ANDROID APPLICATION FOR REMOTE DOOR

LOCK SYSTEM IN COLLEGES

A PROJECT REPORT

Submitted by

S. KRISHNA PRASAD (2013503514)

HARSHAN SHYAM (2013503509)

A. N. KARTHIKEYAN (2013503511)

in partial fulfillment for the Student Innovation Project

of

CENTRE FOR TECHNOLOGY DEVELOPMENT AND TRANSFER

ANNA UNIVERSITY

MADRAS INSTITUTE OF TECHNOLOGY, CHENNAI

ANNA UNIVERSITY: CHENNAI 600 025

December 2016

ANNA UNIVERSITY: CHENNAI 600 025

ii

BONAFIDE CERTIFICATE

Certified that this project report titled “ANDROID APPLICATION

FOR REMOTE DOOR LOCK SYSTEM IN COLLEGES” is a bonafide

work done by “S. KRISHNA PRASAD (2013503514), HARSHAN SHYAM

(2013503509) and A.N.KARTHIKEYAN (2013503511)” under my

supervision, in partial fulfilment for the Student Innovation Project of Centre for

Technology Development and Transfer, Anna University . Certified further, that

to the best of my knowledge the work reported here in does not form part or full

of any other thesis or dissertation on the basis of which a degree or award was

conferred on an earlier occasion to this or any other candidate.

Date:

Place

SIGNATURE

Dr.S.Thamarai Selvi

SUPERVISOR

Professor,

Department of Computer Technology,

Madras Institute of Technology,

Anna University,

Chennai-600 044.

iii

ACKNOWLEDGEMENT

We are highly indebted to our respectable Dean, Dr. A. RAJADURAI and to

our reputable Head of the Department Dr. P. ANANDHAKUMAR,

Department of Computer Technology, MIT Campus, Anna University for

providing us with sufficient facilities that contributed to success in this

endeavor.

We would like to express my sincere thanks and deep sense of gratitude to our

Supervisor, DR.S.THAMARAI SELVI for her valuable guidance, suggestions

and constant encouragement which paved way for the successful completion of

this phase of project work.

We would be failing in our duty, if we forget to thank all the teaching and non-

teaching staff of our department, for their constant support throughout the

course of our project work.

S.KRISHNA PRASAD

HARSHAN SHYAM

A.N.KARTHIKEYAN

iv

ABSTRACT

This project deals with the design and implementation of a surveillance

system using a security camera to remotely open/close the Raspberry pi door

lock system of laboratories in educational institutions via a mobile application .

The proposed security system captures video footage and transmits it via a WIFI

to a static IP, which is viewed using a web browser from a dedicated a smart

device in the admin console. The camera streams live video and records the

motion detected parts in the cloud and/or in the system shared folder for video

analysis. The video analysis result causes a notification to be sent to the mobile

application which in turn controls the raspberry pi to open/close the door lock

system. A Raspberry pi fitted door lock is used so as remotely control opening

and closing mechanism via the mobile application. A security camera is used

for surveillance of the labs from which live footage is sent to the admin console.

Video analysis is done on the footage using MATLAB to detect motion and

human presence. Notification is sent to the mobile application 24/7 when

presence of motion is detected.

v

CHAPTER

NO.

TITLE PAGE

NO.

