Research A rticle
Big Data Analytics Embedded Smart City Architecture for
Performance Enhancement through Real-Time Data Processing
and Decision-Making
Bhagya Nathali Silva, Murad Khan, and Kijun Han
School of Computer Science and E ngineering, Kyungpook Na tional U niversity, Daegu, Republic of Korea
Correspondence should be addressed to Kijun Han; kjhan@knu.ac.kr
Received October ; Accepted December ; Published January
Academic Editor: Jaime Lloret
Copyright © Bhagya Nathali Silva et al. is is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly
cited.
e concept of the smart city is widely favored, as it enhances the quality of life of urban citizens, involving multiple disciplines, that
is, smart community, smart transportation, smart healthcare, smart parking, and many more. Continuous growth of the complex
urban networks is signicantly challenged by real-time data processing and intelligent decision-making capabilities. erefore, in
this paper, we propose a smart city framework based on Big Data analytics. e proposed framework operates on three levels: (1)
data generation and acquisition level collecting heterogeneous data related to city operations, (2) data managemen t and processing
level ltering, analyzing, and storing data to make decisions and events autonomously, and (3) application level initiating execution
of the events corresponding to the received decisions. In order to validate the proposed architecture, we analyze a few major types of
dataset based on the proposed three-level architecture. Further, we tested authentic datasets on Hadoop ecosystem to determine the
threshold and the analysis shows that the proposed architecture oers useful insights into the community development authorities
to improve the existing smart city architecture.
1. Introduction
e novel concept of “connected everyday objects” over the
existing network has been evolved with the emergence of
the smart devices. e tremendous growth of the devices
connected to the network has expanded the boundaries of
conventional networks. is major breakthrough introduced
Internet of ings (IoT) as the third wave of the web aer
static pages web (WWW) and social networking web [, ].
e IoT is an unceasingly growing network, capable of iden-
tifying and sharing data autonomously among heterogeneous
devices, which are uniquely addressable. IoT has become
the spotlig ht of attention among multiple interest groups
due to the advancement of embedded device technology and
rapid increase in the number of devices. e IoT concept has
been matured with the attention of multiple interest groups
and with the advancement of embedded device technology.
is comes up with its productive applications like smart
home,smartcity,smarthealth,andsoforth[–].e
smart city notion is initially coined with the aim of utilizing
public services and resources eciently to increase the quality
of services oered to the urban citizens []. In fact, the
oered services, that is, transportation, parking, surveillance,
electricity, healthcare, and so forth, are optimized with the
autonomous da ta collection via the heterogeneous devices
connected to the urban I oT. It is essential to process a
large amount of data on a real-time basis in order to serve
the service requests eciently. Consequent to the immense
increase in data volume, the general data processing and
analytical mechanisms become impotent to satisfy the real-
time data processing demand. Hence, the collaboration with
Big Data analytics is considered to be the ideal rst step
towards a smarter city. It assures exible and real-time data
processing followed by intelligent decision procedures []. As
a result of adopting Big Data analytics to the urban IoT, this
enhances the quality of services provided by the smart city.
Inaddition,multipleeortshavebeenmadebyaca-
demic and industrial experts to realize the notion of the
Hindawi
Wireless Communications and Mobile Computing
Volume 2017, Article ID 9429676, 12 pages
https://doi.org/10.1155/2017/9429676
W ireless Communications and Mobile Computing
smart city. However, many eorts on individual aspects
of interest are seen in the literature [–] covering water
managemen t, garbage management, parking management,
and so forth. erefore, complete and resilient smart city
architecture has b ecome a cr ucial demand, as lack of integrity
deteriorates the practicability. In addition, it has to facilitate
autonomous behavior, real-time data processing, real-time
decision-making, and smart energy consumption and cus-
tomization. ereupon, the processing and analyzing of the
colossal amount of data become a necessity. Henceforth, the
urban IoT integrates Big Data analytics for the realization of
the smart city []. For example, a smart meter at a residential
building collects the meter reading that is compared with a
predened electricity consumption threshold and, based on
the result, the current energy demand is notied to the smart
grid. Simultaneously, consumers are notied with the current
level of energy consumption, allowing them to manage the
energy utilization eciently. Indeed, the preceding scenario
generates a reasonable amount of data for a single house.
