CHAPTER 1

INTRODUCTION

1.1 INTRODUCTION

The concept of a smart city is popular nowadays and is used to

improve people’s lives. In general, information and communications
technology (ICT) plays an important role in the development of a
smart city. A smart city aims to exchange information using smart
devices and provide various services to the citizens. The
development of a smart city is a critical task because it requires
intelligent choice and the planning infrastructure. One of the
challenging tasks of a smart city is to build an ICT structure. The
second problem of smart cities is the lack of telecommunication
infrastructure. In the development and provision of intelligent
services in smart cities, graphs play an important role.
Graph theory is used in the modeling of highly connected
systems for example; social networks, computer systems, biological
and complex systems. The graph theory models combine the
modeling of system components and devicelevel logic. Big data is a
recent emerging technology and is widely used in smart cities and
telecommunication. In thetelecom industry, the Telecom Call Detail
Records (CDR) are considered a gold mine for data scientists due to
their huge usage and high potential. The challenges associated with
data are it should be clean, free from errors, with no data duplication,
and fewer missing values. In addition, it should be available in real-
time. The use of big data to mine customer behavior is called
customer analytics When a person calls another using a mobile
phone, the CDR event gets generated. A person doesn’t have to use
a mobile phone or GPS. In General, when a person calls another
person, SMS, or even accesses the internet, the CDR is generated in
the database. In many cases, telecom operators store the data in the
system database.

City Hotspot Identification Using Smart Cyber Physical Social System 1

 In most cases, the telecom operators have a separate
department for this purpose. CDRs are the main factor in customer
analytics that need to be carefully investigated. Cyber-physical
systems (CPSs) are known as the next generation of intelligent
systems and are composed of software and hardware that can
control and monitor the physical environments using smart objects
such as actuators and sensors. These are used in the development of
smart cities. The smart objects in CPSS were connected to the real
world using the Internet. Recently, the concepts of CPSs have
become a reality and become the core part of Industry 4.0. This
phenomenon acts as a base for cyberphysical- social systems
(CPSSs).
CPSSs use big data and perform analysis to provide valuable
services. Nowadays, the global world is shifting towards the
advancements and the integration of three aspects cyber, physical,
and social. A CPSS is the integration of a CPS and a cyber social
system (CSS). CPSs are not limited to communication, multimedia, or
entertainment.
The basic structure of a CPSS. In this figure, a reader can see
that cyber, physical, and social spaces are connected. The first
component named the social system mainly comprised of people or
citizens. These people have relations and the relations are formed
based on interaction, personal experiences, observations, and also
perceptions. The second component is named as ‘physical system’.
This component comprised sensors and actuators. The sensing
devices comprised sensors, actuators temperature sensors, etc.
These objects are known as smart objects and were connected using
communication technologies. The communication technologies are
comprised (both wireless and wired) to process the data in the
system and are shown in virtual space. CPSS is a recent and active
research area and a lot of research has been carried out. The
promising feature of CPSS is that it provides an interface between the

City Hotspot Identification Using Smart Cyber Physical Social System 2

objects so that they can send and receive the data and also carry out
necessary actions.
The promising integration feature of CPSS helps to improve the
telecom and user services, especially in smart cities. Researchers
have worked in this direction for example; The researchers proposed
a use case study and performed network analysis using mobile
network telecom data. Herein, the proposed data set is large and
comprised of CDRs, including topological and country information.
Similar work has been carried out byVisan et al. In this research, the
authors highlighted the communication service market problems
faced by telecom operators. Later, they presented various models
and scenarios using telecom big data. Amin et al. proposed CPSs for
the analysis of hotspots in a smart city. The proposed system uses
graph-based metrics for the identification of hotspots. However, the
metrics are very basic and thus the accuracy is compromised and
also the robustness is not discussed.
In General, the provision of valuable services to the users is the
key part of a CPSS. The incorporation of modern communication and
cutting-edge technologies focuses on providing high-quality services
using low latencies. Usually, the telecom big data is comprised of
calls, SMS, and Internet data and big data are telecom transactions
and pass through mobile devices. The challenges associated with big
data are efficient data storage, analysis, and processing. These
challenges became more difficult, especially with the incorporation of
modern techniques named social network analysis (SNA) or machine
learning. These modernresearch methods require suitable big
storage, analysis, and also modern distributed processing solutions.

