Smart Cities
System for Monitoring Microclimate Conditions based on Crowdsensing
Ivan Jezdović, Nevena Nedeljković, Zorica Bogdanović, Aleksandra Labus and Božidar Radenković
Department for e-business, Faculty of Organizational Sciences, University of Belgrade, Jove Ilića 154, Belgrade, Serbia
Keywords: Smart Cities, Internet of Things, Microclimate Conditions Monitoring, Crowdsensing.
Abstract: The aim of this paper is to provide an overview of technologies, application domains and services for smart
city development. We developed, implemented and evaluated a system for monitoring microclimate
conditions based on crowdsensing. The developed system aims to demonstrate one of the ways of
integration of Internet of Things, web and big data technologies. This system measures microclimate
conditions in smart city such as: temperature, humidity, and air pressure. In this way citizens can receive
quick information about microclimate conditions in their environment, such as smart buildings and homes,
and to share information via web application and social media services. As a system support, web
application that provides services of microclimate conditions is developed.
1 INTRODUCTION
More than half of the world's population lives in
cities (Kourtit, Nijkamp and Arribas, 2012). The
trend of further influx of population from rural areas
is expressed. Cities tend to become "smarter" and to
improve quality of life. Information and
communication technologies (ICT) are enabling
further transformation of traditional cities into smart
cities (Mohanty, Choppali and Kougianos, 2016).
The development of smart city is often linked to
the realization of the following elements (Vlacheas
et al., 2013): smart economy, smart mobility, smart
environment, smart people, smart living, and smart
governance. According to these elements, smart city
can be defined as a city that connects the physical
infrastructure, IT infrastructure and business
infrastructure, in order to use the collective
intelligence of the city, and to have an impact on
economic growth and a high quality of life
(Vlacheas et al., 2013). The main aims of the
implementation of a smart city are related to solving
urban problems such as: transportation, health,
education, electricity consumption, environmental
protection (Lee, Phaal and Lee, 2013).
Internet of things (hereinafter: IoT) is important
technology for development of smart city
infrastructure. Internet of things technologies enable
connecting a large numbers of users, intelligent
devices, services and applications on the Internet
(Gubbi et al., 2013). IoT technologies should be
used for building IoT infrastructure for smart city.
IoT infrastructure should enable connecting
intelligent devices in a unique network, and using
different kind of sensors, actuators, tags and readers
in residential and commercial buildings, roads, street
lighting, etc.
Some examples of the implementation of IoT
applications in smart cities are: traffic control
system, smart parking solutions, detection of the air
pollution levels, smog, carbon dioxide, noise,
monitoring weather conditions, alarming in
emergencies, etc. (Bahga and Madisetti, 2014).
Recent trend in smart cities is crowdsensing,
where citizens via smart phones participate in
collecting and sharing data from smart city
environment (Cardone et al., 2013).
The paper gives an overview of the smart cities
concept and its application domains. IoT
technologies and infrastructure for the development
of smart cities are shown. The aim of this paper is to
propose a system for monitoring microclimate
conditions based on crowdsensing. The proposed
system is based on IoT, web and big data
technologies. This system should enable measuring
microclimate conditions by citizens and sharing
information about temperature, humidity, and air
pressure via web application and microblogging
services such as Twitter. A web application that
enables preview of these microclimate parameters is
108
Jezdovi
´
c, I., Nedeljkovi
´
c, N., Bogdanovi
´
c, Z., Labus, A. and Radenkovi
´
c, B.
Smart Cities - System for Monitoring Microclimate Conditions based on Crowdsensing.
DOI: 10.5220/0006430701080115
In Proceedings of the 14th International Joint Conference on e-Business and Telecommunications (ICETE 2017) - Volume 2: ICE-B, pages 108-115
ISBN: 978-989-758-257-8
Copyright © 2017 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

developed. Furthermore, citizen can monitor
microclimate conditions daily, weekly or monthly.
2 SMART CITIES
Smart city represents an urban space that accelerates
economic growth, offers a high quality of life and
makes easy the involvement of citizens in the
management of health, education, public utilities,
business, transportation and public safety services
(Sánchez et al., 2014).