ABSTRACT iv

LIST OF ABBREVIATIONS vii

LIST OF FIGURES Viii

1 INTRODUCTION 1

1.1 INTERNET OF THINGS 1

1.2 IoT COMMUNICATION MODELS 2

1.2.1 DEVICE-TO-DEVICE

COMMUNICATIONS

3

1.2.2 DEVICE-TO-GATEWAY MODEL 4

1.2.3 DEVICE-TO-CLOUD

COMMUNICATIONS

5

1.2.4 BACK-END DATA-SHARING

MODEL

6

1.3 RASPBERRY PI 7

1.4 HARDWARE LAYOUT 8

1.4.1 PROCESSOR / SOC (SYSTEM ON CHIP) 8

1.4.2 POWER SOURCE 9

1.4.3 SD CARD 9

1.4.4 GPIO 9

1.4.5 DSI CONNECTOR 11

1.4.6 AUDIO JACK 11

1.4.7 STATUS LEDS 12

1.4.8 USB 2.0 12

1.4.9 ETHERNET 12

1.4.10 CSI CONNECTER 13

1.4.11 JTAG HEADERS 13

vi

1.4.12 HDMI 13

2 LITERATURE SURVEY 14

2.1 MOTION DETECTION SYSTEM 14

2.1.1 BACKGROUND SUBTRACTION

METHOD

15

2.1.2 WIDE AREA MOTION IMAGERY 17

2.2 HOME AUTOMATION SYSTEM 18

3 PROPOSED WORK 20

3.1 OVERVIEW 20

3.2 ARCHITECTURE 21

3.3 DESCRIPTION OF MODULES 22

3.3.1 USER INTERFACE 22

3.3.2 WIFI ROUTER CONFIGURATION 22

3.3.3 SETTING UP RASPBERRY PI 22

3.3.4 RELAY CIRCUIT 23

3.3.5 MOTION DETECTION 23

3.3.7 MOTION DETECTION FLOW

DIAGRAM

24

4

IMPLEMENTATION AND RESULT 28

5 CONCLUSIONS AND FUTURE WORK 29

6 APPENDIX-1 30

7 REFERENCES 31

vii

LIST OF ABBREVIATIONS

Abbreviation Expansion

IoT Internet of Things

ALG Application-layer gateway

WIFI Wireless Fidelity

API Application programmer interfaces

GPU Graphics processing unit

SD Secure digital

ARM Advanced RISC Machine

GPIO General purpose Input/Output

IRQ Interrupt Request

MIPI Mobile industry processer interface

LCD Liquid crystal display

DSI Display serial interface

CSI Camera serial interface

FDX Full Duplex

JTAG Join test action group

HDMI High definition multimedia interface

WAMI Wide area motion imagery

PIR Passive infrared sensor

LAN Local area network

SAD Subtraction of absolute difference

viii

LIST OF FIGURES

FIGURE

NO.

TITLE PAGE NO.

1.1 Raspberry Pi: Hardware Layout 8

1.2 Raspberry Pi: Pin Diagram 10

3.1 Architecture Diagram

21

3.2 Motion detection flow Diagram 24

4.1 Laboratory view 1 25

4.2 Laboratory view 2 25

4.3 Laboratory view 3 26

4.4 SAD Values Idle set 26

4.5 SAD Values motion detected 27

4.6 Android application user interface 27

4.7 Hardware setup of the system 28

1

CHAPTER 1

INTRODUCTION

1.1 INTERNET OF THINGS

The Internet of Things (IoT) is an important topic in technology

industry, policy, and engineering circles and has become headline news in both

the specialty press and the popular media. This technology is embodied in a

wide spectrum of networked products, systems, and sensors, which take

advantage of advancements in computing power, electronics miniaturization,

and network interconnections to offer new capabilities not previously possible.

An abundance of conferences, reports, and news articles discuss and debate the

prospective impact of the “IoT revolution”—from new market opportunities and

business models to concerns about security, privacy, and technical

interoperability.

The large-scale implementation of IoT devices promises to transform

many aspects of the way we live. For consumers, new IoT products like

Internet-enabled appliances, home automation components, and energy

management devices are moving us toward a vision of the “smart home’’,

offering more security and energy efficiency.

Other personal IoT devices like wearable fitness and health monitoring

devices and network enabled medical devices are transforming the way

healthcare services are delivered. This technology promises to be beneficial for

people with disabilities and the elderly, enabling improved levels of

independence and quality of life at a reasonable cost. IoT systems like

networked vehicles, intelligent traffic systems, and sensors embedded in roads

and bridges move us closer to the idea of “smart cities’’, which help minimize

congestion and energy consumption. IoT technology offers the possibility to

transform agriculture, industry, and energy production and distribution by

2

increasing the availability of information along the value chain of production

using networked sensors.

However, IoT raises many issues and challenges that need to be

considered and addressed in order for potential benefits to be realized.

Some observers see the IoT as a revolutionary fully–interconnected

“smart” world of progress, efficiency, and opportunity, with the potential for

adding billions in value to industry and the global economy. Others warn that

the IoT represents a darker world of surveillance, privacy and security

violations, and consumer lock–in. Attention-grabbing headlines about the

hacking of Internet-connected automobiles, surveillance concerns stemming

from voice recognition features in “smart” TVs, and privacy fears stemming

from the potential misuse of IoT data have captured public attention. This

“promise vs. peril” debate along with an influx of information though popular

media and marketing can make the IoT a complex topic to understand.

1.2 IoT COMMUNICATION MODELS

The discussion below presents the framework and explains key

characteristics of each of the communication models used by the IoT devices.

Device-to-Device Communications

Device-to-Gateway Model

Device-to-Cloud Communications

Back-End Data-Sharing Mode

3

1.2.1 DEVICE-TO-DEVICE COMMUNICATIONS

The device-to-device communication model represents two or

more devices that directly connect and communicate between one

another, rather than through an intermediary application server. These

devices communicate over many types of networks, including IP

networks or the Internet. Often, however these devices use protocols

like Bluetooth, Z-Wave to establish direct device-to-device

communications.

These device-to-device networks allow devices that adhere to a

particular communication protocol to communicate and exchange

messages to achieve their function. This communication model is

commonly used in applications like home automation systems, which

typically use small data packets of information to communicate

between devices with relatively low data rate requirements. Residential

IoT devices like light bulbs, light switches, thermostats, and door locks

normally send small amounts of information to each other (e.g. a door

lock status message or turn on light command) in a home automation

scenario.

From the user’s point of view, this often means that underlying

device-to-device communication protocols are not compatible, forcing

the user to select a family of devices that employ a common protocol.