From these aspects, it is necessary to propose and develop a

big data model that can handle and effectively process, store, and
analyze the big data. In addition, the proposed model should be smart
so that it can provide fast calculation and processing time. Therefore,
based on these facts and ground truths, Herein we proposed a
powerful big data platform with the ability to solve the above-stated

City Hotspot Identification Using Smart Cyber Physical Social System 3

challenges. Our proposed CPSS model is very smart because it can
handle and process large-scale data using different layers.

1.2 EXISTING SYSTEM:

Nattapon et al. presented research on CDRs using a telecom

dataset. In this study, they propose a method to clean large data
using ‘‘filters to filter’ to remove anomalies. Ahmad et al. presented
an advanced framework named the churn prediction SNA model. In
this model, they combine big data and machine learning. Herein,
they suggested various network centrality measures to provide an
equality analysis between each node pair. They perform an analysis
and hence each node pair interacts with the others using links.
It is evident from the literature that SNA and the centrality
measures were used in churn prediction. Modarresi et al. proposed a
graph-based analysis model intending to increase the resilience of
smart homes. Herein, they suggest several topologies using smart
home scenarios. Mededovic et al. explored various centrality metrics
and then concluded that they were used in the analysis of hotspots in
a certain area. Herein, they performed a detailed analysis using two
weeks of telecom data to find the hotspots in the network and also
measure the interaction. In this research, they used Eigenvector as a
key measure to rank the hotspots. Seufert et al. proposed aWi-Fi
hotspot model for the building of a smart city. Herein, the Top-tenWi-
Fi hotspot locations were identified using a publicWi-Fi dataset. They
concluded that the different Wi-Fi locations can be modeled using a
uniform distribution. The angles and the gamma distribution can be
maintained using minimum distance. This is a very simple Wi-Fi
hotspot model and the locations are used to create the spatial
distributions.
Peiyan et al. presented an advanced data-forwarding method
for opportunistic networks. In this research, they explored various
sizes of hotspots in the network. Herein, they propose a Hoten as a
metric used for routing. This metric is used in human mobility.

City Hotspot Identification Using Smart Cyber Physical Social System 4

Another measure named entropy is employed. The function of
entropy is to identify the public and personal Hotspots. Brdar et al.
presented a knowledge retrieval model using telecom data. Herein,
They suggested various centrality measures named closeness and
degree centralit.
Finally, Amin et al. proposed CPSs for the analysis of hotspots
in a smart city. The proposed system uses graph-based metrics for
the identification of hotspots.
However, the accuracy and the robustness are not presented.
Briefly in the above review, we presented various researcher’s work
and they used traditional centrality measures for example Degree,
closeness etc. In summary, these centrality measures are used for
detecting the influencers in small or medium-scale networks. It is
noticed that they are not suitable for large-scale networks. Similarly,
a few measures for example; PageRank, etc. is incompatible with the
telecom data. Because it is used to rank web pages over the Internet.
Therefore, to overcome these issues. Herein, we propose a smart
CPSS model to measure the large traffic areas in smart cities. Our
proposed model is unique in all aspects because we have selected
social and network measures to detect the hotspots. It is noted that
in previous studies, these measures were not used. Thus, it makes
our proposed model more efficient. In addition, our proposed CPSS
model is smarter because it provides accuracy and robustness which
are not supported by the traditional methods. The details of our
proposed conceptual model are discussed in this system.
Disadvantages
 In an existing system, the system doesn't implement USEFUL
SOCIAL NETWORK SIMILARITY AND SOCIAL BEHAVIORAL
MEASURE.
 Social network similarity and behavioral measures not found in
an existing system.

