An Efficient Localization Algorithm Focusing on Stop-and-Go Behavior of

Mobile Nodes

IEEE PerCom 2011

Takamasa Higuchi, Sae Fujii, Hirozumi Yamaguchi and Teruo Higashino

Graduate School of Information Science and Technology, Osaka University

1-5 Yamadaoka, Suita, Osaka 565-0871 Japan

Speaker: Wun-Cheng Li

Outline

• Introduction•Network Model•State Decision Process•Localization Interval•Protocol Design•Simulation•Conclusion

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Introduction

•Location-aware services on cell phones have spread rapidly.▫Car navigation systems▫Pedestrian navigation applications

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Introduction

•However, to provide real-time position information to people indoor is still a big challenge. ▫Exhibition patrons▫Museum visitors▫Customers at shopping malls

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Introduction

•Rely on large amounts of fixed infrastructure for positioning also requires a lot of installation and maintenance costs

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Motivation

•Not all applications require accurate location information.▫Allow a certain range of localization error

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Problem

•To accomplish acceptable accuracy of mobile nodes, frequency of position updates should be sufficiently high.

•How a certain error range enables mobile nodes to locate and reduce excessive localization frequency reduction.

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Goals

•Propose an efficient localization algorithm of mobile nodes to ▫decrease the localization overhead ▫satisfy the constraint of tolerable position error of each

sensor

8

Network Model

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Anchor Nodes

Unknown state Nodes

Moving state Nodes

Static state Nodes

Network Model

•Each mobile node is assumed to have both an ultrasound ranging device and a wireless device

•Applies a Time Difference of Arrival (TDoA) technique to measure the distance

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RF signals

ultrasound signals

10s

20s

5m, (x1 , y1)

A1 (x1, y1)A0

Network Model

•Each node Ai holds position (xi, y𝑖) and speed v𝑖

A1 (x1, y1)v1 = 0.0 m/s

A0 (x0, y0)v0 = 1.1 m/s

A5 (x5, y5)v5 = 0.0 m/s

A4 (x4, y4)v4 = 1.0 m/s

A2 (x2, y2)v2 = 0.0 m/s

A3 (x3, y3)v3 = 0.0 m/s

d1

movement d3

d5

d2

A0

measured distance

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State Decision Process

•Measured distance from Ai to a neighbor Aj is denoted as dj , and the estimated position of Aj as = (xj , yj).

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Aj (xj , yj)

±𝜖 𝑗

dj

Ai

State Decision Process

• represents the set of possible locations.

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A3 (x’3 , y’3)

movement

d3

d1 d4

d2d3

±𝜖1

±𝜖2

±𝜖 4

±𝜖3

Likelihood 2Likelihood 1

Likelihood 3

A1 (x1 , y1) A4 (x4 , y4)

A2 (x2 , y2)

A0

A3 (x3 , y3)

Localization Interval

•Speed vi of Ai is estimated by the following formula.

•𝐼𝑣(𝑣𝑖) is updated in each localization process by the following function.

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Localization Interval

•The failure of movement detection by a single neighbor can be soon recovered by other neighbors.

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A0 (x0, y0)v0 = 0.0 m/s

d’1A1

d1

A0

A0 (x0, y0)v0 = 0.0 m/s

A1

movement

A0

A2

d’1

d1

d2

d’2

movement

Protocol Design

•When a node performs localization, it broadcasts a Request To Measure (RTM) message before sending TDoA measurement signals.

•The Network Allocation Vector (NAV) is used to determines the maximum transmission time delay.

• is determined such that a node which has been delayed for longer time can have shorter backoff time using the following formula.

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Simulation

•QualNet

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PARAMETER SETTINGSEnvironmental scenarios 15m x15mAnchors 4Nodes 30Speed 4.0~8.0 km/hRTM messages maximum range 12mTDoA measurement signals maximum range 6mmax. speed (Vmax) 10.0 km/hcoefficient localization interval ( )𝑐 0.80max. int. of moving nodes ( )𝐼𝑣𝑚𝑎𝑥 3.0 sec.localization int. of static nodes ( )𝐼𝑠 5.0 sec.coefficient of backoff time (𝑎) 0.76tolerable position error (𝜎𝑝𝑚𝑎𝑥) 1.0m

Simulation

•Localization Error

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Simulation

•Tracking Error

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Simulation

•Localization Intervals

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Simulation

• Impact of Ranging Error

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Ran

ging

Err

or[m

]

Conclusions

•This paper proposed a distributed cooperative algorithm to localize mobile nodes with a small number of anchor nodes.

•Automatically adjusts localization frequency according to the estimated speed of nodes to reduce unnecessary localization attempts.

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Thank you!

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