Title Indoor localization based on RF ZigBee protocol

 

Localization in indoor environments is an important aspect with respect to object tracking applications. One of the commercial mechanisms to get to oknow the accurate position of a person/object is global positioning system (GPS). However, this technique is imprecise and does not allow meter or sub-meter accuracy within buildings which is very necessary for smart houses or other wireless network sensors. Currently infrared, RF, and ultrasound signals are principal technologies used for indoor positioning system because unlike outdoor areas, the indoor environment has many challenges on localization due to the dense multi-path effect and building material. Based on the existence of radio connectivity and the attenuation of radio signal with distance, the postion of wireless devices can be estimated especially at low power consumption. The received signal strength indicators (RSSI) has drawn a lot of attention in recent literature since it can be used to estimate the distance from a transmitter to a receiver, thereby estimating the position of a wireless sensor node which is a typical application of ZigBee (IEEE 802.11) protocol standard. There are two common approaches based on RSSI: fingerprints and trilateration. Table 1 summarizes the existing indoor positioning system for ZigBee standards.

Table 1 – Parameter comparison of indoor positioning systems

System

Spacing

Positions

Samples/Pos.

APs

Orientation

Environment

RADAR [1]

Nonuniform

70

80

3

4

Hallway

Saha [2]

Min. 3.12m

19

1200

3

N/A

1-floor

Roos [3]

Uniform 2m

155

40

10

N/A

1-floor

Battiti [4]

N/A

257

N/A

6

N/A

1-floor

Ladd [5

3m

11

1370

5

2

Hallway

Prasithsangaree [6]

1.5m, 3

60

40

2-7

4

1-floor

Youssef [7]

1.5

110

300

4

N/A

Hallway

Xiang [8]

N/A

100

300

5

4

1-floor

Table 2 – Performance comparison of indoor positioning systems

System

Algorithm Type

Accuracy and Precision

RADAR [1]

Nearest Neighbor

Within 7 ft, 38%

Saha [2]

Nearest Neighbor & Neural Network

N/A accuracy, 90%

Roos [3]

Bayesian

<8.28 ft, 90%

Battiti [4]

SVM, Bayesian, Neural Network, Weighted k-Nearest Neighbor

<17 ft, 90%

Ladd [5]

Bayesian

<5 ft, 90%

Prasithsangaree [6]

Weighted k-Nearest Neighbor

25 ft at 75%, 40 ft at 95%

Youssef [7]

Bayesian

<7ft, >90%

Xiang [8]

Bayesian with RSS distribution model

<6ft, 90%

The research project will focus on the object-tracking application which is an algorithmic-software implementation written in Embedded C. The software is able to receive data messages and generate object position estimation data. It acts as an interface that collects raw data that the sensor node generates. Through the application, we will find the most suitable algorithm that can identify and track the object most efficiently.

Work description:

Theory:

  • A survey on RF indoor positioning system using ZigBee protocol
  • Study on RSSI mechanism
  • Study on ZigBee protocol
  • Object tracking algorithm

Practice:

  • Software programming to transmit and receive data
  • Apply RSSI in object tracking

 

Student prerequisites

This subject is dedicated to Vietnamese students as well as foreigner students at Master degree of Electrical Engineering or Telecommunication option. The students who have a fairly good knowledge about wireless sensor networks and C programming are privileged.

   
Supervisors/contacts

Dr. Thanh Huong Nguyen: This email address is being protected from spambots. You need JavaScript enabled to view it.

 

References

 

[1] P. Bahl and V.N. Padmanabhan, “RADAR: an in-building RF-based user location and tracking system”, INFOCOM 2000, Israel

 

[2] S. Saha, K. Chaudhuri, D. Sanghi, P. Bhagwat, “Location determination of a mobile device using IEEE 802.11b access point signals”, WCNC 2003, Los Angeles.

 

[3] T. Roos, P. Myllymaki, H. Tirri, P. Misikangas, J. Sievanen, “A probabilistic approach to WLAN user location estimation”, International Journal of Wireless Information Networks, 2002

 

[4] R. Battiti, M. Brunato, A. Villani, “Statistical learning theory for location fingerprinting in wireless LANs”, Technical report, 2002. http://rtm.science.unitn.it/~battiti/archive/86.pdf

 

[5] A.M. Ladd, K.E. Bekris, G. Marceau, A. Rudys, L.E. Kavraki, D.S. Wallach, “Robotics-based location sensing using wireless Ethernet”, MOBICOM 2002

 

[6] P. Prasithsangaree, P. Krishnamurthy, and P. K. Chrysanthis, “On indoor position location with wireless LANs”, PIMRC 2002, Portugal

 

[7] M. A. Youssef, A. Agrawala, and A. U. Shankar, “WLAN location determination via clustering and probability distributions”, PerCom 2003, Texas

 

[8] Z. Xiang, S. Song, J. Chen, H. Wang, J. Huang, and X. Gao, “A wireless LAN-based indoor positioning technology”, IBM Journal of Research and Development, 2004