LIST OF FIGURES

The Kalman algorithm has been used to increase the accuracy of getting the exact location of the pets. Therefore, with the help of the tracking device and mobile application, pet owners can monitor their pets anytime, anywhere.

INTRODUCTION

Introduction
Background study of the current pets location tracking system that exists in Malaysia 1
Motivation
Project Scope
Project Objective
Innovation/Contribution of Project
Report Organization
Conclusion

When the pet leaves the safe area, the owner will be notified. Users will be able to use Bluetooth to pair the tracking device with the mobile app.

Figure 1.2.1 The increase of stray animals from 2014 to 2018 [7].

LITERATURE REVIEW

Introduction

Types of Location Tracking Technology

GPS
RFID
Wi-Fi
QR code
NFC

The position of each tagged object will subsequently be sent to a gateway via these access points. QR Code ● Able to see the location in real time when the QR code is scanned.

Figure 2.2.7.1 Comparison between different tracking technologies [11]

Summary on Tracking Technologies

Analysis of Location Estimation

When a new GPS-estimated position L(k) is provided to the Kalman filter, the Kalman filtering process is repeated recursively. In addition, the Kalman filter method estimates the speed and location of a GPS receiver and uses them for future estimates.

Figure 2.4.1 Conditional probability density of position based on measurement μ 1 and prediction μ 2 [15]

Location Tracking with velocity estimation

It starts at 0 m/s and works up to the original speed of the GPS sensor through a Kalman recursive filtering method. The estimated position and velocity data will also affect the next estimate, so an incorrectly estimated velocity causes inaccuracy of the location estimate by the Kalman filtering technique.

Velocity Estimator

Direction finder

Replacement of estimated velocity

Calculate ALEE and LER

Review on Existing Pet Location Tracking System

Animal Situation Tracking Service Using RFID, GPS and Sensors
GPS-Arduino based Tracking and Alarm system for protection of wildlife animals
wildCENSE: GPS based Animal Tracking System

Comparison on the Reviewed Location Tracking System
Review on Commercial Mobile Application for Pet Tracking

Tractive
Findster Duo+
Fi Smart Dog Collar
Comparison on Commercial Mobile Applications For Pets Tracking

Proposed Application
Conclusion

The problems that arise are the work and high costs of monitoring all the animals in the group. Ability to track the location of the animal Ability to notify users when the animal is outside the protected area. The combination of WSN and GPS allows tracking the location and movement patterns of wild animals.

In addition, it is able to create safe areas for pets and receive notification when pets leave the safe area. Tractive ● Can set a safe zone, which the user will be notified when pets leave the safe zone. Findster ● Can set a safe zone, which the user will be notified when pets leave the safe zone.

For the implementation of the proposed system, the strengths, weaknesses and modules of the existing systems have been evaluated.

Figure 2.5.3 Comparison of LER graph for Kalman filter and average GPS accuracy [15]

SYSTEM METHODOLOGY

Introduction
Design Specifications

Project Methodology
Requirements Planning Phase
System design phase
Development phase
Cutover

Justification of RAD
User Requirements

Functional Requirements
Non-functional requirements

System Design Diagram
UML Diagram

Activity Diagram
Login Activity Diagram
Home Page Activity Diagram
Sequence Diagram
Login Sequence Diagram
Class Diagram

Conclusion

In addition, several prototypes of the proposed project will be built for user review. In the development phase, the refinement of the prototypes developed in the previous phase will be improved and modified according to feedback from the user. In addition, Android Studio and Firebase will be used to build the mobile app and store user data in the firebase database.

Then the data will be retrieved in the mobile application and show the location of the pet using the application. In this final phase, features, functions, interfaces and everything related to the project will be finalized. Fast delivery: users can preview a prototype of the proposed project at the beginning of development.

The diagrams will help in better understanding the various features and functions of the proposed project.

Figure 3.6.1.1 Pet Location Tracking Mobile Application use case diagram

SYSTEM DESIGN

Introduction

System Setup and Configurations

Arduino UNO and SIM808 set up for GPS function
Tracking Device Setup
Tracking Device
Android Studio Setup
Firebase Setup with Android studio

The most important parts of the code to obtain GPS codes can be seen in the list below. Listing 3.3.1.3 will show the PHP file implemented to create an interface to connect the Firebase to the Arduino. The server setup is done on a free website hosting server called 000webhost.

In order for users to configure the safe zone radius, data from Firebase must be obtained to calculate the distance between the pet's location and the safe zone radius. Listing 4.2.3.9 Arduino Section Code to Get Data from Server to SIM808 The listing above shows the function of getting data from a serial monitor and using gps.distanceBetween() to calculate the distance between the coordinates of the tracking device and the coordinates set by the user. If the calculated distance is greater than the limit distance, the pet is outside the safe area.

The code below is to send a notification to the pet owner's device when their pet is outside the safe zone.

Figure 4.2.3.1 Arduino UNO with SIM808 shield

Verification Plan (Testing)

Test Steps Step Description Data Used Expected Result Actual Result Pass/Fail 1 Test a valid email address and password. Redirect new user to create a new pet profile or redirect existing user to main menu.

SYSTEM IMPLEMENTATION

Introduction

Tool Involved

The coordinates obtained from the remote database will be fed into Kalman Filtering to improve the accuracy of location detection.

