15EC401M-MULTIDISCIPLINARY DESIGN REPORT

on

Wi-Fi Fingerprint Based Indoor Positioning System

by

RA1511004010244 R.RAVINDRA VIKRAM

RA1511004010424 PARAM MAHALINGAM

RA1511004010447 S.SAI PRASANNA

RA1511004010552 A.V RANJIT SREYUS

FACULTY OF ENGINEERING AND TECHNOLOGY

DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING

SRM INSTITUTE OF SCIENCE AND TECHNOLOGY

SRM Nagar, Kattankulathur-603203,

Kancheepuram District, Tamil Nadu

November 2018
 SRM INSTITUTE OF SCIENCE AND TECHNOLOGY

FACULTY OF ENGINEERING AND TECHNOLOGY

DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING

SRM Nagar, Kattankulathur-603203.

Kancheepuram District, Tamil Nadu

BONAFIDE CERTIFICATE
Registration Nos.: RA1511004010244

RA1511004010424

RA1511004010447

RA1511004010552

Certified to be the bonafide record of work done by R.Ravindra Vikram, Param Mahalingam,

S.Sai Prasanna and A.V Ranjit Sreyus of ECE B.Tech Degree course in 15EC401M

Multidisciplinary Design in SRM Institute of Science and Technology, Kattankulathur during

the Academic year 2018-2019 (ODD Semester).

Course In charge

Date Year Coordinator

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 TABLEOFCONTENTS

ABSTRACT iii

LIST OF FIGURES iv

1 INTRODUCTION 1

2 LITERATURE REVIEW 2

3 PROBLEM STATEMENT AND OBJECTIVES 6

4 SYSTEM DESIGN AND ARCHITECTURE 7

4.1 Adopted Methodology.................... .......... 8
4.2 Hardware and Software Requirements.................... 9

5 REALISTIC CONSTRAINTS AND DELIVERABLES 10

6 CONCLUSIONS AND FUTURE SCOPE 11

7 REFERENCES 12

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 ABSTRACT
The growing commercial interest in indoor location based services (ILBS) has spurred recent
development of many indoor positioning techniques. Due to the absence of global positioning
system (GPS) signal, many other signals have been proposed for indoor usage. As GPS signal
cannot penetrate well in indoor environment, various other signals have been investigated for
localization purpose. Among many signals, the use of Wi-Fi signal Wi-Fi (802.11) emerges as a
promising one due to the pervasive deployment of wireless LANs (WLANs). In particular, Wi-Fi
fingerprinting has been attracting much attention recently because it does not require line-of-
sight measurement of access points (APs) and achieves high applicability in complex indoor
environment. Using Wi-Fi signals and their RSSI (received signal strength intensity) the location
of a mobile user in indoor environment is estimated and displayed in a map like platform. In
addition to that Heat map of a Wi-Fi access point and walking trajectories of the mobile user is
also displayed

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 LIST OF FIGURES

1. (LAPM scheme to reduce handoff latency)...............................4

2. Propagation model with mean and variance............................ 4
3. RSSI stabilization............................ . 5
4. System Model.........................7
5. RSSI measurement (in dBm)......................7
6. A sample of a fingerprint database…………………….8
7. Error minimization ……………….8

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 1. INTRODUCTION
Traditional localization services like GPS work on methods of triangulation (working with
angles) and trilateration (working with distances) both methods require LoS, AoA.For LoS, an
indoor environment causes obstruction and the signal reception will be poor if not null. In the
case of AoA the structure of the indoor environments cause signal reflections which decrease
accuracy. The additional cost of hardware in schemes like bluetooth, FM radio, ultrasound, RFID
and light makes these traditional methods expensive. These techniques, even if implemented,
result in localization depicted in a 2-D manner only. 2-D results have low precision in the case of
indoor environments like multi-storied buildings. Thus multi path effects, additional cost and low
precision deem traditional localization services poor for indoor environments. In the case of Wi-
Fi (802.11) usage as signal for indoor positioning, it does not require LoS/AoA measurements,
no additional cost is required as extensive usage of WLANs are present in indoor environments,
Wi-Fi reception enabled mobile devices collaborate with WLANs to give better accuracy. Wi-Fi
therefore is best suited for indoor positioning purposes.