For example, the family of devices using the Z-Wave protocol is not

natively compatible with the ZigBee family of devices. While these

incompatibilities limit user choice to devices within a particular

protocol family, the user benefits from knowing that products within a

particular family tend to communicate well.

4

1.2.2 DEVICE-TO-GATEWAY MODEL

In the device-to-gateway model, or more typically, the device-to-

application-layer gateway (ALG) model, the IoT device connects

through an ALG service as a conduit to reach a cloud service. In

simpler terms, this means that there is application software operating

on a local gateway device, which acts as an intermediary between the

device and the cloud service and provides security and other

functionality such as data or protocol translation.

Several forms of this model are found in consumer devices. In

many cases, the local gateway device is a smartphone running an app

to communicate with a device and relay data to a cloud service. This is

often the model employed with popular consumer items like personal

fitness trackers. These devices do not have the native ability to connect

directly to a cloud service, so they frequently rely on smartphone app

software to serve as an intermediary gateway to connect the fitness

device to the cloud.

The other form of this device-to-gateway model is the emergence

of “hub” devices in home automation applications. These are devices

that serve as a local gateway between individual IoT devices and a

cloud service, but they can also bridge the interoperability gap between

devices themselves. For example, the SmartThings hub is a stand-alone

gateway device that has Z-Wave and Zigbee transceivers installed to

communicate with both families of devices. It then connects to the

SmartThings cloud service, allowing the user to gain access to the

devices using a smartphone app and an Internet connection.

5

1.2.3 DEVICE-TO-CLOUD COMMUNICATIONS

In a device-to-cloud communication model, the IoT device

connects directly to an Internet cloud service like an application

service provider to exchange data and control message traffic. This

approach frequently takes advantage of existing communications

mechanisms like traditional wired Ethernet or Wi-Fi connections to

establish a connection between the device and the IP network, which

ultimately connects to the cloud service.

This communication model is employed by some popular

consumer IoT devices like the Nest Labs Learning Thermostat44 and

the Samsung SmartTV. In the case of the Nest Learning Thermostat,

the device transmits data to a cloud database where the data can be

used to analyze home energy consumption.

Further, this cloud connection enables the user to obtain remote

access to their thermostat via a smartphone or Web interface, and it

also supports software updates to the thermostat. Similarly with the

Samsung SmartTV technology, the television uses an Internet

connection to transmit user viewing information to Samsung for

analysis and to enable the interactive voice recognition features of the

TV. In these cases, the device-to-cloud model adds value to the end

user by extending the capabilities of the device beyond its native

features.

However, interoperability challenges can arise when attempting

to integrate devices made by different manufacturers. Frequently, the

device and cloud service are from the same vendor. If proprietary data

6

protocols are used between the device and the cloud service, the device

owner or user may be tied to a specific cloud service, limiting or

preventing the use of alternative service providers. This is commonly

referred to as “vendor lock-in’’, a term that encompasses other facets

of the relationship with the provider such as ownership of and access to

the data. At the same time, users can generally have confidence that

devices designed for the specific platform can be integrated.

1.2.4 BACK-END DATA-SHARING MODEL

The back-end data-sharing model refers to a communication

architecture that enables users to analyze smart object data from a

cloud service in combination with data from other sources. This

architecture supports “the user’s desire for granting access to the

uploaded sensor data to third parties”. This approach is an extension of

the single device-to-cloud communication model, which can lead to

data silos where “IoT devices upload data only to a single application

service provider’’. A back-end sharing architecture allows the data

collected from single IoT device data streams to be aggregated and

analyzed.

For example, a corporate user in charge of an office complex

would be interested in consolidating and analyzing the energy

consumption and utilities data produced by all the IoT sensors and

Internet-enabled utility systems on the premises. Often in the single

device-to-cloud model, the data each IoT sensor or system produces

sits in a stand-alone data silo.

7

An effective back-end data sharing architecture would allow the

company to easily access and analyze the data in the cloud produced

by the whole spectrum of devices in the building. Also, this kind of

architecture facilitates data portability needs. Effective back-end data

sharing architectures allow users to move their data when they switch

between IoT services, breaking down traditional data silo barriers. The

back-end data-sharing model suggests a federated cloud services

approach or cloud applications programmer interfaces (APIs) are

needed to achieve interoperability of data.

1.3 RASPBERRY PI

Raspberry Pi is a credit-card sized computer manufactured and designed

in the United Kingdom by the Raspberry Pi foundation with the intention of

teaching basic computer science to school students and every other person

interested in computer hardware, programming and DIY-Do-it Yourself projects

The Raspberry Pi has a Broadcom BCM2835 system on a chip (SoC),

which includes an ARM1176JZF-S 700 MHz processor, VideoCore IV GPU

and was originally shipped with 256 megabytes of RAM, later upgraded (Model

B & Model B+) to 512 MB. It does not include a built-in hard disk or solid-state

drive, but it uses an SD card for booting and persistent storage, with the Model

B+ using a MicroSD.

