section 4 presents
learning scenarios and means of learning evaluation, while
section 5 points out on conclusions and further work.
Abstract—The paper presents the results of the “Smart
People for Smart Cities!” project funded by European Structural
Funds Human Resources. “1 Decembrie 1918” University of Alba
Iulia, Romania in partnership with UTI SA Romania, UNINOVA
Portugal and INFOR ELEA Italy have collaborated to bridge
and enhance people’s skills and competences for the development
of smart city applications within the university curricula of
Applied Electronics, Computer Science and Environmental
Engineering specialties. The primary goal was to create an
enhanced learning and teaching environment, goal that was
achieved through specific objectives: (i.) to set up a research testing laboratory where real conditions of a smart city is
simulated; (ii.) to develop and implement a software instrument
for students’ evaluation; (iii.) to develop and implement a
software solution for processing data provided by various types
of sensors used in the smart city environment. The project
successfully complemented the academic curricula with a module
of practical applications, which simulate existing automation
projects on the market, integrates data obtained from sensors
and provides statistical models. Smart City Lab (SCL) computing
system covers all stages of data processing in automated systems
II.
LEARNING
Smart learning (s-learning) means a new learning paradigm
which serves learners to have an efficient learning environment
that offers personalized mobile contents and easy adaption to
current education model. And it also allows learners to have a
convenient communication environment and rich resources [1],
[2]. Table 1 shows some learning options with specific features
and tools, namely: e-learning, m-learning, u-learning, and slearning. Electronic (e-learning) is defined as the use of
computer network technology, primarily over an intranet or
through the Internet, to deliver information and instruction to
individuals [3]. Mobile learning (m-learning) includes the
ability to learn everywhere at every time without permanent
physical connection to cable networks. This can be achieved by
the use of mobile and portable devices such as PDA, cell
phones, portable computers, and Tablet PC [4]. Smart learning
(s-learning) is an important and new paradigm of learning
today. The concept of s-learning plays an important role in the
creation of an efficient learning environment that offers
personalized contents and easy adaptation to current education
model. It also provides learners with a convenient
communication environment and rich resources [1], [2].
Ubiquitous (u-learning) environment is a situation or setting of
pervasive or omnipresent education or learning. Education is
happening all around the student, but the student may not even
be conscious of the learning process. Source data is present in
the embedded objects and students do not have to do anything
in order to learn. They just have to be there [5].
Keywords—Urban computing, ubiqitous learning, smart city
computing system, smart environment
I. INTRODUCTION
Modern smart cities offer the necessary infrastructure and
services to foster a number of formal and informal learning
activities in the city landscape. This paper presents our
proposal on how urban computing system may leverage the
learning process and improve the university curricula for the
specialties of Applied Electronics, Computer Science and
Environmental Engineering. We have developed the Smart
City Lab (SCL) computing system together with various
educational scenarios and activities to be performed by
exploiting SCL. In this paper we present the concept and
architecture of the SCL, the methodology we followed for
designing learning scenarios and the experience we gained.
Learning is a cognitive process of acquiring skills or
knowledge. In our approach both students and teachers have
experimented novel technological means and methods in the
process of exploration of the effects of urban computing on the
learning process. We applied our methodology in the city of
Alba Iulia. Results from our experience demonstrate the
potential of exploiting urban computing in the learning process
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SMART LEARNING ENVIRONMENT AND UBIQUITOUS
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This approach is supported also by [8] stating that a
ubiquitous learning environment provides an interoperable,
pervasive, and seamless learning architecture to connect,
integrate, and share three major dimensions of learning
resources: learning collaborators, learning contents, and
learning services. Therefore ubiquitous learning is
characterized by providing intuitive ways for identifying right
learning collaborators, right learning contents and right
learning services in the right place at the right time [8].
Table 1. Learning Systems
Learning
system
Features
Tools
E-learning
Computer-based
training
Intranet, Internet
M-learning
Flexibility of location
Mobile and portable devices
S-Learning
Personalised content,
efficient and effective
education
Smart mobile devices, PDA, PC
U-Learning
Pervasive education
Mobile and PC devices, sensors,
camera
III.
