International Journal of Engineering Trends and Technology (IJETT) – Volume 8 Number 9- Feb 2014
A Robust SSD Position Fingerprint Approach for
Wireless Networks
P.V.Sindhu 1 , Ms.S.P.Godlin Jasil 2
1
P.V.Sindhu1
Student of post graduation, Department of Computer Science and Engineering, Sathyabama University,
Chennai, India.
2
Ms. S.P.Godlin Jasil2
Faculty of Computer Science and Engineering, Sathyabama University, Chennai, India.
Abstract-- Fingerprint-based method is widely adopted for
indoor localization purpose. The analytically robust position
fingerprint definition, the signal strength difference (SSD) is
used for Localization purpose. Two well-known localization
algorithms (K nearest Neighbour and Bayesian Inference) are
used to demonstrate the robustness of SSD. The difference of the
Signal Strengths between the user with the different access
points, are analysed to identify a highest and nearest access
point from the user’s point of position. While user requesting
data from one access point and moving to the next access point,
the Signal strength difference (SSD) will be calculated and then
automatically the data will be delivered to the user from the
highest and nearest access point, which has strongest signal. So
that it can reduce the data loss problem in mobile computing
environment.
Keywords-- Position Fingerprint, Signal strength difference
(SSD), Access point, Wi-Fi.
I.
INTRODUCTION
Received Signal Strength (RSS) varies significantly across
multiple different device hardware even it is used under the
same wireless conditions. The analytical robust position
fingerprint definition, SSD is used for overcoming few
problems in mobile computing environment. The signal
strength difference (SSD) is a robust position Fingerprinting
technique. It is used for calculating the difference between
signal strengths. Two well-known localization algorithms (K
Nearest Neighbour and Bayesian Inference) are used when
proposed fingerprint is applied. These two algorithms
demonstrate the robustness and working of SSD. Calculation
of Signal strengths is done when used an SSD Approach.
User’s Signal Strength is calculated so that the difference of
the Signal Strength between the user with the different access
points, are analysed to identify a highest and nearest access
point from the user. While requesting data from one access
point and moving to the next access point, the Signal strength
difference (SSD) will be calculated and then automatically
ISSN: 2231-5381
the data will be delivered to the user from the highest and
nearest access point, which has strongest signal. So that it can
reduce the data loss & time consumption problem in mobile
computing environment.
II.
RELATED ARTICLES
This paper proposed the evaluation shows that the
solutions can address the signal strength heterogeneity
problem. According to them, heterogeneous wireless client’s
measure signal strength differently. This is the basic
elementary problem for indoor position fingerprinting, and it
has a high impact on the position precision. So this presents
an automatic mapping-based method that avoids calibration
by learning from online measurements [1].
This paper gives a description of Ekahau Site Survey is a tool.
It is used for Wi-Fi network planning, for Real-Time Position
Systems. It runs on Microsoft Windows and supports 802.11
a/b/g/n/ac wireless networks. It provides a ground-level view
of WLAN (Wireless Local Area Network) coverage and
performance based on the data collected during passive and
active surveys. It is optimized for only 802.11n family of WiFi. It is also capable of performing predictive surveys that
facilitates WLAN planning at pre-deployment stage [2].
This paper focuses on Received signal strength, which is one
of the most capable strategies in wireless communications.
The stationary signal strength is the averaged using RSS
value and the result is a stationary Gaussian process; which
includes some experimental results [3].
This paper discusses about indoor localization techniques,
which uses position fingerprints. Position Based Services
(LBS) are the applications of mobile computing, where an
LBS relies on a user’s position, which is used to deliver
context aware applications. In this paper, they investigated
many aspects of fingerprint-based position systems, in order
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International Journal of Engineering Trends and Technology (IJETT) – Volume 8 Number 9- Feb 2014
to improve their accuracy. WLAN can be implemented with
the least effort, but WLAN aims to provide local wireless
access to fixed network architectures [4].
