School Papers

ABSTRACT- A “Smart City” generally means a technologically

ABSTRACT-
A “Smart City” generally means a technologically advanced city that is able to
understand its environment through analyzing its data so that it immediately
makes changes to solve issues and to improve the residents’ quality of life.
The huge volume, high velocity and wide variety of city’s data require the
utilization of “Big Data” technologies to gain valuable insights from it. The 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. This paper reviews the
applications and, hence, the potentials where Big Data technology can drive a
city to be smart. Starting from investigating the visibility of the city, which
means collecting data from all networks, devices and sensors

that
embedded in its infrastructure. Continuing to explain how can this data become
valuable by passing different processing stages, and by applying advanced
analyzing Big Data platforms on data? The smartness of the data driven city is
achieved by visualizing the data in useful shape in order to improve any city’s
system application. The literature review also shows the practical applications
of Big Data in a Smart City in the domains of smart energy, smart public safety
and smart traffic systems.

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INTRODUCTION

Smart cities face
serious challenges prior to widespread acceptance, but their integrated use of
Big Data, and other technologies to solve contemporary urban issues should
eventually lead to their adoption.

Big data offer the
potential for cities to obtain valuable insights from a large amount of data
collected through various sources, and the IoT allows the integration of
sensors, radio-frequency identification, and Bluetooth in the real-world
environment using highly networked services.

 The
combination of the IoT and big data is an unexplored research area that has
brought new and interesting challenges for achieving the goal of future smart
cities. These new challenges focus primarily on problems related to business
and technology that enable cities to actualize the vision, principles, and
requirements of the applications of smart cities by realizing the main smart
environment characteristics.

The concept of smart cities (SC) emerged as a
strategy to mitigate unprecedented challenges of continuous urbanization, while
at the same time provide better quality of life to the citizens. City smartness
is realized by means of the advances of Information and Communication
Technologies (ICT) and as a result SCs are usually characterized by an
extensive use of digital technologies in various city domains in combination
with a holistic view of the city where different domains should be closed
integrated.

This article is a systematic literature review on
BIG DATA ANALYTICS frameworks in SCs aiming at answering three basic research
questions. RQ1: What types of BIG DATA ANALYTICS frameworks are available for
the smart city context? RQ2: What are the functional gaps in the current
available frameworks? Finally, RQ3: What conceptual guidelines for designing
integrated scalable BIG DATA ANALYTICS frameworks, relevant for smart city
contexts, can be found in the literature? The literature review analyzed 10
articles addressing BD applications in SCs.

I.                  
WHAT IS BIG
DATA?

The term Big Data refers to all the data that is being generated across
the globe at an unprecedented rate. This data could be either structured or
unstructured. Today’s business enterprises owe a huge part of their success to
an economy that is firmly knowledge-oriented.

Data drives the modern organizations of the world and hence making sense of this data and
unraveling the various patterns and revealing unseen connections within the
vast sea of data becomes critical and a hugely rewarding endeavor indeed. There
is a need to convert Big Data into Business
Intelligence that enterprises can readily deploy. Better data
leads to better decision making and an improved way to strategize for
organizations regardless of their size, geography, market share, customer
segmentation and such other categorizations.

Hadoop
is the platform of choice for working with extremely
large volumes of data. The most successful enterprises of tomorrow will be the
ones that can make sense of all that data
at extremely high volumes and speeds in order to capture
newer markets and customer base.

 

Ø 
5 V’S OF BIG DATA:-

Big Data has certain characteristics and hence is defined using
4Vs namely:

 

Volume: The amount of data that businesses can collect is
really enormous and hence the volume of the data becomes a critical factor in Big Data analytics.

 

Velocity: The rate at which new data is being generated all
thanks to our dependence on the internet, sensors, and machine-to-machine data
is also important to parse Big Data in a timely manner.

Variety: The
data that is generated is completely heterogeneous in the sense that it could
be in various formats like video, text, database, numeric, sensor data and so
on and hence understanding the type of Big Data is a key factor to unlocking its value.

Veracity: Knowing
whether the data that is available is coming from a credible source is of
utmost importance before deciphering and implementing Big Data for business
needs.

