INDOOR WIRELESS LOCALISATION NETWORK USING A

MOBILE PHONE INTERFACE

Matthew D’Souza

Autonomous Systems Laboratory, CSIRO ICT Centre, Brisbane, Australia

Montserrat Ros

School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong, Australia

Adam Postula

School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia

Keywords: Indoor Localisation, Wireless Sensor Network, Mobile Phone.

Abstract: In recent years, indoor localisation and movement tracking of people and objects has generated interest for a

variety of applications ranging from transport to health care. We present a localisation network designed to

track people in an indoor environment. The localisation network consists of static nodes placed at

predetermined locations in a building. Users carry a mobile node to track their current position. The mobile

node has onboard motion sensors to detect a person’s heading direction and motion state. A dynamic

tracking mode was used to determine a person’s position. The dynamic tracking model was implemented

using a Multi-Hypothesis Estimation algorithm. The dynamic tracking model determines the mobile node’s

position by using the mobile node’s proximity to static nodes, mobile node’s motion sensor information and

the building’s floor-plan. We found that by using a multi-hypothesis estimation algorithm, robust

localisation accuracy, could be achieved in real-time. The position resolution of the localisation network

was found to have a maximum error between 1m and 3.5m. Further work involves extensive testing the

localisation network with multiple mobile nodes and over a larger test region. Other areas involve

investigating how multiple mobile nodes placed on a user can be used to improve the estimate of the user’s

position.

1 INTRODUCTION

Tracking the position of people or objects has uses

for a wide range of applications in transport

management, agriculture and health domains. Our

paper presents a wireless localisation network that

uses inertial sensors to track a person's position in an

indoor environment. The localisation network also

used a dynamic tracking model based on a Multi-

Hypthosis Estimation algorithm. The dynamic

tracking model allowed the localisation network to

achieve robust localisation accuracy with low cost

inertial sensors and radio transceivers.

The localisation network consists of static nodes

placed at predetermined positions throughout a

building. The user carries a mobile node which

localises their position. The static nodes are used to

determine the presence of the user within a

particular region of a building. The user’s position is

determined by the dynamic tracking model. The

dynamic tracking model uses the mobile node’s

movement information (heading), proximity

information from the nearest static node and

building’s floor-plan to track the mobile node. The

user can also view their current position using a

mobile phone.

Our localisation network architecture is based on

the localisation network by Klingbeil et al (Klingbeil

and Wark, 2008). One of the disadvantages of that

Localisation Network was the use of power

consuming sensors in their mobile node, such as a

magnetometer to detect a user’s heading with respect

D’Souza M., Ros M. and Postula A. (2009).

INDOOR WIRELESS LOCALISATION NETWORK USING A MOBILE PHONE INTERFACE.

In Proceedings of the International Conference on Wireless Information Networks and Systems, pages 45-50

DOI: 10.5220/0002261200450050

 SciTePress

Figure 1: Localisation Network Overview.

to magnetic north. Another disadvantage was the use

of a relatively large mobile node, which was found

to be too cumbersome to attach to people.

Our localisation network uses the Fleck Nano

wireless sensor platform to implement the mobile

node and the Fleck

-3 wireless platform for the

static nodes. The Fleck Nano platform was designed

to be a small, inexpensive wireless sensor with

minimal computation resources that can be used to

complement other sensor platforms.

The contributions of this paper can be

summarised as:

• Implementation of a localisation network for

position tracking.

• Experimental use of small, unobtrusive,

wireless sensors for position and motion

monitoring.

• Use of a Multi-Hypthesis Estimation based

dynamic position tracking model.

• Development of a mobile phone user interface.

This paper is organized into 6 sections. Section 2

presents a review of related work. Section 3

discusses the implementation of the localisation

network. Section 4 presents the dynamic position

tracking model. Section 5 discusses the testing

conducted. Conclusions and futher areas of

investigation are discussed in Section 6.

2 RELATED WORK

Different types of wireless technologies, such as

GPS have been investigated for outdoor and indoor

location systems. Unfortunately, GPS is not suitable

for indoor use and this has led to research into the

use of other wireless technologies including UWB

(Schwarz et al., 2005), ultrasonic and GSM (Otsason

et al., 2005) platforms.

Lamarca et al (Hightower et al., 2006, LaMarca

et al., 2005) describes the Placelab geophysical

location system which localises users in an urban

environment. The Placelab system uses the Received

Signal Strength Indicators (RSSI) from Wireless

LAN hotspots and GSM broadcast towers to

determine a user's position. The Placelab software

uses a database of known wireless LAN hotspots

and GSM broadcast towers. The Placelab software

can be used with a PDA or laptop with wireless

LAN or GSM connectivity. Localisation accuracy is

stated as being less then GPS, with 20-25m using

wireless LAN and 100 to 150m for GSM broadcast

towers.

