International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 3, Issue 1, January 2014
ISSN 2319 - 4847
POSITION LOCATION METHODOLOGY BASED
ON RSSI USING RFID
Ms.Aarti Vaidya1, Prof.A.Meshram2 ,Prof.A.Sakhare3
1
M.tech. 3rd sem, Department of Computer Science and Engineering, G.H.Raisoni Academy of Engineering and Technology,
Nagpur, India
2
Prof. Department of Computer Science and Engineering, G.H.Raisoni Academy of Engineering and Technology , Nagpur,
India
3
Prof. Department of Computer Science and Engineering, G.H.Raisoni Collage of Engineering and Technology , Nagpur,
India
ABSTRACT
The proposed method enables self-recognition of a persons’ current position by utilizing high frequency passive radio-frequency
identification (RFID) tags. The system is used by each and every person who wants to locate the person within the network
established by the system. The locating network is set up by the use of RFID readers and tags. Computer server is built to send
and receive the request and result of location in association with RFID tags and reader. The readers will emit the frequency
allocated to particular person and will find the tags present in their respective area. By using frequency matching, method the
position of person is detected and finally gets position information on the server in the map of particular organization and also
the guide message. The system is built without use of GPS technology.
Keywords:- RFID, Readers, Tags, Localization
1.Introduction
Localization: Finding the exact location of any object that resides on earth surface by using various detecting technologies
is localization. The localization may be indoor or outdoor.
Various technologies used for position detection
Many different technologies have been emerging for position location. There are three approaches by which we can find
the locations of anybody are as follows:
1. GPS (global positioning system)
2. RFID system (Radio frequency identification)
3. NFC (near field communication).
GPS is generally used for outdoor applications whereas RFID and NFC can be used for outdoor as well as indoor
applications. But RFID and NFC are more suitable for indoor application because of the related range used by their reader
and tag.
1. GPS: A Global Positioning System is a system designed to help in navigation on the Earth. GPS satellites cover the
whole earth in four directions, and a group on the equator. Radio signals are taken from satellites in space in orbit around
the Earth. , signals can be received by the GPS unit from 6 to 12 satellites at a time. An atomic clock is contained by
every satellite which is carefully set by NORAD several times every day. [9].GPS devices may provide latitude and
longitude information. Some may calculate altitude information. But this information related to altitude may not be
accurate and rely on exclusively to pilot aircraft. The radio signals can give good time and position of the satellite. The
current time is subtracted by a GPS receiver from the time the signal was sent. The difference tells that how long before
the sender sent the signal.. The time difference multiplied by the speed of light is the distance to the satellite [7].
Trigonometry is used by GPS unit to calculate where it is from each satellite's position and distance. Four satellites are
required for solving the geometric equations .[10]
2. RFID: Radio Frequency Identification (RFID) is used for the identification of any particular object uniquely. Some
applications are creating demand for high-accuracy, low cost indoor location methods [2]. However, due to the higher
complexity of indoor environments, navigation systems i.e. GPS that perform well outdoors ,but may work poorly indoors.
As name suggests, each RFID is provided with the frequency. Frequency can be of any range from very low frequency to
ultra high frequency. It is observed that frequency is inversely proportional to the wavelength (f=c/ where
f:=frequency:=velocity, :=wavelength)[1] .So, the distance to be covered can be adjusted by using the frequency . RFID
Volume 3, Issue 1, January 2014
Page 370
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 3, Issue 1, January 2014
ISSN 2319 - 4847
readers and RFID tags are used for object’s identification. An RFID reader generally acts as sender as well as receiver. It
captures an encoded radio signal to interrogate the tag. The RFID tag receives the message and then responds with its
identification and other information.[2] RFID tags can be passive, active or battery-assisted or passive. Active tag is
having its own battery and sends its id periodically. Battery assisted passive tag has small battery and can work after
coming in contact with the reader. Simple passive tags are without battery. The reader has capacity to read simple tags
frequency after coming in range of reader. To create a specific interrogation zone, fixed readers are set. The reading area
is created when tags go in and out of the interrogation zone [5].
3. NFC: Near Field Communication is a short-range high frequency wireless communication technology. It enables the
exchange of data between devices .The distance can be over about a 10 cm distance.NFC is an upgrade in the RFID
technology.NFC combines the interface of a smartcard and a reader into a single device. Sometimes it works like
Bluetooth system .The connection between two NFC devices is established at once .[23]
2. LITERATURE SURVEY
Existing Position Location application and related algorithms using RFID Technique
2.1. Received Signal Strength Indicator (RSSI)
[1] A radio frequency identification (RFID) wireless sensor- network location tracking system was implemented by the
wireless communication technology (WCT), micro-sensors and location tracking algorithm (LTA), and developed to be a
network node communication system.
