localization for mobile sensor networks acm mobicom 2004 lingxuan hudavid evans department of...
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Localization for Mobile Sensor Networks
ACM MobiCom 2004ACM MobiCom 2004
Lingxuan Hu David Evans
Department of Computer Science
University of Virginia
Localization
• Location Awareness
• Importance– Environment monitoring– VehicleTracking– Location based routing – save significant
energy– Improve caching behavior– Security enhanced (wormhole attacks)
Determining Location
• Direct approaches– GPS
• Expensive (cost, size, energy)• Only works outdoors, on Earth
– Configured manually• Expensive• Not possible for ad hoc, mobile networks
• Indirect approaches– Small number of seed nodes
• Seeds are configured or have GPS
– Dependence on special hardware– Requirement for particular network topologies
Hop-Count Techniques
DV-HOP [Niculescu & Nath, 2003]Amorphous [Nagpal et. al, 2003]
Works well with a few, well-located seeds and regular, static node distribution. Works poorly if nodes move or are unevenly distributed.
r
1
1
2
23
3
33
4
4
4
44
5
5
6
7
8
Local Techniques
Centroid [Bulusu, Heidemann, Estrin, 2000]:Calculate center of all heard seed locations
APIT [He, et. al, Mobicom 2003]:Use triangular regionsDepend on a high density of
seeds (with long transmission ranges)
Environment considered
• Conditions– No special hardware for ranging is
available– The prior deployment of seed (beacons)
nodes is unknown– The seed density is low– The node distribution is irregular– Nodes and seeds can move
uncontrollably.
Scenarios
NASA Mars TumbleweedImage by Jeff Antol
Nodes moving, seeds stationary
Nodes and seeds moving
Nodes stationary, seeds moving
MCL: Initialization
Initialization: Node has no knowledge of its location.
L0 = { set of N random locations in the deployment area }
Node’s actual position
MCL Step: Predict
Node’s actual position
Predict: Node guesses new possible locations based on previous possible locations and maximum velocity, vmax
Prediction
Assumes node is equally likely to move in any direction with any speed between 0 and vmax.
Can adjust probability distribution if more is known.
MCL Step: Filter
Node’s actual position
Filter: Remove samples that are inconsistent with observations
Seed node: knowsand transmits location
r
Filtering
Indirect SeedIf node doesn’t hear a seed, but one of your neighbors hears it, node must be within distance (r, 2r] of that seed’s location.
Direct SeedIf node hears a seed,the node must (likely) bewith distance r ofthe seed’s location
S S
Resampling
Use prediction distribution to create enough sample points that are consistent with the observations.
Results Summary
• Effect of network parameters:– Speed of nodes and seeds– Density of nodes and seeds
• Cost Tradeoffs:– Memory v. Accuracy: Number of samples– Communication v. Accuracy: Indirect seeds
• Radio Irregularity: fairly resilient• Movement: control helps; group motion hurts
Convergence
Node density nd = 10, seed density sd = 1
The localization error converges in first 10-20 steps
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
0 5 10 15 20 25 30 35 40 45 50
Est
imate
Err
or
(r)
Time (steps)
vmax=.2 r, smax=0
vmax=r, smax=0
vmax=r, smax=r
00.20.40.60.81
1.21.41.61.82
2.22.42.62.83
0.1 0.5 1 1.5 2 2.5 3 3.5 4
Est
imate
Err
or
(r)
Seed Density
MCL
Centroid
Amorphous
Seed Density
nd = 10, vmax = smax=.2r
Better accuracy than other localization algorithms over range of seed densities
Centroid: Bulusu, Heidemann and Estrin. IEEE Personal Communications Magazine. Oct 2000.
Amorphous: Nagpal, Shrobe and Bachrach. IPSN 2003.
Radio Irregularity
nd = 10, sd = 1, vmax = smax=.2r
Insensitive to irregular radio pattern
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
0 0.1 0.2 0.3 0.4 0.5
Est
imate
Err
or
(r)
Degree of Irregularity (r varies ±dr)
MCL
Centroid
Amorphous
Future Work: Security
• Attacks on localization:– Bogus seed announcements
• Require authentication between seeds and nodes
– Bogus indirect announcements• Retransmit tokens received from seeds
– Replay, wormhole attacks• Filtering has advantages as long as you get one
legitimate announcement
• Proving node location to others
Summary
• Mobility can improve localization:– Increases uncertainty, but more observations
• Monte Carlo Localization– Maintain set of samples representing
possible locations– Filter out impossible locations based on
observations from direct and indirect seeds– Achieves accurate localization cheaply with
low seed density
THANK YOU
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