The cities that have predisposition to become
smart can use IoT technologies and crowdsensing
applications to collect and share data in the real time
(Cardone et al., 2013). Crowdsensing is ICT tool
which focuses in different areas such as
environment, citizen collaboration, urban traffic
systems, health/fitness and social networking
(Farkas and Lendák, 2015). The advantage of
crowdsensing is the usage of services based on the
sensing data (Petkovic et al., 2015) and it is a
cheaper way to implement smart technologies in the
cities because they do not require expensive
infrastructure (Talasila et al., 2013).
With the development of the smart phones and
advanced technologies (GPS, microphone, camera,
etc.), the citizens can collect data from the urban
areas. Along these lines people can be a part of the
smart cities and their services (Farkas and Lendák,
2015).
One of the crowdsensig solutions is e-
participation. People can post collected information
to the social media such as Twitter, Facebook,
Instagram, Foursquare, etc., and that can be called
participatory-social sensing. The data that can be
collected includes:
 environmental measurements (temperature,
humidity, air pressure and emissions of the
harmful gases in order to get level of the air
pollution),
 geolocations,
 traffic information (collision during rush hour,
police presence, traffic jams, accidents and
traffic noise),
 different parameters for detection of fire and
the reconstruction of infrastructure.
Data collected and evaluated in real time can be
used in numerous analyses and validations. The
shared information through social media needs to be
analysed and used by the different organizations and
government. The obtained information can make
cities smarter and better for living. The final goal
that needs to be achieved is to raise consciousness
and environmental awareness of the citizen
(Bellavista et al., 2015).
2.1 Smart City: Application Domains
The development of smart city implies application of
IoT solutions that enable: the implementation of
smart parking, automation of traffic signals, lights
on roads; installation of sensors in the road
infrastructure, buildings, houses, apartments,
hospitals, educational institutions; installation of
sensors for the detection of fire, electricity and water
consumption, air pollution, temperature, humidity,
radiation, etc.
Areas of application of IoT solutions in smart
cities can be categorized in several application
domains (Radenković et al., 2017): administration,
participation program and public safety; buildings
and houses; health care; education, traffic and
energetics. Smart city application domains are
shown in Figure 1.
Figure 1: Smart city: application domains.
2.1.1 Administration, Participation Program
and Public Safety
The development of an efficient management system
for smart city implies transparent, efficient and
effective operation of all departments of the city
government (Sabri et al., 2015), which includes:
connectivity through information technology,
disclosure of information via the Internet, keeping
public records of requests of citizens and enterprises,
resolving requests in the shortest time etc.
Participation of individuals and society in
general is necessary for realization of the smart city
(Estrada, Soto and D'Arminio, 2013). The usage of
Smart Cities - System for Monitoring Microclimate Conditions based on Crowdsensing
109

IoT applications increases the possibility of the e-
participation of the citizens. That can encourage
sharing ideas and information via social media in
order to improve the citizen’s access to information,
public services and public decision-making which
impacts the well-being of society.
Important domain in smart city is public safety.
The most common solutions in this area are related
to the installation of a large number of cameras for
monitoring different parts of the city (Bahga and
Madisetti, 2014). This system can be improved by
using IoT solutions and construction of necessary
infrastructure for the collection, consolidation,
analysis and visual presentation of information in
real time. These activities allow effective planning
and quick response of the competent government
departments.
2.1.2 Smart Buildings and Houses
This domain refers to the automation system within
a building or a house (heating, water, electricity,
waste, etc.) into a single entity (Davidović and
Labus, 2016). This includes communication between
parts of the building, and communication with other
buildings in the city in order to achieve energy
savings and reduced maintenance costs (Radenković
et al., 2017).
Smart buildings and houses are built to provide
adequate conditions for life and make it safe and
effective. Their characteristics are comfort, heat,
sound and physical isolation and protection. Also
rational and economical use of energy can increase
the quality of life. Placing suitable sensors within the
buildings or houses, such as vibration sensors,
sensors for monitoring pollution levels, temperature
and humidity (Goncalves, 2014), should reduce the
need for expensive periodic structural testing by
human operators and allow proactive maintenance
and restoration actions, as well as providing real
time information about the weather conditions.