This paper discusses an effective technique in locating a
source,
which
is
based
on
intersections
of hyperbolic curves defined by the time differences of arrival
of a signal received at a number of sensors is proposed. This
approach is non-iterative and also gives an explicit solution.
Received signal strength (RSS) when referred to a signal
propagation model, is used to find user position, where it is
one of the most capable strategy in wireless connections. This
paper develops simple methods which are based on relative
signal-strength capacity that is, the difference in inactive
signal strength measured at the user position from multiple
base transceiver stations (BTSs) [5].
This paper is focused to discuss about the context-awareness.
Current indoor localization systems need to be manually
adapted to work optimally with specific hardware and
software. Evaluating the efficiency is a difficult task. One
type of context information is position information of
wireless network clients. This paper focuses on Research in
indoor localization of wireless network clients based on
signal strength. Not much of this research is directed towards
handling the issue of adapting signal strength based indoor
localization system to the hardware and software of a specific
wireless network user, it can be a tag or a PDA or a laptop.
Therefore current indoor localization systems need to be
manually adapted to work optimally with specific hardware
and software [6].
This paper describes that, RSS based position estimation.
This technique of position estimation has been proposed as a
low-cost, low-complexity solution for many position-aware
applications. This work studies radio propagation path loss
model, which is assumed, known a priori in position aware
applications. This paper presents a detailed study on the RSSbased joint estimation of unknown positions and coordinates
the distance-power gradient which is a parameter of path loss
model. A nonlinear least-square estimator improves the
performance of the algorithm. From simulation results, which
are based on CRB, shows that the proposed joint estimator is
especially useful for estimation of position in unknown or
changing environments [7].
This paper is focused to discuss the Wireless LAN. WLAN is
efficiently used for effective indoor positioning system. It is
sufficiently robust to enable for a variety of position aware
applications without requiring special-purpose hardware or
complicated training and calibration procedures.
This paper demonstrates probabilistic techniques that
allows for remarkably accurate localization. This system is
sufficiently robust to enable a variety of position-aware
applications without requiring special-purpose hardware or
complicated training and calibration procedures [8].
III.
EXISTING SYSTEM
In the existing system, the popular position fingerprint,
Received Signal Strength (RSS) varies significantly across
multiple different device hardware even it is used under the
same wireless network conditions. The system while using
RSS was not that effective when compared to SSD. If the user
in mobile computing environment requests for a data from
internet, the server will send the data to the user from that
particular tower region or an access point. Also if the gets the
signal from the next tower, the data consumption from the
new tower will be a very time consuming process. There is a
chance for data loss and request time out. It is also a tough
task to find the users movement using RSS mechanism.
Disadvantages of existing system are, There is a chance for
data loss & Request time out, while the getting the data from
nearest tower. The data consumption from the new tower will
be a very time consuming process. It is difficult to predict the
user’s movement using RSS mechanism.
IV.
PROPOSED SYSTEM
In the Proposed System, using an SSD Approach is
to identify best matched tower from the user’s point of
position. The user’s Signal Strength is calculated, so that the
difference of the Signal Strength between the user with the
different towers are analysed to identify a best matched or
nearest Tower from the user point of view. The disadvantages
of existing system can be improved in proposed system.
Advantages of proposed system are, automatically data
delivered to the user. There is a chance for reducing the time
consumption and data loss problem in mobile computing
environment. It can be easy to track the user movement using
SSD Approach.
V.
IMPLEMENTATION
This paper focuses on implementing two algorithms; they are
KNN Algorithm and Bayesian Algorithm. Two well-known
localization algorithms are used, when our proposed
fingerprint is used. They are K Nearest Neighbour and
Bayesian Inference.
KNN Algorithm is used to find the k closest training
points (small || xi − x0 ||) according to some metric which is a
distance calculation measure for ex. euclidean, manhattan,
etc.). It uses predicted class with majority vote and predicted
value with average weighted by inverse distance. It is a
Memory-based model. In its basic form, it is one of the most
simple machines learning Methods.
