IoT-Based Smart City Development using Big
Data Analytical Approach
M. Mazhar Rathore, Student Member, IEEE, Awais Ahmad, and Anand Paul, Member, IEEE
1
Abstract— To meet the needs of urban public and the city
development smartly, the use of IoT devices, such as sensors,
actuators, and smartphones, etc., and the smart system is the
very fast and valuable source. However, interconnecting
thousands of IoT devices while communicating with each other
over the Internet to establish a smart system, results in the
generation of huge amount of data, termed as Big Data. To
integrate IoT services in order to get real-time city data and then
processing such big amount of data in an efficient way aimed at
establishing smart city is a challenging task. Therefore, in this
paper, we proposed and developed a smart city system based on
IoT using Big Data Analytics. We use sensors deployment
including smart home sensors, vehicular networking, weather
and water sensors, smart parking sensors, surveillance objects,
etc. The complete architecture and implementation model is
proposed, which is implemented using Hadoop ecosystem in a
real environment. The system implementation consists of various
steps that start from data generation and collecting, aggregating,
filtration, classification, preprocessing, computing and finished at
decision making. The efficiency in Big Data processing is
achieved using spark over Hadoop. The system is practically
implemented by taken smart systems as city data source to
develop smart city. The evaluation results show that the proposed
system is scalable and efficient.
Index Terms—Smart City, Big Data, Hadoop.
D
I. INTRODUCTION
UE TO the incorporation of ubiquitous and pervasive
computing, the trend of living is now changing.one of the
report notified that in 2050, seventy percent of the world
population will live in cities [1]. Hence, a rapid increase has
been seen in the transition of the population towards cities.
Therefore, for urbanization, it is an utmost important factor to
comprehend the demand for service profiling to enhance the
efficiency and may bring the recent advancement in the city
management. Presently, few organization are on the way with
their IoT platforms for live monitoring, planning and gathering
urban process parameters. For instance, Japan’s broadband
access is providing the facility of communication between
This study was supported by the Brain Korea 21 Plus project (SW Human
Resource Development Program for Supporting Smart Life) funded by
Ministry of Education, School of Computer Science and Engineering,
Kyungpook National University, Korea (21A20131600005).
M. Mazhar Rathore is with Kyungpook National University, Daegu, Korea
(e-mail: rathoremazhar@gmail.com).
Awais Ahmad is with Kyungpook National University, Daegu, Korea. (email: awais.ahmad@live.com).
Anand Paul is Associate Professor in School of Computer Science and
Engineering in Kyungpook National University, Daegu, Korea. (e-mail:
paul.editor@gmail.com).
9781509011476/16/$31.00©2016IEE
people, people and things, and things and things [2]. Similarly,
S. Korea’s smart home enables their people to access things
remotely [3]. Singapore next generation I-Hub [4] intentions
to comprehend the next generation “U” type network through
a secure and ubiquitous network [5]. Hence, The use of IoT
for smart systems results in enhancing the number of things to
be interconnected with each other, which results in the
overwhelming amount of the heterogeneous data, referred to
big data. Analyzing such data based on the user needs and
choices, the cities would become even smarter. However, the
discussed system works at a limited level, without considering
the importance of Big Data generation and handling. Such
activities should be followed by the amount of data collected,
offline and real-time big data processing and analysis, and
decision making. Usually, data collection and analysis
techniques are difficult to achieve in such environment.
Therefore, there is a need to incorporate smart technology that
could efficiently collect the data, performed analysis, take
real-time decisions and predict the future for better city
planning and development.
Having understood the feasibility and potential of the IoT
and the smart systems, in this paper, we propel the concept of
smart systems toward the smart city development based on big
data analytics. In the paper, we proposed the complete
architecture to the developed smart city and did urban
planning using IoT-based Big Data analytics. The system
provides the guidance to all the government authorities to
make their cities smarter and intelligent in order to take a
decision at real-time based on current city scenarios. The
complete system description is depicted in next sections.
II. MOTIVATION
Urban planning and development smart city applications
have major impact area on the life of citizens [1]. This
includes the effect on the citizen in terms of health and safety,
disaster management, pollution control, and so on so forth.