A classical case of using wireless beacons for

navigation is the Active Badge project, presented in

(Want et al., 1992). The Active Badge project

achieved a 5-10m accuracy using infrared. The

main drawback of this platform is that it required

line of sight between beacons.

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An extension of the Active Badge project was

the ORL location system by (Ward et al., 1997)

which developed a prototype network of ultrasonic

beacons to perform real-time tracking of tagged

mobile devices in an office environment. Other

ultrasonic location systems such as the Cricket Mote

(Priyantha et al., 2000).

3 LOCALISATION NETWORK

An overview of the localisation network, seen in

Figure 1, consisted of static nodes placed at

predetermined positions on a building level. The

mobile node is carried by a user, which localises

their current position. The static nodes are used to

determine a mobile node’s regional position and

general heading. The base node displays the current

position of the mobile nodes. This section describes

an overview of the network topology and also

describes the implementation of the localisation

network.

3.1 Base Node

The base node was implemented using the Fleck

-3

wireless sensor platform (Corke et al., 2007). This

platform has been used for a variety of wireless

sensor applications particularly for environmental

monitoring (Corke et al., 2007). The Fleck

-3 uses

the Atmega128 micro-controller along with the

Nordic NRF905 radio transceiver operating in the

ISM band. The Fleck

-3 also incorporates a real-

time clock chip to reduce micro-controller overheads

for timing operations.

Figure 2: Fleck Nano Packet Delivery Ratio (%) vs

Antenna Angular Direction.

An operating system called Fleck Operating

System (FOS) was used to provide a priority-based,

non-preemptive (cooperative) threading

environment. This has the advantage of a simple

concurrent programming model. All application

software on the static and base nodes ran on top of

FOS. The static node can be seen in Figure 1.

The base node is connected via a serial

connection to a Personal Computer (PC). The PC

implements the dynamic position tracking model

used to track the location of the mobile node. The

model displays a building floor-plan with the current

position of the mobile node.

3.2 Static Node

The Static Node was also implemented using the

Fleck

-3 platform. The primary function of the

static node is to compute the packet delivery ratio of

nearby mobile nodes. The packet deliver ratio is

calculated by counting the number of messages

received from the mobile node. Once the mobile

node’s current packet delivery ratio has been

calculated, it forwards this and the mobile node’s

accelerometer data to the base node.

The static nodes are connected to the base node

via a wireless multi-hop network. The wireless

multi-hop network employs the Link Quality Multi-

Hop Network Routing communication protocol

(Stephen et al., 2008). The advantage of using a

wireless multi-hop network is that static nodes only

have to be within range of at least one other static

node. This allows static and base nodes to be easily

deployed in an indoor environment. Each static node

will relay a received message to either the base

station or the nearest static node neighbour.

3.3 Mobile Node

The Fleck Nano platform was used to implement the

mobile node. It consists of a Nordic 915Mhz RF

transceiver, onboard microcontroller, a 3-axis

accelerometer and 2-axis gyroscope for motion

detection. Figure 1 shows the Fleck Nano as a

mobile node. It uses a coin cell battery as a power

source. The dimensions are 25mm x 20mm. The

Fleck Nano’s small physical profile and onboard

accelerometer is advantageous for our application

because it is unobtrusive. The range of the Fleck

Nano’s RF transceiver’s range was limited to

approximately 7m by setting the lowest transmission

power level. This allows improves the localisation

resolution of the mobile node.

INDOOR WIRELESS LOCALISATION NETWORK USING A MOBILE PHONE INTERFACE

Figure 3: Mobile Phone User Interface Application.

The packet delivery ratio was used to estimate

the link quality of the RF Channel because the Fleck

Nano’s Nordic RF transceiver does not detect the

RSSI of intercepted transmissions. Packet delivery

ratio is a measure of many packets was received

over a wireless link. The directional sensitivity of

the Fleck Nano was measured in terms of antenna

angular direction and packet delivery ratio. Figure 2

shows the angular direction sensitivity of the Fleck

Nano’s antenna, which was tested by rotating the

Fleck Nano away from a static node. The Fleck

Nano has a high packet delivery ratio in its forward

direction (0 degrees) but a poor packet delivery ratio

in its reverse direction (180 degrees).

