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© 2020 IJRAR August 2020, Volume 7, Issue 3 www.ijrar.org (E-ISSN 2348-1269, P- ISSN 2349-5138)

IJRAR2002557 International Journal of Research and Analytical Reviews (IJRAR) www.ijrar.org 436

Smartect Security and Automation using IoT & Image

Processing

1Kumar R, 2Deepak Jangir, 3H S Rakshak, 4Krishan Jangir, 5Yezdan R 1Professor, 2Student, 3Student, 4Student, 5Student

1Department of Information Science & Engineering, 1MVJ College of Engineering, Bangalore, India

Abstract: The Smartect Security and Automation system is aimed at providing smart security solutions to the average consumer. It becomes

very tedious and sometimes impossible to prevent certain security mishaps such as trespassing, attempts of robbery and fire safety. Also,

certain monotonous tasks such as opening and closing of gates, turning off of appliances, etc. can be automated to provide ease of access in

our daily lives. Thus, Smartect aims at providing solutions to all of these through the use of technology such as the Arduino mega, Machine

Learning concepts such as - Image Recognition, Deep Learning and Natural Language Processing. The system will be fed with photos of the

home owner providing a biometric feature for access control and to detect trespassing, deep learning algorithms would be used to differentiate

between trespassing humans and animals. Cryptographic Techniques such as block-chaining and hashing would also be used to secure this

data and the IOT server as well. With time, the system would also be able to predict the building owner’s day to day pattern and hence further

upgrading the system’s safety. Thus, Smartect aims at providing the complete package for all safety, security and ease of access needs.

I. INTRODUCTION

In urbanization you think big, because you think decades ahead‖-Kushal Pal Singh. Consider this, over half of the world’s population

lives in urban areas, it is expected by 2050 that this coefficient will grow to 66 per cent. Research tells us that on an average, people spend a high

percentage of their lives inside buildings i.e. offices, schools and homes. Our entire lives revolve around these buildings and infrastructure.

Buildings also represent one of the largest capital expenses for businesses, probably next only to the CTC‘s being paid to employees. The

beginning of the industrial revolution signaled the beginning of a new era to the Homo sapiens with rapid development, a population explosion

and increasing longevity in lives due to the development of science and technology. This trend continues in the modern area with the IOT and

Artificial intelligence sparking a revolution this time around.

The coming decades will bring about a significant shift in the way buildings are developed and used. Land shortage will lead to a tendency

to construct taller buildings and dig for space underground. Such type of buildings will give rise to new kinds of problems, one that challenges

the safety and security of the people. It is to address these sorts of problems that modern technology must be used.

The Internet of Things offers innovative solutions to meet these goals. Smart buildings harness IoT connectivity, sensors and the cloud storage

to remotely monitor and control a range of building systems, right from HVAC systems to lighting and security systems. The ultimate aim is to

get all the components of the buildings communicating with each other so that there is improved performance, ease of access and an overall

feeling of safety. Why go smart? The future belongs to those who can continuingly use the available limited energy resources to the optimum,

and saving on energy means saving on the expenses and bills. Lastly, the security of the human resource inside the building is better guarded

through this wonderful new piece of technology.

Consider a situation where you are the last person to leave office, you suddenly realize that you have not locked your door or that you forgot

to turn off the lights. To prevent you from getting the boot the next morning technology comes to your rescue. The smart door lock system kicks

in and automatically locks he door once the last the resident is out of the building. Or supposing a burglar has intruded into your building space,

it would prove to be a meal to find him, unless your compound safety mechanism kicks in and has eyes on him from the word go. Not only do

you save time with these tactics but also make the entire process so very efficient. This is where IOT based smart buildings can really turn into

a savior of sorts.

Image recognition based on Deep Learning also enhances the access control mechanisms. Thus, it would further enhance the proposition that

to maintain high levels of security, human intervention is no longer required. The security guard might easily be overpowered or undergo

deception. But it is next to impossible to cheat the algorithms.

II. OBJECTIVE

The main objective of the proposed system is to develop a security module that can be implemented in a large-scale due to its economic

feasibility, ease of access and a sense of reliability in the field of automated security.