Value: Last but not least, big data must have
value. That is, if you’re going to invest in the infrastructure required to
collect and interpret data on a system-wide scale, it’s important to ensure
that the insights that are generated are based on accurate data and lead to
measurable improvements at the end of the day.

 

Ø BIG
DATA TECHNOLOGIES:-

 

Big
data is an evolving term that describes any voluminous amount of
structured, semi structured and unstructured data that has the
potential to be mined for information.

Big data technologies are important in providing more
accurate analysis, which may lead to more concrete decision-making resulting in
greater operational efficiencies, cost reductions, and reduced risks for the
business.

To harness the power of big data, you would require an
infrastructure that can manage and process huge volumes of structured and
unstructured data in realtime and can protect data privacy and security.

 Some technologies used for big data
analytics:-

1.      Hadoop: – Hadoop is an Apache open source
framework written in java that allows distributed processing of large datasets
across clusters of computers using simple programming models. A Hadoop
frame-worked application works in an environment that provides distributed
storage and computation across clusters of computers. Hadoop is designed to
scale up from single server to thousands of machines, each offering local
computation and storage.

 

2.      MongoDB: – MongoDB
is an open-source document database that provides high
performance, high availability, and automatic scaling. MongoDB obviates the
need for an Object Relational Mapping (ORM) to facilitate development.

 

3.      MapReduce: – MapReduce is a programming model for writing applications that
can process Big Data in parallel on multiple nodes. MapReduce provides
analytical capabilities for analyzing huge volumes of complex data. MapReduce
divides a task into small parts and assigns them to many computers. Later, the
results are collected at one place and integrated to form the result dataset.

4.      Hive:- Hive
is a data warehouse infrastructure tool to process structured data in Hadoop.
It resides on top of Hadoop to summarize Big Data, and makes querying and
analyzing easy. Initially Hive was developed by Facebook, later the Apache
Software Foundation took it up and developed it further as an open source under
the name Apache Hive. It is used by different companies. For example, Amazon
uses it in Amazon Elastic MapReduce.

 

5.      Apache Pig:- Apache Pig is an abstraction over MapReduce. It is a
tool/platform which is used to analyze larger sets of data representing them as
data flows. Pig is generally used with Hadoop; we can perform all the data
manipulation operations in Hadoop using Pig.

II.               
WHAT IS A
SMART CITY?

A smart
city is an urban area that uses different types of electronic
data collection sensors to supply information used to manage assets and
resources efficiently. This includes data collected from citizens,
devices, and assets that is processed and analyzed to monitor and manage
traffic and transportation systems, power plants, water supply networks, waste
management, law enforcement, information systems, schools, libraries,
hospitals, and other community services.

The smart
city concept integrates information and
communication technology (ICT),
and various physical devices connected to the network (the Internet of things or IoT) to optimize the
efficiency of city operations and services and connect to citizens. Smart
city technology allows city officials to interact directly with both community
and city infrastructure and to monitor what is happening in the city and how
the city is evolving.

Vision
of smarter cities:-


Environmental sustainability and efficiency


Sustainable homes and buildings


Efficient use of resources


Efficient and sustainable transportation

– Better
urban planning – livable cities

Smart
City Applications:-

·        
Smart parking:
Monitoring of parking spaces availability in the city.

 

·        
Structural Health:
Monitoring of vibrations and material conditions in buildings, Bridges and
historical monuments.

 

·        
Noise Urban maps:
Sound monitoring in bar areas and centric zones in real time.

 

·        
Smartphone detection:
Detect smart phones and in general any device which works with Wifi or
Bluetooth interfaces.

 

·        
Electromagnetic field levels: Measurement of the energy radiated by cell stations and WiFi
routers.

 

·        
Traffic Congestion:
Monitoring of vehicles and pedestrian levels to optimize driving and walking
routes.

 

·        
Smart lighting:
Intelligent and weather adaptive lighting in street lights.

 

·        
Waste management:
Detection of rubbish levels in containers to optimize the trash collection
routes.

 

Smart
roads: Intelligent Highways with warning
messages and diversions according to climate conditions and unexpected
events like accidents or traffic jams.

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