The Foundation provides Debian and Arch Linux ARM distributions for

download. Tools are available for Python as the main programming language,

with support for BBC BASIC (via the RISC OS image or the Brandy Basic

clone for Linux), C, Java and Perl.

8

1.4 HARDWARE LAYOUT

Fig 1.1 Raspberry Pi : Hardware Layout

1.4.1 PROCESSOR / SOC (SYSTEM ON CHIP)

The Raspberry Pi has a Broadcom BCM2835 System on Chip

module. It has a ARM1176JZF-S processor.

9

1.4.2 POWER SOURCE

The Pi is a device which consumes 700mA or 3W or power. It is powered

by a MicroUSB charger or the GPIO header. Any good smartphone charger will

do the work of powering the Pi.

1.4.3 SD CARD`

The Raspberry Pi does not have any onboard storage available. The

operating system is loaded on a SD card which is inserted on the SD card slot

on the Raspberry Pi. The operating system can be loaded on the card using a

card reader on any computer.

1.4.4 GPIO

General Purpose Input Output General-purpose input/output (GPIO) is a

generic pin on an integrated circuit whose behaviour, including whether it is an

input or output pin, can be controlled by the user at run time. GPIO pins have

no special purpose defined, and go unused by default. The idea is that

sometimes the system designer building a full system that uses the chip might

find it useful to have a handful of additional digital control lines, and having

these available from the chip can save the hassle of having to arrange additional

circuitry to provide them.

10

Fig 1.2 Raspberry Pi : PIN Diagram

GPIO capabilities may include:

GPIO pins can be configured to be input or output

GPIO pins can be enabled/disabled

Input values are readable (typically high=1, low=0)

Output values are writable/readable

Input values can often be used as IRQs (typically for wakeup events)

11

The production Raspberry Pi board has a 26-pin 2.54 mm (100 mil)

expansion header, marked as P1, arranged in a 2x13 strip. They provide 8 GPIO

pins plus access to I²C, SPI, UART), as well as +3.3 V, +5 V and GND supply

lines. Pin one is the pin in the first column and on the bottom row.

1.4.5 DSI CONNECTOR

The Display Serial Interface (DSI) is a specification by the Mobile

Industry Processor Interface (MIPI) Alliance aimed at reducing the cost

of display controllers in a mobile device. It is commonly targeted at LCD

and similar display technologies. It defines a serial bus and a

communication protocol between the host (source of the image data) and

the device (destination of the image data).

A DSI compatible LCD screen can be connected through the DSI

connector, although it may require additional drivers to drive the display.

6) RCA Video RCA Video outputs (PAL and NTSC) are available on all

models of Raspberry Pi. Any television or screen with a RCA jack can be

connected with the RPi.

1.4.6 AUDIO JACK

A standard 3.5 mm TRS connector is available on the RPi for stereo

audio output. Any headphone or 3.5mm audio cable can be connected directly.

Although this jack cannot be used for taking audio input, USB mics or USB

sound cards can be used.

12

1.4.7 STATUS LEDS

There are 5 status LEDs on the RPi that show the status of various

activities as follows:

OK - SDCard Access (via GPIO16) - labelled as "OK" on Model B

Rev1.0 boards and "ACT" on Model B Rev2.0 and Model A boards

POWER - 3.3 V Power - labelled as "PWR" on all boards

Raspberry PI Seminar Report

FDX - Full Duplex (LAN) (Model B) - labelled as "FDX" on all

boards

LNK - Link/Activity (LAN) (Model B) - labelled as "LNK" on all

boards

10M/100 - 10/100Mbit (LAN) (Model B) - labelled (incorrectly) as

"10M" on Model B Rev1.0 boards and "100" on Model B Rev2.0

and Model A boards

1.4.8 USB 2.0

Port USB 2.0 ports are the means to connect accessories such as mouse or

keyboard to the Raspberry Pi. There is 1 port on Model A, 2 on Model B and 4

on Model B+. The number of ports can be increased by using an external

powered USB hub which is available as a standard Pi accessory.

1.4.9 ETHERNET

Ethernet port is available on Model B and B+. It can be connected to a

network or internet using a standard LAN cable on the Ethernet port. The

Ethernet ports are controlled by Microchip LAN9512 LAN controller chip.

13

1.4.10 CSI CONNECTOR

CSI – Camera Serial Interface is a serial interface designed by MIPI

(Mobile Industry Processor Interface) alliance aimed at interfacing digital

cameras with a mobile processor.

The RPi foundation provides a camera specially made for the Pi which

can be connected with the Pi using the CSI connector.

1.4.11 JTAG HEADERS

JTAG is an acronym for ‗Joint Test Action Group', an organisation that

started back in the mid 1980's to address test point access issues on PCB with

surface mount devices. The organisation devised a method of access to device

pins via a serial port that became known as the TAP (Test Access Port). In 1990

the method became a recognised international standard (IEEE Std 1149.1).

Many thousands of devices now include this standardised port as a feature to

allow test and design engineers to access pins.