City Hotspot Identification Using Smart Cyber Physical Social System 5

1.3 PROPOSED SYSTEM
The motivation of our research is to propose and develop a
smart CPSS model that can efficiently process telecom data and
perform data analytics. The proposed CPSS acts as a solution to the
challenges associated with the extraction of large-scale data. The
hotspots have a high density as compared to the other areas of a
city. Thus, hotspot identification is useful to telecom operators and
companies to focus only on specific areas in providing high-quality
services.
The secondary motivation of this proposal is to provide a real-
time big data model that will help telecom decision-makers. It is
evident from the literature that, telecom operators and companies
always take care of providing good services to the customers. As the
influential hotspots in a network increase the service-providing
features. Thus, it has importance in the telecom domain.
Our proposed CPSS model is smart and comprised of three
layers. Each layer has different functionality and hence, different
functions have been performed by each layer. Our proposed model
initially extracts the hotspots as high-traffic areas from a graph and
later performs Social network analytics (SNA). Herein, we suggested
social network similarity and social behavioral measures. These
measures are used to quantify the importance of each node. Thus,
our proposed CPSS model identifies Top-10 high influencers based on
suggested metrics and it favors accurate analysis of telecom data.
In previous studies, traditional centrality methods were used.
Our proposed model is unique in all aspects because we have
selected social and behavioral measures to detect the hotspots or
high communication areas. Thus, it makes our proposed model more

City Hotspot Identification Using Smart Cyber Physical Social System 6

efficient. In addition, our proposed CPSS model is efficient because it
provides accuracy and robustness which are not supported by the
traditional methods.

In this proposal, we confirmed that social network similarity and

behavioral measures are useful in the identification of high
communication areas. This will help the telecom operators to perform
accurate analysis of large-scale telecom data.
Advantages
• This research provides big data analysis using telecom data. Thus,
it helps the telecom operators and the companies to identify the
hotspots (high communication areas) in a smart city. It has a
benefit for the telecom companies so that they can pay more
attention to these areas in providing more good services in target
areas.
• The proposed CPSS model is smart because it helps to identify the
high communication areas in a smart city.

• In this proposal two research fields can be combined, i.e. Graph

theory and communication.

1.4 PROBLEM STATEMENT:

Hotspots or high-traffic communication areas have a high

activity and density compared to the other areas in a smart city.
Hotspot analysis is a classical problem concerned with spatial
analysis. Telecommunication operators and companies always care to
identify the hotspots in a city to improve the quality of service.

1.5 OBJECTIVES
Integrate Cyber-Physical and Social Data Sources:
 Collect real-time data from physical sensors (e.g., traffic
cameras, IoT devices) and social platforms (e.g., Twitter, check-
ins) to identify urban activity patterns.
Detect and Analyze Urban Hotspots:

City Hotspot Identification Using Smart Cyber Physical Social System 7

  Identify high-activity zones or "hotspots" in the city based on
population density, traffic, social activity, and environmental
conditions.

Develop a Smart Data Processing Framework:

 Design an intelligent system that fuses heterogeneous data
sources using advanced analytics, machine learning, or
statistical techniques.
Ensure Real-Time Monitoring and Updates:
 Enable continuous monitoring and dynamic hotspot detection
with real-time data input and analysis.
Support Urban Planning and Resource Allocation:
 Provide insights for government agencies or city planners to
manage crowd control, traffic flow, public safety, and
emergency response.
Preserve Privacy and Data Security:
 Implement privacy-preserving techniques to ensure that user
and sensor data are handled securely and ethically.
Visualize Hotspot Data for Decision-Making:
 Develop user-friendly dashboards or heat maps to visually
represent hotspot information for easy interpretation and
decision support.

City Hotspot Identification Using Smart Cyber Physical Social System 8

 CHAPTER 2

LITERATURE REVIEW

In recent years, the concept of integrating cyber, physical, and

social systems has become increasingly prominent in the
development of smart cities, particularly for urban hotspot
identification.

Zheng et al. (2014) introduced the idea of urban computing

by combining data from GPS trajectories, social media, and
environmental sensors to detect and analyze crowd dynamics in
urban areas. Similarly,

Gao et al. (2017) utilized geotagged social media data to

identify event-based hotspots and their temporal patterns,
demonstrating that social platforms can provide rich contextual
insights for city planning.

Perera et al. (2014) emphasized the role of context-aware

computing in smart environments, showing that fusing data from IoT
sensors with social behavior significantly enhances the accuracy of
urban analytics.