SMART CITY LAB CONCEPT AND ARCHITECTURE
The Smart City Lab (SCL) computing system aims at
simulating within the laboratories of "1 December 1918"
University Alba Iulia situations and events based on specific
sensors that identify smart city features. By definition, cities in
which all vital systems (transport, parking, water, sewer,
lighting, waste management) are "smart" or "connected", are
called "smart cities". For the scope of this paper we consider
the definition of a smart city as in [9]:
“…are territories with a high capacity for learning and
innovation, which is built-in to the creativity of their
population, their institutions of knowledge creation and their
digital infrastructure for communication”. …. [and are
concerned] with people and the human capital side of the
equation, rather than blindly believing that IT itself can
automatically transform and improve cities.”
From the technological perspective, smart cities have been
defined as cities with great presence of ICT technologies.
These have permeated commercial applications of intelligentacting products and services, artificial intelligence, and
thinking machines to enter and leverage the city’s
infrastructure. Smart homes and smart buildings are example of
systems equipped with a multitude of mobile terminals and
embedded devices as well as connected sensors and actuators
[10]. In this context a smart city becomes the extension of a
smart space to the entire city scale.
The SCL computing system is based on specific hardware
such as: a large variety of sensors, data concentrators, gateway
routers, database server, etc. The hardware covers four areas
of interest in a smart city: (i.) smart management of waste, (ii.)
air pollution, (iii.) noise pollution and (IV.) intelligent
management of street lighting. The software has been
designed as modules that interface with hardware and backoffice modules to interpret the collected data, and to display
and interface with additional applications such as student
evaluation and counselling applications, student physical and
emotional status evaluation applications. Data collected from
sensors can be analyzed, processed and presented as reports
and graphics. Further on, event workflows and alarm
transmissions can be defined. As a lab solution, it allows
transfer of data to additional applications developed by
students. The application is a secured solution based on data
collection from sensors arranged in simulated environments of
a smart city. The information read by sensors are recorded
with a variable frequency that can be defined by the user and
then can be stored in the data warehouse. Based on this
information, graphs/reports can be generated, issuing of
alarms and tracking of workflows for predetermined events
can be also achieved.
The main characteristics of context‐aware and ubiquitous
learning are given in [6] and discussed under the following
eight aspects: mobility, location awareness, interoperability,
seamlessness, situation awareness, social awareness,
adaptability, and pervasiveness. More detailed descriptions of
these aspects are given as follows:
• Mobility: The continuousness of computing while learners
move from one position to another.
• Location awareness: The identification of learners’
locations.
• Interoperability: The interoperable operation between
different standards of learning resources, services, and
platforms.
• Seamlessness: The provision of everlasting service sessions
under any connection with any device.
• Situation awareness: The detection of learners’ various
situated scenarios, and the knowledge of what learners are
doing with whom at what time and where.
• Social awareness: The awareness of learners’ social
relationship, including what do they know? What are they
doing at a moment? What are their knowledge competence
and social familiarity?
• Adaptability: The adjustability of learning materials and
services depending on learners’ accessibility, preferences,
and need at a moment.
• Pervasiveness: The provision of intuitive and transparent
way of accessing learning materials and services, predicting
what learners need before their explicit expressions.
In addition to the context‐aware ubiquitous learning
characteristics presented in [6], the potential criteria of a
context-aware ubiquitous learning environment are formulated
as in [7]:
• Offers more adaptive supports to the learners by taking into
account their learning behaviors and contexts in both the
cyber world and the real world.
• Actively provide personalized supports or hints to the
learners in the right way, in the right place, and at the right
time, based on the personal and environmental contexts in
the real world, as well as the profile and learning portfolio
of the learner.
• Adapt the subject content to meet the functions of various
mobile devices.