2.1.3 Smart E-Health
One definition of e-health is that it represents a
combination of medical and information
technologies, referring to health services and
information delivered from the Internet. The primary
goal is to improve the efficiency, quality of health
services and treatment methods (Solanas, 2014).
From the aspect of IoT, the components of e-health
are (Vukićević et al., 2016; Rodić-Trmčić et al.,
2016):
 Telemedicine - refers to healing patients from
long distance and involves the use of
advanced technologies such as wearables
(small electronic devices that consist of one or
more sensors that can monitor the health status
of the patients).
 Mobile health - means the provision of health
and medical services in public health using
mobile devices and services.
2.1.4 Smart Education
Smart cities provide a large number of educational
services (Wolff, Kortuem and Cavero, 2015). The
goal is to motivate and engage citizens in the
educational process and to improve the overall level
of the education.
The important part of the concept of smart
education is to make smart classrooms. The smart
classrooms are made of advanced multimedia
technologies and their advantage is to increase
efficiency of the process of knowledge transfer
(Simić et al., 2016). The measurements that can be
monitoring are parameters of physical environment
such as temperature, air pollution, light, sound,
smell, etc. New generation of classrooms are
equipped with IT infrastructure and modern
technologies for teaching: computers, mobile
devices, projectors, smart interactive whiteboards,
document cameras, microphones, cameras, etc.
Technical equipment allows faster and more
interesting approach to teaching and assessment and
easy way to support the teaching process.
2.1.5 Smart Traffic
Transportation control is of strategic importance in
the big cities. It is expected that the IoT application
provide interactive management of the central
system for monitoring and regulation of traffic.
Based on the obtained data, traffic flows can be
analysed and improved in real time.
The problem of traffic congestion is becoming
serious because of population growth, process
urbanization and motorization. Using ICTs and
intelligent transport systems (ITS) for monitoring
the traffic in the city, can increase safety, make
traffic more effective, delay and long period of
travelling and reduce environmental pollution.
As part of smart traffic, smart parking is based
on sensors implemented by or in the road
infrastructure. Intelligent displays and smart parking
services can help to find a free parking slot in the
city. The benefits of smart parking are faster time to
locate a parking space, which means we can
decrease emission of harmful gases from the car,
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reduce traffic congestion and noise pollution
produced by cars.
2.1.6 Smart Grid
One of the main objectives of smart cities is
connecting various energy sources in a single
network and easy access to the system for energy
distribution. A digital electricity network that store,
distribute and act according to the collected
information in order to improve efficiency, increase
the reliability of the economy, save and control
electricity consumption and service provision of
electricity supply is called smart grid. Smart grid
represents combination of hardware and software
components and uses ICTs to produce and delivery
electric energy. Benefits of using smart grids are
(Lukic et al., 2016):
 efficient and reliable delivery of the electricity,
 increasing the number of the electric vehicles,
 users have greater control over power
consumption,
 reduction of global emissions of carbon
dioxide.
Using advanced technologies, it is possible to
provide a service for monitoring the energy
consumption of the whole city. This would help the
governments to get a clear and detailed view of the
amount of energy required by the different services
(public lighting, traffic lights, control cameras, etc.).
3 IoT INFRASTRUCTURE FOR
SMART CITIES
IoT infrastructure and services in cities can
contribute to the optimization of traffic, energy
consumption, administrative and other processes
(Wang, Ali and Kelly, 2015). From the aspect of
communication, management and data processing,
multi-layered architecture of IoT in the smart cities
consists of the following layers (Jin et al., 2014):
 layer for measuring and sensing,
 Network-Centric IoT layer,
 Cloud-Centric IoT layer,
 Data-Centric IoT layer.
The lowest of the IoT infrastructure is the
sensing layer. It consists of intelligent (smart)
devices that collect and process information from the
environment. Such a device must have the following
physical components (Sanchez et al., 2014): power,
memory, processor and communication interface.
The IoT solutions are usually based on the
following smart devices: microcomputers,
microcontrollers, sensors, actuators and modules.
Network-Centric layer in IoT infrastructure is
responsible to provide a communication channel
from sensors to the Internet, including the use of
various technologies and network devices such as
routers, base stations, and others.