Moreover, data processing and decision-making should be
carried out in a timely manner. Nevertheless, thousands of
residential and public infrastructures in the city generate
aprodigiousamountofdatarelatedtoasingletaskas
mentioned above. us, the unication of data sources and
Big Data analytics is considered to be an expedient solution
to facilitate real-time operation of the smar t city.
Even though the smart city has become a buzzword in
themoderntechnologicalera,theactualimplementationis
still in its infancy. In this regard, multiple eorts are made
to implement a realistic smart city. An urban IoT, “Padova
Smart City,” was implemented to provide ICT solutions for
the city administration []. e framework consists of a data
collection system, street lighting monitoring system, and a
gateway. By means of the collected environmental parame-
ters, that is, temperature, humidity, and light, it assures the
operation of streetlights. SmartSantander test bed in North
Spain is used in [] to determine the potential benets of
Big Data analytics for smart cities. e authors have ana-
lyzed temperature, trac, seas on, and working days to
dene a network with many interacting parts, which behave
according to individual rules. Smart city architecture from a
data perspective is proposed in []. e architecture con-
sists of six layers covering multiple aspects of a smart city.
Moreover, three-tier pyramidal architecture is proposed in
[] to facilitate transactions among heterogeneous devices
across a wireless ubiquitous platform. However, most of the
proposed architecture types focus on specic area of interest
such as lighting, trac congestion, and water managemen t.
us, the claim is valid that there is a necessity of realistic
smart city architecture competent enough to make real-time
intelligent decisions to upli the quality of urban IoT services.
Figure presents the overview of a conventional smart city
that consists of smart community, smart transportation,
smart grid, smart water management, and so forth.
In this paper , Big Data analytics are integrated with the
smart city architecture to propose a realistic and feasible
framework for the deployment of smart cities. e pro-
posed architecture is c apable of rea l-time intelligent decision-
making, autonomous data collecting, and user-centric energy
customizing. However, the decision and control management
is the most inuential component for the realization of a
smart city. Hence, the attainment of real-time and prompt
decisions has become the utmost goal of the proposed
scheme. Also, fusion techniques work to expedite the pro-
cessing of the enormous amount of collected data in Big
Data analytics. In this study, Hadoop is chosen as the storage
and pro cessing medium for the heterogeneous data. e
Hadoop processing is followed by the generation of intelligent
decisions related to the smart city operations. Finally, the
actions or events corresponding to the decisions are executed.
e rest of the paper is organized as follows. Section
presents a detailed description of the recent literature and
smart city management based on Big Data analytics. Section
gives a brief description of the proposed architecture. e
results and analysis are presented in Section . Finally, the
conclusion is outlined in Section .
2. Related Work
e rapid development of the smart city system diverts the
focus of many researchers and architects towards an ecient
communication and standard architectural design. Standard-
izing the smart city models can provide various benets to
the researchers and engineers in dierent contexts, nam-
ing standalone communication p aradigm, detailed layering
architecture, processing of information in real time, and so
forth. In addition, the smart city architecture covers a variety
of research approaches ranging from abstract concepts to a
complete set of services. Re cently, the researchers are working
on deriving various solutions to present generic architecture
of IoT-based smart city. Similarly, various schemes have
been proposed in the current literature that follows thorough
experimentation and test bed based simulations to overcome
the challenges. A scheme based on experimenting a complete
set of smart city services on various test bed modules has
been proposed in []. e authors in [] developed the
physical implementation of a large-scale IoT infrastr ucture
in a Santander city. e experimental facility is designed to
be so user-friendly so that the experimenter can test the
facility in dierent urban environments and smart city plan-
ning. A variety of new mechanisms were developed follow-
ing the Santander city requirements. ese mechanisms
include mobility support, security and surveillance systems,
large-scale support, scalability, and heterogeneity in a smart
city environment. e test bed results show that the proposed
architecture covers several challenges in the current litera-
ture.However,thedatacollectedfromvarioussensorsisnot
tested for future urban planning and designing. erefore, the
architecture can guarantee better services in one environment
but may show poor performance in another environment.