Different projects related to monitoring of cyclist, cars, public
car parking, etc. are undergoing that utilizes sensors services
for the collection of specific collection of data. Apparently,
different other service domain applications are identified that
utilizes smart city IoT infrastructure to provision operations in
air, noise, pollution, and surveillance system in the cities.
However, in the development of any metropolitan, the
transport system has a key role. Even a country can only
progress rapidly if his transportation system and the facilities
of transportation to the citizen are remarkable. A good
transportation system makes the task to be performed
promptly. The smarter transportation has a lot of other
benefits, such as reduction of pollution, facilitation of citizens,
rapid development, economy improvement, and many more.
The recent research consists of a very few research findings in
the field of smart traffic as well as in the smart city. Moreover,
in this electronic era, where billions of devices are connected
on the internet generate millions of terabytes of Big Data. The
big data generated by the various IoT systems is used to
analyze different aspects of smart city. A similar concept is
followed using the IoT paradigm and the big data concepts for
urban planning. Thus, to analyze such amount of data and
make intelligent decisions is a major challenge. Therefore,
based on the need of the citizens and authorities, we focus on
the building of city the more smartly by providing real-time
information regarding the city for the citizens as well
authorities.
Therefore, the rationale behind our intentions is to enrich
the vast deployment of ICT resources in developing the entire
system. For this very reason, we come to know that the
advancement of recent technology in the embedded system
depicts the trends of ICT. Therefore, a system is required that
could inhale all of the recent developments in the field of
smart cities, due to which a remarkable growth can be seen in
a near future. The design of this system requires all the
capabilities of sensing the environment and analyzing the
sensing information. Therefore, various real-time action could
be welcomed due to these technological resources. Moreover,
it can be seen that integrating a large amount of data to
perform the efficient analysis are already performed at their
best. However, with large scale environment, it is unavoidable
that the huge portion of data is left disjoint. As a result, such
data cannot provide us a better understanding of the situation
so that we may plan for future.
III. BACKGROUND AND RELATED WORK
Big data and its analysis are at the verge of modern science
and business, where author highlights the identity of number
of sources on big data such as online transactions, emails,
audios, videos, search queries, health records, social
networking interactions, images, click-streams, logs, posts,
search queries, health records, social networking interactions,
mobile phones and applications, scientific equipment, and
sensors [6]. The proposed model is using conventional
database tools. The challenge is to capture, form, store,
manage, share, analyze, and visualize the big data.
Various studies have been performed on smart cities
building in a limited way [7] and statistical features of the big
data as well [8-12]. In the majority of the cases, we do not rely
on their statistical measurements. With a view to manifest the
statistical measurements of the big data sets, there is a need to
represent them in some other forms, i.e., how to characterize
big data sets as the majority of the data processing tasks rely
on some appropriate data representation. For instance, in case
of image processing scenario, the wavelet transform [13], in
case of remote sensing scenario, multi-resolution
representation, such as image segmentation [14], image denoising [15], image restoration [16], image fusion [17], change
detection [18], feature extraction [19], and image
interpretation. Hence, assessment of statistical measurements
of big data in the wavelet transform domain in real-time or
offline is a key challenging area.
The publicity big data is used for transformation and
governance of cities with the help of data flood that provide
much more sophisticated, wider scale understanding and
control of urbanity. To the best of our knowledge, there is no
such industrial definition of big data, but a survey of emerging
trends signify a number of key features, such as volume
indicates a lot of data to be aggregated for processing and
analyzing while velocity refers to the high-speed processing
and analysis (e.g., online streaming, real-time remote sensing,
social websites data, e-health data, and so forth.). On the other
hand, the term Variety refers to the vastly varied structures
(e.g., Machine-to-Machine, Internet of Things, Wireless
Sensor Network, and so forth). Interested readers are referred
to [20] [21] [22] [23] [24].
In case of collaboration interactions within scientific
communities through different knowledge fields, results in
new products. Since the role of communities and networks as
an essential condition of novelties is discussed in [1]. The IoT
is expected to be a substantial support for the IT infrastructure
of smart cities [25]. A scheme is also presented that deploys
sensors in a cloud that provide IoT services are presented in
[26]. This work discussed different commercial cloud-assisted
remote sensing platforms and highlights their capabilities.