Wang et al. (2018) proposed a cyber-physical-social

computing framework that integrates traffic flow data, environmental
sensors, and social media check-ins to detect high-density zones and
crowd movements in real-time.

City Hotspot Identification Using Smart Cyber Physical Social System 9

 Additionally, Yu et al. (2016) developed a real-time urban
sensing platform leveraging mobile phone data and social
interactions to dynamically detect and visualize urban hotspots. Their
approach showed that combining cyber-physical data (such as GPS)
with social signals (like call records or tweets) enables timely and
accurate urban monitoring.

Li et al. (2019) also contributed a model that incorporates

spatio-temporal patterns from both physical and social sensors,
highlighting the need for efficient data fusion algorithms to process
heterogeneous data.

While these studies confirm the effectiveness of cyber-physical-

social integration for hotspot detection, they also point out
challenges related to privacy, scalability, and real-time processing.
The literature collectively supports that a Smart Cyber-Physical-Social
System (CPSS) approach can significantly improve city hotspot
identification and decision-making for urban development.

City Hotspot Identification Using Smart Cyber Physical Social System 10

 CHAPTER 3

REQUIREMENTS & DOMAIN INFORMATION

3.1 REQUIREMENT SPECIFICATIONS:

Requirement Specifications describe the Arti-craft of Software

Requirements and Hardware Requirements used in this project.

3. 1.1 Software Requirements:

The functional requirements or the overall description

documents include the product perspective and features, operating
system and operating environment, graphics requirements, design
constraints and user documentation. The appropriation of
requirements and implementation constraints gives the general
overview of the project in regards to what the areas of strength
and deficit are and how to tackle them.

 Operating System - Windows 8 or above

 Coding Language - Java/J2EE (JSP, Servlet)

 Front End - J2EE

 Back End - MySQL

3.1.2 Hardware Requirements:

Minimum hardware requirements are very dependent on the

particular software being developed by a given Enthought Python/
Canopy/VS Code user. Applications that need to store large
arrays/objects in memory will require more RAM, whereas

City Hotspot Identification Using Smart Cyber Physical Social System 11

applications that need to perform numerous calculations or tasks
more quickly will require a faster processor.

➢Processor - Pentium–IV
➢RAM - 4 GB(min)
➢Hard Disk - 1 TB

3.2 DOMAIN INFORMATION:

Java Technology

Java technology is both a programming language and a

platform.

The Java Programming Language

The Java programming language is a high-level language.The

most programming languages, you either compile or interpret a
program so that you can run it on your computer. The Java
programming language is unusual in that a program is both compiled
and interpreted. With the compiler, first you translate a program into
an intermediate language called Java byte codes —the platform-
independent codes interpreted by the interpreter on the Java
platform. The interpreter parses and runs each Java byte code
instruction on the computer. Compilation happens just once;
interpretation occurs each time the program is executed. The
following figure illustrates how this works.

Fig:3.1 Java Programming

The Java Platform

A platform is the hardware or software environment in which a

program runs. We’ve already mentioned some of the most popular

City Hotspot Identification Using Smart Cyber Physical Social System 12

platforms like Windows 2000, Linux, Solaris, and MacOS. Most
platforms can be described as a combination of the operating system
and hardware. The Java platform differs from most other platforms in
that it’s a software-only platform that runs on top of other hardware-
based platforms.

The Java platform has two components:

 The Java Virtual Machine(Java VM)

 The Java Application Programming Interface (Java API)

You’ve already been introduced to the Java VM. It’s the base for
the Java platform and is ported onto various hardware-based
platforms.The Java API is a large collection of ready-made software
components that provide many useful capabilities, such as graphical
user interface (GUI) widgets. The Java API is grouped into libraries of
related classes and interfaces; these libraries are known as packages.
The next section, What Can Java Technology Do? Highlights what
functionality some of the packages in the Java API provide.The
following figure depicts a program that’s running on the Java
platform. As the figure shows, the Java API and the virtual machine
insulate the program from the hardware.

Fig:3.2 Java platform

Native code is code that after you compile it, the compiled code
runs on a specific hardware platform. As a platform-independent
environment, the Java platform can be a bit slower than native code.
However, smart compilers, well-tuned interpreters, and just-in-time
byte code compilers can bring performance close to that of native
code without threatening portability.

