RFID technology has been widely used for object tracking in indoor environment due to
their low cost and convenience for deployment. Existing RFID localization approaches rely
on signal strength to measure the distance between RFID reader and tags. However, because
of the environmental complexity and inferences, the measurement of distance by signal
strength is not accurate, which causes large error in localization. In this paper we
develop a novel algorithm to improve the RFID localization accuracy. Our algorithm is
based on particle swarm optimization. More importantly, we add reference tags in the
deployment, and design a parameter named Correction Factor in PSO to measure the distances
more accurately by signal strength. The result shows that compared with the previous
method without the correction factor, our proposed approach can increase the accuracy by
50%. This method has good application prospect in equipment management.
NiL., LiuY.,
LauY.C. and PatilA.,
Landmarc: Indoor location sensing using active rfid,
Wireless Networks10 (2004), 701–710. doi:
10.1023/B:WINE.0000044029.06344.dd
2.
ShangguanL., LiZ.,
YangZ., LiM.
and LiuY.,
Otrack: Order tracking for luggage in mobile rfid
systems, INFOCOM, 2013 Proceedings IEEE,
2013, pp. 3066–3074. doi: 10.1109/INFCOM.2013.6567119
3.
WuK., XiaoJ.,
YiY., GaoM.
and NiL., Fila:
Fine-grained indoor localization, INFOCOM, 2012 Proceedings
IEEE, 2012, pp. 2210–2218. doi:
10.1109/INFCOM.2012.6195606
4.
StoleruR., HeT.
and StankovicJ.,
Walking gps: A pratical solution for localization in manually deployed
wireless sensor networks, Local Computer Networks, 2004 29th
Annual IEEE International Conference on, 2004, pp.
480–489. doi: 10.1109/LCN.2004.136
5.
WangY., JiaX.
and LeeH.K.,
An indoors wireless positioning system based on wireless local area
network infrastructure, 6th Int Symp on Satellite Navigation
Technology Including Mobile Positioning & Location Services,
54.
6.
OuyangR., WongA.-S. and WooK.T., Gps
localization accuracy improvement by fusing terrestrial toa
measurements, Communications (ICC), 2010 IEEE International
Conference on, 2010, pp. 1–5. doi:
10.1109/ICC.2010.5502809
7.
NikitinP., MartinezR., RamamurthyS., LelandH., SpiessG. and RaoK.,
Phase based spatial identification of uhf rfid tags,
RFID, 2010 IEEE International Conference on,
2010, pp. 102–109. doi: 10.1109/RFID.2010.5467253
8.
Hekimian-WilliamsC.,
GrantB., LiuX.,
ZhangZ. and KumarP.,
Accurate localization of rfid tags using phase
difference, RFID, 2010 IEEE International Conference on,
2010, pp. 89–96. doi: 10.1109/RFID.2010.5467268
9.
HightowerJ., WantR.
and BorrielloG.,
Spoton: An indoor 3d location sensing technology based on rf signal
strength, University of Washi-ngton, Department and of Computer Science and
Engineering.
10.
PriyanthaN.B.,
ChakrabortyA. and
BalakrishnanH.,
The cricket locationsupport system, Proceedings
of 6th ACM Tnterna-tional Conference on Mobile Camputing and Networking (ACM MOB-ICOM)
8, 2000, pp. 32–43. doi: 10.1145/345910.345917
11.
BahlP. and PadmanabhanV.N.,
Radar: An in-buil-ding rf-based user location and tracking
system, INFOCOM 2000 Nineteenth Annual Joint Conference of the
IEEE Computer and Communications Societies Proceedings IEEE 2,
2000, pp. 775–784. doi: 10.1109/INFCOM.2000.832252
12.
RobertsonP., AngermannM. and
KhiderM.,
Improvingsimultaneous localization and mapping for pedestrian navigation
and automatic maping of buildings by using online human-based feature
labeling, Position Location and Navigation Symposium (PLANS),
2010 IEEE/ION, 2010, pp. 365–374.
doi: 10.1109/PLANS.2010.5507304
13.
LiuT., YangL.,
LinQ., GuoY.
and LiuY.,
Anchor-free backscatter positioning for rfid tags with high
accuracy, INFOCOM, 2014 Proceedings IEEE,
2014, pp. 379–387. doi: 10.1109/INFOCOM.2014.6847960
14.
PintoA., MoreiraA. and CostaP., A
localization method based on map-matching and particle swarm
optimization, Journal of Intelligent & Robotic
Systems77 (2015), 313–326. doi:
10.1007/s10846-013-0009-2
15.
WongC.J.Y., WK. and
LinQ.Z.,
Error detection in arithmetic coding with artificial
markers, Computers & Mathematics with Applications62 (2011), 359–366. doi:
10.1016/j.camwa.2011.05.017
16.