The nature of sensors is to generate a massive volume of big
data. Thus, the distribution of context to sensor data for
originating intelligible and persuasive information plays a
critical role [25]. Also, context-aware computing has proven
to be successful in understanding sensory data. Various other
works about context aware in term of IoT perspective is
discussed in [27]. Due to the fact that the deployment of a
maximum number of sensors along with numerous
heterogeneous devices and systems, and due to unreliable
nature of the majority of objects, the Quality of Service could
be reduced. To solve such problem, the cognitive management
framework for IoT is proposed [28]. In this scheme, intelligent
and autonomous implementation of different application are
enabled by mapping cognition and proximity with related
objects for the application.
Due to the fact that urban planning and development
applications can be benefited from a smart city IoT
capabilities can be grouped into impact areas [1]. This
includes the effect on the citizen in terms of health and safety,
the transportation system in terms of mobility and pollution,
and so on so forth. Different projects related to monitoring of
cyclist, cars, public car parking, etc. are undergoing that
utilizes sensors services for collection of specific collection of
data. Apparently, different other service domain applications
are identified that utilizes smart city IoT infrastructure to
provision operations in air, noise, pollution, vehicle mobility,
and surveillance system in the cities. The recent research
consists of a very few research findings in the field of smart
city as well as in urban areas. Similarly, a compact system is
not yet built which is more scalable and efficient. The big data
is used to analyze different aspects of the smart city and then
uses the knowledge obtain from the past generated data for the
betterment of cities. A similar concept is followed using the
IoT paradigm and the big data concepts for urban planning.
Thus, we tried to come up with a solution that is applicable in
the smart city and as well as in the urban areas. The proposed
system is implemented and tested on the Hadoop framework
with a spark to get the real time effects in the case of real-time
smart city decision. Moreover, Hadoop and MapReduce is
used for large historical data for urban planning and future
enhancements.
placed on the front screen of the car. We get the
IV. EASE OF USE IOT-BASED SMART CITY
A. System Overview
The primary concept of the smart city is to get right
information at the right place on the right device at the right
time to make the city related decision with easiness and to
facilitate the citizens more quick and fast ways. To develop
the IoT-based smart city, we deployed several wireless and
wired sensors, surveillance cameras, emergency buttons in
streets, and other fixed devices. The main challenge in this
regard is to achieve smart city system and link smart system
generated data at one place. We do this by placing the main
data hub linking all smart system to have all smart system data
at a central place. Figure 1 shows the sensors and smart
system deployment in order to generate data using a central
hub for building the smart city. In order to get Real-time city
data, we proposed to deploy many sensors at different places
to achieve smart homes, smart parking, weather and water
systems, vehicular traffic, environment population and
surveillance system. These systems are used by the authorities
to make an intelligent decision based on the real-time data to
establish the smart city.
In a smart home, the home is continuously monitored by
sending data generated from the sensors, e.g., the smoke and
temperature to detect a fire at real-time, the electricity and gas
consumption to effectively manage the power, gas, and water
consumption to the houses and different areas of the city. The
smart parking helps in managing the vehicles coming and
going out of different car parking zones. Thus by smart car
parking analysis, the need for new car parking at specific areas
can be identified. In our system, the citizens easily get the
information of the nearest free slot and suitable place for
parking at real-time. Weather and water system provides the
weather related data like temperature, rain, humidity, pressure,
wind speed and water levels at rivers, lakes, dams, and other
reservoirs. In the world, most of the flood occur due to the rain
and similarly few by snow melting and dam breakage.
Therefore, we use rain measuring sensors and snow melting
parameters in order to predict the flood earlier. We also
predict about the water reservoirs in advance in order to meet
the need of the water to the citizens. Vehicular traffic
information is the most significant source of a smart city.
Through this type of data source and with useful real-time
analysis the citizen and as well as government can get more
benefits. In our smart city system, we are getting the traffic
information by GPRS, vehicular sensors, as well as the sensors
Fig. 1. Sensors Deployment
location of each vehicle the number of vehicles between two
pairs of sensors placed at the various location of the city.
Moreover, if any accident happens, the front screen will be
damage and the sensor will send the alert to the police, traffic
authorities, and hospital. Moreover, a city can never be smart
with unhealthy citizens. Therefore, while designing smart city,
we put a separate module to get environment data which
includes gases information such as particular metals, carbon
monoxide sulfur dioxide, ozone, and noise as well as. The
citizens are alerted when any of the toxic gas is more in the
air.