1.4.12 HDMI

HDMI – High Definition Multimedia Interface HDMI 1.3 a type A port is

provided on the RPi to connect with HDMI screens.

1.5 ORGANIZATION OF THESIS

The thesis is organized as follows. Chapter 1 discusses Introduction of

IoT and Raspberry Pi, its layout and gives an overview of its components.

Chapter 2 presents the literature review on motion detection and home

automation. Chapter 3 discusses the Chapter 4 discusses the simulation results

and concludes with future work.

14

CHAPTER 2

LITERATURE SURVEY

2.1 MOTION DETECTION SYSTEM

Badri Narayan Subudhi et. al. [1] (2016) proposed a novel background

subtraction (BGS) technique to detect local changes corresponding to the

movement of the objects in video scenes. It presents an efficient combination of

six local features; three existing and three newly proposed. For background

modelling and subtraction, a statistical parametric biunique model is proposed.

A few simple statistical parameters are used to characterize each feature. For

background subtraction, the multi-valued features computed at each pixel

location are compared with those of the computed parameters corresponding to

that feature.

Komal Rahangdale et. al. [2] (2016) proposed an approach to the problem

of automatically tracking people and detecting unusual or suspicious

movements in CCTV videos. It proposes a system that works for surveillance

systems installed in indoor environments like entrances/exits of buildings,

corridors, etc. It presents a framework that processes video data obtained from a

CCTV camera fixed at a particular location. First, the foreground objects are

obtained using background subtraction. These foreground objects are then

classified into people and suspicious objects. These objects are tracked using a

blob matching technique.

Sunanda R. Hanchinamani et. al. [3] (2016) presented a method in which

the video is first converted to streams and then applied to convolution filter

which removes high frequency noise components to obtain smoothened images.

The smoothened images are then applied to background subtraction algorithm

15

with adaptive threshold which gives detected object present in background

image. The detected object is then applied to convolution filter to remove the

spurious distorted pixels which improves the quality of image.

Chia-Jui Yang, Ting Chou et. al. [4] (2016) proposes an intelligence

surveillance system in indoor environments, which support the functions of

people detection, people tracking, and behaviour analysis. Strong variation of

lightness by switching lights and frequent crossing of people are two major

design challenges of the proposed system, which will decrease the detection

accuracy. Therefore, we propose a mechanism of updating background to react

to the variation of lightness.

2.1.1. BACKGROUND SUBTRACTION METHOD

Identifying moving objects from a video sequence is a fundamental and

critical task in many computer-vision applications. A common approach is to

perform background subtraction, which identifies moving objects from the

portion of a video frame that differs significantly from a background model.

There are many challenges in developing a good background subtraction

algorithm. First, it must be robust against changes in illumination. Second, it

should avoid detecting non-stationary background objects such as moving

leaves, rain, snow, and shadows cast by moving objects. Finally, its internal

background model should react quickly to changes in background such as

starting and stopping of vehicles.

In paper [5], the author proposes a two-stage foreground propagation that

uses clues to adapt to the environment and detect moving objects in a non-

16

stationary camera. The first stage creates a weight matrix to instantaneously

regulate the background model by responding to clues from frame differencing

and background subtraction. The regulated background model is less affected

by inaccurate motion compensation. In the second stage, an iterative approach is

taken to refine the threshold for each pixel location by initially using pixels with

high foreground probability as clues.

Foreground regions detected from the refined threshold are less likely to

be false detections and capture true object regions with completeness.

Experimental results showed that the two-stage foreground propagation had

significantly higher recall with comparable precision and outperformed other

method.

To overcome the limitation in traditional background subtraction

algorithms, several methods have been proposed to detect moving objects in a

non-stationary camera. Panoramic methods [6, 7] use image registration to

create a background model with a panorama, and the background model is used

to detect moving objects. The panoramic background model suffers from

accumulated stitching errors and infrequent updates, resulting in false

detections.

Frame differencing methods [8] and accumulative frame differencing

methods [9, 10] are not affected by these modeling errors because they use few

previous frames as references to localize the moving object. However, frame

differencing methods and accumulative frame differencing methods cannot

properly segment the complete object and need alternative means to segment

the moving object. [11] is a motion segmentation method, and it segments the

foreground from the background with the assumption that the trajectories of

moving objects and those of the background do not have the same subspace.

17

2.1.2 WIDE AREAS MOTION IMAGERY

Wide Area Motion Imagery (WAMI) enables the surveillance of tens of

square kilometers with one airborne sensor. Each image can contain thousands

of moving objects. Applications such as driver behavior analysis or traffic

monitoring require precise multiple object tracking that is dependent on initial

detections. However, low object resolution, dense traffic, and imprecise image

alignment lead to split, merged, and missing detections.

In paper [12], the author provides a detailed overview of existing methods

for moving object detection in WAMI data. Also proposing a novel combination

of short-term background subtraction and suppression of image alignment errors

by pixel neighbourhood consideration, In total, eleven methods are

systematically evaluated using more than 160,000 ground truth detections of the

WPAFB 2009 dataset. Best performance with respect to precision and recall is

achieved by the proposed one.