City Hotspot Identification Using Smart Cyber Physical Social System 13

Client Server

Over view:

With the varied topic in existence in the fields of computers,

Client Server is one, which has generated more heat than light, and
also more hype than reality.

This technology has acquired a certain critical mass attention

with its dedication conferences and magazines. Major computer
vendors such as IBM and DEC, have declared that Client Servers is
their main future market. A survey of DBMS magazine revealed that
76% of its readers were actively looking at the client server solution.
The growth in the client server development tools from $200 million
in 1992 to more than $1.2 billion in 1996.Client server
implementations are complex but the underlying concept is simple
and powerful. A client is an application running with local resources
but able to request the database and relate the services from
separate remote server.

What is a Client Server?

Two prominent systems in existence are client server and file

server systems. It is essential to distinguish between client servers
and file server systems. Both provide shared network access to data
but the comparison dens there! The file server simply provides a
remote disk drive that can be accessed by LAN applications on a file-
by-file basis.

The client server offers full relational database services such as

SQL-Access, Record modifying, Insert, delete with full relational
integrity backup/ restore performance for high volume of
transactions, etc. the client server middleware provides a flexible
interface between client and server, who does what, when and to
whom.

Front end or User Interface Design

The entire user interface is planned to be developed in browser

City Hotspot Identification Using Smart Cyber Physical Social System 14

specific environment with a touch of Intranet-Based Architecture for
achieving the Distributed Concept.The browser specific components
are designed by using the HTML standards, and the dynamism of the
designed by concentrating on the constructs of the Java Server Pages.

Communication or Database Connectivity Tier

The Communication architecture is designed by concentrating

on the Standards of Servlets and Enterprise Java Beans. The database
connectivity is established by using the Java Data Base Connectivity.

Java Virtual Machine (JVM)

Beyond the language, there is the Java virtual machine. The

Java virtual machine is an important element of the Java technology.
The virtual machine can be embedded within a web browser or an
operating system. Once a piece of Java code is loaded onto a
machine, it is verified. As part of the loading process, a class loader is
invoked and does byte code verification makes sure that the code
that’s has been generated by the compiler will not corrupt the
machine that it’s loaded on. Byte code verification takes place at the
end of the compilation process to make sure that is all accurate and
correct. So, byte code verification is integral to the compiling and
executing of Java code.

Java Source Java bytecode JavaVM

Java . Class

Fig:3.3 Java Virtual Machine

JavaScript

JavaScript is a script-based programming language that was

developed by Netscape Communication Corporation. JavaScript was
originally called Live Script and renamed as JavaScript to indicate its
relationship with Java. JavaScript supports the development of both
client and server components of Web-based applications. On the

City Hotspot Identification Using Smart Cyber Physical Social System 15

client side, it can be used to write programs that are executed by a
Web browser within the context of a Web page. On the server side, it
can be used to write Web server programs that can process
information submitted by a Web browser and then updates the
browser’s display accordingly.Even though JavaScript supports both
client and server Web programming, we prefer JavaScript at client
side programming since most of the browsers supports it. JavaScript
is almost as easy to learn as HTML, and JavaScript statements can be
included in HTML documents by enclosing the statements between a
pair of scripting tags.

<SCRIPTS>..</SCRIPT>.

<SCRIPT LANGUAGE = “JavaScript”>

JavaScript statements

</SCRIPT>

JavaScript Vs Java

JavaScript and Java are entirely different languages. A few of

the most glaring differences areJava applets are generally displayed
in a box within the web document; JavaScript can affect any part of
the Web document itself.While JavaScript is best suited to simple
applications and adding interactive features to Web pages; Java can
be used for incredibly complex applications.

Advantages

 JavaScript can be used for Sever-side and Client-side scripting.

 It is more flexible than VBScript.

 JavaScript is the default scripting languages at Client-side since

all the browsers supports it.