III. RELATED WORK

International Conference on Communication and Signal Processing, April 2-4, 2015, India i-Learning loT: An Intelligent Self

Learning System for Home Automation Using loT - Vishwajeet Hari Bhide, Dr. Sanjeev Wagh

Internet of Things (loT) is extension of current internet to provide communication, connection, and internetworking between various devices

or physical objects also known as "Things." In this paper we have reported an effective use of loT for Environmental Condition Monitoring and

Controlling in Homes. We also provide fault detection and correction in any devices connected to this system automatically. Home Automation

is nothing but automation of regular activities inside the home. Now a day's due to huge advancement in wireless sensor network and other

computation technologies, it is possible to provide flexible and low-cost home automation system. However, there is no any system available in

market which provide home automation as well as error detection in the devices efficiently. In this system we use prediction to find out the

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required solution if any problem occurs in any device connected to the system. To achieve that we are applying Data Mining concept. For

efficient data mining we use Naive Bayes Classifier algorithm to find out the best possible solution. This gives a huge upper hand on other

available home automation system, and we actually manage to provide a real intelligent system. The Internet of Things (loTs) is nothing but

connecting everyday objects like Internet TV s, smart phones, sensors and or actuators to the Internet where this device linked together

intelligently which enables new forms of communication between people and things, and between things themselves. Now anyone, can have

connectivity with anything for anytime and anyplace and it is possible to create these connections which extend and possibly create an advanced

dynamic network. Modern advances in electronics and communications Technologies have led to the miniaturization and improvement of the

performance of computers, sensors and networking. These changes have given rise to the development of several home automation technologies

and systems. According to, home automation can be useful to those who need to Access home appliances while away from their home and can

incredibly improve the lives of the disabled.

2nd IEEE PERCOM Workshop on Security Privacy and Trust in The Internet of Things 2017 Blockchain for IoT Security and

Privacy: The Case Study of a Smart Home - Ali Dorri, Salil S. Kanhere, Raja Jurdaky and Praveen Gauravaramz

Internet of Things (IoT) security and privacy remain a major challenge, mainly due to the massive scale and distributed nature of IoT

networks. Blockchain-based approaches provide decentralized security and privacy, yet they involve significant energy, delay, and computational

overhead that is not suitable for most resource-constrained IoT devices. In our previous work, we presented a lightweight instantiation of a BC

particularly geared for use in IoT by eliminating the Proof of Work (POW) and the concept of coins. Our approach was exemplified in a smart

home setting and consists of three main tiers namely: cloud storage, overlay, and smart home. In this paper we delve deeper and outline the

various core components and functions of the smart home tier. Each smart home is equipped with an always online, high resource device, known

as” miner” that is responsible for handling all communication within and external to the home. The miner also preserves a private and secure

BC, used for controlling and auditing communications. We show that our proposed BC-based smart home framework is secure by thoroughly

analyzing its security with respect to the fundamental security goals of confidentiality, integrity, and availability. Finally, we present simulation

results to highlight that the overheads (in terms of traffic, processing time and energy consumption) introduced by our approach are insignificant

relative to its security and privacy gains.

Network-Level Security and Privacy Control for Smart-Home IoT Devices - Vijay Sivaramany, Hassan Habibi Gharakheiliy, Arun

Vishwanath, Roksana Boreli, Olivier Mehani University of New South Wales, IBM Research-Australia, NICTA, Australia

The increasing uptake of smart home appliances, such as lights, smoke-alarms, power switches, baby monitors, and weighing scales, raises

privacy and security concerns at unprecedented scale, allowing legitimate and illegitimate entities to snoop and intrude into the family’s activities.