Surveys such as Radke et al. [13] summarize change detection methods,

whereby the specific characteristics of WAMI and their impact on the detection

method are not considered. Although various different detection approaches

exist in the literature [14, 15], no evaluation of detection performance has been

presented so far. Instead, authors usually focus on developing and evaluating

multiple object tracking algorithms that implicitly handle inaccurate detections,

e.g. by allowing many to many correspondences (i.e. detection and track

sharing) in multiple hypothesis tracking [16]. Common evaluation measures

such as precision and recall are reported either estimated tracks and not

detections [17] or within a limited evaluation setup.

18

2.2. HOME AUTOMATION SYSTEM

In paper [18], the author used the inexpensive Raspberry Pi to automate

the tasks at home such as switching appliances on & off over WiFi (Wireless

Fidelity) or LAN(Local Area Network) using a personal computer or a mobile

or a tablet through the browser. The proposed work was done by using the

dedicated Android application. The conventional switch boards were added with

a touch screen or replaced with a touch screen to match the taste of the user's

home decor. PIR (Passive Infrared Sensor) sensor was used to detect human

detection and automate the on and off functionality.

Home automation uses different types of network protocols such as Wi-

Fi, Bluetooth and ZigBee. However, existing home equipment often requires

network communication enabled power plugs or devices that have a unique

communication protocol specified by the company. Although these equipment

have standard communication capability, each device is limited to communicate

within a same network protocol enabled devices. In order to solve this issue

between various network protocols in smart homes, Ho-Kyeong Ra, Sangsoo

Jeong, Hee Jung Yoon, and Sang Hyuk Son [19] presented a Smart Home

Automation Framework (SHAF) wherein a central server manages multiple

nodes in a smart home through ZigBee communication.

Home automation system uses the portable devices as a user interface.

They can communicate with home automation network through an Internet

gateway, by means of low power communication protocols like Zigbee, Wi-Fi

etc.

In paper [20], controlling home appliances via Smartphone using Wi-Fi

as communication protocol and raspberry pi as server system is facilitated. The

user here will move directly with the system through a web-based interface over

19

the web, whereas home appliances like lights, fan and door lock are remotely

controlled through easy website. An extra feature that enhances the facet of

protection from fireplace accidents is its capability of sleuthing the smoke in

order that within the event of any fireplace, associates an alerting message and

an image is sent to Smartphone. If the web affiliation is down or the server isn't

up, the embedded system board still will manage and operate the appliances

domestically. By this we provide a climbable and price effective Home

Automation system.

20

CHAPTER 3

PROPOSED WORK

3.1 OERVIEW

The proposed system architectures generally incorporate a raspberry-pi

computer for the purposes of network management and provision of remote

access. Raspberry-pi can be configured according to our required system.

The user will communicate to raspberry-pi through wifi network. The system is

flexible and scalable, allowing additional appliances designed by multiple

vendors, to be securely and safely added to the home network with the

minimum amount of effort.

The wifi network should be having adequate strength also. We can use a

wifi-modem for steeping a wifi. The users have an android interface for using

the system through the mobile phone which is also connected to the same

network as the raspberry-pi. The camera for motion detection is also connected

in the same network. The raspberry-pi board is configured for each appliances.

So, according to user intervention the matched out will make high and the

corresponding relay will switch on and device start function.

The system is scalable and allows multi-vendor appliances to be added

with no major changes to its core. The project consists of the following

modules:

User Interface

Wifi Router configuration

Raspberry Pi

Relay Circuit

Motion Detection and Appliances

21

3.2. ARCHITECTURE

Fig 3.1 Home automation: Architecture

22

3.3. DESCRIPTION OF MODULES

3.3.1 USER INTERFACE

Android provides a variety of pre-build UI components such as structured

layout objects and UI controls that allow you to build the graphical user

interface for your app. Android also provides other UI modules for special

interfaces such as dialogs, notifications, and menus. The interface allows user to

view device status and to control device. It provides notifications from the

motion detection system when the room is idle. It consists of separate buttons to

on/off the appliances.

3.3.2. WIFI ROUTER CONFIGURATION

The wifi unit provides the medium for communication. It can be also

configured to make security services. the wifi should be configured with a

certain address and user commands will be directing through wifi unit. We may

use sudo nano /etc/network/interfaces for configuring wifi with raspberry-pi.

3.3.3. SETTING UP RASPBERRY PI

The Raspberry Pi is a low cost single-board computer which is controlled

by a modified version of Debian Linux optimized for the ARM architecture.

Here we are using modelB ,700 MHz ARM processor with 512 MB RAM. The

setting up of raspi consists of selecting raspbian OS from noobs package. the

noobs package consists of raspbian, arclinux, pidora, open ELEC, Risc OS

operating system. After the os selection we need to configure raspberry-pi using

Raspi-config command. We can enter into raspi desktop using startx command.

To interface with raspberry pi, VNC Viewer and Putty are used.