Similarly, the demands of the user in an IoT-based smart
environment rapidly change. Hence, it decreases the chances
of understanding the context and dynamicity of the IoT-
basedsmartuser.Ontheotherhand,theIoTisnotyet
matured to deploy it as generic standard for designing smart
services such as smart homes and smart cities because
of the following two major reasons: () the current IoT-
based solutions are limited to specic application domain
Wireless Communications and Mobile Computing
Smart transportation
system
Smart car
parking system
Smart electricity system
Smart community
Smart natural gas system
Smart waste
management system
Smart water management system
Smart decision and
control system
GSM/WIFI/3G/4G
Smart meter
Acoustic sensor
F : Typical smart city architecture.
and () new technologies and optimization techniques are
good in one area but may be not in another. For example,
wireless s ensor networks (WSN) suered high packet loss
in a heterogeneous wireless environment. In addition, the
deployment of IoT for one particular purpose such as waste
management, air quality, noise pollution, and so forth does
not reect a standard solution [–]. Similarly, wireless local
area network can provide low-cost services but it provides a
narrow coverage compared to other technologies. erefore,
the researchers have come up with several solutions which
ultimately lead to a generic communication model covering
a wide set of s ervices [–]. Moreover, a generic commu-
nication model can be achieved by integrating the WSN with
the existing infrastructure and, thus, helps in achieving a real
IoT environment with multifaceted architecture [].
In order to design ecient and generic smart city archi-
tecture, the Big Data that is obtained from the existing smart
city should be carefully examined and analyzed. e process
of collection of data can be done by placing sensors in various
locations in a smart home or smart city environment. Oine
processing of Big Data can help in designing and planning
of the urban city environment. However, it does not help
in performing real-time decisions. Various techniques based
on Hadoop ecosystem are developed to analyze the data for
better usage and designing of the services for a smart city. For
example, architecture called City Data and Analytics Platform
(CiDAP) has been proposed in []. e authors developed
layered architecture of data processing between the data
sources and applications. e entire architecture consists of
dierentpartssuchasdatacollectionunit(IoTbroker)and
IoT agent (a repository to store data), a Big Data processing
module, and a city model communication server providing
the communication facilities with an external object. e data
from dierent applications is collected and is passed to the
city model server. e city model server processes the data
and passes it to the IoT broker . e IoT broker separates
the data based on the sensors’ IDs and assigns an index
number to the data. Finally, the IoT broker sends data to IoT
agent for further processing. e proposed scheme achieves a
higher throughput in processing of the data. Similarly, various
other projects are developed based on Big Data analytics such
as SCOPE [] and FIRWARE []. ese projects help in
various aspects and provide dierent mechanisms to deal
with Big Data in the real-time environment. However, they
are not openly available to the researchers and engineers for
use in dierent environments.
e wireless-based technologies such as wireless sensor
network, wireless LAN, G/G, and LTE play a vital role
in providing always best-connected services in the smart
city environment []. ese technologies are employed in
various elds and sectors of the smart city such as health care,
transportation, schools, universities, and marketing. More-
over, these technologies enable a real-time communication
with the smart cities devices. us, the data generated by the
smart city sensors can be eciently processed to take real-
time decisions. However, real-time decisions require fast and
ecient data processing tools. For example, Hado op presents
a solution to process the big amount of data in possible time.
W ireless Communications and Mobile Computing
T : e amount of data collected in one year.
Collection frequency /day /hour / min / min
Records collected m . b . b . b
Terabytescollected .tb tb tb tb
m: million, b: billion, and tb: terabyte.
In addition, employing any existing tool to process Big Data
depends on three properties of Big Data, that is, velocity,
variety, and volume. However, processing a huge amount of
data in the minimum possible time and performing real-time
decision are a challenging task. erefore, the recent research
presents several models to process the data in the oine form.
us,theoutcomescanbeusedformanagementofurban
planning. In order to elaborate the idea of urban planning
based on Big Data analytics, we present a few example
scenarios. e energy consumption recorded by smart meters
inatimespanofoneyearisshowninTable[].e
information clearly illustrates the exponential growth of data
generation. e amount of data collected was calculated
assuming kilobytes per record [].
e table shows that the amount of data collected by
million meters per mins in one year is equal to TB.
us, this high amount of data cannot be processed at once.
erefore, sophisticated tools and techniques are required
toprocessthedataandcomeupwithproperplanningand
management. Similarly, processing the parking data from
various parking garages in a smart city can help in designing
smart parking systems. e vehicular data from various roads
of a city can be used to design a smart transportation system.
