Department of Computer Science and Information Engineering

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INDOOR LOCALIZATION SYSTEM USING RSSI MEASUREMENT OF WIRELESS

SENSOR NETWORK BASED ON ZIGBEE STANDARD

Department of Computer Science and Information Engineering National Cheng Kung University, Taiwan R.O.C.

Authors： Masashi Sugano, Tomonori Kawazoe, Yoshikazu Ohta, and Masayuki Murata

Publisher： Wireless Sensor Networks 2006

Present： Yu-Tso Chen

Date： March, 25, 2009

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Outline

1. Introduction 2. Localization System Model 3. Effective Data Collection 4. Experiment Setup and Results 5. Conclusions and Future Works

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Introduction

Sensing data are meaningless if the sensor location is unknown.

RSSI has a larger variation because it is subject to the deleterious effects of fading or shadowing.• RSSI-based approach therefore needs more data than other

methods to achieve higher accuracy.

Collecting a amount of data causes an increase in traffic & in the energy consumption.• We devised a data-collection technique in which sensors

recognize the number of surrounding sensors.

Localization System Model

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Sensor Node Placement

There is two ways in which a sensor node can learn its position.

• Sensor node’s position in the sink node’s database• Can’t handle large number of randomly placed

sensor nodes.

• Places a few beacon nodes that know their own positions

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Data Collection

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1. Measure the power of the packet and transform the RSSI into Distance

Position Estimation Calculation at the Sink Node

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2. Sensors send the following data to sink nodeSensor ID, Target ID, Packet Number, and Sensor-to-Target Distance

3. Use a maximum-likelihood (ML) estimation to estimate the position of a target

4. ML estimation of a target’s position can be obtained using the Minimum Mean Square Error (MMSE) [18]

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2 2 2

2 2 2

2 2 2

( ) ( ) ( )

( ) ( ) ( )

( ) ( ) ( )

AP A p A p

BP B p B p

CP C p C p

d x x y y

d x x y y

d x x y y

AB

C

P

Position Estimation Calculation at the Sink Node

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Position Estimation Calculation at the Sink Node

Effective Data Collection

A user can decide the number of data to collect based on prior knowledge.• Targets can inform sensors of the number of data

by sending packets

Sensor nodes send data depends on the deployment density of sensor nodes itself and the distance between the sensor node and the target.

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R is the communication range and Mi is the number of sensor nodes.

Define the number of data required by the system as Z

Sensor node i sends data if the measured distance is less than Di Di depends on the density around sensor node i

Effective Data Collection (cont.)

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2iMDensityR

2 2i

i

M Z

R D i

i

ZD R

M

Implementation of Localization System

We set the threshold value of RSSI in each sensor node

Sensor node decides to transmit a packet to a sink node only when the received signal from a target exceeds this value• We can change the number of data to collect by

changing this threshold value

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Position Estimation Procedure

1. Sensor nodes’ positions are stored in a database on a PC. • The RSSI threshold is set in sensor nodes.

2. A measurement demand message is broadcast to sensor nodes from a target.

3. Sensor node measures RSSI, if exceeds the preset threshold value, a sensor node transmits the target ID and sequence number to the sink node

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Position Estimation Procedure (cont.)

4. Sink node collects the ID and sequence number of the target, and the ID and RSSI of each sensor node.

If three or more RSSI values with the same target ID and sequence number are collected, the target’s position can be estimated.

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Experimental Results

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Positions of sensor nodes and targets in the conference room

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Predicted & Actually Obtained Data Collection Numbers

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Conclusions & Future Works

Density of sensor nodes was set to 0.27 nodes/m2 the position estimation error could be reduced to 1.5-2 m.

The collected numbers of data could be controlled by changing the RSSI threshold.