Cloud-Centric layer is responsible to make
available data and services to users. The role of the
cloud technology is to create an environment in
which the management and use of sensors can be
offered as a service to end users.
Application layer consists of applications that
use the data collected in the sensing layer to control
different devices and smart buildings in the city.
3.1 Technologies for Smart City
Development
Most of the existing solutions for smart cities are
based on the integration of wireless communication
technology, with the aim to create a flexible and
scalable infrastructure. A special segment in the IoT
infrastructure is a smart city solution that provides
clients with mobility and continuity of network
connection. The basic requirement is to enable the
usage of various access technologies and provide
communication with smart devices and objects from
different locations.
Enabling technologies for the smart city
development are: network technologies and
protocols, mobile technologies, cloud computing and
big data.
3.1.1 Network Technologies and Protocols
Internet of things is based on the use of computer
networks to connect intelligent devices and
applications. Intelligent devices can be connected in
Personal Area Networks (PAN), Local Area
Networks (LAN), Metropolitan Area Networks
(MAN), Wide Area Networks (WAN) and sensor
networks (Olivieri еt al., 2015). When it comes to
sensor networks, the most common application is
Wireless Sensor Network (WSN) that allows
wireless connection of sensors in sensor fields.
Sensors are used to collect and send the raw data.
Communication between two devices is established
via direct connection or through access points
installed on the network. Such communication is
called M2M, and usually takes place using the IP
protocol.
Smart Cities - System for Monitoring Microclimate Conditions based on Crowdsensing
111

3.1.2 Mobile Technologies
Mobile technologies that have contributed to the
development and implementation Internet of things
are: mobile networks and the mobile Internet,
Bluetooth, Radio Frequency Identification (RFID),
Worldwide Interoperability of Microwave Access
(WiMAX), Global Positioning System (GPS), Near
Field Communication (NFC), ZigBee, etc.
3.1.3 Cloud Computing
Management of the smart city and data collected
from sensors and with crowdsensing technology,
should be stored at a reliable infrastructure such as
the cloud computing. Cloud computing enables
delivering computer resources on demand such as:
infrastructure, servers, storage, applications, services
and development environments. Generally and in the
IoT context, there are three approaches in the use of
cloud computing services (Despotović-Zrakić et al.,
2013):
 Infrastructure as a Service (IaaS) - for
development of smart cities infrastructure
based and IoT,
 Platform as a Service (PaaS) - suitable for IoT
project management,
 Software as a Service (SaaS) - in the IoT
provides web-based applications for working
with sensors, actuators, and other intelligent
devices.
3.1.4 Big Data
The need for applying big data technology is often
explained using three "V" model, under which are
the main characteristics of big data (Laney, 2013):
volume, variety and velocity.
IoT systems with sensor networks generate large
amounts of data. A variety of sensors in short time
intervals monitors parameters in the environment,
such as the health status of people, plants and
animals, buildings, atmospheric phenomena,
earthquakes, river flows and events in the universe
(Vanschoren et al., 2014). In addition to sensor
networks, a large amount of data is generated by the
mobile crowdsensing. Using crowdsensing
techniques users, from their mobile devices, can
send essential information about the state of traffic
or noise pollution.
Due to the huge amount of data, it is necessary to
use big data technology for their proper storage. It is
necessary to integrate the collected data with data
from other sensor systems, and then perform
analysis. The analysis results should be available in
real time and displayed in visual form suitable for
users. Based on the results, fast notifying and
alerting are performed in emergency situations and
prediction of future states of the system.
4 SYSTEM FOR MONITORING
MICROCLIMATE
CONDITIONS BASED ON
CROWDSENSING
Using a larger number of stations that measure the
microclimate conditions such as temperature,
humidity and air pressure, combined with
geolocation data, residents of smart cities will be
provided with more accurate information. If we take
into account how social networks such as Facebook
and Twitter have a stake in our daily life, an
important part of the system is the station`s
possibility to send microclimate condition directly
on the social network. This allows residents of smart
cities fast access to fresh information on the
microclimate conditions in their part of town.
This paper presents a system for monitoring
microclimate conditions based on crowdsensing
(Figure 2). System is developed as a project of E-
business Department, which exist at the Faculty of
Organizational Sciences, University of Belgrade.