Moreover,thisdatacanbeusedinthedevelopmentofroads
and bridges in various places in t he smart city. Similarly,
several examples of using Big Data analytics in planning
and developing of smart cities services are presented in
recent literature [, ]. However, real-time decision-making
and processing on such a large amount of data are still a
challengingjob.Inaddition,anecientsmartcitycanbe
built by c onsidering the following two points: () generic
communication model and () real-time Big Data analytics.
e above literature reveals some important challenges
that need to be addressed, for example, designing a generic
communication model, real-time Big Data analytics, and
acquisition of d ata from sensors in a smart city. erefore,
in this paper, we identify the nee d for an ecient and generic
communication model for future smart cities based on Big
Data analytics and integration of WSN.
3. Proposed Scheme
e proposed smart city architecture comprises three levels:
() data generation and acquisition level, () data manage-
ment and processing level, and () application level. A brief
overview of the proposed smart city architecture is provided
in the next subsection followed by detailed description of
three levels of the proposed framework.
3.1. Overview. e layering architecture and working ow
of the proposed smart city architecture are illustrated in
Figure . Both layering and workow are presented in a
top-down manner starting from data generation and acqui-
sition level to data management and processing level to
applicationlevel.eproposedcityarchitectureencompasses
smart community development department, smart trac
control department, smart weather forecast department, and
smart hospital and health department. e aforementioned
components are liable for the collection of heterogeneous
data within the city suburbs, thus acting as the bottom
level of the proposed framework. ese components are fur-
ther connected with the smart decision and control system
via heterogeneous access technologies such as GSM, Wi-Fi,
G, and G. e autonomous decision-making uplis the
reliability as well as the pract i cability of the proposed scheme.
Upon receiving the collected data, intelligent decisions are
carried out by the smart decision and control system, situated
in the middle level of the smart city framework. Moreover,
the middle level regulates the events conforming to the made
decisions. e event generation is taken place at the top level
(application level), upon the reception of autonomous deci-
sions.
e utmost goal of this study was to exploit realistic smart
city architecture to enhance the data processing ecacy to
enable real-time decision-making. In this paper, we propos ed
smart city architecture that incorporates Big Data analytics.
In fact, there are previous studies, which integrated Big Data
analytics into the smart city architecture. However, the pro-
posed scheme is not a conventional Big Data embedded smart
city as it performs explicit data ltration using Kalman lter
(KF) prior to the Big Data processing. Data ltration is per-
formed to further expedite the data processing. e KF
applies threshold based ltration to distinguish between valu-
ableandnoisydata.us,itreducestheloadthatrequiresfur-
ther processing. Similarly, we occupied a Hadoop two nodes’
cluster for the Big Data processing. As shown in the Results
and Data Analysis, the unication of data ltration and sys-
tem architecture has enhanced the throughput of the smart
city, while reducing t he processing time. us, the proposed
scheme was able to fulll the demand for smart city architec-
ture capable of processing data and making decision in real
time.
3.2. Data Generation and Acquisition Level. Arealisticsmart
city not only includes a prodigious amount of data but
also includes complex and comprehensive computation and
multiple ap plication domains. e realization of the smart
city implementation relies on all forms of data and com-
putation due to their indispensability []. e smart city
notion aims to optimize residential resources, to reduce
trac congestion, to provide ecient healthcare services,
andtoperformthewatermanagement.eacquisitionof
data associated with the daily operational activities become
vital in terms of achieving the preceding aims. However,
the data acquisition has become tedious and challenging
due to the massive amount of data created by people and
other connected devices. For the sake of further processing,
the phenomena of interest from the real world are sensed
and identied. Consequently, conversion into digital data
employs various mechanisms. Low-cost and energy ecient
Wireless Communications and Mobile Computing
Data generation and
acquisition level
Smart home
Data sources
Veh ic u lar
transportation system
Data
Data Data
Data
Communication technologies
ZigBee Bluetooth Wi-Fi Data and cellular networks
Data management
and processing level
Data fusion Data analysis Data processing Data storing
Kalman lter
Filtered
big data
Noise removal
MapReduce
Useful
data
Map Reduce
HBASE
Real-time
lookups
HDFS
Distributed
storage
Event management
Decision management
Events
Classify
events
Generate
decisions
Intelligent
decisions
Application level
Unicast
decisions
Identify departments
Service
events
Generate smart city
service events
End user
Notify
Smart
community
development
Departments
Smart trac
control dept.
Smart
weather
forecast dept.
Smart hospital
and health
dept.
Data ow
Level entry
Level exit
Services
events
Resources
events
Weather and forecast
system
E-health services
F : Working of the proposed architecture.