System Requirements

The Arduino UNO R3 is a microcontroller board with 14 digital input/output pins, 6 analog inputs, a 16MHz ceramic resonator, a USB connector, a power connector, an ICSP header, and a reset button. To get started, it can be easily connected to a computer via a USB connection or powered by an adapter or batteries. The library of DFRobot_sim808, SoftwareSerial and TinyGPS++ is used to get the data from the module and set up a virtual safe zone.

Table 5.3.2 Computer Hardware Requirements Hardware that I will be using:

User Interface of Application

Splash Screen and Tracker Request
Login and Sign Up Screen
Connecting to the Tracking Device
Create a pet profile
Home Page
Live Tracking and View History
Safe Zone Function
News Feed Module
Settings Page

If the data is successfully saved, the application will direct users to the home page. Users can view the live location and configure the safe zone on the home page. The bottom navigation bar shows that users can view the news feed, publish a post, view the home page, and configure their settings.

Users can navigate to their pet's location when they click the first button in the bottom right. Other than that, users can see the places where their pet has gone when they click on the up arrow at the bottom of the screen, then it will pop up a sliding panel to show the history. Users can post news feed when they click on the second button from the bottom navigation bar.

Users can set up profile image, username, full name, edit pet information, enable notification and contact customer support.

Conclusion

Comparison of results in coordinates after using Kalman Algorithm

This chapter discusses the comparison of results in coordinates after filtering the coordinates with the Kalman algorithm. From the table above, you can see that there is at least 0.06 to 0.07 difference between latitude coordinates before Kalman filtering and after Kalman filtering for latitude and there is a range of 0.2 to 0.25 difference between the longitude coordinates. The results show that the coordinates are closer to the real-time location after filtering the coordinates with the Kalman algorithm.

Therefore, the above results can show that the Kalman algorithm can improve the accuracy for locating the pets. Furthermore, the real-time location has also been recorded, which proves that the coordinates after filtering with Kalman Algorithm are closer to the real-time location.

Table 6.2.2 Comparison table between coordinates before Kalman Algorithm and real-time location

Test Cases for Completed Module

Login module for pet owners
Connecting to Tracking Device using Bluetooth Module
Pet Profile Module
Safe Zone Module
News Feed Module
Post Image and Status Module

Test Case Module: Connecting to a Tracking Device with Bluetooth Unit Testing Test Description: To test if the application is connected to the tracking device. Redirect users to the Bluetooth settings page and remind them to turn on Bluetooth to pair the tracking device. Test Case Module: Unit Testing the Pet Profile Module Test Description: Create a new pet profile for testing.

Test case module: News Feed module unit testing Test Description: To test the news feed function. Test case module: Post Image and status module unit testing Test Description: To test the post image and status function.

Tracking Device

Analysis of Survey

Demographic data
Survey results

The survey results in table 6.5.2.1 showed the combined responses in percentages and numbers. The results in Table A and Table B in the Appendix showed that respondents agreed that "The location accuracy provided by the tracking device should be accurate." and of the respondents agreed that "I worry about my pet's health when wearing sensors and electronic devices". From the table above, it shows that the tracking device and the mobile app features have equal percentage of agreement, which determines that the users expressed that the mobile app feature is as significant as the tracking device.

Aside from that, the biggest disagreement is about the statement "The tracking device does not need to be water resistant", which concludes that users demand a tracking device that is water resistant. In addition, the respondents agree with the statement "I can contact the administration to solve the problems I encountered while using the application", who indicated that users needed support when they encountered problems with the mobile application or the tracking device. The functional and non-functional requirements set out in Chapter 3 are the results of the online survey among 42 pet owners.

Table 6.5.2.1 Percentage of combined respondents' responses by preferred features

Algorithm Results

It can be observed that the differences of the actual result with the coordinates without filtering with Kalman algorithm are higher than the differences between the actual result and the coordinates with Kalman algorithm.

Project Challenges

Timeline

Gantt chart of FYP1
Work done in FYP1 on last semester
Planning for current semester (FYP2)
Gantt chart of FYP 2 timeline

The planning for this semester would be complete for each module and functionality that is specified in the project goals and user requirements. During the development phase, each module will be improvised and the problems faced by FYP1 will be solved. Apart from that, other GPS chips will be studied and tested to have a smaller tracking device and a battery saving device.

In addition, the UML diagram is updated as changes occur during development. From week 10, each module is tested to ensure that the functions function properly and are free of errors. Furthermore, the unit tests of each completed module are discussed in detail.

The results and analysis of the study and the project timeline for FYP 1 and FYP2 and final planning for the current semester were also documented.

Table 6.8.2.1 Gantt chart of Overall FYP1 timeline

CONCLUSION AND RECOMMENDATION

Introduction
Findings
Limitation
Future Recommendation
Conclusion

The tracking device will upload the location coordinates (X(k), Y(k)) and time k to the remote database (Firebase) and the obtained data will be filtered using the Kalman algorithm to increase the accuracy. Apart from that, the size and weight of the tracker may not be suitable for small pets. The project is only available for Android users to use the pet location tracking mobile application with purchased tracking device.

Due to cost and skill constraints, inability to research other GPS chips to compare and analyze tracking device effectiveness and size. The tracking device and live tracking must be configured in sleep mode when the users have not reported a lost pet. Users can link multiple tracking devices to an account and create different pet profiles on different tracking devices.

Available: https://tractive.com/blog/en/tech/pet-tracking-device-comparison-bluetooth-vs-gps-tracker.