The Localization Services Market is valued at $11,994 million, and it is expected to touch
$61,897 million by the year 2022, viewed by a CAGR 26.6%. Localization services employ
location information based on smartphones, mobile devices for offering schemes and services.
The rapid development and usage of mobile phones with localization application in various
domains like Data Science enhance the growth of the localization market. On the other hand, the
expense involved in implementing localization services with security constraints limit the
development of the localization services. The usage of localization services has grown
throughout various organizations like defense, media, entertainment, advertising & marketing,
retail, big data and others (education, BFSI, and healthcare). Government measures to develop a
standard for technologies in defense along with drastic modernization & urbanization in the
growing markets hope to benefit the economic growth of the defense and government sectors.
Advertising, big data & analytics, entertainment, business intelligence, navigation, social
networking and others including disaster management and emergency support are the sectors for
application in the localization services sector. In addition, the rising demand for localization
services, such as defense, media, entertainment, advertising & marketing, retail, big data and
others (education, BFSI, and healthcare) shows promising growth for global localization
services.

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 2. LITERATURE REVIEW

The following papers were analyzed during research for the project. They were deemed
important due to the impact they have on our project, and the techniques employed.

1. Wi-Fi Fingerprint-Based Indoor Positioning:Recent Advances and Comparisons

(IEEE COMMUNICATIONS SURVEYS & TUTORIALS, VOL. 18, NO. 1, FIRST
QUARTER 2016) Suining He, Student Member, IEEE, and S.-H. Gary Chan,
Senior Member, IEEE

In this survey the system model is defined to address the problem statement. Wi-Fi fingerprint
based indoor positioning, the most important measurement is of the RSSI (received signal
strength intensity). The process of fingerprinting goes through 2 phases, an offline phase
followed by an online phase. in the offline phase, RSSI is collected from all access points (APs)
at a particular known location (site survey). Similarly, with respect to many survey techniques
(which are mentioned in the following section), site survey is done for the indoor environment
resulting in a database. The database contains RSSI vectors at each known location from the APs
whose locations are unknown. These known locations are called point(s) of reference (PoR) and
the database is the fingerprint of the indoor environment. In the online phase, the mobile user's
RSSI value is compared with the fingerprints stored. The position of the user is estimated based
on the minimal error obtained by comparison and the respective PoR is obtained as output.

2. S Sezer, S Scott-Hayward, P-K Chouhan, B Fraser, D Lake, J Finnegan, N Viljoen, M

Miller, N Rao, Are we ready for SDN?Implementation challenges for software-defined
networks. Commun Mag IEEE 51, 36 (2013)

Recently, the OpenFlow protocol has been considered in the wireless access network to provide
the fine-grained packet control, and SDN makes the separation of underlying physical
infrastructure and the network services. The OpenRoads was the first project in the SDN-based
WLAN environment that enables wireless network slicing using the FlowVisor to assign
different SSIDs to the MT. The OpenRoads introduced the OpenFlow-based testbed to control
mobility between the WiFi and the WiMax base stations. The proposed prototype using the
OpenFlow protocol is to enable flow-based scheme with the fine-grained packet control in home
networks. CloudMAC is a distributed architecture that enables transferring the processing of
MAC layer functions on the central servers to minimize the IEEE 802.11 AP pressure, and the
AP is responsible only for the MAC frames forwarding among the virtual APs using the
OpenFlow. Contrarily, Cloud-MAC did not declare the switching procedure of associated
stations among APs simultaneously that is required for per-user handover. Besides, the
CloudMAC drives all traffic towards the Cloud which increases the load of the control plane. In

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contrast, the LAPM scheme provides the seamless mobility without any change on the MTs to
reduce the network complexities and the deployment cost. An SDN framework Odin to empower
the seamless mobility in WLAN, builds the logical virtual access point (LVAP), which is similar
to the concept of the virtual APs in the CloudMAC architecture. The LVAP offers a dedicated
logical connection to the client with a unique BSSID. During the handoff process, the client does
not necessitate re-association with the target AP. The Odin offers seamless handover in a
wireless network to reduce the handover delay in comparison with the IEEE 802.11 traditional
handover. However, the handover process depends on a formal parameter RSSI which could lead
to a load imbalance situation. The SDN-based handover approach proposes for IEEE
802.11WLAN in which the handover procedure depends on neighbor AP response and permits
the MT to connect with several APs simultaneously. It also enables fast switching among APs to
improve the performance of the video streaming applications. However, the scope of the
proposed approach is limited to video streaming applications without the inclusion of video
conferencing and peer-to-peer-based applications. SDWLAN presented an architecture that
sustains client-unaware handoff on 802.11 AP MAC layer and provides a unified control
platform for wireless APs and wired backbone. The wireless access switch (WAS) is a device
that configured with the OpenFlow protocol for transferring several module functions of AP onto
the centralized controller. However, it is challenging to incorporate WAS into the existing
WLAN environment.The proposed LAPM scheme has advantages over the abovementioned
SDN-based solutions with the main feature of the mobility management. The LAPM scheme is
primarily distinctive from those techniques that support individual mobility of MTs with the load
balancing approach among PAPs in the overlapping signal range without any amendments at the
MTs.