Last but not least, the most important thing for the people of
the smart city is the security concerns. Security is achieved by
the proposed system by continuous monitoring the video of
the whole city. However, it is very difficult to analyze all city
videos and detect any mishap with anyone at real time by the
system. To overcome this limitation, we propose new
scenarios, which increase the security of the system of the
whole city. We put various emergency buttons including
microphones at different places of the city with surveillances
cameras. When any mishap happens with anyone like robbery,
car stolen, etc. He can just push the emergency button at any
near place, and it will send the message to the nearest police
station etc. Thus, the police or security agencies can start
monitoring the nearby locations through surveillance cameras
and can quickly locate the imposter.
The Analytical Architecture and Implementation Model
Based on the needs of the smart city, we proposed an
architecture and the implementation to analyze IoT-based
smart systems to establish smart city by real-time analysis.
The system complete architecture and implementation model
is given in Figure 2. It shows the full details of all the steps
performed from data generation to decision making and
applications. Initially, every system will generate their data,
such as smart home generated data, vehicular data, smart
parking data, etc. At every system, there is relay node,
collecting data from all sensors and then sending to the
Intelligent City Building through GW, The Internet, and the
central data-Hub. Intelligent City Building is the main analysis
system that is responsible for all activities from data
collection, filtration to Decision making. It contains various
servers equipped with many advanced analytical algorithms at
various levels. At data preprocessing level, the data is
collected by fast network cards and driver, so that it cannot
lose any packets. As the sensors have a lot of metadata, and
the sensors also generate the heterogeneous type of data.
Therefore, all the unnecessary metadata and the redundant
data are discarded at filtration server. The classification server
classifies the incoming data from the various system by the
message type and the identifier. After classification, the
classified data is converted to the organized form, i.e.,
understandable to the Hadoop ecosystem, such as sequence
file or tabular for with parameters information.
Since we are dealing with a large amount of data (termed as
big data). Therefore, we need a system that could efficiently
process a large size of real-time (smart city) as well as offline
(urban planning) data. To meet these requirements, we used
Hadoop ecosystem, which contains Master nodes, and various
data nodes under the Master node. The Hadoop ecosystem has
HDFS file storage, which divides the data into an equal
amount of chunks and stored them in multiple data nodes.
Later, the parallel processing is performed on these chunks
using MapReduce system. Hadoop basically used for batch
processing, However, in order to use it for real-time analytics,
we used Spark over the Hadoop system. All the processing
calculations, results generation are done at Hadoop ecosystem.
Finally, the decision making is performed based on the results
generated by Hadoop ecosystem. The decision-making
approach uses machine learning, pattern recognition, soft
computing and decision models. The generated results are
used for many smart city activates such as shown as
applications in Figure 2.
B. System Implementation and Data Generation?
We implement the whole system on Hadoop ecosystem taking
it as the Intelligent City Building. All the smart systems are
connected to the main system, which collects the real-time
data. All the remaining activities are performed by the Hadoop
ecosystem. For the city vehicular network data collection, we
developed a hardware-based vehicular network with two
vehicles and a base station, as shown in Figure 3, to generate
real data such as location, speed, time, etc. The base station is
attached to the system by USB port that transmits the
vehicular information to the system. For the other smart
systems, we used existing smart systems datasets, which we
replayed from another computer system to make the real-time
environment. The real-time traffic is processed by Hadooppcap-lib, Hadoop-pcap-scr-de libraries and converted into
sequence file to make it capable of processing on Hadoop.
Each facility of the smart city is implemented as a separated
class or sub-module. Citizens have limited access to the results
of these modules, and the government has full access to them.
Fig. 2. System Architecture and Implementation Model.
Fig. 3. The developed vehicular network
As it is very difficult at this time to implement all the smart
systems, therefore we take existing datasets of the mentioned
smart systems from various reliable resources. The details of
each dataset including the datasets size, the area information,
description, the number of parameters, and the reference are
given in Table 1.