In this paper we first illustrate these threats using real devices currently available in the market. We then argue that as more such devices emerge,

the attack vectors increase, and ensuring privacy/security of the house becomes more challenging. We therefore advocate that device-level

protections be augmented with network-level security solutions, that can monitor network activity to detect suspicious behavior. We further

propose that software defined networking technology be used to dynamically block/quarantine devices, based on their network activity and on

the context within the house such as time-of-day or occupancy-level. We believe our network-centric approach can augment device-centric

security for the emerging smart-home. The home is becoming increasingly “smart”, driven by emergence of Internet-connected appliances that

form part of the emerging breed of Internet-of-Things (IoT) devices. This enables consumers to remotely monitor and manage their home

environment lighting systems can be controlled remotely, smoke alarms can alert our mobile phone when a fire is detected, we can monitor our

kids from afar, and our health/fitness data can be sent instantly from the home to the cloud for analysis. Surveys in the US indicate personal or

family safety, property protection, lighting/energy management, and pet monitoring as top motivations for use of such devices, with 51% of

those surveyed willing to pay in excess of $500 for a fully-equipped smart-home. With increasing deployments of such Internet-connected

devices in the house come increasing risks to both privacy and security: an eavesdropper can illegitimately snoop into family activities, even a

legitimate entity (such as the device manufacturer) may be gathering data about users that they are not aware of, and of course a malicious

entity may remotely take over control of the IoT devices, using it to either harm the household or to use it as a launchpad for attacking other

domains. Indeed, in early 2014 it was revealed that there was a large-scale attack on IoT devices including TVs and fridges, in which hackers

were believed to have broken into more than 100,000 everyday consumer gadgets.

Deep Food: Deep Learning-based Food Image Recognition for Computer-aided Dietary Assessment - Chang Liu, Yu Cao, Yan Luo,

Guanling Chen, Vinod Vokkarane, Yunsheng Ma, The University of Massachusetts Lowell, One University Ave, Lowell, MA, 01854,

USA

Worldwide, in 2014, more than 1.9 billion adults, 18 years and older, were overweight. Of these, over 600 million were obese. Accurately

documenting dietary caloric intake is crucial to manage weight loss, but also presents challenges because most of the current methods for dietary

assessment must rely on memory to recall foods eaten. The ultimate goal of our research is to develop computer-aided technical solutions to

enhance and improve the accuracy of current measurements of dietary intake. Our proposed system in this paper aims to improve the accuracy

of dietary assessment by analyzing the food images captured by mobile devices (e.g., smartphone). The key technique innovation in this paper

is the deep learning-based food image recognition algorithms. Substantial research has demonstrated that digital imaging accurately estimates

dietary intake in many environments and it has many advantages over other methods. However, how to derive the food information (e.g., food

type and portion size) from food image effectively and efficiently remains a challenging and open research problem. We propose a new

Convolutional Neural Network (CNN)-based food image recognition algorithm to address this problem. We applied our proposed approach to

two real-world food image data sets (UEC-256 and Food-101) and achieved impressive results. To the best of our knowledge, these results

outperformed all other reported work using these two data sets. Our experiments have demonstrated that the proposed approach is a promising

solution for addressing the food image recognition problem. Our future work includes further improving the performance of the algorithms and

integrating our system into a real-world mobile and cloud computing-based system to enhance the accuracy of current measurements of dietary

intake. Keywords: Deep learning, food image recognition, dietary assessment




IV. RESEARCH METHODOLOGY

In this section we will discuss the methodology that we are using to avoid accidents. Block diagram of the proposed system is shown below

Fig 4.1 Block Diagram of Face Recognition/Pedestrian System

4.1 Block Diagram Description

This project mainly contains two modules

4.1.1 Arduino Mega:

The Arduino Mega is mainly based on microcontroller board of ATmega2560. In this proposed system, the Arduino mega connects all the

sensors and the body of the robot which helps the microcontroller control the movement and the functionalities of the robot.

4.1.2 Arduino Uno:

The Arduino Uno is an open-source microcontroller board based on the Microchip ATmega328P microcontroller and developed by

Arduino.cc. The board is equipped with sets of digital and analog input/output pins that may be interfaced to various expansion boards and other

circuits.

Different steps for implementing Smartect Security and Automation system are presented below

In “Design and Implementation of Security System [1] Facial Recognition using Open CV–Open Source Computer Vision is a library of

programming functions mainly aimed at real-time computer vision. Its main functionality is to Read and write images. Facial Detection and

detecting other key features of the face. Detection of shapes such as circles, rectangles etc. in images, to name a few.

