Error-Correcting Sequence-Based Localization for Wireless Networks

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Error-Correcting Sequence-Based Localization for
Wireless NetworksA New Paradigm

• Bhaskar Krishnamachari
• Autonomous Networks Research Group
• Dept. of EE-Systems
• USC Viterbi School of Engineering
• http//ceng.usc.edu/anrg
• bkrishna_at_usc.edu

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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

• 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 low-error communication within 0.1 dB
  of theoretical Shannon limit

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Error Correcting Localization
encoder
ideal signals (RSS, TDOA, AOA, etc.)
corrupted signals
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
DB
AC
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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
B
2
A
C
5
3
D
4
F
E
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Constraint Violations
1
B
2
A
C
4
3
D
5
F
E
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Illustration
NO ERRONEOUS CONSTRAINTS
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Illustration
13.9 ERRONEOUS CONSTRAINTS
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Illustration
22.2 ERRONEOUS CONSTRAINTS
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Illustration
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 (?)
• Standard deviation of log-normal shadowing model
  (s)
• Node Deployment Parameters
• Number of reference nodes (a)
• Reference node density (ß)
• Scanning resolution (?)
• 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-coo
  perating nodes, different accuracy and precision
  requirements, etc.)
• different attack models and assumptions