Error-Correcting Sequence-Based
Localization for Wireless Networks:
A New Paradigm
Location esitmate for 123745968
Bhaskar Krishnamachari
Autonomous Networks Research Group
Dept. of EE-Systems
USC Viterbi School of Engineering
http://ceng.usc.edu/~anrg
bkrishna@usc.edu
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ARO Workshop, Seattle, June 1415, 2005
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Overview
• Location information is a fundamental building block for
self-organized wireless ad-hoc and sensor networks. It is
important for
–
–
–
–
stamping sensor measurements
target tracking
topology formation
routing and querying
• Thus far, the primary focus in designing localization
algorithms has been on functionality.
• Critical challenges of fault-tolerance and security have
been largely ignored.
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Securing Localization
• Localization algorithms can be made secure and robust
in a number of complementary ways:
– developing tamper-proof hardware
– securing measurements through cryptographic algorithms
– patches to existing algorithms to address identified vulnerabilities
– developing a fundamentally new class of localization algorithms
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Thesis
• A new class of sequence-decoding localization
algorithms, with the potential to automatically detect and
correct errors introduced by the environment as well as
malicious attackers, will be a key component of future
tactical wireless networks.
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Traditional Forward Error Correction
encoder
original message
higher dimension
codeword
channel with error
decoder
corrupted
packet
“nearest”
correct codeword
received message
• FEC is at the heart of modern high-performance wireless
communication.
• A major field of research for several decades
• Latest FEC techniques (turbo codes, LDPC codes) can provide low5
error communication within 0.1 dB of theoretical Shannon limit
Error Correcting Localization
encoder
ideal signals
corrupted signals
(RSS, TDOA, AOA, etc.)
noise/environmental errors
malicious errors
decoder
codeword
corrupted
codeword
“nearest”
correct codeword
decoded
location
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Ecolocation
•
A novel RF-only sequence-based error-correcting localization technique
currently under development
•
Empirically shown to have superior performance compared to state of the
art techniques
•
“Tip of the iceberg”
Reference:
Yedavalli, Krishnamachari, Srinivasan, Ravula, “Ecolocation: A sequence based
technique for RF-only localization in wireless sensor networks,” IPSN 2005.
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Ecolocation
• Basic idea: look at the sequence indicating relative
ranking of RSSI measurements, not absolute values
• Each sequence ideally corresponds to a unique location
region
• Provides a way to decode location with high accuracy, even
given a possibly erroneous sequence.
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The Basic Algorithm
 Unknown node sends a beacon.
 Nearby reference nodes measure RSSI and send to computation
point.
 Sequence is determined and expressed as a set of ordering
constraints.
 Most likely location is computed based on this measured
sequence
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Illustration
• Sequence: ADBC
DC
B
AD
BC
D
C
A
AB
AC
DB
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Motivation
• Ordered sequence is inherently more robust to amplitude
fading fluctuations than absolute signal strengths
• Many corrupt sequences do not correspond to any valid
locations - hence error is easily detected and can be
corrected in most cases by mapping to nearest valid
sequence. Specifically, the number of feasible codeword
sequences is only O(n4) out of n! possible (corrupt)
sequences.
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Location Determination
 Consider a grid of location points in the environment
 Determine ideal sequence for a given possible location of the
unknown node
 Look at the measured sequence and compare with above to
determine number of satisfied/violated constraints
 Identify location(s) that maximizes the number of satisfied
constraints
• Optimizations: Multiresolution search/Greedy approaches
can significantly cut down on search time and computation
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An alternative approach
• Precompute regions in the location space corresponding to
feasible error-free sequences (not all possible sequences are
feasible)
• Determine the feasible sequence that “best” matches received
sequence and return the corresponding location
• Can yield a much faster solution, can also be optimized
through multi-resolution/greedy approaches
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Order Constraints
1
2
A
5
F
3
4
B
C
D
E
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Constraint Violations
RSSI as a function of distance
-90
-80
B
-70
-60
RSSI (dBm)
1
-50
-40
-30
-20
2
A
4
F
3
5
-10
C
0
0
2
6
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16.12
17.08 18
Distance (feet)
D
E
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Illustration
Location estimate for 123456789
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NO ERRONEOUS CONSTRAINTS
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Illustration
Location esitmate for 123745968
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13.9% ERRONEOUS CONSTRAINTS
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Illustration
Location estimate for 234567891
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22.2% ERRONEOUS CONSTRAINTS
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Illustration
Location estimate for 913276584
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47.2% ERRONEOUS CONSTRAINTS
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Evaluation
• Simulation Model:
– RSS samples generated using log-normal shadowing model
• Simulation Parameters:
– RF Channel Characteristics
• Path loss exponent (η)
• Standard deviation of log-normal shadowing model (σ)
– Node Deployment Parameters
•
•
•
•
Number of reference nodes (α)
Reference node density (β)
Scanning resolution (γ)
Random placement of nodes
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RF-only State of the Art
• Pattern Recognition (e.g. RADAR)
• Centroids
• Approximate Point in Triangle (APIT)
• RSSI-based Maximum Likelihood Estimation (MLE)
• RSSI-based Minimum Mean Squared Error Estimation (MMSE)
• Proximity (nearest reference, an extreme special case of
ECOLOCATION)
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Experiments with Real Measurements
• Outdoors: Parking Lot.
– Eleven MICA 2 motes placed randomly in an unobstructed 144
sq. m area
– Locations of all motes estimated and compared with true position
• Indoors: 3rd floor of EE building
– Twelve MICA 2 motes placed randomly in an obstructed 120 sq.
m area in an office building
– unknown node placed at five locations for position estimation
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Empirical Results (1)
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Empirical Results (2)
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Empirical Results (3)
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Empirical Results (4)
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Observations
• Ecolocation is self-configuring - it does not require prior
measurement of environment. It is robust and efficient in dense
settings.
• Can be easily extended to 3D environments and to incorporate other
available information (including antenna orientations, operational
area constraints)
• Most importantly, Ecolocation can also detect and mitigate induced
errors from malicious nodes. (Each adversary can forge at most n-1
constraints out of n(n-1)/2 )
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Research Agenda
• Intermediate term: develop Ecolocation
– Full, optimized testbed implementation of Ecolocation taking into
account resource constraints on energy, computation, and
communication
– Quantifying security using different adversarial models
– Theoretical analysis of gains from error correction (is there an
equivalent to coding gain in communications?)
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Research Agenda
• Long term: Develop and analyze a wide range of
sequence/codeword-based error correcting localization
algorithms suitable for different contexts:
– with other signal measurement modalities (angles, TDoA-based
ranges, etc.)
– under different density/mobility assumptions
– for network localization (multiple unknown nodes)
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Additional Thoughts
• Enable multiple “competing” solutions
• Develop a “standard suite” of benchmark problems for
comparisons
– realistic empirical traces or real common test-bed
– different environmental operating conditions (density, mobility,
resource constraints, indoor/outdoor, interference)
– different modalities (pure RF, multimodal TDoA)
– different localization requirements (single/multiple unknown
node, cooperating/non-cooperating nodes, different accuracy
and precision requirements, etc.)
– different attack models and assumptions
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