Data Gathering- This step involves extraction of unique features of a person’s face that can be used to differentiate him from another person.

Trainer- The data from the dataset is taken and is used to train the open CV module. This is done with the help of a specific training module.

Recognition-Here a face is captured on the camera and if this face was already captured before, the recognizer returns a “prediction” marking

the face with an id and also marking the accuracy of the match.

In “Smartect Security and Automation system”, [2] Motion Sensor – OpenCV is again used for motion detection. The basic principle behind

motion detection in cameras is to differentiate between the foreground and background.i.e.to discern between small lighting changes and actual

motion. we plan to implement is a camera that turns on only when it detects motion and click pictures when any sort of motion is detected.

Training of the system will first be only the background. Subsequently it would be trained with other objects in the image to improve accuracy

of detection. Also integrated is a drop-box account which would store the photos taken. All of these steps are done to improve storage efficiency

of our system.

In “Smartect Security and Automation system”, [3] Fire Safety Mechanism –This system is equipped with smoke and flame sensor. When a

fire is sensed, this system automatically unlocks the door and opens the windows to remove the excess smoke. The system would reduce and

limit the building’s electric supply to only emergency lights to reduce further damages. The nearest fire station would be contacted automatically

for assistance. This system has two-fold mechanisms for fire detection, thus preventing any false positive alarms. The flame sensor will generate

certain output on detection of flame. Measured input from the Sensor is then fed to the amplifier. Amplifier then sends analog data to the analog

output pin of the module. In this, we are going to interface LED using the Flame Sensor Module KY-026 in Raspberry Pi.

In “Smart Security and Automation system”, [4] Human Detection – Digital image processing by using the package and features of OpenCV

library and coded with one of the high-level languages like Python and is booted with the help of a Raspberry Pi microcontroller. This is fitted

with a RaspiCam to capture the motion images of objects passing through it. The model utilizes image samples in top-view or even over-view

and predefined models containing to great numbers motion variations are set, which helps in identification of humans entering the surroundings.

A set of Passive Infra-Red (PIR) sensors are installed to instruct the system to capture images of a person or an object that crosses it (passes




around it). This system of human detection is combined with sensor feedback mechanism which is being used along with the capability to send

data to local servers via Bluetooth signals.

In “Smart Security and Automation system”, [5] Encryption module –We Using the python library called “cryptography” and a packet named

“fornet”, we encrypt the needed files. The files we would be encrypting here are the photos of the user. The module generates a 32-bit hash key,

which is stored in a file in storage and retrieved when necessary. Thus, keeping in mind with privacy laws all over the world.

In “IoT Application Development: Home Security System” [6], The proposed solution makes use of USB Webcam as an image capturing

unit, Electric Door Strike as an actuator and Telegram which has an amazing feature as Telegram Bot which provide APIs to build solutions

which is compatible with Raspberry Pi IoT infrastructure.

In "Web-based online embedded door access control and home security system based on face recognition," [7] The paper describes about the

implementation and deployment of wireless control system and accessibility in to a home environment for authenticated people only. A wireless

network technique, ZigBee and image processing technique PCA based, dedicatedly make the security system alive as per the request.

In “Smart Home Automation using Arduino UNO Microcontroller”, it describes he design and prototype implementation of Smart Home

Automation System is proposed. The system incorporates microcontroller and Bluetooth module to provide automation capability to various

household activities. A microcontroller offers high performance and flexibility.

In” Embodiment of IOT based Smart Home Security System” [9], paper reflects on the IoT base Smart Home System as well as home

security System. built is low-cost smart home operated by Android application wirelessly inside the home and also from anywhere in the world

using GSM system. Smart electrical monitoring system where user can operate wirelessly their light, fan, TV, freeze etc. smart Security system

where user can maintain the security system using application even, he/she can get any type security alert living anywhere in the world Smart

gardening system using android application and GSM system has been developed.