Hyper Text Markup Language

Hypertext Markup Language (HTML), the languages of the

World Wide Web (WWW), allows users to produces Web pages that
include text, graphics and pointer to other Web pages

City Hotspot Identification Using Smart Cyber Physical Social System 16

(Hyperlinks).HTML is not a programming language but it is an
application of ISO Standard 8879, SGML (Standard Generalized
Markup Language), but specialized to hypertext and adapted to the
Web. The idea behind Hypertext is that instead of reading text in rigid
linear structure, we can easily jump from one point to another point.
We can navigate through the information based on our interest and
preference. A markup language is simply a series of elements, each
delimited with special characters that define how text or other items
enclosed within the elements should be displayed.

Advantages

 A HTML document is small and hence easy to send over the net.
It is small because it does not include formatted information.

 HTML is platform independent.

 HTML tags are not case-sensitive.

JDBC

In an effort to set an independent database standard API for

Java; Sun Microsystems developed Java Database Connectivity, or
JDBC. JDBC offers a generic SQL database access mechanism that
provides a consistent interface to a variety of RDBMSs. This
consistent interface is achieved through the use of “plug-in” database
connectivity modules, or drivers.

If a database vendor wishes to have JDBC support, he or she

must provide the driver for each platform that the database and Java
run on. To gain a wider acceptance of JDBC, Sun based JDBC’s
framework on ODBC. As you discovered earlier in this chapter, ODBC
has widespread support on a variety of platforms.

City Hotspot Identification Using Smart Cyber Physical Social System 17

 JavaProgram Interpreter

Compilers My Program

Fig:3.4 JDBC

You can think of Java byte codes as the machine code

instructions for the Java Virtual Machine (Java VM). Every Java
interpreter, whether it’s a Java development tool or a Web browser
that can run Java applets, is an implementation of the Java VM.

Networking

TCP/IP stack

The TCP/IP stack is shorter than the OSI one:

Fig:3.5 TCP/IP stack

TCP is a connection-oriented protocol; UDP (User Datagram

Protocol) is a connectionless protocol.

IP datagram’s

The IP layer provides a connectionless and unreliable delivery

system. It considers each datagram independently of the others. Any
association between datagram must be supplied by the higher layers.

City Hotspot Identification Using Smart Cyber Physical Social System 18

The IP layer supplies a checksum that includes its own header. The
header includes the source and destination addresses. The IP layer
handles routing through an Internet. It is also responsible for breaking
up large datagram into smaller ones for transmission and
reassembling them at the other end.

UDP

UDP is also connectionless and unreliable. What it adds to IP is a

checksum for the contents of the datagram and port numbers. These
are used to give a client/server model - see later.

TCP

TCP supplies logic to give a reliable connection-oriented

protocol above IP. It provides a virtual circuit that two processes can
use to communicate.

Internet addresses

In order to use a service, you must be able to find it. The

Internet uses an address scheme for machines so that they can be
located. The address is a 32-bit integer which gives the IP address.
This encodes a network ID and more addressing. The network ID falls
into various classes according to the size of the network address.

Network address

Class A uses 8 bits for the network address with 24 bits left over
for other addressing. Class B uses 16-bit network addressing. Class C
uses 24-bit network addressing and class D uses all 32.

Subnet address

Internally, the UNIX network is divided into sub networks.

Building 11 is currently on one sub network and uses 10-bit
addressing, allowing 1024 different hosts.

Host address

8 bits are finally used for host addresses within our subnet. This
places a limit of 256 machines that can be on the subnet.

City Hotspot Identification Using Smart Cyber Physical Social System 19

Total address

The 32-bit address is usually written as 4 integers separated by

dots.

Port addresses

A service exists on a host, and is identified by its port. This is a

16 bit number. To send a message to a server, you send it to the port
for that service of the host that it is running on. This is not location
transparency! Certain of these ports are "well known".

Sockets

A socket is a data structure maintained by the system to handle

network connections. A socket is created using the call socket. It
returns an integer that is like a file descriptor. In fact, under Windows,
this handle can be used with Read File and Write File functions.

#include <sys/types.h>

#include <sys/socket.h>

intsocket(int family, int type, int protocol);

Tomcat 6.0 web server

Tomcat is an open-source web server developed by Apache

Group. Apache Tomcat is the servlet container that is used in the
official Reference Implementation for the Java Servlet and Java Server
Pages technologies. The Java Servlet and Java Server Pages
specifications are developed by Sun under the Java Community
Process. Web Servers like Apache Tomcat support only web
components while an application server supports web components as
well as business components (BEAs WebLogic, is one of the popular

City Hotspot Identification Using Smart Cyber Physical Social System 20

application servers). To develop a web application with jsp/servlet
install any web server like JRun, Tomcat etc. to run your application.