23

3.3.4. RELAY CIRCUIT

A relay is an electrically operated switch. Relays are used where it is

necessary to control a circuit by a low-power signal (with complete electrical

isolation between control and controlled circuits), or where several circuits must

be controlled by one signal.in our system the output from raspberry pi is directly

given to relay circuit. According to the out of raspberry pi, corresponding relay

will turn on and makes its device working. We are using a NPN transistor in

relay and it works based on concept of emf. The relay can be selected according

to our application purpose. Our system ends up with the working of relay

circuit. In this system, we can add devices very easily into system. Also it can

be configured with more security and functional services.

3.3.5. MOTION DETECTION

The motion detection system will detect idleness and this can be notified

to the server that is running in the Raspberry pi. This can be communicated to

the android app via the wifi network in which the whole system is connected.

The appliances like electric door, fan and light are connected to the GPIO pins

of the raspberry pi through the relay circuit. Depending upon the button pressed

by the user on the android app, the appliance is selected in the relay circuit and

the appliance can be turned on/off.

24

3.3.6. MOTION DETECTION FLOW DIAGRAM

Fig 3.2 Motion detection: flow control

25

CHAPTER 4

IMPLEMENTATION AND RESULTS

Fig 4.1 Laboratory View 1

Image is captured using camera and transmitted using WiFi. Fig 4.1 , 4.2 ,

4.3 shows the snapshot that is captured and analysed using SAD( Subtraction of

Absolute Differences) method.

Fig 4.2 Laboratory View 2

26

Fig 4.3 Laboratory View 3

The result of SAD is analysed and when its value is more than certain

threshold there is motion in the laboratory. If it is below the threshold and at the

same instance if the timestamp is also exceeded then “idleness” is marked and

reported. The threshold can vary based upon the level of sensitivity to be

measured.

Fig 4.4 SAD values idle set

27

Fig 4.5 SAD values Motion Detected

In Fig 4.4, 4.5 the final values are computed and analysed based on

which inference has to be made. In this case the threshold is used as 10 and the

timestamp is used as 15 minutes. When the final values are less than

10(threshold) for 15 minutes then it is reported. If any final value is more than

10(threshold) in this instance then the timestamp is reset and started again.

Figure 4.6 shows the user interface screen of the developed android

application. It contains a switch button each for light, fan and the door.

Fig 4.6 Android application user interface

28

Fig 4.7 Hardware setup of system

The above figure Fig 4.7 is the hardware setup of the system on the

whole. It contains the raspberry pi, the relay circuit and the door lock.

Fig 4.8 Hardware setup of system

The above figure Fig 4.8 is the product after final fabrication

29

Fig 4.9 Hardware setup of system

The above figure Fig 4.9 shows the organisation of the components inside

the box.

30

CHAPTER 5

CONCLUSION AND FUTURE WORK

The proposed system is at an affordable price and has an ease of

installing it anywhere required, with minimum maintenance cost. The motion

detection module has been implemented in MATLAB. Safety always a prime

priority for man. Hence, leveraging exciting new technology to extend the

proposed home automation system to increase safety through fire and carbon-

monoxide detectors is definitely on the cards. Energy efficient technology is the

order of the day. So another crucial future extension may include power

tracking and notifying customers of their power budgets and carbon footprints.

Closed and private networks wherein all devices communicate with each other

in a private network facilitates the extension of the proposed system to a total

personal home security system.

31

APPENDIX-1

IMPLEMENTATION TOOL

MATLAB (matrix laboratory) is a multi-paradigm numerical computing

environment and fourth-generation programming language. A proprietary

programming language developed by MathWorks, MATLAB allows matrix

manipulations, plotting of functions and data, implementation of algorithms,

creation of user interfaces, and interfacing with programs written in other

languages, including C, C++, Java, Fortran and Python.

MATLAB is used to analyze and design the systems and products

transforming our world. The matrix-based MATLAB language is the world's

most natural way to express computational mathematics. The Built-in graphics

make it easy to visualize and gain insights from data. The desktop environment

invites experimentation, exploration, and discovery.

MATLAB is used to take users ideas beyond the desktop. One can run

your analyses on larger data sets, and scale up to clusters and clouds. MATLAB

code can be integrated with other languages, enabling one to deploy algorithms

and applications within web, enterprise, and production systems. Although

MATLAB is intended primarily for numerical computing, an optional toolbox

uses the MuPAD symbolic engine, allowing access to symbolic computing

abilities. An additional package, Simulink, adds graphical multi-domain

simulation and model-based design for dynamic and embedded systems.

USAGE OF MATLAB IN THIS PROJECT:

MATLAB is mainly used here for video analysis. The live video from ip

camera is captured and analyzed. This analysis is done based on SAD (Sum of

absolute Difference). If the result is below a threshold value then it means that

the system is idle. If idleness is detected then notification is sent to the server.

MATLAB is also used for sending notification to the server. MATLAB is also

used to store the live videos which can be viewed whenever needed.