V. SYSTEM HARDWARE AND SOFTWARE CONFIGURATION

5.1 Hardware Components

5.1.1 Arduino Mega 2560 REV3

Fig 5.1 Arduino Mega 2560 REV3

The Arduino Mega 2560 is a Board based on the ATmega2560 microcontroller. It has 54 digital input / output pins (of which 15 could be

used as PWM outputs), 16 analog inputs, four UARTs (hardware serial ports), a 16 MHz crystal oscillator, a USB link, a power jack, an ICSP

header and a reset button. It comprises everything you need to help the microcontroller; just connect it to a computer using a USB cable or

power it with AC to DC Adapter or a battery

5.1.2 Arduino Uno REV3

Fig 5.2 Arduino Uno REV3

Arduino Uno is a board based on the ATmega328P (datasheet) Microcontroller. It has 14 digital input / output pins (of which 6 can be used

as PWM outputs), 6 analog inputs, a 16 MHz ceramic resonator (CSTCE16M0V53-R0), a USB interface, a power jack, an ICSP header and a

reset button. It contains all the requirements to support the microcontroller; just connect it to a computer with a USB cable or power it with AC

to DC Adapter or with a Battery.




5.1.3 M2Q Gas Sensor

Fig 5.3 MQ2 Gas Sensor

Gas sensor module (MQ2) is useful for detecting gas leakage (in home and industry). Combustible gas and smoke can be observed. The Gas

sensor output voltage increases as gas concentrations rise. Rotating the potentiometer will change the sensitivity.

5.1.4 Flame Sensor

Fig 5.4 Flame Sensor

Flame Sensor can be used in the range of 760 nm- 1100 nm to detect fire source or other wave length light sources. It is based on the YG1006

sensor which is a high speed NPN silicone phototransistor with high sensitivity. The sensor is sensitive to infrared radiation due to its black

epoxy. Sensor can be a great addition to locating the source of fire in a fire fighting system, it can be used as a system eye. The Signal LED will

light up when the sensor detects flame and the D0 pin goes LOW.

5.1.5 PIR Sensor

Fig 5.5 PIR Sensor

PIR sensors allow you to sense motion, which is almost often used to detect whether a person has moved inside or beyond the range of

sensors. They're small, inexpensive, low-powered, easy to use and don't wear out. They are most commonly used in appliances and gadgets

which are used in homes or businesses. They are also called PIR, "Passive Infrared," "Pyroelectric," or "IR Motion" Sensors.

5.1.6 Fire Extinguisher

Fig 5.6 Fire Extinguisher

A fire extinguisher is an external fire safety system used, often in emergencies, to extinguish or prevent small fires. This is not intended for

use on an out - of -control fire, such as one that has reached the ceiling, endangering the user or otherwise needing a fire brigade's expertise.




5.1.7 16x2 LCD Display

Fig 5.7 16x2 LCD Display

A LCD is a flat-panel screen or other electronically modulated optical device that uses the light-modulating properties of liquid crystals in

conjunction with polarizers. Liquid crystals do not specifically emit light; rather, they use a backlight or reflector to create colored or monochrome

images.

5.1.8 CCTV

Fig 5.8 CCTV

Closed-circuit television, also known as video surveillance, is the use of video cameras to relay a signal over a small range of monitors to a

particular location.

5.1.9 SG90 Servo Motor

Fig 5.9 SG90 Servo Motor

A servo motor is an electric device that can precisely move or rotate an object. When you want to rotate and object at a certain angle or distance,

then you use a servo motor. It consists of a basic motor running via the servo mechanism.

5.1.10 MFRC-522 RC522 RFID

Fig 5.10 RC522 RFID




The MFRC522 is a highly integrated reader or writer IC that supports ISO / IEC 14443 A / MIFARE mode for contactless communication at

13.56 MHz The MFRC522 reader. Without additional active circuitry, the internal transmitter of the MFRC522 can drive a reader or writer

antenna designed to communicate with ISO / IEC 14443 A / MIFARE cards and transponders. The receiver module provides reliable and

efficient implementation for ISO / IEC 14443 A / MIFARE compatible cards and transponders to demodulate and decode the signals. The

digital module administers the full functionality of ISO / IEC 14443 A framing and error detection (parity and CRC).