Figure 2: System for monitoring microclimate conditions
based on crowdsensing.
The proposed system collects data from different
types of crowdsensing stations and enables sharing
data directly on crowdsensing platform and social
networks. The crowdsensing platform integrates web
application, web services and Hadoop. This paper
shows the implementation of the crowdsensing
station that measures the microclimate conditions
such as temperature, humidity and air pressure
(Figure 3).
Although the system is developed in the
academic environment, the simplicity of the system
allows ordinary citizens to build it by themselves
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and use it to send and receive data from the server.
Participating in this process they become a part of
crowdsensing network.
Figure 3: Crowdsensing station for monitoring
temperature, humidity and air pressure.
Crowdsensing station for monitoring
temperature, humidity and air pressure consists of:
microcomputer, microcontrollers and sensors.
Devices connected in a real environment can be seen
in Figure 4.
Figure 4: Connected devices in a real environment.
DHT11 sensor measures the temperature and
humidity, while a barometer measures the air
pressure. Both sensors are connected to the Arduino
microcontroller. Arduino is getting its power from
the Raspberry Pi which is powered by a +5.1V
micro USB supply. Raspberry Pi and Arduino
communicate through Serial Communication. This
type of communication is essential for all micro-
controllers to communicate with other devices.
Raspberry Pi via web service sends the data to the
database for further analysis and storage. Users can
access weather data through web application.
Connection to the Internet is realized through
Raspberry Pi device via Ethernet adapter or WiFi
module.
The use of the above mention devices is optional.
The system can be implemented using other
microcomputers and microcontrollers such as xMega
or ESP8266, but it is important that system meets
the requirements of web services and platform.
As a system support, a responsive web
application was developed (Figure 5). The
application design was developed using HTML and
CSS technologies, while logic is based on JavaScript
and PHP programming language.
Figure 5: The screenshot of web applications.
Microcomputer, Raspberry Pi communicates
with Web application through web services. Web
services are based on the REST architecture. All
data is represented in JSON format. Simultaneously
all sent data are processed by using Hadoop (Figure
6). In addition to the data collected from the sensors,
by using Hadoop, data on weather conditions
obtained from an external web service are also
processed. For visualization we used Tableau
Desktop tool. It is data visualization software that
allows multiple data sources and different data chart
types.
Figure 6: Chart view from big data visualization tool.
Smart Cities - System for Monitoring Microclimate Conditions based on Crowdsensing
113

Hadoop provides the ability to store the large
scale of data (generated from sensors, video/audio
devices or the social media). Storing and processing
data on the Internet requires scalability, fault
tolerance, availability and that can be enabled by
using cloud computing. Collected data from the
social media (Twitter) is generated in an effort to
analysed information (temperature, humidity, air
pressure, etc.) provided by the people and gave them
the feedback on weather conditions in all central
cities of the state through social services.
Raspberry Pi device has built-in option to tweet
the data obtained from the sensor at the desired time
intervals. This option is achieved by using a tweeter-
master library for Python programming language.
Example of tweeting weather conditions is shown in
Figure 7.
Figure 7: Tweet of the data obtained from the sensors.
5 CONCLUSIONS
This paper presents a system for monitoring
microclimate conditions based on crowdsensing.
The developed system was developed within the
Department for e-business, at the Faculty of
Organizational Sciences, University of Belgrade.
By using the proposed system users become a
part of crowdsensing network that allows obtaining
precise information about microclimate conditions
based on their location. The main advantages of the
present solution are ease of use, low cost of
equipment needed for the implementation and easy
upgrade. Because of these characteristics, the
proposed system is suitable to be used by a large
number of users.
Plans for further development are:
 creation of database of users with health
problems that depend on weather conditions
and adequate notification system,
 implementation of the GPS sensor which will
collect information about the position and the
place where the measurement is performed.
Combination of GPS and microclimate data,
allows us to group data based on the location
and their mutual comparison from which we
can draw more accurate data.
 development of the proposed crowdsourcing
platform that enable sharing information from
the crowdsensing stations by citizens.
ACKNOWLEDGEMENTS
The authors are thankful to Ministry of Education,
science and technological development of the
Republic of Serbia, grant no. 174031.