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The main goal of such systems is to record immediately
and to set-up parameters of interest for:
•
Real-time monitoring of living conditions;
•
Increasing efficiency and labor productivity;
•
Cost reduction;
•
Minimize response time in extreme situations
The proposed solution is based on 4 sensors kits: intelligent
street lighting kit, air quality testing kit, smart waste
management kit and noise pollution monitoring kit.
Each kit contains one data concentrator that collects data
from sensors and send it to the Meshlium gateway using
ZigBee communications protocol. This communicates with the
4 concentrators, aggregates data and send them to the server.
The server runs Portal Services and Web Services.
The functional modules of the application (back-office) are
implemented on each user terminal and are connected to the
server, providing real time access to the stored data as in Fig. 1.
Fig. 2. Software architecture of SCL
Data collections from sensors and storing into a database is
done using a communication service. This service is bidirectional due to the fact that collects data from sensors and
also sends commands to the sensor kits.
Back-office is modular with components such as:
- User administration module
- Components/sensors management module
- Communication settings module
- Reports/graphics module
- Events workflow module
- Data export module
A. User Administration Module
This functionality allows information management of users
and rights:
• Users - operators who have access to the
application. It manages the following information:
Name, Code, Password, Email
• Groups - allows creating groups that contain one or
more users.
• Rights - allows grant / revoke rights to various
functionalities of the application.
Fig.1. Hardware architecture of SCL
The functionalities of the SCL computing system are:
• Unique identification of sensors and unique
identification of data type for each sensor;
• Data collections from sensors and storing into a
database
• Users and rights management;
• Handling data from the database to issue alarms in
case of escaping from a range of preset parameters;
• Defining alarms based on data collected from
sensors;
• Defining of event workflows;
• Data exports into csv, xls formats
B. Components/Sensors Management Module
The application can uniquely identify each sensor node and
each hardware architecture node. In hardware architecture, the
nodes are represented by data concentrators and gateways. It
defines the following entities:
Sensor - at hardware level, each sensor is identified through
a unique address. Starting from this unique address, other
attributes can be defined for each sensor: sensor ID, name,
type, measured parameter, measurement unit, status, etc.
Node - is represented by the data concentrator or
gateway. The attributes that can be defined for each node are:
ID, name, type (concentrator or gateway), status, parent, etc.
The system strictly identifies sensors in order to keep the
accuracy of retrieved data. Nodes (concentrators) are
identified through type of data transfers that are transmitted by
the central system.
Software applications ensure the implementation of business
requirements through the sensor communication software and
the back - office module (Fig. 2.).
C. Communication Settings Module
The application is able to control and modify the
communication parameters between different components.
Different components of the system and also the
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eight
educational
affordances,
namely:
knowledge
construction, applying, synthesis, evaluation, interactivity,
collaborative learning, game based learning, and context
aware learning. SCL is designed as ubiquitous learning that
provides context aware information and self learning
opportunities for learners. In SCL learning is not viewed only
as a form of delivered instruction, undertaken within the
confines of traditional educational environments. It is
understood as a social process that happens at a time and place
of the learner's choosing. It is collaborative, evolving and
informed by a process of self paced development. It combines
‘aware’ classroom, capable of understanding about the context
of its students; it is also a digitally enhanced outdoor space as
a cityscape. SCL builds a real time interactive classroom with
tele education experience by bringing pervasive computing
technologies. The used approach is to move the user interface
of a real time tele education system from the desktop into the
3D space of an augmented classroom so that in this classroom
the teacher could interact with the remote students with
multiple natural modalities just like interacting with the local
students.
Following we present a number of representative learning
scenarios exploiting the smart city environment.
Fig. 3. Presents the interface for the city of Alba Iulia that
implements several Smart city applications. One application
scenario is the Smart Factory. This application is focused on
reducing maintenance costs and ensuring quality in the
manufacturing process of porcelain.
communication status is identified. For instance, one can
select:
- ZigBee communication settings between data
concentrators and gateways;
- Ethernet / Wi-Fi / 3G communication settings between
gateway and database server;
- Views communication status between different
components.