The two inertial sensors integrated with the

monitoring node were an accelerometer and a

gyroscope. Accelerometers measure the acceleration

force caused by walking. The accelerometer used on

the mobile node was the SCA3100 3-axis

accelerometer from VTI Technologies (VTI

Technologies). The SCA3100 has a sensitivity of

20mg and a maximum range of ±2g. The dynamic

position tracking model used the accelerometer

information to determine if the mobile node was

moving.

The gyroscope was used to determine the

heading of the user by measuring how much the user

has turned horizontally. Gyroscopes measure the

angular velocity of a moving object. The angular

velocity can be used to measure the directional

heading the object is moving in. The 2-axis

gyroscope module used was the IDG-300

(InvenSense) from Invensense. The IDG-300 has a

maximum range of ±500°/s.

Figure 4: Graphical User Display showing the Floor-Plan.

Red Circles represent Static Nodes, Green Dots represent

position estimates, Blue Dots high light nearest Static Node and

Red line illustrates path of Mobile Node.

3.3.1 Mobile Phone User Interface

A Nokia N96 Mobile Phone can be used to view the

user’s current position. The Mobile Phone

communicates via a Wireless LAN connection to the

base node’s PC as seen in Figure 1. The mobile

phone interface is shown in Figure 3. The mobile

phone application dynamically updates the display

using the coordinates calculated by the mobile node.

The application was implemented using Python

scripting.

4 DYNAMIC POSITION

TRACKING MODEL

The Dynamic Position Tracking model used a Monte

Carlo Multi-hypothesis estimation algorithm or

Particle Filtering. The model runs on a PC and

positions are updated every time a packet is received

from a mobile node. Figure 4 shows the graphical

user interface, which displays the current position of

a mobile node on the building’s floor-plan. The

model computes the mobile node’s position by

combining three key pieces of information:

• Proximity of static nodes determined by the

mobile node’s packet delivery ratio.

• Motion and heading information derived from

the onboard inertial sensors.

• Position of the mobile node on the floor-plan.

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Figure 5: Separation Distance between Mobile and Static Nodes (m) vs Packet Delivery Ratio (%).

As shown in Figure 4, each green dot or particle

represents a random estimate of the mobile node’s

position. The red circles represent the static nodes.

The blue dots highlight the static node within range

of the mobile node. The mobile node’s path is

shown by the red line. The dynamic tracking model

uses the static-mobile node proximity and the mobile

node’s motion information (acceleration movement

and angular heading) to estimate the next position of

each particle (green dot). The position of the mobile

node is computed by averaging the coordinates of

the particles’ positions.

4.1 Proximity Estimation

The proximity of the mobile node to static nodes is

determined by the packet delivery ratio. Figure 5

shows the linear relation between the packet delivery

ratio and the separation distance between the static

node and sequence number. The separation distance

between the static and mobile node is used by the

dynamic tracking model to estimate a region where

the mobile node is likely to be situated.

4.2 Heading Estimation

The packet delivery ratio is also used to estimate the

heading of the mobile node. Figure 2 shows the

relation between the packet delivery ratio and the

mobile node’s antenna angular direction. The

mobile node’s onboard motion accelerometer and

gyroscope sensors are also used to determine it’s

heading. The acceleration sensor is used to

determine if the mobile node is moving and the

gyroscope is used to estimate the direction the user

has turned. The gyroscope can be used to detect

sudden changes in heading that cannot be detected

using the packet delivery ratio and antenna angular

direction relation.

4.3 Floor-Plan Map

The floor-plan map was used by the dynamic

tracking model to ensure that the mobile node’s

estimated position was valid. Validity is determined

by checking if the mobile node has to transverse

through a wall or barrier, in-order to move to its

predicted position. If this is found to be the case,

then the dynamic tracking model will then re-

estimate the mobile node’s position until it

determines that the node’s position is in a valid

location.

5 EVALUATION

Initial testing of the localisation network involved

placing static nodes in the level of a building and

having a user walk around while carrying a mobile

node. Testing was also performed using a robot that

moved at a constant velocity. Initial test showed that

the static nodes should be placed 5m apart provide

the most optimal coverage for the mobile nodes’

packet delivery ratio to be reliably estimated and for

the mobile node to relay its onboard motion data to

the base node via the static nodes. The position

resolution of the localisation network was found to

have a maximum error between 1m and 3.5m.

6 CONCLUSIONS AND FURTHER

WORK

In this paper we presented a localisation network

that tracks people in an indoor environment. The

localisation network consisted of static nodes placed

at known positions in a building. Users carry a

mobile node to track their current position. The

INDOOR WIRELESS LOCALISATION NETWORK USING A MOBILE PHONE INTERFACE

static nodes communicated with a base node that

was connected to a PC. The dynamic tracking

model, implemented on the PC, determined a user’s

position based on a Multi-Hypothesis Estimation

algorithm.

