1 mobile-assisted localization in wireless sensor networks nissanka b.priyantha, hari balakrishnan,...
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Mobile-Assisted Localization in Wireless Sensor Networks
Nissanka B.Priyantha, Hari Balakrishnan, Eric D. Demaine, Seth Teller
IEEE INFOCOM 2005 March
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Outline Introduction MAL: Mobile-Assisted Localization
Distance Measurement Movement strategy
AFL: Anchor-free Localization Proposed in 2003 April
Simulation Conclusion
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Introduction Knowledge of sensor location enable
1. Sensed data useful to application2. Efficient routing protocol
Node location information is also useful in indoor environment
Reference node (known location)
Mobile device(like PDA)
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Introduction cont. How reference node know location
Manually configuring Cumbersome and error-prone
Automatically localize1. Each node obtain distance to neighbor2. Computes a coordinate assignment for all node
This paper shows that use mobile device to measure pairwise node distance Then fed the distance information into AFL
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Indoor Localization Problems Obsturction occlude line-of-sight
EX. hard to obtain distance between nodes placed inside and outside a room
The lack of omni-directional ranging hardware Directional antenna usually pointed toward to user
Sparse node deployment Hard to obtain a rigid structure (necessary to obtai
n a unique solution) Geometric dilution of precision (GDOP)
Incur large error in its position estimate
1 2
43
2,31,4
1
42,3
Globally rigid
A graph is globally rigid if it has a unique embedding
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GDOP
nodeThe intersection point
is not precise
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GDOP
nodeThe intersection point
is not precise
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MAL: three distance measurement approach1. Measure distance between two node at a
time
2. Measure distance between three nodes at a time
3. Measure distance between four or more nodes at a time
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Measure distances among two nodes
Assumption:At least need 3 mobile position
no,n1,m0,m1,m2 are coplanar
m0,m1,m2 are collinearUse a larger number of points would reduce GDOP
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Measure distances among three nodes
Assumption:
At least need 6 mobile position
no,n1,n2 are non-collinear
m0~m5 are coplanar no three of which are collinear
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Measure distances among four or more nodes Require at least [(3j-5)/(j-3)] mobile
position Ex. J=4 then [(3j-5)/(j-3)] = 7
At least need 7 mobile position
Assumption n1,n2,n3,n4,m1,m2,m3,m4,m5,m6,m7 No four of which are coplanar
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Movement Strategy: use 3 nodes at a time
Location 1
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Movement Strategy
Location 2
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Movement Strategy
Location 3
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Movement Strategy
Location 4
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Movement Strategy
Location 5
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Movement Strategy
Location 6
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Movement StrategyMoving around the (perimeter of )visibility region of the node
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Movement Strategy
Find node L
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Movement Strategy
Location 1
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Movement Strategy
Location 2
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Movement Strategy
Location 3
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Movement Strategy
Location 4
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Movement Strategy
Location 5
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Movement Strategy
Location 6
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Movement Strategy
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Movement StrategyMoving around the visibility region of the node
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Distance information graph
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AFL: Anchor-Free Localization2003 April
Coordinate of nodei
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AFL: Anchor-Free LocalizationSelect n1 to maximize h0,1
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AFL: Anchor-Free Localization
Select n2 that has the maximize hop count from n1
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AFL: Anchor-Free Localization
Find n3 that has the equal hop count to n1 and n2
n3 maximize h1,3 + h2,3
3 hop
3 ho
p
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AFL: Anchor-Free Localization
Find n4 that has the equal hop count to n1 and n2 and have max hop count to n3
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AFL: Anchor-Free Localization
Select n5 to minimize |h1,5- h2,5|Pick the node that minimizes |h3,5 - h4,5|
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AFL: Anchor-Free Localization Coordinate of nodei
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AFL: Anchor-Free Localization
True position AFL Phase1a(X1,Y1)
b(X2,Y2)
Eab= || dm(a,b) – (X1-X1)2+(Y2-Y2)2 ||2
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AFL: Phase 2
Measure distance
“Compute” distance
Node i Node i’s neighbor
AFL use a optimization algorithm to minimize the Graph Error
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Didn’t know the one hop range and obstruction effect
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Performance Evaluation
Inter-node distance estimate error %
CD
F o
f the
% e
dge
erro
r
Cause byphysical obstacle such as furniture
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Coordinates obtained after running AFL
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MAL Performance
Radius larger reduce more GDOP error
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MAL PerformanceEdge
length error
We can obtain a large number of mobile distance estimates to reduce the distance error
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Conclusion We evaluate the algorithm using real-
world experiment
The average distance error is less than 1.5%
With sufficient distance samples, AFL can produces coordinate assignment
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Mobile-assisted A mobile collecting distance information betw
een nodes and itself
The challenge is to design movement strategies To collect sufficient distance
The pairwise node distances then be fed into a localization algorithm Anchor-Free Localization (AFL) Algorithm