Bayesian Algorithm gives the maximum likelihood
estimation of the class and prior, posterior probabilities of the
objects in a closet. The probabilities of the objects help in
finding out the nearest object.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 8 Number 9- Feb 2014
While user is in mobile computing environment and
requesting a data from the one access point and moving to the
next access point, the Signal strength difference will be
calculated and then automatically the data will be
downloaded to the user from the next tower which is nearest
& having strongest signal. So that it can reduce the time
consumption &data loss problem in mobile computing
environment. It will be easy to track the user movement using
SSD.
definition, the Signal Strength Difference (SSD) is used as
Position Fingerprinting technique.
SSD Approach includes the calculation of Signal
Strength Differences. User’s point of position is the point
from which access point user is getting signal for his internet
data access. When the User is in mobile computing
environment, the SSD approach helps in finding out the
difference between Signal strengths of User’s point of
position and to the next access point, which is nearer to the
user and has highest signal strength. So it helps in reducing
Data loss and Time consumption problem in mobile
computing environment.
ACKNOWLEDGMENT
I would like to wish to the Head of the Department of
Computer Science & Engineering, Ms. Bharati madam for the
encouragement, which lead to enhancement of the paper work.
To my guide, Ms. S.P.Godlin Jasil madam for the support and
guidance in the improvement of the paper.
REFERENCES
[1] M.B. Kjærgaard, “Indoor Position Fingerprinting with
Heterogeneous Clients,” Pervasive and Mobile Computing,
vol. 7, no. 1, pp. 31-43, Feb. 2011.
[2] Ekahau, “http://www.ekahau.com, 2012”.
[3] J. Bardwell, “A Discussion Clarifying Often-Misused
802.11 WLAN Terminologies,” http://www.connect802.com/
download/techpubs/2004/you_believe_D100201.pdf, 2011.
Figure 4.1: General block diagram of
outdoor positioning.
Wi-Fi in indoor &
Above Figure gives general block diagram of Wi-Fi in
indoor & outdoor positioning. In Indoor positioning, the radio
frequency of signals will be permitted to 100 meters or within.
They cannot be beyond that range. The Wi-Fi equipped
devices can be connected to internet through fixed internet
connection setup like LAN, BROADBAND or a DSL
connection.
In an Outdoor positioning, it provides the extension of
connectivity. The radio frequency of signals will be up to 300
meters or beyond. Using an SSD Approach we can find out
the nearest and highest frequency radio frequency signals.
Here, we do not need any fixed connections.
VI.
CONCLUSION
Fingerprint-based methods are most widely adopted for the
purpose of indoor localization purpose because of their costeffectiveness. An analytically a robust position fingerprint
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[4] M. Hossain, H. Nguyen Van, Y. Jin, and W.-S. Soh,
“Indoor Localization Using Multiple Wireless Technologies,”
Proc. IEEE Int’l Conf. Mobile Adhoc and Sensor Systems
(MASS),http://www.ece.nus.edu.sg/stfpage/elesohws/mass07.
pdf, Oct. 2007.
[5] B.-C. Liu, K.-H. Lin, and J.-C. Wu, “Analysis of
Hyperbolic and Circular Positioning Algorithms Using
Stationary Signal-Strength-Difference Measurements in
Wireless Communication,” IEEE Trans. Vehicular
Technology, vol. 55, no. 2, pp. 499-509, Mar. 2006.
[6] M.B. Kjærgaard, “Automatic Mitigation of Sensor
Variations for Signal Strength Based Position Systems,” Proc.
Second Int’l Workshop Position and Context Awareness,
2006.
[7] X. Li, “RSS-Based Position Estimation with Unknown
Pathloss Model,” IEEE Trans. Wireless Comm., vol. 5, no. 12,
pp. 3626-3633, 2006.
[8] A. Haeberlen, E. Flannery, A.M. Ladd, A. Rudys, D.S.
Wallach, and L.E. Kavraki, “Practical Robust Localization
over Large-Scale 802.11 Wireless Networks,” Proc. ACM
MobiCom, pp. 70-84, 2004.
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