The base and static nodes were implemented

using the Fleck

-3 platform. Our localisation

network uses the Fleck Nano platform for mobile

inertial sensing. The Fleck Nano platform is ideal for

our purposes because it has an onboard integrated

microcontroller and wireless transceiver, an

accelerometer for inertial sensing and also has a

small form factor. The user was also able to use a

mobile phone to view their current position. The

mobile phone was connected via a WLAN link to

the base node’s PC.

We also developed a dynamic position tracking

model for improving the localisation tracking of the

mobile node. The model used a Monte Carlo Multi-

hypothesis estimation algorithm. The model

determines the mobile node’s position by combining

the mobile node’s proximity to static nodes, heading

from onboard gyroscope and antenna angular

position and the building’s floor-plan. We found that

by using multi-hypothesis estimation we could

achieve robust localisation accuracy, in realtime.

The position resolution of the localisation network

was found to have a maximum error between 1m

and 3.5m.

Further work involves extensive testing the

localisation network with multiple mobile nodes and

over a larger test region. Other areas of investigation

involve looking at how multiple mobile nodes

placed on a person, can be used to improve the

estimate of the person’s position. We will also

investigate the use of the motion sensitive mobile

phones for estimating a person’s position.

ACKNOWLEDGEMENTS

The authors acknowledge the support provided to

this project by the Urban Interfaces Project,

Australasian CRC for Interaction Design (ACID).

REFERENCES

Corke, P., Valencia, P., Sikka, P., Wark, T. & Overs, L.

(2007) Long-duration solar-powered wireless sensor

networks. Proceedings of the 4th workshop on

Embedded networked sensors. Cork, Ireland, ACM.

Hightower, J., LaMarca, A. & Smith, I. E. (2006) Practical

Lessons from Place Lab. Pervasive Computing, IEEE,

5, 32-39.

InvenSense IDG-300 Integrated Dual-Axis Gyroscope,

2009,

http://www.invensense.com/products/idg_300.html.

Klingbeil, L. & Wark, T. (2008) A Wireless Sensor

Network for Real-Time Indoor Localisation and

Motion Monitoring. Proceedings of the 7th

international conference on Information processing in

sensor networks. IEEE Computer Society.

LaMarca, A., Chawathe, Y., Consolvo, S., Hightower, J.,

Smith, I., Scott, J., Sohn, T., Howard, J., Hughes, J.,

Potter, F., Tabert, J., Powledge, P., Borriello, G. &

Schilit, B. (2005) Place Lab: device positioning using

radio beacons in the wild. Pervasive Computing. Third

International Conference, PERVASIVE 2005.

Proceedings (Lecture Notes in Computer Science Vol.

3468). Springer-Verlag. 2005, 116-33.

Otsason, V., Varshavsky, A., LaMarca, A. & de Lara, E.

(2005) Accurate GSM indoor localization. UbiComp

2005: Ubiquitous Computing 7th International

Conference, UbiComp 2005. Proceedings (Lecture

Notes in Computer Science Vol. 3660) . Springer-

Verlag. 2005, 141-58.

Priyantha, N. B., Chakraborty, A. & Balakrishnan, H.

(2000) The Cricket location-support system. MobiCom

2000. Proceedings of the Sixth Annual International

Conference on Mobile Computing and Networking.

ACM. 2000, 32-43.

Schwarz, V., Huber, A. & Tuchler, M. (2005) Accuracy of

a commercial UWB 3D location/tracking system and

its impact on LT application scenarios.

Stephen, R., Wen, H. & Peter, C. (2008) An empirical

study of data collection protocols for wireless sensor

networks. Proceedings of the workshop on Real-world

wireless sensor networks. Glasgow, Scotland, ACM.

VTI Technologies Data Sheet: SCA3100-D04 3-Axis High

Performance Accelerometer With Digital SPI

Interface, 2009, http://www.vti.fi/midcom-

serveattachmentguid-

15dea0383c2711ddbf604fe1cb479fb09fb0/sca3100-

d04_accelerometer_datasheet_82_688_00_d.pdf.

Want, R., Hopper, A., Falcao, V. & Gibbons, J. (1992)

The active badge location system. ACM Transactions

on Information Systems, 10, 91-102.

Ward, A., Jones, A. & Hopper, A. (1997) A new location

technique for the active office. Personal

Communications, IEEE [see also IEEE Wireless

Communications], 4, 42-47.

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