CHAPTER 4

SYSTEM METHODOLOGY

4.1 ARCHITECTURE OF PROPOSED SYSTEM:

The proposed system architecture integrates cyber, physical,

and social components to effectively identify urban hotspots in real
time. It consists of three major layers: the data acquisition layer, the
data processing and fusion layer, and the hotspot analysis and
visualization layer. In the data acquisition layer, physical sensors
(such as GPS, CCTV, and environmental monitors) collect real-time
urban data, while social sources like geotagged tweets, mobile check-
ins, and event feeds contribute contextual information. This
heterogeneous data is then transmitted to the data processing and
fusion layer, where advanced data fusion techniques and machine
learning algorithms analyze spatial-temporal patterns, filter noise,
and detect anomalies.

City Hotspot Identification Using Smart Cyber Physical Social System 21

 Fig;4.1 Architecture Diagram

4.2 MODULES

Server
In this module, the Admin has to login by using valid user name
and password. After login successful he can do some operations such
as View All Users and Authorize, View All Datasets, View All Datasets
by Chain using CPSS model, View City Hotspot Identification Status
Results, View Cyber Physical Social System Results.
View and Authorize Users
In this module, the admin can view the list of users who all
registered. In this, the admin can view the user’s details such as,
user name, email, address and admin authorizes the users.
User

In this module, there are n numbers of users are present. User

should register before doing any operations. Once user registers,
their details will be stored to the database. After registration

City Hotspot Identification Using Smart Cyber Physical Social System 22

successful, he has to login by using authorized user name and
password. Once Login is successful user will do some operations like
Register and Login, View Profile, Upload Datasets, Find City Hotspot
Identification Status Type, Find City Hotspot Identification Status
Type By CPSS model.

4.3 SYSTEM DESIGN

Input Design

Input Design plays a vital role in the life cycle of software

development, it requires very careful attention of developers. The
input design is to feed data to the application as accurate as possible.
So inputs are supposed to be designed effectively so that the errors
occurring while feeding are minimized. According to Software
Engineering Concepts, the input forms or screens are designed to
provide to have a validation control over the input limit, range and
other related validations.This system has input screens in almost all
the modules.

Error messages are developed to alert the user whenever he

commits some mistakes and guides him in the right way so that
invalid entries are not made. Let us see deeply about this under
module design.

Output Design

The Output from the computer is required to mainly create an

efficient method of communication within the company primarily
among the project leader and his team members, in other words, the
administrator and the clients.

The output of VPN is the system which allows the project leader
to manage his clients in terms of creating new clients and assigning
new projects to them, maintaining a record of the project validity and
providing folder level access to each client on the user side
depending on the projects allotted to him. After completion of a

City Hotspot Identification Using Smart Cyber Physical Social System 23

project, a new project may be assigned to the client. User
authentication procedures are maintained at the initial stages itself.

4.3.1 UML Diagrams

The Unified Modeling Language (UML) is a universally useful

visual displaying language that is utilized to determine, imagine,
develop, and report the curios of a product framework. It catches
choices and comprehension about frameworks that must be
developed. There are various types of diagrams in UML, each used for
a different purpose.

Use Case Diagram

City Hotspot Identification Using Smart Cyber Physical Social System 24

 Fig:4.2 Use Case Diagram

City Hotspot Identification Using Smart Cyber Physical Social System 25

Class Diagram

Fig 4.3 Class Diagram

This diagram shows the process of execution of the system and

the behavior of the system. It is a set of activities that represents the
flow by which actions are taking place in the system. Each activity
represents the action performed in every step.