5.1.11 LEDs

Fig 5.11 LED

A light-emitting diode is a light source with a semiconductor that emits light as the current flows through it. In the semiconductor, electrons

recombine with holes in the nucleus, releasing energy in the form of photons.

5.1.12 Breadboard

Fig 5.12 Breadboard

A breadboard is a plastic rectangular board with a set of tiny holes inside it. These holes allow you to easily insert electronic components to

prototype (meaning building and testing an early prototype of) an electronic circuit, such as this one with a battery, switch, resistor, and LED

(light-emitting diode).

5.1.12 Jumper Cables

Fig 5.13 Jumper Cables

Jumper wires are simply wires with connector pins at either end, so that they can be used to connect two points without soldering each other.

Usually, jumper wires are used with breadboards and other prototyping devices to make adjustments to a circuit as needed simple.




VI. TEST CASES AND RESULTS

6.1 Face Detection

Table 6.1 Face Detection Test Case

TEST CASE

ID

TEST CASE EXPECTED

RESULT

ACTUAL RESULT REMARKS

1 Face_detection() Face will either be

recognized or not.

Face is recognized. Successful

2 train() Images of different

faces are fed into the

system

Image module

OpenCV trained.

Successful

6.2 Pedestrian Detection

Table 6.2 Pedestrian Detection Test Case

TEST CASE

ID

TEST CASE EXPECTED

RESULT

ACTUAL RESULT REMARKS

1 Pedestrian_detection() Human will be

detected.

Humans are detected. Successful

2 train() Training data is fed to

system.

Training data is fed to

system

Successful

6.2 Fire Detection Test Case

Table 6.3 Result Generation Test Case

TEST CASE

ID

TEST CASE EXPECTED RESULT ACTUAL RESULT REMARKS

1 detect() Fire and smoke are

detected, doors open

automatically, exhaust

is turned on.

Fire and smoke are

detected, doors open

automatically, exhaust

is turned on.

Successful

2 train() The input image is

processed into HSB

Frame.

The input image is

processed into HSB

Frame.

Successful




VII. EXPERIMENTAL RESULTS AND DISCUSSION

7.1 Snapshot 1: Overall Project Snapshot

Fig 7.1 Overall Project Snapshot

7.2 Snapshot 2: Smoke Sensor & Flame Sensor – Fire Safety Module

Fig 7.2 Working of Smoke Sensor and Flame Sensor

The smoke and flame sensors are placed in the room where there is a high chance of fire for example kitchens, laboratories, indoor

fireplaces etc. The smoke and flame sensors keep sensing for smoke, gas leakage & fire 24/7. The sensors are triggered when they sense

smoke or fire (flame). Also, to prevent false positives in case of smoke produced due to Incense Sticks or any other reason, we have

implemented an image recognition module that detects the incoming video frame and converts it to frames. It then converts the video into

HSV colour space which is closer to how humans perceive colour and then takes it takes an upper and lower range of orange colour and

shows pixels detected in that range. Once smoke or fire is sensed, the data is sent to the Arduino Mega 2560 REV3 for processing and

the Arduino is automated to take further actions. The Arduino is programmed to limit the power supply of the building to prevent further

damages. Emergency LEDs are used as they consume less power. The windows of the building are automatically opened to remove excess

smoke. An exhaust fan is also switched on to remove excess smoke from the building




7.3 Snapshot 3: Automated Fire Extinguisher

Fig 7.3 Shows the Woking of the Automated Fire Extinguisher and Displays Messages

7.4 Snapshot 4: Face Recognition & RFID

Fig 7.7 Shows the Working of Face Recognition & RFID Module

A camera (mobile phone/webcam) is placed at the entrance. This immediately captures a photo when the user clicks on the capture button

in the GUI. To protect the privacy of the individual and according to the GDPR, the photo is also encrypted and stored in the database.

The photo is then sent to the OpenCV module used for image recognition. An RFID is also setup as an alternative.