Technology used: The Space Signal Attenuation Model (SAM), Received Signal Strength Indicator (RSSI), Link Quality
Indicator (LQI), and Network Node Location Calculus (NNLC) were adopted in this system. The RFID WSN system
provided real-time position for object recognition, and return the information as an object path information was recorded
on the sensors.
[2]
The
method
that
enables
self
recognition
of
a
mobile
vehicle’s
current position by utilizing ultrahigh frequency (UHF) passive radio-frequency identification (RFID) tags. It can be used
in real industry environments such as complex storage warehouses where many different goods are dispersed throughout
a wide area.
Technology used: The particular method makes use of two UHF RFID readers with identical emission configuration
attached to a vehicle to identify a reference RFID tag. By utilizing the received signal strength indicator obtained by the
readers from the reference RFID tag, the precise position of the moving vehicle can be obtained. If more than one antenna
is attached to a monostatic reader, they would function by switching to a single active antenna per each reading. Also,
readers provide estimated RSSI similar to analog-to-digital converter (ADC) output.
2.1.1 Positioning Algorithms based on RSSI
a) Lateration:. For all the mobile nodes, each anchor node measures the power received from the mobile and, by the
computing the distance, draws a circle around itself.[17] The anchor node assumes that the mobile should be at one of the
points on the circumference of the circle. If the calculus of the distances is exact, given three anchor nodes it is possible to
discover where the mobile node is by the intersection of three circles. In case of overlapping , the problem is to find
which ones are correct and which are false. In order to do this “minimum path discovery” is implemented. For each
estimated position the distance between it and the others is computed. Two equally sized sets are created. The first set
collects the estimated positions with smaller respective distances. The other set contains the remaining nodes. Comparing
the sets by the sum of the distances between them, it is possible to find the cluster containing the closest estimated
coordinates.
b) Minimum Maximum: This algorithm is very simple and robust in the presence of noisy channels. For all mobile
nodes, an anchor node measures the power received, but, in the minimum maximum case, the computation of the distance
allows a square to be drawn around the fixed node, with sides equal to twice the estimated distance. The anchor node
assumes that the mobile is within this region. The same thing happens for all other mobile nodes. By overlapping the
square regions it is possible to identify a mobile node within a rectangle whose area gets smaller as the number of anchor
nodes increases. The algorithm’s output is the centroid of this rectangle.
c) Ring Overlapping Circles: This algorithm is complex to implement than the previous two. It works on comparing the
received power from the anchor nodes. Therefore it does not need an accurate estimate of the distance among nodes.
Circles are drawn by anchor nodes around itself by measuring the received power to any other fixed node. The
circumferences of these circles are centered on an anchor node and ideally means in a channel which only contains path
Volume 3, Issue 1, January 2014
Page 371
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 3, Issue 1, January 2014
ISSN 2319 - 4847
loss, will intersect the others. In this way each anchor node draws boundaries around itself that identify different regions
of the space. By comparing the received signal strength from the mobile node (for all the mobile nodes) with the one
coming from the other anchor nodes, each anchor node can apportion the mobile node to one of these regions. The same
procedure is repeated for each anchor node. Several overlapping regions, i.e. a slice is found. The output of this algorithm
is the centroid of the region’s slice where the majority of anchor nodes have established the mobile to be..This algorithm
does not need to estimate the distances among nodes. It just compares the received powers. This is very important for the
realization of cheap sensors.
d) Modifying Minimum Maximum: If an anchor node assumes that the mobile position is represented by a square
region, instead of a circumference around itself, there is an additive estimation error. To remove errors, two solutions
are proposed: one is impracticable because of the high complexity required, but the other provides the basis for the
implementation of MINMAX+. MINMAX+ is a modified version of the MINMAX function which aims to correct the
bias.[10] .This method use both lateration and minimum maximum positioning. The new approach called Cooperative
Overlapping Circles and Minimum Maximum (COCMM) is introduced. The core idea of this method is to always use the
square region provided by the minimum maximum approach to estimate node positions, but in case of a good channel the
algorithm corrects the minimum maximum output using lateration. For a channel with low noise levels, lateration can
potentially provide good positioning estimates that should be inside the square box built by MINMAX. This square is
therefore used to select the cluster of estimated locations obtained by lateration. If the square region does not contain any
estimate computed by lateration, the output of COCMM will be the same as the one for MINMAX.. It is observed that,
the bias of both MINMAX and COCMM become smaller with an increase in the number of anchor nodes.[16]
2.2 The mathematical tool for positioning: Kalman filtering
A global localization system combining odometry data with radio frequency identification (RFID) readings is proposed
.RFID tags are placed at the ceiling of the environment and can be detected by a mobile robot unit traveling below
them.[3]
Technology used. This is possible using a suitable tag’s antenna in ultrahigh frequency band, expressly designed to obtain
regular and stable RFID detection regions, which allows us to consider an efficient Kalman filtering approach to fuse
RFID readings with the vehicle odometry data.
