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, barcode
scanner, Wi-Fi router



                Manual Shopping is the
traditional way of shopping where the customers choose their wished 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 shopper
as well as cashier. To overcome this law, the customer himself can scan the
barcode using his mobile while making purchase, retrieve essential details of
all products from shops database and generate bill himself. 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 Barcode 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 barcode scanner for the purchasing and
navigation of items for store that will be self-checking and automatic payment
transaction. Here comes the term indoor navigation and barcode scanning. Indoor
positioning is still a challenging problem because satellite-based approach do
not work properly inside buildings.


                Barcodes are ubiquitously used
to identify products, goods or deliveries. Devices to read barcodes are all around,
in the form of pen type readers, laser scanners or LED scanners. Camera-based
readers, as a new kind of barcode reader, have recently gained much attention.
The interest in camera-based barcode recognition is built on the fact that
numerous mobile devices are already in use, which provide the capability to
take images of a fair quality. This describes the hardware system architecture for
implementing the barcode reading system in mobile phones and its process. The
camera device and application processors are necessary hardware components for
the system. The application processors is 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 barcode.


Related Work


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


Author: Gennady Berkovich

1 This paper outlines
the software navigation engine that was developed by SPIRIT Navigation for
indoor positioning on commercial smart-phones. 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
globally. 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

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 devices. 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, our 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


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

This paper propose 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 maps. Based on our 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.

Existing System


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. Customers have to Wait in the billing line to scan the products.


In foreign countries there are some
mall which use indoor navigation. To use this system user should go to the
particular LED/LCD screen and search for product location. 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.




Also at the billing section user need
to scan for each  product and does the
total. There is not a technology for scanning all the product at the same time
so that user can do the shopping in minimum time as possible. 






Methodologies to implement the system modules:

1.       Point
out product

2.       Scan

3.       Payment



Point out product:

      Now day’s
mall getting bigger and bigger. It very difficult to find the expected product
in mall. User search all over mall for expected product. Propose system provide
the better way to search the expected product. User just need to search product
in mobile then it will point out the product where user could get the product.


Scan barcode:

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



payment is done by carry debit card, credit card or cash. But in propose system
user can pay the bill from mobile by online. So customer not need to carry any
kind of card or cash.




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

4) Recalculate the new cluster centre

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 result. 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
generated. 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.






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



As per the product
cost, bill will 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 :


of the functions and every module must be well.

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



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

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



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




1.       Data Migration.

2.       Interfaces with other systems.

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




      Propose system effectively used in mall
for notify towards expected product. It also reduce affords of customer and
shopper at the time of bill payment. Propose system could be used in shops for
billing purpose. Propose system could be used in canteen for selecting food and
bill payment.






Customer register himself using his credentials and sets username &
Password to use the application for the first time. Then user will LOGIN into
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


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 have to repeat this process till he ends
the shopping. After that application will create the QR-Code of the total
product with the MRP and details. So, that at the billing time, employee would
scan the QR-Code and does fast billing process.


In a step aimed at promoting shopping methods and make
people life easier, we are going to build this mobile application that could
play an important role in Indian society as a whole. 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.



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       iBeacon (2014) 
Online. Available: https://developer.apple.com/ibeacon/Getting-Started-with-iBeacon.pdf

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

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.





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