REFERENCES
Bahga, A. and Madisetti, V., 2014. Internet of Things: A
Hands-On Approach. VPT.
Bellavista, P, Cardone, G, Corradi, A, Foschini, L,
Ianniello, R., 2015. Crowdsensing in Smart Cities:
Technical Challenges, Open Issues, and Emerging
Solution Guidelines, pp. 23.
Cardone, G., Foschini, L., Bellavista, P., Corradi, A.,
Borcea, C., Talasila, M. and Curtmola, R., 2013.
Fostering participaction in smart cities: a geo-social
crowdsensing platform. IEEE Communications
Magazine, 51(6), pp.112-119.
Davidovic, B. and Labus, A., 2015. A smart home system
based on sensor technology. Facta Universitatis,
Series: Electronics and Energetics, 29(3), pp.451-460.
Despotović-Zrakić, M., Simić, K., Labus, A., Milić, A.
and Jovanić, B., 2013. Scaffolding Environment for е-
Learning through Cloud Computing. Educ. Technol.
Soc, 16(3), pp.301-314.
Estrada, J. E. P., Soto, A. R. P. and D'Arminio, P., 2013,
June. The new era of the smart cities Open Innovation
based on human-centered computing methodologies
applied in the PEOPLE project. In Information
Systems and Technologies (CISTI), 2013 8th Iberian
Conference on (pp. 1-6). IEEE.
Farkas, K. and Lendák, I., 2015, June. Simulation
environment for investigating crowd-sensing based
urban parking. In Models and Technologies for
Intelligent Transportation Systems (MT-ITS), 2015
International Conference on (pp. 320-327). IEEE.
Goncalves, R., Carvalho, N. B., Pinho, P. and Roselli, L.,
2014, June. Smart environment technology as a
possible enabler of smart cities. In Microwave
Symposium (IMS), 2014 IEEE MTT-S International
(pp. 1-3). IEEE.
Gubbi, J., Buyya, R., Marusic, S. and Palaniswami, M.,
2013. Internet of Things (IoT): A vision, architectural
elements, and future directions. Future generation
computer systems, 29(7), pp.1645-1660.
Jin, J., Gubbi, J., Marusic, S. and Palaniswami, M., 2014.
An information framework for creating a smart city
through internet of things. IEEE Internet of Things
Journal, 1(2), pp.112-121.
Kourtit, K., Nijkamp, P. and Arribas, D., 2012. Smart
ICE-B 2017 - 14th International Conference on e-Business
114

cities in perspective–a comparative European study by
means of self-organizing maps. Innovation: The
European journal of social science research, 25(2),
pp.229-246.
Laney, D., 2013. 3D Data management: Controlling data
volume, velocity and variety. Meta Group. Application
Delivery Strategies,(February 2001).
Lee, J. H., Phaal, R. and Lee, S. H., 2013. An integrated
service-device-technology roadmap for smart city
development. Technological Forecasting and Social
Change, 80(2), pp.286-306.
Li, C. M., Liu, B., Qin, R. F. and Yang, N., 2016. An
urban mobile monitoring system integrating remote
sensing and environmental sensors. In Design,
Manufacturing and Mechatronics: Proceedings of the
2015 International Conference on Design,
Manufacturing and Mechatronics (ICDMM2015) (pp.
510-519).
Lukić, J., Radenković, M., Despotović-Zrakić, M., Labus,
A. and Bogdanović, Z., 2016. A hybrid approach to
building a multi-dimensional business intelligence
system for electricity grid operators. Utilities Policy,
41, pp.95-106.
Mohanty, S.P., Choppali, U. and Kougianos, E., 2016.
Everything you wanted to know about smart cities:
The Internet of things is the backbone. IEEE
Consumer Electronics Magazine, 5(3), pp.60-70
Olivieri, A. C. and Rizzo, G., 2015, July. Scalable
approaches to integration in heterogeneous IoT and
M2M scenarios. In Innovative Mobile and Internet
Services in Ubiquitous Computing (IMIS), 2015 9th
International Conference on (pp. 358-363). IEEE.