LiuY., YangJ.,
MengQ., LvZ.,
SongZ. and GaoZ.,
Stereoscopic image quality assessment method based on binocular
combination saliency model, Signal Processing125 (2016),
237–248.
17.
YangJ., XuR.,
LvZ. and SongH.,
Analysis of camera arrays applicable to the internet of
things, Sensors16(3) (2016),
421.
18.
JiangD., YingX.,
HanY. and LvZ.,
Collaborative multi-hop routing in cognitive wireless
networks, Wireless Personal Communications86(2) (2016),
901–923.
19.
YangJ., LinY.,
GaoZ., LvZ.,
WeiW. and SongH.,
Quality index for stereoscopic images by separately evaluating adding and
subtracting, PloS One10(12) (2015),
e0145800.
20.
YangJ., ZhouJ.,
LvZ., WeiW.
and SongH., A
real-time monitoring system of industry carbon monoxide based on wireless sensor
networks, Sensors15(11) (2015),
29535–29546.
21.
LinY., YangJ.,
LvZ., WeiW.
and SongH., A
self-assessment stereo capture model applicable to the internet of
things, Sensors15(8) (2015),
20925–20944.
22.
YangJ., ChenB.,
ZhouJ. and LvZ.,
A low-power and portable biomedical device for respiratory monitoring
with a stable power source, Sensors15(8) (2015),
19618–19632.
23.
JiangD., XuZ.
and LvZ., A
multicast delivery approach with minimum energy consumption for wireless multi-hop
networks, Telecommunication Systems
(2015), 1–12.
24.
LvZ., HalawaniA., FengS.,
LiH. and RéhmanS.U.,
Multimodal hand and foot gesture interaction for handheld
devices, ACM Transactions on Multimedia Computing,
Communications, and Applications (TOMM)11(1s) (2014),
10.
25.
LiuY., YangZ.,
WangX. and JianL.,
Location, localization, and localizability,
Journal of Computer Science and Technology25 (2010), 274–297. doi:
10.1007/s11390-010-9324-2
26.
XiaoJ., WuK.,
YiY. and NiL.,
Fifs: Fine-grained indoor fingerprinting system,
Computer Communications and Networks (ICCCN), 2012 21st International Conference
on, 2012, pp. 1–7. doi: 10.1109/ICCCN.2012.6289200
27.
LinQ., ZhuQ.,
HuangP., ChenJ.,
MingZ. and YuJ.,
A novel hybrid multiobjective immune algorithm with adaptive differential
evolution, Computers & Operations Research62 (2015),
95–111.
28.
DuL.W.X., HZ. and
ZhuY.,
Combining quantum-behaved pso and k2 algorithm for enhance gene network
construction, Current Bionfinormatics8 (2013), 133–137. doi:
10.2174/157489313804871515
29.
LiJ.Q. and HeS.Q.,
An intelligent wire less sensor networks system with multiple servers
communication, International Journal of Distributed Sensor
Networks7 (2015), 1–9. doi: 10.1155/2015-960173
30.
GopakumarA. and
JacobL.,
Localization in wireless sensor networks using particle swarm
optimization, IET Conference on Wireless, Mobile and Multimedia
Networks, 2008, pp. 227–230. doi:
10.1049/cp:20080185
31.
WangJ. and KatabiD.,
Dude, where’s my card?: Rfid positioning that works with multipath and
non-line of sight, ACM SIGCOMM Computer Communication
Review43 (2013), 51–62. doi:
10.1145/2534169.2486029
32.
ZhuZ., XiaoJ.,
LiJ.-Q., WangF.
and ZhangQ.,
Global path planning of wheeled robots using multi-objective memetic
algorithms, Integrated Computer-Aided Engineering22(4) (2015),
387–404.
33.
LinC.J.Y. and ZQ.,
A novel micro-population immune multiobjective optimization
algorithm, Computers & Operations Research40 (2013), 1590–1601. doi:
10.1016/j.cor.2011.11.011
34.
ZhangD., MaJ.,
ChenQ. and NiL.,
An rf-based system for tracking transceiver-free objects,
Pervasive Computing and Communications, 2007 PerCom ’07 Fifth Annual IEEE
International Conference on, 2007, pp.
135–144. doi: 10.1109/PERCOM.2007.8
35.
YangL., ChenY.,
LiX.-Y., XiaoC.,
LiM. and LiuY.,
Tagoram: Real-time tracking of mobile rfid tags to high precision using
cots devices, Proceedings of the 20th Annual International
Conference on Mobile Computing and Networking ACM, 2014, pp.
237–248. doi: 10.1145/2639108.2639111