3. Mobility management in IEEE 802.11 WLAN using SDN/NFV technologies (Gilani et

al EURASIP Journal on Wireless Communications and Networking (2017) 2017:67)
Syed Mushhad M. Gilani, Tang Hong, Wenqiang Jin Guofeng Zhao, H. Meng Heang and
Chuan Xu
In the online (query) phase, a user (or target) samples or measures an RSSI vector at his/her
position and reports it to the server. Using some similarity metric, the server compares the
received target vector with the stored fingerprints. The target position is estimated based on the
most similar “neighbors”, the set of RPs whose fingerprints closely match the target’s RSSI.The
key issue in the online phase is the handoff latency of the mobile user in real time scenario

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 Figure 1. (LAPM scheme to reduce handoff latency)

For reducing handoff latency, SDN (Software Defined Networks) are used Logical APs are used
which are in constant connection with both the mobile devices as well as physical APs. This is
known as Logical AP mobility management (LAPM). According to the centralized controller in
the SDN hand-offs take place. The Logical AP address issues of signal strength, load, latency,
throughput issues. First the load and signal strength information is obtained from the physical AP
If the RSSI > a threshold (set by SDN) the load conditions are checked If either the load or
threshold conditions are not satisfactory hand-off is started. Hand-off latency is minimized as
Logical AP is always connected with all nearby physical APs.
4. W. Zhuo, B. Zhang, S. Chan, and E. Chang, “Error modeling and estimation fusion for
indoor localization,” in Proc. IEEE ICME, Jul. 2012, pp. 741–746.

Figure 2: Propagation model with mean and variance

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 To simulate raw signal strength readings we used the propagation equation to which we
added a random normal error with mean=0 and variance=0.71d where d is the distance
calculated. This error model is based on analysis of the variation in radio propagation
experiments. By tracking the cumulative average of the position estimate we saw that after
many estimates (around 300) the average stabilized. In many of the trials this stabilized
average was very close (approximately 1m) to the true position. We also see that repeated
trials gave similar results. The approach will use a training phase to learn what to expect at a
specified location, using characteristics such as the mean and variance of the signal strength
from each Wi-Fi access point at that location, and the mean and variance of the position
estimate over a number of iterations. During a verification phase, the application will
compare real-time position estimates to these ‘expected’ characteristics of each known
location.

5. Indoor Location Using Trilateration Characteristics B Cook[1, 2, 3], G Buckberry[2], I

Scowcroft[2], J Mitchell[1], T Allen[3] (RESEARCH GATE, SIEMENS
COMMUNICATIONS) 1 University College London, 2 Siemens Communications, 3
Nottingham Trent University

We have performed experiments to measure signal propagation over distance using both Wi-Fi
signals and raw carrier signals generated in the 2.4GHz band. As we are interested in the
repeatability of position estimates, rather than the accuracy, we looked at how quickly the
average signal stabilised.The Wi-Fi experiment consisted of taking 100 readings at each of 4
locations. Although the amount of variation in signal strength was different for each position we
found that the cumulative average stabilised after about 40 measurements (Figure 4). Preliminary
tests on a PDA show that it would take approximately 42 seconds to take 40 readings, which
would limit location updates to approximately once per minute. This should be sufficient as
we would expect users to remain in one location for longer than this.

Figure 3: Graphs of RSSIs for Wi-Fi showing average RSSI stabilizing after about 40 measurements

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 3. PROBLEM STATEMENT
Indoor environments involve obstacles signal fluctuations, noise and multipath effects. There are
many positioning systems that have been exploited for this purpose like GPS, LIDAR, Beacon,
RFID etc. Wi-Fi fingerprint based indoor positioning is an exciting technique which can be used
for indoor localization due to the fact that Wi-Fi (802.11) overcomes the drawbacks conventional
location based services which rely on line of sight/angle of arrival measurements, signal
fluctuations, obstacles etc. Proposed work focuses on the fingerprinting, positioning and tracking
the walking trajectory of users in an indoor environment for which algorithms are simulated for
improved results.

OBJECTIVES

1. Design a localization system using Fingerprinting technique

2. Achieve better accuracy than conventional models (GPS, RFID, trilateration

and triangulation)

3. Contribute to the active research in this domain

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 4. SYSTEM DESIGN AND ARCHITECTURE

Figure 4.System Model

Figure 5. RSSI measurement (in dBm) at a known location from Access Points (Yellow Green
Red). The vector gives the fingerprint of the location.

