Akanksha Ambavade1, Sagar Rathod 2,
Prashant More3, Anuja Doke 4

Prof. S.V.Athawale 5
(Guide), Computer Department, A.I.S.S.M.S. C.O.E,

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1, 2, 3, 4Computer Department, A.I.S.S.M.S. C.O.E.

 

 

Abstract- Big
shopping malls usually provide a directory to their available shops, but these
directories are most of the time static and do not provide any interactivity
features to the visitors. In this work, we present a mobile shopping mall
navigator. The main reason behind our conceptual idea of our proposed project
is because we feel that when visitors often change their plan to go to other
shops instead of the ones in their minds, it can be full of effort especially
considering the crowded levels and location of the navigation material. The
application developed is practical and feasible. Smart-Phones have become very popular these
days, so we have combined the idea. Smart-Phone application helping you in an
alienated mall. The idea revolves around our Smart-Phones & the

“Wi-Fi” provided by the
mall. An application that needs real-time, fast, & reliable data
processing.

 

 

Technical Keywords –  Indoor navigation, QR-Code  scanner, Wi-Fi router

 

I.     
Introduction

                Manual
Shopping is the traditional way of shopping where the customers choose their
desired product and carry the products along with them. Traditional shopping is
a tedious and time consuming job. In traditional shopping, the customer has to
wait in long queues at the cash counter. This consumes lot of time and energy
of both the customer as well as cashier. To overcome these flaws, the customer himself
can scan the QR-Code using his mobile while making purchase, retrieve essential
details of all products from shops database and generate bill himself 11. This
bill can be sent to the customer’s mobile through online banking service thus
the user can make quick payment and leave the shop early. The QR-Code of the
product is scanned by the customer and move to the wish list if they are
interested in choice of item by using the proposed mobile application. In order
to develop an Android Application that uses a QR-Code scanner for the
purchasing and navigation4 of items for store that will be self-checking and
automatic payment transaction1. Here comes the term indoor navigation5 and QR-Code
scanning. Indoor positioning is still a challenging problem because
satellite-based approach does not work properly inside buildings1.

 

                QR-Codes
are ubiquitously used to identify products, goods or deliveries. Devices to
read QR-Codes are all around, in the form of pen type readers, laser scanners or
LED scanners. Camera-based readers, as a new kind of QR-Code reader, have
recently gained much attention. The interest in camera-based QR-Code
recognition is built on the fact that numerous mobile devices are already in
use, which provide the capability to take images of a fair quality11. This
describes the hardware system architecture for implementing the QR-Code reading
system in mobile phones and its process. The camera device and application
processors are necessary hardware components for the system. The application
processors are needed to implement the camera interface, LCD controllers, DSP
for image processing, and application host in CPU for real-time computations.
The application processor works for displaying the menu and preview of the
display and computing of code recognition and decoding in real-time. With these
systems, the user can control the position of the camera of Smart-Phone and
decides the capture timing of QR-Code5.

 

II.  
Related Work

 

Accurate
and reliable real-time indoor positioning on commercial Smart-Phones

 

Author: Gennady Berkovich

This paper outlines the software navigation engine that was
developed by SPIRIT Navigation for indoor positioning on commercial Smart-Phones1.
A distinctive feature of our approach is concurrent use of Wi-Fi and BLE
modules, together with the floor premises plan are used for hybrid indoor
positioning in the navigation engine. Indoor navigation software uses such
technologies as PDR and map matching. There is no need to enter initial
position manually where it can be determined by GPS/GNSS (Global Navigation
Satellite Systems) receiver. The automatic recovery of tracking in this case
allows continuing tracking and increasing availability of indoor navigation.
Positioning results given for different indoor environments in a shopping mall
with accuracy of about 1-2 m.