2.3 Combination of RSSI (technology) and kalman filtering (tool)
A system that localizes and identifies trolleys in the entry area of a distribution center is presented in .The area is
monitored by an RFID reading system (consisting of four antennae and a fixed RFID reader) and an inexpensive pair of
webcams. The trolleys are equipped with a passive RFID tag and an optical marker.[4]
Technology used: The system uses a Particle Filter to combine Received Signal Strength Indication (RSSI) data from the
RFID system and image data from the webcam and propose procedures to improve RFID and camera localization
accuracy. The resulting system achieves an accuracy of less than 30 cm in static and the most likely dynamic scenarios.[4]
2.4 Comparative study:
2.4.1 Comparison Table for Technologies:
Volume 3, Issue 1, January 2014
Page 372
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 3, Issue 1, January 2014
ISSN 2319 - 4847
SR.NO.
TECHNOLOGY
DISTANCE
MAKE USE OF
COST
ADVANTAGES
DISADVANTAGES
1.
GPS
WHOLE EARTH
SATELLITES,
TRANSMITTER,
LESS
EASY
WORKING
LIKE GSM
ACCURACY IS LESS
LESS
MORE
RECEIVER
2.
30CM TO 3KM
RFID
READERS, TAGS,
SUITABLE
FOR
BASED
ON
FREQUENCY USED
AVR (ADVANCED
VIRTUAL
RISC
CONTROLLER)
INDOOR.
FAST
DATA
TRANSFER
3.
10 TO 20 CM
NFC
NFC
ENABLED
CARDS
VERY EXPENSIVE
FAST
AS
NEWLY
INTRODUCED
TRANSFER
DATA
DISTANCE
RANGE
CANNOT BE INCREASED
2.4.2 Comparison Table for various positioning algorithms based on RSSI
NO
ALGORITHM
OUTPUT REGION
RESOUR
ROBUSTN
PERFORMA
ERROR
CE
ESS
NCE
S
LESS
MEDIUM
ADEQUATE
LESS
VERY
HIGH
GOOD
AVERAG
UTILIZA
TION
1.
LATERATION
CIRCUMFERENCE
2.
MINIMUM
MAXIMUM
CENTROID
RING
OVERLAPPING
CIRCLES
CENTROID
3.
OF
RECTANGLE
LESS
OF
THE
MORE
REGION’S SLICE WHERE
THE
MAJORITY
E
VERY
ADEQUATE
MORE
GOOD
LESS
HIGH
OF
ANCHOR NODES
4.
MODIFYING
MINIMUM
MAXIMUM
COMBINATION
LESS
LESS
3. CONCLUSION
This paper focuses on various technologies for the position location such as Global Positioning System, Radio Frequency
Identification and Near Field Communication. Also proposes the system for the position location of the person using
RFID for the indoor application for the organization using the reader, tag and AVR controller. Out of these various
technologies, the RFID technique has been chosen. RFID technology is generally used for the indoor application .Cost
involved in RFID is very less. As no. of readers increases more exact location can be found .So, accuracy is more than
that of GPS. Network can be easily established. No use of internet for the application.
Volume 3, Issue 1, January 2014
Page 373
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 3, Issue 1, January 2014
ISSN 2319 - 4847
Using Received signal strength indicator, position detection is possible. Kalman filtering is an interesting and
powerful mathematical tool to optimally and dynamically process data hampered by white Gaussian noise. Filtering also
allows one to interpret measurements coherently with mathematical formulas. It is possible using Kalman filtering to
realize tracking applications, achieving a good performance after a specific delay to achieve the best filter state.
Paper also finds that RFID technology along with RSSI and Kalman filtering is best combination for position
detection for indoor application.
REFERENCES
[1] Yeong-Lin Lai, Member and Jay Cheng,” A 2.45-GHz RFID Wireless-Sensor-Network Location Tracking System” ,
2013 IEEE 17th International Symposium on Consumer Electronics (ISCE)
[2] Emidio DiGiampaolo and Francesco Martinelli,” A Passive UHF-RFID System for the Localization of an Indoor
Autonomous Vehicle”, IEEE Trans. Ind. Electron., VOL. 59, NO. 10, October 2012
[3] Sangdo Park and Hongchul Lee,” Self-Recognition of Vehicle Position Using UHF Passive RFID Tags” , IEEE Trans.