City Hotspot Identification Using Smart Cyber Physical Social System 26

Sequence Diagram

Fig 4.4 Sequence Diagram

City Hotspot Identification Using Smart Cyber Physical Social System 27

Flowchart Diagrams

Fig: 4.5 Flowchart Diagram for User

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 Fig: 4.6 Flowchart Diagram for Server

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4.3.2 Data Flow Diagram:

Fig:4.7 Data Flow Diagram

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 CHAPTER 5

EXPERIMENTATION & ANALYSIS

5.1 EXPERIMENTATION
The experimentation for identifying city hotspots using a Smart
Cyber Physical Social System involved integrating data from multiple
sources, including IoT sensors, social media platforms, and mobile
GPS data across selected urban regions. The data was collected over
a defined period and preprocessed to remove noise and
inconsistencies. Clustering algorithms like DBSCAN and K-Means were
applied to detect high-density zones of human activity, while
temporal patterns were analyzed to identify peak usage times.
Sentiment analysis on geotagged social posts provided insights into
the public perception of various areas. Visualization tools and GIS
mapping were used to represent hotspots spatially, enabling effective
analysis and validation of system performance against real-world
patterns.

City Hotspot Identification Using Smart Cyber Physical Social System 31

5.2 RESULTS

5.2.1 Screen Shots

Fig:5.1 Home Page

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 Fig:5.2 User Registration

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 Fig:5.3 User Login

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 Fig:5.4 Server Login

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5.3 TESTING

System Testing

The purpose of testing is to discover errors. Testing is the

process of trying to discover every conceivable fault or weakness in a
work product. It provides a way to check the functionality of
components, sub-assemblies, assemblies and/or a finished product It
is the process of exercising software with the intent of ensuring that
the software system meets its requirements and user expectations
and does not fail in an unacceptable manner. There are various types
of tests. Each test type addresses a specific testing requirement.

Types Of Tests

Unit testing

Unit testing involves the design of test cases that validate that
the internal program logic is functioning properly, and that program
inputs produce valid outputs. All decision branches and internal code
flow should be validated. It is the testing of individual software units
of the application .it is done after the completion of an individual unit
before integration.

Integration testing

Integration tests are designed to test integrated software

components to determine if they actually run as one program.
Testing is event driven and is more concerned with the basic outcome
of screens or fields. Integration tests demonstrate that although the
components were individually satisfaction, as shown by successfully
unit testing, the combination of components is correct and consistent.

Functional test

Functional tests provide systematic demonstrations that

functions tested are available as specified by the business and
technical requirements, system documentation, and user manuals.

City Hotspot Identification Using Smart Cyber Physical Social System 36

Functional testing is centered on the following items:

Valid Input: identified classes of valid input must be accepted.

Invalid Input : identified classes of invalid input must be rejected.

Functions: identified functions must be exercised.

Output: identified classes of application outputs must be exercised.

Systems/Procedures: interfacing systems or procedures must be

invoked.

System Test

System testing ensures that the entire integrated software

system meets requirements. It tests a configuration to ensure known
and predictable results. An example of system testing is the
configuration-oriented system integration test. System testing is
based on process descriptions and flows, emphasizing pre-driven
process links and integration points.

White Box Testing

White Box Testing is a testing in which in which the software

tester has knowledge of the inner workings, structure and language of
the software, or at least its purpose. It is purpose. It is used to test
areas that cannot be reached from a black box level.

Black Box Testing

Black Box Testing is testing the software without any knowledge

of the inner workings, structure or language of the module being
tested.

City Hotspot Identification Using Smart Cyber Physical Social System 37

 CHAPTER 6
CONCLUSION

Herein, we proposed a smart CPSS model on a big data platform

by using telecom data. The smart CPSS model is divided into different
layers and each layer has different functionality. At first, the data
collection layer receives raw telecom data. The next step is to pass
through the data processing layer. The data processing layer
performs different functions, for instance, processing, storage and
analysis, etc. Then, it constructs a graph and performs a social
network analysis (SNA). Herein, the high communication areas in a
city were identified and secondly, Top-10 hotspots were discovered
using social network similarity and social behavioral measures. It is
evident from the results that our proposed big data analysis has
shown that the ranking of hotspots remains practiced under these
metrics. In addition, we found that the variance of results is
significantly smaller for Milan. This project is helpful the traffic
forecasting. In the future, will perform a detailed analysis of the
complete dataset that comprises every week’s data for Trento.

City Hotspot Identification Using Smart Cyber Physical Social System 38

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