 

Indoor positioning of wheeled
devices for Ambient Assisted Living: A case study

 

Author: Payam Nazemzadeh, Daniele Fontanelli,
David Macii, Luigi Palopoli

Indoor
navigation is a well-known research topic whose relevance has been steadily
growing in the last years thrust by considerable commercial interests as well
as by the need for supporting and guiding users in large public environments,
such as stations, airports or shopping malls. People with motion or cognitive
impairments could perceive large crowded environments as intimidating. In such
situations, a smart wheeled walker able to estimate its own position autonomously
could be used to guide users safely towards a wanted destination. Two strong
requirements for this kind of applications are: low deployment costs and the
capability to work in large and crowded environments. The position tracking
technique presented in this paper is based on an Extended Kalman Filter (EKF)
and is analysed through simulations in view of minimizing the amount of sensors
and devices in the environment.

 

Methods
and Tools to Construct a Global Indoor Positioning System

 

Author: Suk-Hoon Jung, Gunwoo Lee, Dongsoo Han

A
GIPS is a system that provides positioning services in most buildings in
villages and cities globally2. An unsupervised learning-based method is
adopted to construct radio maps using fingerprints collected via crowd sourcing
and a probabilistic indoor positioning algorithm is developed. An experimental
GIPS, named KAILOS was developed integrating the methods and tools.  The more volunteers who participate in
developing indoor positioning systems on KAILOS-like systems, the sooner GIPS
will be realized.

 

Interactive
android-based indoor parking lot vehicle locator using QR-code

 

Author: Siti Fatimah Abdul Razak, Choon Lin Liew, Chin Poo Lee, Kian Ming
Lim

In
this study, we report on an android based application development aimed to
provide navigation services to locate parked vehicles in an indoor parking
space of shopping malls. We utilize the motion sensor, bar code scanner
function and camera function built in smart-phones. This application is able to
show the route from user current location to his parked vehicle based on an
indoor map of the parking area stored in a database.

 

Mitigating
the antenna orientation effect on indoor Wi-Fi positioning of mobile
phones

 

Author: Da Su, Zhenhui Situ, Ivan
Wang-Hei Ho

In
this paper, we implement a practical and convenient indoor positioning system
based on the fingerprint method and Kalman filter on Android mobile devices3.
This paper discusses the positioning algorithms and addresses various
challenges in practical application, such as the effect of antenna orientation
and signal fluctuation. Specifically, an improved mapping algorithm based on
k-nearest neighbour (K-NN) is introduced to tackle the orientation effect, and
an orientation-based fingerprint database is established through studying the
received signal strength patterns in different directions to handle the large
fluctuation caused by orientation change. Finally, their experimental result
indicates that the proposed IPS can achieve up to 1.2 meters accuracy, is sufficient
for various navigation services in indoor environments (e.g., shopping malls).

 

GROPING:
Geomagnetism and Crowd sensing Powered Indoor Navigation

 

Author: Chi Zhang, Kalyan P. Subbu, Jun Luo, Jianxin Wu

This
paper proposes GROPING as a self-contained indoor navigation system independent
of any infrastructural support. It relies on geomagnetic fingerprints that are
far more stable than Wi-Fi fingerprints, and it exploits crowd sensing to
construct floor maps than expecting individual venues to supply digitized maps12.
Based on their experiments with 20 participants in various floors of a big
shopping mall, GROPING is able to deliver a sufficient accuracy for
localization and thus provides smooth navigation experience.

III.
Existing System

 

In traditional shopping, people have to
search exact product in the mall with wide range of available brands. Sometimes
they will ask for help in searching product to assistant but may be they also
don’t know the exact position. On other hand, customers have to wait in the
billing line to scan the products.

 

In foreign countries there are some malls
which use indoor navigation. To use this system user should go to the
particular LED/LCD screen and search for product location2. But on the
weekends or holidays there is too much rush, so there can be number of people
waiting in queue to search their product, which is little bit time consuming.

 

 

 

               

                      Fig.1. Billing
Section                                                              
Fig.2. Navigation System

 

Also at the billing section user need
to scan each product and does the total. There is no technology to scan the
entire products at the same time so that user can do the shopping in minimum
time as possible11. 