Ind. Electron, VOL. 60, NO. 1, January 2013
[4] Andreas Parr, Robert Miesen, Fabian Kirsch, Martin Vossiek,” A Novel Method for UHF RFID Tag Tracking Based
on Acceleration Data”, 2012 IEEE International Conference on RFID
[5]Fynn Schwiegelshohn, Theresa Nick and J¨urgen G¨otze,” Localization Based on Fusion of RFID and Stereo Image
Data”, 978-1-4673-6033-3/13 2013 IEEE
[6] S. S. Saab and Z. S. Nakad, “A standalone RFID indoor positioning system using passive tags,” IEEE Trans. Ind.
Electron., vol. 58, no. 5, pp. 1961–1970, May 2011.
[7]
Shuai Shao and Robert J. Burkholder,”Passive UHF RFID Tag Localization Using Reader Antenna Spatial
Diversity”, 978-1-4673-0946-2012 IEEE
[8]
P. Dinesh, P. Kishore, Vivekanandan B. and S. Moorthi,” RFID based Navigation System forUnmanned Material
Handling Vehicles using FPGA” ,2013 International Conference on Intelligent Systems and Signal Processing (ISSP)
[9] Lun-Wu Yeha, Ming-Hsiu Hsu, Hong-Ying Huanga, Yu-Chee Tseng,“Design and implementation of a self-guided
indoor robot based on a two-tier localization” , in Pervasive and Mobile Computing , vol. 8,no. 2, pp.271-281, April
2012
[10] W. Gueaieb and M. S. Miah, “An intelligent mobile robot navigation technique using RFID technology,” IEEE
Trans. Instrum. Meas., vol. 57, no. 9, pp. 1908–1917, Sep. 2008.
[11] Yu Zhou,Wenfei Liu, Peisen Huang “Laser-activated RFID-based Indoor Localization System for Mobile Robots” ,
in Proceedings of the IEEE International Conference on Robotics and Automation, pp.4600 - 4605, 2007
[12] Byoung-Suk Choi, Joon-Woo Lee, Ju-Jang Lee “An improved localization system with RFID technology for a mobile
robot” , in Proceedings of the conference of IEEE Industrial Electronics, IECO, pp.3409 - 3413,2008
[13] C. Hekimian-Williams, B. Grant, and P. Kumar, "Accurate localization of RFID tags using phase difference",
RFID'10, IEEE, pp. 89-96, 2010.
[14] P. Vorst, S. Schneegans, Y. Bin, and A. Zell, "Self-Localization with RFID snapshots in densely tagged
environments", IROS'08, IEEE, Nice, France, pp. 1353-1358, 2008.
[15] D. Joho, C. Plagemann, and W. Burgard, "Modeling RFID signal strength and tag detection for localization and
mapping", ICRA'09, IEEE, Kobe, Japan, 2009.
[16] K. Chawla and G. Robins, "An RFID-based object localisation framework", Int. journal of radio frequency
identification technology and applications, vol. 3, pp. 2-30, 2011.
[17] Y. Zhang, M.G. Amin, and S. Kaushik, "Localization and tracking of passive RFID tags based on direction
estimation," Int. Journal of Antennas and Propagation, vol. 2007, p.9, 2007.
[18] B.S. Choi and J.J. Lee, "Mobile robot localization in indoor environment using RFID and sonar fusion system",
IROS'09, IEEE, St. Louis, USA, 2009.
Volume 3, Issue 1, January 2014
Page 374
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 3, Issue 1, January 2014
ISSN 2319 - 4847
[19] A. Milella, D. Di Paola, G. Cicirelli, and T. D'Orazio, "RFID tag bearing estimation for mobile robot localization",
ICAR'09, IEEE, Munich, Germany, pp. 1-6, 2009.
[20] A. Parr, R. Miesen, F. Kirsch, and M. Vossiek, "A novel method for UHF RFID tag tracking based on acceleration
data", RFID'12, IEEE, Orlando, USA, pp. 110-115, 2012.
[21] L. M. Ni, Y. Liu, Y. C. Lau, and A. P. Patil, "LANDMARC: Indoor Location Sensing Using Active RFID," Wireless
Networks, vol. 10, pp. 701-710, 2004.
[22] G.y. Jin, X.y. Lu, and M.S. Park, "An Indoor Localization Mechanism Using Active RFID Tag", SUTC'06, IEEE,
Newport Beach, USA, 2006
Volume 3, Issue 1, January 2014
Page 375