Perpendicular Intersection: Locating Wireless Sensors with Mobile Beacon

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Perpendicular Intersection: Perpendicular Intersection: Locating Wireless Sensors Locating Wireless Sensors with Mobile Beaconwith Mobile Beacon

Zhongwen Guo, Ying Guo, Feng Hong, Xiaohui Yang, Yuan He, Yuan Feng, Yunhao Liu

Ocean University of China

Hong Kong University of Science and Technology

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Outline

Introduction Observations on RSSI Our Scheme: PI (Perpendicular Intersection) Design of Optimal Trajectory Experiments Conclusion and Future Work

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Introduction

Locating sensor nodes is a crucial issue in WSN applications. OceanSense

https://www.cse.ust.hk/~liu/Ocean http://osn.ouc.edu.cn

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Existing Approaches (1)

Range-Based Approaches TOA, TDOA, AOA

Require additional hardware support Expensive in manufactory cost and energy consumption

RSSI-based (Received Signal Strength Index) Easy to implement; Based on the log-normal shadowing model Rely on absolute RSSI values (unstable & irregular) Inaccurate due to channel noise, interference, attenuation,

reflection, and environmental dynamics

00

( ) ( ) 10 logT

dRSSI d P PL d X

d

Environment-dependent !

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Existing Approaches (2)

Range-Based Approaches Mobile-assisted

Avoid cumulative errors of coordinate calculations and unnecessary communication overhead

Still rely on absolute RSSI values

Range-Free Approaches Rely on connectivity measurements (e.g. hop-count) Accuracy and precision affected by node density and

network conditions

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More about OceanSense

Sparsely deployed restricted floating sensors Unstable wireless communications Varying network connectivity

The existing localization approaches cannot well support such a practically complex scenario.

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Design Goals

A localization approach which is easy to implement in practice which has better accuracy, especially under dynamic and

complex environments. which is time and energy efficient in locating a network of

wireless sensors.

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Observations on RSSI

The closer a node is to the signal sender, the larger RSSI value it perceives.

-15 -10 -5 0 5 10 15210

215

220

225

230

235

240

245

250

Distance Interval (m)R

SS

I

10m20m30m40m50m

Outdoor observation

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Observations on RSSI

The closer a node is to the signal sender, the larger RSSI value it perceives.

Indoor observation

-8 -6 -4 -2 0 2 4 6 8200

205

210

215

220

225

230

235

240

245

250

Distance Interval (m)R

SS

I

4m8m12m16m

A

-8

-4

-2

-1

1

2

4

8(m)

Static Sensor Node

O

Mobile Node A’ PositionTrajectory of Node A

16(m)1284

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PI (Perpendicular Intersection)

N

P1(x1,y1)

H’(x’, y’)

H’’(x’’, y’’)

(x, y)

start

stop start

stop M

Virtual Triangle (VT)

P2(x2,y2)

P3(x3,y3)

No longer absolute RSSI values!

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Theoretical Estimation Error

Given the velocity of the mobile beacon as V and the broadcast frequency as F, the distance between two beacon points is V/F.

or

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Optimal Mobile Trajectory (1)

The optimal mobile trajectory to locate a network of sensor nodes satisfies the following requirements: All the sensor nodes can be located.

The optimal trajectory consists of multiple joint VTs, which cover the entire deployment area.

It is the shortest trajectory. The mobile beacon traverses the entire area in the shortest time and

consumes the minimum energy cost.

The localization latency of a sensor node is minimized. The node should be located as soon as the mobile beacon traverses along

the two sides of the VT.

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The node is in the VT which has largest sum of RSSI values. Side length of a VT = R (transmission range)

P1

H

P2 N1

N1'

P3

P4

L

14


Trajectory length:

Localization latency:

P1

H

P2 N1

N1'

P3

P4

L

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Analysis on Overhead

Communication Cost Zero communication cost among the sensor nodes to be located. The number of beacon messages received by a sensor node when the

mobile beacon traverses one VT side is FR/V. A sensor node receives the beacon messages from at most 6 sides.

