an efficient localization algorithm focusing on stop-and-go behavior of mobile nodes
DESCRIPTION
An Efficient Localization Algorithm Focusing on Stop-and-Go Behavior of Mobile Nodes. 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 - PowerPoint PPT PresentationTRANSCRIPT
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
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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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