V. DATA ANALYSIS AND IMPACT ON SOCIETY
The data analysis using the developed system. The analysis
results can be used for many purposes and benefited the
society from common citizen to businessman and the
government authorities. Here, we are highlighting some of the
impacts and benefits gains by the citizens and the government
by the real data analysis.
Figure 4 are the graphs generated to show the average speed
of the vehicles on the road when the intensity of the vehicles is
low and high respectively. Which shows the higher speed
when the intensity of the traffic is low and lower average
speed when then the intensity of the traffic is higher.
Similarly, Figure 5 shows the intensity of the traffic between
two points at a various time. Figure 6 is the graph generated
by the system to show the estimated time to reach to the other
point based on the average speed of the vehicles and the
intensity of the vehicles. The system also checks out the over
speed vehicles. The over speed area of the Madrid Highway
with respect to a number of vehicles are shown in figure 7.
From this analysis, A citizen can decide which is the suitable
routes to reach the destination depending on the on the current
traffic scenario. Moreover, the government can control traffic
and make optimize plan at runtime to decrease the intensity of
the traffic on the crowded road. Open the blocked road due to
any incident at the run time. Similarly, the government can do
many things from current traffic analysis.
S#
1
2
3
4
5
Datasets
Water Usage
TABLE I
DATASETS DETAILS
Area
Description
Surrey,
Canada
Madrid traffic Madrid
Vehicular
Cologne,
Mobility Traces Germany
Parking lots
Aarhus,
Denmark
pollution
Aarhus,
Denmark
6
City traffic
7
Weather
61263 houses meter
reading
Location, speed
Mobility, 400 km2, 24
hours, 700 cars
Eight parking lots
Fig. 6. Est. time to reach the destination depending on the traffic intensity
No. of attr. Ref.
11
[29]
5
5
[30]
[31]
7
[32]
449 sensors, Ozone,
8
NO,NO2, particle
matters, etc.
Aarhus, Sensors b/w 2 points.
9
Denmark Avg.speed, time to
reach.
Aarhus, temperature, humidity, 7
Denmark rain, pressure, etc.
[32]
Fig. 7. Location of Speed Violation on Madrid highway
[32]
[32]
Fig. 8. Free Spaces at various parking lots at different times
Fig. 4. The speed of vehicles at low intensity of traffic between two points
Fig. 9. Usage of various parking lots at different times
Fig. 5. No. of vehicles b/w two sources and destination at various time
Fig. 10. Water usage of various areas of Surrey City
Fig. 11. Pollution Level at different time of the day
The car parking analysis in the city are shown in Figure 8
and Figure 9 that describe the free and consumed slots of
several parking lots on various timeslots. From such type of
analysis results, the citizens are updated to select the best
suitable parking lot near their location. He can save his fuel
without manually searching the free car garage. In a nutshell,
It makes the profit equilibrium between the low as well as
high-profit merchants by diverting customer to free parking
lots. Moreover, The government can make urban planning
build more parking areas near the places where most of the
people normally go.
From smart home data, the government can provide a lot of
services to the citizens and can also make smart real-time
decisions at run time such as fire detection and control, energy
management, etc. Here we are just taking one case of water
management, which benefits the government regarding proper
control and planning of water usage. Figure 10 shows the
consumption of water for various calculated by the system
from each home consumption. The developed system
continuously monitors the environment for toxic gases such as
ozone (O3), carbon monoxide, sulfur dioxide (SO2), nitrogen
oxide, and particulate environment for toxic gases such as
ozone (O3), carbon monoxide, sulfur dioxide (SO2), nitrogen
oxide, and particulate matter. The monitoring graph for a short
time, when some of the gases exceed, is shown in Figure 11.
The system generates alerts when any of the gas exceeds the
dangerous threshold limit. It alters the people especially the
patients with the particular diseases snot to go out. The
government can also do urban planning, i.e., plan for traffic,
city and industrial building and shifting to other places based
on the city pollution.