The upper bound of communication cost of a sensor node is 6FR/V . Computation Overhead

The computation overhead on a sensor node is O(FR/V). Storage Overhead

PI only needs to store at most 14 vertices with their corresponding RSSI values, which cost 70 bytes.

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Experiments (1)

A prototype system with 100 TelosB motes Various environments:

library hall, laboratory, racket court, parking lots, and the sea. The mobile beacon is also a TelosB mote. A base station is deployed to collect the localization results. PI is compared with two existing approaches

TRL [1]: a range-based approach using trilateration. (3 vertices) BI [2]: a mobile-assisted localization approach that exploits Bayesian

inference to improve the estimation accuracy. (3 vertices + 3 random)

[1] J. Hightower and G. Borriello. Location systems for ubiquitous computing. IEEE Computer, 34(8):57 – 66, August 2001.

[2] M. Sichitiu and V. Ramadurai. Localization of wireless sensor networks with a mobile beacon. Proceedings of IEEE MASS, 2004.

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Experiments (2)

Hall Experiment In a hall of our library, which is about 450m2. The side length of a VT R=15m. The moving velocity V=0.1m/s The broadcast frequency F=1time/sec.

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Experiments (3)

Hall Experiment – Group 1 14 sensor nodes are deployed randomly.

RSSI values perceived by node N6

Ave. Estimation error: PI 1.2175m BI 2.4921m TRL 3.3631m

Deployment areaBase Sation

Unknown node

A Pillar

Wall

N1

N2 N3

N4

N5

N6

N7

N8

Mobile beacon trajectory

Mobile beaconN9

N10

N11

N12

N13

N14

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Experiments (4)

Hall Experiment – Group 2 35 sensor nodes are deployed randomly.

Deployment areaBase Sation

Unknown node

A Pillar

Wall


Mobile beacon


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Experiments (5)

Laboratory Experiment In the laboratory of computer software center, a room of 324m2 with

120 computers and desks inside. People sit, stand, or walk in the room. R=9m. V=0.1m/s. F=1time/sec.

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Experiments (6)

Deployment area

Base Sation

Unknown node

Wall

N1N2 N3

N4

N5

N6

N7


Obstacle

Door

Mobile beacon

N9

N8

N12

N10

N11

Laboratory Experiment – Group 1 12 sensor nodes are deployed.


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Experiments (7)

Laboratory Experiment – Group 2 100 sensor nodes are deployed.

Deployment area

Base station

Sensor node

Wall

Obstacle

Door

Mobile beacon

Trajectory ofMobile beacon


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Experiments (8)

Racket Court Experiments R=15m. V=0.1m/s. F=1time/sec.


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Experiments (9)

Parking Lots Experiments R=15m. V=0.1m/s. F=1time/sec.

Ave. Estimation error: PI 1.0952mBI 2.2079m TRL 3.7324m

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Experiments (10)

Offshore Experiment V=1.5m/s. F=1time/sec.

0 20 40 60 80 100 1200

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40

60

80

100

120

Deployment area (m)

Dep

loym

ent a

rea

(m)

1

2

1 20

5

10

15

Node ID

Loca

tion

Err

or (

m)

PI ErrorBI ErrorTrilateration ErrorAverge PI ErrorAverge BI ErrorAverge Trilateration Error

Ave. Estimation error: PI 7.3391 m BI 7.9094 m TRL 9.1620 m

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Experiments (11)

Impact of Different Factors Evaluated with the hall experiments. V is set at 0.05m/s, 0.1m/s, 0.2m/s

and 0.4m/s, F is set at 0.5, 1, 2, and 4 times per second, respectively.

The intersection point of the two curves in the right figure represents a good setting in practice, which sets appropriate trade-off between the localization accuracy and the communication cost.

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Conclusion and Future Work

Conclusion We propose a mobile-assisted localization approach: Perpendicular

Intersection (PI). Easy to implement Accurate in dynamic and complex environments Time and energy efficient

We examine the performance of PI by implementing a prototype system.

Further Work Improve the prototype system, introducing an automatic mobile

beacon. Large-scale field tests on the OceanSense platform. Extend PI in the underwater acoustic sensor networks.

28Thanks !

Perpendicular Intersection: Locating Wireless Sensors with Mobile Beacon

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