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 4.1ADOPTED METHODOLOGY

Figure 6. A sample of a fingerprint database

Figure 7. Error minimization

 RSSI value and the AP connected are taken as input.

 RSSI input and fingerprint vector of the AP are subtracted and absolute value is stored in
the same vector.
 The first element of the vector is checked for least error.
 The element of the vector corresponding to least error in RSSI value gives the RP of
mobile user and location is estimated.

Here we presume that a mobile user is connected to a single AP at a time. Hence the RSSI value
of the connection is subtracted from all the elements of the vector in the fingerprint database

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 4.2 HARDWARE AND SOFTWARE REQUIREMENTS

Hardware requirements:

The hardware requirements of the project are constrained towards cost. The system was designed
to minimize the cost and create an effective system.

Hardware component Typical example

Transmission cables CAT-5 cables

Wireless Access Points CISCO WRT300N

Network Switches CISCO 2960

Network Routers CISCO 2811

Software requirements:

The software for the project is difficult to be chosen to maximize the open-source
components.The software is typically licensed and is suitable economically on very large scale
applications. However for small scale applications these requirements may not be needed to
minimize cost.

OpenFlow Protocol Stack (for SDN/NFV virtual controller), Commview WLAN analyzer

Cost Modeling

COMPONENT NAME EQUIVALENT COST (in INR)

CAT-5 cables 10000/-

CISCO WRT300N (Access Points-500) 100000/-

CISCO 2960 (Network Switches-30) 50000/-

CISCO 2811 (Network Routers-10) 75000/-

OpenFlow Protocol Stack license (NEC) 300000/-

NETCOST 535000/-

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 5. REALISTIC CONSTRAINTS AND DELIVERABLES

 FINGERPRINTING ACCURACY

The RSSI measurements for the purpose of fingerprinting must be of minimum error in
advance to improve the efficiency and reduce the complexity of the system.

 HANDOFF LATENCY

Handoff latency decreases the performance of the system as it makes the process of
obtaining real time input highly complicated. Proposed SDN/NFV models are required to
address this constraint.

 ACCESS POINT LOAD IMBALANCE

Load imbalance is a common issue in highly used WLAN environments where data
traffic is always at maximum flow. This may result in congestion and reduce the
throughput in the network thus unreliable information from the Access Points in real time
scenario. Proposed SDN/NFV models are required to address this constraint.

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 6. CONCLUSIONS AND FUTURE SCOPE

To find the exact position of the users using WLAN in an indoor environment based on
fingerprinting has been simulated and it is evident from the result that the proposed method
performs better than the conventional localization methods.

 As this is a relatively developing research area, further research in the existing

localization algorithms will allow us to optimize the model from both the software as
well as hardware perspective respectively. Extensive research has been carried out, not
only in the academic field but also into the practical fields. Our model aims at designing a
new prototype relating to both these domains.

 This prototype can be extended to a tracking system which inherits a similar approach
using Wi-Fi fingerprinting

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 7. REFERENCES

Wi-Fi Fingerprint-Based Indoor Positioning:Recent Advances and

Comparisons (IEEE COMMUNICATIONS SURVEYS &
TUTORIALS, VOL. 18, NO. 1, FIRST QUARTER 2016)
Suining He, Student Member, IEEE, and S.-H. Gary Chan,
Senior Member, IEEE
2. Mobility management in IEEE 802.11 WLAN using SDN/NFV
technologies (Gilani et al. EURASIP Journal on Wireless
Communications and Networking (2017) 2017:67)
Syed Mushhad M. Gilani, Tang Hong, Wenqiang Jin,
Guofeng Zhao, H. Meng Heang and Chuan Xu
3. Indoor Location Using Trilateration Characteristics
B Cook[1, 2, 3], G Buckberry[2], I Scowcroft[2], J Mitchell[1],
T Allen[3] (RESEARCH GATE, SIEMENS COMMUNICATIONS)
1 University College London, 2 Siemens Communications, 3
Nottingham Trent University
4. L. Ni, Y. Liu, Y. C. Lau, and A. Patil, “LANDMARC: Indoor loca-
tion sensing using active RFID,” in Proc. IEEE PerCom, Mar.
2003, pp. 407–415.
5. W. Zhuo, B. Zhang, S. Chan, and E. Chang, “Error modeling and
estimation fusion for indoor localization,” in Proc. IEEE ICME,
Jul. 2012, pp. 741–746.

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