 

 

IV.
PROPOSED METHODOLOGY

 

 

Methodologies to implement the system modules:

1.       Point
out product

2.       Scan
QR-Code

3.       Payment

 

 

Point out product:

     
Now-a-day’s malls are getting bigger and bigger. It is very difficult to
find the expected product in mall. User search all over mall for needed
product. Propose system provide the better way to search the desired product. User
just needs to search product in mobile then it will point out the product where
user will get the product.

 

Scan QR-Code:

     When user wants to add product in cart
he/she scan the QR-Code of product and select the quantity. Then it will
automatically add the products into the cart. After selecting required products
user can pay the bill.

 

Payment:

     Traditionally,
payment is done by debit card, credit card or cash. But in propose system user
can pay the bill online. So customers don’t need to carry any kind of card or
cash.

 

V. ALGORITHM

 

Let  X = {x1,x2,x3,……..,xn}
be the set of data points and V = {v1,v2,…….,vc}
be the set of centres.

1) Randomly select ‘c’ cluster centres.

2) Calculate the distance between each data
point and cluster centres.

3) Assign the data point to the cluster
centre whose distance from the cluster centre is minimum of all the cluster
centres.

4) Recalculate the new cluster centre
using:

Where, ‘ci’ represents the
number of data points in ith cluster.

5) Recalculate the distance between each data
point and new obtained cluster centres.

6) If no data point was reassigned then
stop, otherwise repeat from step 3).

 

K-means clustering algorithm:

      K-means
simple and easy way to classify a given data set through a
certain number of clusters (assume k clusters).
The main idea is to define k centres, one for each cluster.
These centres should be placed in a cunning way because of
different location causes different result8. So, the better choice is to
place them as much as possible far away from each other.
The next step is to take each point belonging to a given
data set and associate it to the nearest centre. When no point is
pending, the first step is completed and an early group age is done. At
this point we need to re-calculate k new centroids as barycentre of the
clusters resulting from the previous step. After we have these k new centroids,
a new binding has to be done between the same data set
points and the nearest new centre. A loop has been generated8. As a
result of  this loop we  may  notice that the k centres change
their location step by step until no more changes  are done or 
in  other words centres do not move any more.

Fig.3. Flow
Chart

 

VI. MODULES

 

·        
User

·        
QR-Code
scanner

·        
Payment

 

User:

User login into
application. Search the required product location. Then scan the QR-Code to add
the product into cart. Then user will pay the bill.

 

QR-Code scanner:

QR-Code holds the
all information about product like name, amount, etc.  Users scan the product QR-Code to add it into
cart. Product will add to cart by scanning QR-Code.

 

Payment:

As per the product
cost, bill will be generate by system. User can pay the bill by credit/debit
card or online payment. If user pay the bill by credit/debit card then system
will ask card details like card no, expiry date, bank name, etc. if user pay
the bill online then system will ask bank details.

 

Component design:

Fig.4. component design

Performance Requirement:

     Performance
of the functions and every module must be well.

     The
overall performance of the software will enable the users to work efficiently.

 

Safety Requirement:

     The
application is designed in modules where errors can be detected and fixed
easily.

     This
makes it easier to install and update new functionality if required.

 

Security Requirement:

    To access the system, person have to
register him/herself in database. Only authorized users can make payment
online.

 

VII.
TECHNIQUE USED

 

1.       Data Migration.

2.       Interfaces with other systems.

3.       Set up and maintenance of security
rights and access permissions.

 

Scope:

 

      Propose system effectively used in mall
to notify the expected product. It also reduces efforts of customer and shopper
at the time of bill payment. Propose system can be used in shops for billing purpose.
Propose system can be used in canteen for selecting food and bill payment.