VI. SYSTEM EVALUATION
We implemented the system using several (4-5) Hadoop
data nodes. However, for evaluation purpose, we take single
node setup on UBUNTU 14.04 LTS coreTMi5 machine with
3.2 GHz processor and 4 GB memory. Since the system is
based on big data analytics, therefore, the systems is evaluated
with respect to efficiency and response time using offline
traffic as well as real-time traffic. The processing time results
with respect to offline datasets size and the throughput
analysis result is shown in Figure 12 and Figure 13
respectively. It is evident in the graph that when the data size
is increased the processing time proportionally increased, both
Fig. 12. Processing time corresponding to the data size
Fig. 13. Throughput corresponding to the data size
data size and processing time are directly proportional to each
other. Moreover, throughput is also directly proportional to
data size because of the parallel processing nature of Hadoop
system, which is the major achievement of the system.
VII. CONCLUSION
The smart city has a major impact on country’s economy.
Strong and smart city system helps in taking a quick and
intelligent decision. This paper focuses on the implementation
of the smart city by the use of the IoT-based smart system.
Various smart systems are used to get real-time city data to
make a decision. The Hadoop ecosystem is used to process
Big Data generated by all the smart systems deployed in the
city. The System is practically implemented and tested on real
data. In future we are planning the actual deployment of all
Smart systems, testing the accuracy of the system, considering
security issues.
ACKNOWLEDGMENTS
This research was supported by Kyungpook National
University Bokhyeon Research Fund, 2015. This study was
also supported by the Brain Korea 21 Plus project (SW
Human Resource Development Program for Supporting Smart
Life) funded by Ministry of Education, School of Computer
Science and Engineering, Kyungpook National University,
Korea (21A20131600005). This work was also supported by
the IT R&D Program of MSIP/IITP. [10041145, SelfOrganized Software Platform (SoSp) for Welfare Devices].
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Muhammad Mazhar Ullah Rathore
received his Master’s degree in Computer
and Communication Security from the
National University of Sciences and
Technology, Pakistan in 2012. Currently, he
is pursuing his Ph.D. with Dr. Anand Paul at
Kyungpook National University, Daegu,
South Korea. His research includes Big Data Analytics,
Network Traffic Analysis and Monitoring, Remote Sensing,
IoT, Smart City, Urban Planning, Intrusion Detection, and
Computer and Network Security. He is an IEEE student
member. He is also a nominee of Best Project Award in 2015
IEEE Communications Society Student Competition for his
project ‘‘IoT based Smart City’’. He is serving as a reviewer
for various IEEE, ACM, Springer, and Elsevier journals
Awais Ahmad received his BS (CS) from
the University of Peshawar and Masters
(Telecommunication and Networking) from
Bahria University, Islamabad Pakistan in
2008 and 2010 respectively. During his
Master’s research work he worked on
energy efficient congestion control schemes
in Mobile Wireless Sensor Networks
(WSN). Currently, he is pursuing his Ph.D. at Kyungpook
National University, Daegu South Korea. There he got
research experience on Big Data Analytics, IoT/SIoT, 4G/5G,
Machine-to- Machine Communication, and Wireless Sensor
Network. He also received three prestigious awards: (i) IEEE
Best Research Paper Award: International Workshop on
Ubiquitous Sensor Systems (UWSS 2015), in conjunction
with the Smart World Congress (SWC 2015), Beijing, China,
(ii) Research Award from President of Bahria University
Islamabad, Pakistan in 2011, (iii) Best paper nomination
award in WCECS 2011 at UCLA, USA, and (iv) Best paper
award in the 1st Symposium on CS&E, Moju Resort, Korea in
2013.
Anand Paul, (SM’**) received the Ph.D.
degree in Electrical Engineering from the
National Cheng Kung University, Tainan,
Taiwan, in 2010. He is currently working as
an Associate Professor in the School of
Computer Science and Engineering,
Kyungpook National University, South
Korea. He is a delegate representing South Korea for M2M
focus group and for MPEG. His research interests include
Algorithm and Architecture Reconfigurable Embedded
Computing. He is IEEE Senior member and has guest edited
various international journals and he is also part of Editorial
Team for Journal of Platform Technology, ACM Applied
Computing review and Cyber---Physical Systems. He serves as
a reviewer for various IEEE /IET/Springer and Elsevier
journals. He is the track chair for Smart human computer
interaction in ACM SAC 2015, 2014. He was the recipient of
the Outstanding International Student Scholarship award in
2004---2010, the Best Paper Award in National Computer
Symposium and in 2009, and International Conference on
Softcomputing and Network Security, India in 2015.