7.5 Snapshot 5: Human Recognition

Fig 7.5 Shows the Working of Human/Pedestrian Recognition Module

To prevent unauthorized entry of people, cameras are placed near the compounds. These cameras are activated when motion is detected

by the motion sensors, and clicks several photos of the intruder as soon as an alert is sounded. All the images are stored in a folder and

the and folder undergoes extraction of positive and negative images. This model is done using OpenCV2. The model is trained in such a




way as to detect for people in the given photographs. It makes a box around the figures detected in the image and writes output frames to

disk. Background subtraction is done in the image and compared with the original image. It finally alerts the user if intrusion (humans)

are detected and hence if animals/birds are not detected.

7.6 Snapshot 6: Encryption/Decryption Key

Fig 7.6 Encryption/Decryption Key

Encryption is the process of encoding a message or information in such a way that only authorized parties can access it and those who are

not authorized cannot. Encryption does not itself prevent interference but denies the intelligible content to a would-be interceptor. Using

the python library called “cryptography” and a packet named “fornet”, we encrypt the needed files.

7.7 Snapshot 7: Application GUI

Fig 6.7 Shows the GUI Application & Controller

Tcl/Tk has long been an integral part of Python. It provides a robust and platform independent windowing toolkit, that is available to

Python programmers using the Tkinter package, and its extension, the Tkinter.tix and the Tkinter.ttk modules.

VIII. ACKNOWLEDGMENT

The satisfaction and euphoria that accompany a successful completion of any task would be incomplete without the mention of people

who made it possible, success is the epitome of hard work and perseverance, but steadfast of all is encouraging guidance.

So, with gratitude we acknowledge all those whose guidance and encouragement served as beacon of light and crowned our effort

with success. We are thankful to our principal, Dr. P. Mahabaleshwarappa, for his encouragement and support throughout the project work.

We are also thankful to our HOD, Prof. Sanchari Saha for her incessant encouragement & all the help during the project work. We consider it

a privilege and honour to express our sincere gratitude to our guide Prof. Kumar R, Assistant Professor, Department of Information Science &

Engineering for his valuable guidance throughout the tenure of this project work, and whose support and encouragement made this work

possible.

It’s also a great pleasure to express our deepest gratitude to all faculty members of our department for their cooperation and

constructive criticism offered, which helped us a lot during our project work. Finally, we would like to thank all our family members and

friends whose encouragement and support was invaluable.




IX. REFERENCES

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Gyeongsan, Gamasil-gil 50, 712-701, Republic of Korea

[2] Narasimha Prasad, L V Shankar Murthy P, Kishor Kumar Reddy C. Analysis of Magnitude for Earthquake Detection using Primary

Waves and Secondary Waves. In, International Conference on Human Computer Interactions (ICHCI) in September 2014, IEEE.

[3] AJAYI Olasupo Opeyemi, OLAIFA Oluwaseun. Detecting Intrusion in Large Farm Lands and Plantations in Nigeria Using Virtual

Fences. In, University of Lagos, Nigeria, 2016

[4] R. Angeline, Adithya S, Abishek Narayanan. Fire Alarm System Using IOT. In, International Journal of Innovative Technology and

Exploring Engineering (IJITEE), Volume-8, Issue-6S3, April 2019.

[5] Md. Wahidur Rahman, Md. Harun-Ar-RashidRahabul Islam, Dr. Mohammad Motiur Rahman. Embodiment of IOT based Smart Home

Security System. In, International Journal for Research in Applied Science & Engineering Technology (IJRASET) ISSN: 2321-9653;

IC Value: 45.98; SJ Impact Factor: 6.887; Volume 6 Issue IX, Sep 2018.

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[8] Ali Dorri, Salil S. Kanhere, Raja Jurdak and Praveen Gauravaram. Blockchain for IoT Security and Privacy. In, The University of New

South Wales Sydney, Australia & CSIRO, March 2017.

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[10] Dr. Rajeshwari M Banakar, Raj G Anvekar. IoT Application Development: Home Security System. In, IEEE Technological Innovations

in ICT for agricultural and Rural Development (TIAR), 2017.


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