32

REFERENCES

[1] B. N. Subudhi, S. Ghosh, S. C. K. Shiu, and A. Ghosh, "Statistical

feature bag based background subtraction for local change

detection", Information Sciences.

[2] Komal Rahangdale and Mahadev Kokate, “EVENT DETECTION

USING BACKGROUND SUBTRACTION FOR

SURVEILLANCE SYSTEMS”, International Journal of

Engineering Applied Sciences and Technology, 2016 Vol. 1,

Issue 4, ISSN No. 2455-2143, Pages 25-28 .

[3] Sunanda R. Hanchinamani, Sayantam Sarkar and Satish S

Bhairannawar, “Design and Implementation of High Speed

Background Subtraction Algorithm for Moving Object Detection”,

6th International Conference On Advances In Computing &

Communications, ICACC 2016, 6-8 September 2016, Cochin,

India

[4] C. J. Yang, T. Chou, F. A. Chang, C. Ssu-Yuan and J. I. Guo, "A

smart surveillance system with multiple people detection, tracking,

and behavior analysis," 2016 International Symposium on VLSI

Design, Automation and Test (VLSI-DAT), Hsinchu, 2016, pp.1-4.

[5] WonTaek Chung, YongHyun Kim, Yong-Joong Kim and DaiJin

Kim, "A two-stage foreground propagation for moving object

detection in a non-stationary," 2016 13th IEEE International

Conference on Advanced Video and Signal Based Surveillance

(AVSS), Colorado Springs, CO, 2016, pp. 187-193.

[6] S. Ali and M. Shah. Cocoa: tracking in aerial imagery. In Defense

and Security Symposium, pages 62090D–62090D. International

Society for Optics and Photonics, 2006.

[7] C. Guillot, M. Taron, P. Sayd, Q. C. Pham, C. Tilmant, and J.-M.

Lavest. Background subtraction adapted to ptz cameras by

keypoint density estimation. In Proceedings of the British Machine

Vision Conference, pages 34–1, 2010

[8] C. Aeschliman, J. Park, and A. C. Kak. Tracking vehicles through

shadows and occlusions in wide-area aerial video. Aerospace and

Electronic Systems, IEEE Transactions on, 50(1):429–444, 2014.

33

[9] S. Bhattacharya, H. Idrees, I. Saleemi, S. Ali, and M. Shah.

Moving object detection and tracking in forward looking infra-red

aerial imagery. In Machine Vision Beyond Visible Spectrum,

pages 221–252. Springer, 2011

[10] S. Bhattacharya, H. Idrees, I. Saleemi, S. Ali, and M. Shah.

Moving object detection and tracking in forward looking infra-red

aerial imagery. In Machine Vision Beyond Visible Spectrum,

pages 221–252. Springer, 2011

[11] L. W. Sommer, M. Teutsch, T. Schuchert and J. Beyerer, "A

survey on moving object detection for wide area motion imagery,"

2016 IEEE Winter Conference on Applications of Computer

Vision (WACV), Lake Placid, NY, 2016, pp. 1-9.

[12] L. W. Sommer, M. Teutsch, T. Schuchert and J. Beyerer, "A

survey on moving object detection for wide area motion imagery,"

2016 IEEE Winter Conference on Applications of Computer

Vision (WACV), Lake Placid, NY, 2016, pp. 1-9.

[13] R. Radke, S. Andra, O. Al-Kofahi, and B. Roysam. Image

Change Detection Algorithms: A Systematic Survey. IEEE

Trans. Image Process., 14(3):294–307, 2005.

[14] V. Reilly, H. Idrees, and M. Shah. Detection and Tracking of Large

Number of Targets in Wide Area Surveillance. In ECCV, 2010.

[15] I. Saleemi and M. Shah. Multiframe Many-Many Point

Correspondence for Vehicle Tracking in High Density Wide

Area Aerial Videos. IJCV, 104(2):198–219, 2013.

[16] J. Xiao, H. Cheng, H. Sawhney, and F. Han. Vehicle Detection

and Tracking in Wide Field-of-View Aerial Video. In CVPR, 2010

[17] J. Prokaj, M. Duchaineau, and G. Medioni. Inferring Tracklets for

Multi-Object Tracking. In CVPRW, 2011.

[18] M. Asadullah and A. Raza, "An overview of home automation

systems," 2016 2nd International Conference on Robotics and

Artificial Intelligence (ICRAI), Rawalpindi, Pakistan, 2016, pp.

27-31.

34

[19] H. K. Ra, S. Jeong, H. J. Yoon and S. H. Son, "SHAF: Framework

for Smart Home Sensing and Actuation," 2016 IEEE 22nd

International Conference on Embedded and Real-Time Computing

Systems and Applications (RTCSA), Daegu, 2016, pp. 258-258.

[20] Ghosh, "Intelligent appliances controller using Raspberry Pi," 2016

IEEE 7th Annual Information Technology, Electronics and Mobile

Communication Conference (IEMCON), Vancouver, BC, 2016,

pp. 1-5.