 

VIII.
FEATURES OF THE PROJECT

 

Navigation:

 

     Registration/Login:
Customer register himself using his credentials and sets username &
Password to use the application for the first time. Then user will LOGIN in our
android app using his username & password. Then user will input the product
name and location automatically taken by Latitude & Longitude values of
receiver. After that system will show the path towards the product.

 

Billing System:

 

      By using navigation
system user reach to the destination. Then customer has to scan QR-Code of the
product and add it to cart. Customer has to repeat this process till he ends
the shopping11. After that application will create the QR-Code of the total
product with the MRP and details. So, that at the billing time, employee will
scan the QR-Code and does fast billing process.

 

      

Fig.5. Manual billing
system v/s self billing system

 

 

IX. Conclusions

In a step aimed for promoting shopping methods and
make people life easier, we are going to build this mobile application that
will play an important role in Indian society. The usage of Pocket PC mall
navigator as a shopping mall navigator, in addition to helping the users to
find shops efficiently and effectively, were able to create awareness in using
smart mobile devices for flexibility in almost every task among the shopping
mall.

 

References

1       
Gennady Berkovich
“Accurate and Reliable Real-Time Indoor
Positioning on Commercial Smart-Phones”, IEEE           International Conference on Indoor Positioning and Indoor
Navigation, pp 670-677, Oct 2014.

2        Suk-Hoon Jung,
Gunwoo Lee and Dongsoo Han “Methods and Tools to Construct a Global Indoor Positioning
System”           IEEE Transactions on
System, man and Cybernetics system,  pp
2168-2216, Jun 2016.

3       Dasu,  Zhenhui
Situ, Ivan Wang-Hei Ho “Mitigating the Antenna Orientation Effect on Indoor
Wi-Fi positioning system of           Mobile
Phones” IEEE 26 th International Symposium On Personal, Indoor
and Mobile Radio Communication(PIMRC)           Services,
Applications and business, pp 2105-2109, 
Sep 2015.

4        Ultekin, Oguz
Bayat “Smart Location-Based Mobile Shopping Android Application”,
Journal of Computer and           Communications,
pp 54-63, Feb 2014.

5        Prof. Seema
Vanjire, Unmesh Kanchan, Ganesh Shitole, Pradnyesh Patil , “Location Based
Services on Smart-Phone    through the
Android Application”, International Journal of Advanced Research in
Computer and Communication        Engineering
Vol.3, Issue 1, pp 417-421, Jan 2014.

6        P. E. Rybski,
S. A. Stoeter, M. Gini, D. F. Hougen, and N. Papanikolopoulos, “Performance
of a distributed system using           shared
communications channels”, IEEE Trans. on communication and Automation,
Volume 22(5), pp 713-727, Oct 2002.

7       M. Batalin and G. S. Sukhatme  Coverage, “Exploration and deployment by a
ibeacons and communication network”,           Telecommunication
Systems Journal, Special Issue on Wireless Sensor Networks, Volume 26(2), pp
181-196,  Jan 2004.

8       https://en.wikipedia.org/wiki/K-means_algorithm

9       An indoor geo-location system for wireless lans, in
Parallel Processing Workshops, 2003. Proceedings. 2003 International           Conference on, pp 29-34, Oct 2003.

10    Location Fingerprint analyses toward efficient indoor
positioning, in Pervasive Computing and Communications, 2008,           PerCom 
2008. Sixth Annual IEEE International Conference, pp 100-109, March
2008.

11    Object recognition using a tag. In 1997 International
Conference on Image Processing (ICIP 97) 3-Volume Set-Volume 1,           IEEE, IEEE Computer Society Press, pp
877-880, Oct 1997.

12    Chi Zhang, Kalyan P. Subbu, Jun Luo, and Jianxin Wu,
Member IEEE, “GROPING: Geomagnetism and
Crowd sensing

         Powered
Indoor Navigation”, IEEE Transactions
on mobile computing, Volume 14, No. 2, pp 387-400, Feb 2015.

 

 

 

Author