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The term Wi-Fi, commercially used at least as early as August 2000, was coined by a brand-consulting firm called Interbrand Corporation. The Wi-Fi Alliance had hired Interbrand to determine a name that was "a little catchier than 'IEEE 802.11b Direct Sequence'"
Wi-Fi based indoor positioning
The increasing demand for location based services inside buildings has made
indoor positioning a significant research topic. The applications of indoor
positioning are many, for instance, indoor navigation for people or robots,
inventory tracking, locating patients in a hospital, guiding blind people,
tracking small children or elderly individuals, location based advertising,
ambient intelligence etc. Although the Global Positioning System is the most
popular outdoor positioning system, its signals are easily blocked by most
construction materials making it useless for indoor positioning. This study
deals with indoor positioning using the Wireless Ethernet IEEE 802.11(Wi-Fi)
standard that has a distinct advantage of low cost over other indoor wireless
technologies – it has relatively cheap equipment and in many areas usually a
Wi-Fi network already exists as a part of the communication infrastructure
avoiding expensive and time consuming infrastructure deployment.
The increasing demand for location based services
inside buildings has made indoor positioning a significant
research topic. This study deals with indoor positioning using
the Wireless Ethernet IEEE 802.11 (Wireless Fidelity, Wi-Fi)
standard that has a distinct advantage of low cost over other
indoor wireless technologies. The aim of this study is to
examine several aspects of location fingerprinting based
indoor positioning that affect positioning accuracy. Overall,
the positioning accuracy achieved in the performed
experiments is 2.0 to 2.5 meters.
What is Wi-Fi?
• “Wireless Fidelity”
• Wi-Fi, also spelled Wifi or WiFi, is a technology that allows an
electronic device to exchange data or connect to the
internet wirelessly using microwaves in the 2.4 GHz and 5 GHz
• The Wi-Fi Alliance defines Wi-Fi as any "wireless local area
network (WLAN) products that are based on the Institute of
Electrical and Electronics Engineers' (IEEE) 802.11 standards"
Three Main Components:
1. Tags, or transponders, affixed to objects and carry identifying
2. Readers, or transceivers(Access Points), read/recieves and
interface with user-end databases.
3. User-end databases, correlate data stored on tags with physical
objects i.e. a host and system software.
The Basic Operation of Wi-Fi positioning System is :
• A device having wifi is attached to the object that needs to be identified.
• When this tagged object is presented in front of a suitable wifi access
point(reader), the device transmits its identification data to the access
• The AP then reads the data and has the capability to forward it over
suitable communication channels.
• This application can then uses this unique data to identify the object and
presents it to the aggregation and computation server software.
• The computation server performs the required location estimation
algorithms and sends the output indication on the radio map.
Location based positioning systems usually work in two phases:
1) Calibration Phase (Offline Phase)
2) Positioning Phase (Online Phase)
• In the calibration phase, a mobile device is used to measure RSS values (in
dBm) from several APs at the chosen calibration points in the area of
interest. An average of several samples recorded per location is stored.
• In the positioning phase, a mobile device measures the RSS values in an
unknown location and applies a location estimation algorithm to estimate
its current location using the previously created radio map.
• Weighted k-Nearest Neighbours (WKNN)
• WKNN has one tuning parameter, the number of nearest neighbours
considered k, which is used to control the locality of the location
calculation. When k =1 , the algorithm acts as a simple look-up table.
• Making use of the two different Wi-Fi frequency bands: 2.4 GHz (IEEE
802.11b/g) as well as 5 GHz (IEEE 802.11a).
• Device orientation information was used ie. east, west, north and
• The impact of varying the number of used APs was studied in more
detail and without regarding the origin of an AP.
• Here, the best results are achieved mostly using about 20 APs
• The dependence of average positioning error (in meters) on the choice of
the method (WKNN versus k-Nearest Neighbours[KNN]) and selected number
of nearest neighbours k is shown (from 1 to 10).
• The smallest positioning error in this experiment was 2.35 meters using
WKNN method while that of KNN was 2.48 meters.
• Contactless: An Wi-Fi tag can be read without any physical contact
between the tag and the AP/reader.
• Cost Effective: Wi-Fi allows cost effective LAN deployment indoor as well
• Absence of line of sight. A line of sight is generally not required
• Better Security: Wi-Fi Protected Access encryption (WPA2)
• Variable range: 2.4 GHz - 5 GHz bandwidth
• Support for multiple tag reads.
• Easy availability: Easily available for most new electronic devices such as
laptops, mobile phones etc
• Tracking of goods.
• Tracking of persons and animals.
• Access the Internet.
• Wireless data transfer .
• Large area connectivity.
• Remote access to devices
• It was observed that a positioning system can benefit from the availability of
additional weakly-sensed APs as well as APs working in 5 GHz frequency band
(using IEEE 802.11a/n). RSS readings from these APs gave a notable
improvement in positioning accuracy. In fact, in this study, using exclusively
the APs from other buildings nearby, the positioning error was still a decent
• This Positioning System is based on RSS value which is a measurement of the
power present in a received radio signal to determine the location of an
• Cost effective
•F. Thomas and L. Ros, Revisiting trilateration for robot localization. IEEE
ransactions on Robotics, 21(1), 2005, pp. 93-101.
•Widyawan, M. Klepal and D. Pesch, “Influence of Predicted and Measured
Fingerprint on the Accuracy of RSSI-based Indoor Location Systems, ” in
Proceedings of 4th Workshop on Positioning, Navigation, and Communication
2007 (WPNC’07), 2007, pp. 145-151.
•J. Yim, S. Jeong, K. Gwon and J. Joo, Improvement of Kalman filters for WLAN
based indoor tracking. Expert Systems with Applications, 37(1), 2010, pp. 426-
•G. Jekabsons, V. Zuravlyov, Refining Wi-Fi based indoor positioning. In:
Proceedings of 4th International Scientific Conference Applied Information and
Communication Technologies (AICT), Jelgava, Latvia, 2010, pp. 87-95.
•O. M. Badawy and M. Hasan, Decision tree approach to estimate user location
in WLAN based on location fingerprinting. In: Proceedings of 24th National
Radio Science Conference, Ain Shams Univ., Egypt, 2007, pp. 1-10.
•M. Brunato and R. Battiti, Statistical learning theory for location fingerprinting
in wireless LANs. Computer Networks and ISDN Systems, 47(6), Elsevier, 2005,