Petkovics, Á., Simon, V., Gódor, I. and Böröcz, B., 2015,
December. Crowdsensing solutions in smart cities:
introducing a horizontal architecture. In Proceedings
of the 13th International Conference on Advances in
Mobile Computing and Multimedia (pp. 33-37). ACM.
Radenković, B., Despotović-Zrakić, M., Bogdanović, Z.,
Barać, D., and Labus, A., 2015. Elektronsko
poslovanje. Beograd: Fakultet organizacionih nauka.
Radenković, B., Despotović-Zrakić, M., Bogdanović
, Z.,
Barać, D., Labus, A., Bojović, Ž., 2017. Internet
inteligentnih uređaja. Beograd: Fakultet
organizacionih nauka. ISBN: 978-86-7680-339-2
Rodić-Trmčić, B., Labus, A., Bogdanović, Z., Babić, D.
and Dacić-Pilčević, A., 2016. Usability of m-Health
services: A health professional's perspective.
Management: Journal for Theory and Practice
Management, 21(80), pp.45-54.
Sabri, S., Rajabifard, A., Ho, S., Namazi-Rad, M.R. and
Pettit, C., 2015. Alternative planning and land
administration for future smart cities [leading edge].
IEEE Technology and Society Magazine, 34(4), pp.33-
73.
Sánchez, L., Elicegui, I., Cuesta, J. and Muñoz, L., 2014,
June. On the energy savings achieved through an
internet of things enabled smart city trial. In
Communications (ICC), 2014 IEEE International
Conference on (pp. 3836-3841). IEEE.
Sanchez, L., Muñoz, L., Galache, J. A., Sotres, P.,
Santana, J. R., Gutierrez, V., Ramdhany, R., Gluhak,
A., Krco, S., Theodoridis, E. and Pfisterer, D., 2014.
SmartSantander: IoT experimentation over a smart city
testbed. Computer Networks, 61, pp.217-238.
Simić, K., Despotović-Zrakić, M., Bojović, Ž., Jovanić,
B. and Knežević, Đ., 2016. A platform for a smart
learning environment. Facta universitatis-series:
Electronics and Energetics, 29(3), pp.407-417.
Solanas, A., Patsakis, C., Conti, M., Vlachos, I.S., Ramos,
V., Falcone, F., Postolache, O., Pérez-Martínez, P.A.,
Di Pietro, R., Perrea, D.N. and Martinez-Balleste, A.,
2014. Smart health: a context-aware health paradigm
within smart cities. IEEE Communications Magazine,
52(8), pp.74-81.
Talasila, M., Curtmola, R. and Borcea, C., 2013, April.
Improving location reliability in crowd sensed data
with minimal efforts. In Wireless and Mobile
Networking Conference (WMNC), 2013 6th Joint IFIP
(pp. 1-8). IEEE.
Vanschoren, J., Vespier, U., Miao, S., Meeng, M.,
Cachucho, R. and Knobbe, A., 2014. Large-Scale
Sensor Network Analysis: Applications in Structural
Health Monitoring. In Big Data Management,
Technologies, and Applications (pp. 314-347). IGI
Global.
Vlacheas, P., Giaffreda, R., Stavroulaki, V., Kelaidonis,
D., Foteinos, V., Poulios, G., Demestichas, P., Somov,
A., Biswas, A.R. and Moessner, K., 2013. Enabling
smart cities through a cognitive management
framework for the internet of things. IEEE
Communications Magazine, 51(6), pp.102-111.
Vukićević, S., Stamenković, Z., Murugesan, S.,
Bogdanović, Z. and Radenković, B., 2016. A new
telerehabilitation system based on internet of things.
Facta Universitatis, Series: Electronics and
Energetics, 29(3), pp.395-405.
Wang, P., Ali, A. and Kelly, W., 2015, August. Data
security and threat modeling for smart city
infrastructure. In Cyber Security of Smart Cities,
Industrial Control System and Communications
(SSIC), 2015 International Conference on (pp. 1-6).
IEEE.
Wolff, A., Kortuem, G. and Cavero, J., 2015, April.
Towards smart city education. In Sustainable Internet
and ICT for Sustainability (SustainIT), 2015 (pp. 1-3).
IEEE.
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