D. Reports / Graphics Module
The application is able to issue specific reports for both
parameters collected from sensors and communication area and
the status of each sensor / node.
It will also offer features for generate the following
graphics:
• Integration of collected parameters as time function;
• Uptime communication;
• Uptime sensors / nodes.
E. Events Workflow Module
Workflows module for event implementation is designed to
define certain actions that the system must perform if a sum of
conditions are met. In this case, there are defined workflow
settings for normal operation and also tasks to be executed in
the case of certain events. The module is very important, if the
application is interfaced with additional systems to
communicate and transmit tasks to third parties. Interface
defining workflow site is graphical and intuitive.
F. Data Export Module
Data export module represents the module in which
collected data can be exported in various formats such as: xls,
csv, and pdf formats.
This module is important for both data visualization, and
data collection and transfer to additional applications, unless
Web Services connectors are not used.
G. Additional Application Interfaces using Web Services
Given the open feature of the application, it is imperative to
offer a mechanism to native interface with additional
applications. This is designed by using external connectors of
web service type. In this way the application can transmit
information collected by additional applications and also
receive information. Using this module in combination with the
workflow event module, one can elaborate logic diagrams that
perform certain actions using certain inputs, with bidirectional
information running thought these connectors. An example is
the connection with the Ambient Intelligence Environment for
Curriculum Development [11] that integrates modules for
student’s
assessment,
environment
assessment
and
personalized and adapted teaching.
IV.
Fig. 3. SCL interface for Alba Iulia city
A. Case study 1
The objective of this case study is to monitor critical
processes, environmental variables throughout a porcelain
factory, parameters that affect product quality and working
conditions. It monitors air temperature around working robots
and conveyors, light intensity in various working areas and
CO2 concentration in the workers' area and in real time, using a
PC, tablet, or smartphone and an Internet connection. The
integration of sensor technology with Cloud platform allows
data from sensor nodes to transmit directly to the Cloud, for
analysis and use it in a number of applications [12].
EDUCATIONAL SCENARIO AND EVALUATION
The educational scenarios focused on the educational
affordances of the context aware ubiquitous learning
environment. The methodology established types of
educational affordances that can be provided by a context
aware ubiquitous learning environment. SCL can provide
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Battery,
communications
module:
802.15.4,
220V
charger/adapter. The unit houses a battery in its enclosure to
allow the system to measure and transmit data even without a
connected power supply. The battery capacity is 6600mAh:
this means that application autonomy can range from several
days to several weeks without recharging the battery,
depending on sampling frequency. The unit nodes can operate
under battery power or on the grid to continuously recharge
the battery [12].
A multiprotocol gateway and can be configured with different
communication protocols such as Wi-Fi, Ethernet and
802.15.4, enabling the system to receive sensor data from the
nodes, parse it and store it in a local database. The use of
802.15.4 communication technology allows sensor data to be
stored or read as it is measured, so that users can visualize data
“live” in real time. By introducing an IP address in a browser
all the Smart Factory information can be visualized on the
Web interface. Software is pre-configured – data is accessible
anywhere, anytime. Each unit node is programmed and tested
in laboratories so that, once in position, it can start monitoring
when the “ON” switch is pushed. It monitors corresponding
parameters at different intervals because not all processes have
the same priority or critical level. After each measurement,
data is transmitted to the gateway using 802.15.4. Other
information such as battery level is sent to provide robustness
to the solution. Using the cloud connection allows the unit to
start sending data immediately without the need to configure a
own server system. From the outset, the sensor data is
available for download and local analysis, or it can be
transferred to another server. Once the data is synchronized
with the cloud platform, it can be accessed from anywhere.
Fig. 4. The sensor solution in four key areas
B. Sensors in the porcelain factory
In the factory, the porcelain production process is performed
throughout several phases mainly executed by robots. The
final phase of the control is achieved manually by workers. At
this point in the process, it is crucial to control the defects on
the surface and structure, the quality of the color. Temperature
control is required at a high sampling rate and high rate of
accuracy in the readings. Because of all the above, costs of
maintenance of these automated machines is high. Ensuring a
right temperature control not only reduces the rejection of
products but lowers the maintenance costs. Humidity sensors
are necessary because humidity can alter the behavior of the
object during the glazing phase and affect the final quality.
Light sensors are needed to maintain a constant luminosity,
and are calibrated for color analysis, since color looks
different depending on ambient light. Sensors that can capture
Volatile Organic Compound (VOC) readings are very
important. In the process of producing the porcelain ware
different types of solvents are used that volatilize during the
drying process. Environmental rules state that evaporated
VOCs from the solvents must be captured, recycled or
destroyed in a controlled manner, to keep them from polluting
the atmosphere. Sensors measure VOCs and ensure
compliance. Finally, Noise sensors monitor working
conditions in the factory. The hardware configuration consists
of four types of sensor devices and a multiprotocol Internet
gateway. The unit nodes used in this case study are modular
and can monitor and measure a wide range of factors: up to
seven initial parameters such as: temperature, CO2, VOC,
humidity, luminosity (Luxes accuracy), microphone (dBA).
Each unit node contains the appropriate sensors and
electronics needed to control them, and are formatted from the
factory to adapt to each specific use. The encapsulated nodes
also contain:
C. Evaluation and Lessons Learned
In our evaluation we exploited both quantitative and
qualitative data. Quantitative data from questionnaires that
students filled in and qualitative data from observations, user
trials and interviews. Our aim was to answer research issues in
three different areas. The first issue was relevant with the
usability of the SCL computing system. The second issue was
to study whether people can actually learn by interacting with
the application. Finally we studied the benefit of using an
urban computing system like SCL in the learning process.
Aiming to evaluate the usability of our system we asked 16
teachers to fill in a questionnaire after using the application.
All participants answered that it was easy to use SCL, except
two persons who were neutral. The overall rating of SCL
performance (Fig. 5) was good as expected, as one participant
rated it neutral. In order to assess the benefit of using an urban
computing system like SCL in the learning process we
interviewed both students and teachers. Students expressed
their excitement about being able to use smart phones in
learning activities and taking a class outside their classroom in
combination with applications in the smart classroom. It was
evident that students were eager to participate in our study and
go out interacting with SCL in the smart factory application as
well in the smart classroom. Teachers, also, expressed positive
remarks; they found interesting to use novel technologies
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teaching specific subjects such as Sensors, Measurement, Data
Processing and were encouraged that their students were
passionate on carrying out outdoors learning activities.
which students can become totally immersed in the learning
process. In extension, smart city learning leverages the
infrastructure and services that modern smart cities offer in
order to transform them into open learning environments.
Further work will integrate new case studies and new web
service applications will be developed by students.
ACKNOWLEDGMENT
The author acknowledge the European Social Fund and the
Romanian Government for support and funding and the
partners of the research project POSDRU/156/1.2/G/137166
entitled “Smart People for Smart Cities! – Adapting the study
programs for Applied Electronics, Computer Science and
Environmental Engineering to the requirements of the 21st
century”.
Fig. 5. Evaluation of SCL performance
REFERENCES
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and bringing new interactions back into the classroom. We
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CONCLUSIONS AND FURTHER WORK
In this work we have investigated how smart city technologies
may enhance the learning process of students hearing courses
of Applied Electronics, Computer Science and Environmental
Engineering. We have developed the Smart City Lab
computing system with various educational scenarios and
activities were performed exploiting SCL. Results from our
experience demonstrated the potential of exploiting urban
computing in the learning process and the benefits of learning
out of the classroom. This evolution and especially mobile and
smart city teaching enables educators to get their classes out of
their classrooms, facilitates learners to continue their learning
in the city landscape and allows realization of the learning
process when and where they desire. Teaching methods like
projects and field trips can benefit from these technologies as
they can enrich the smart city learning experience. Ubiquitous
learning on the other hand means something more than
mobility, a ubiquitous learning environment is any setting in
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