Localization in Wireless Sensor Networks

1
Localization in Wireless Sensor Networks

• Shafagh Alikhani
• ELG 7178
• Fall 2008

2
Outline

• Wireless Sensor Networks
• Localization What? Why?
• Classification of Localization Algorithms
• Examples of Localization Techniques

3
Wireless Sensor Networks

• a large number of
• self-sufficient nodes
• nodes have
• sensing capabilities
• can perform
• simple computations
• can communicate
• with each other

4
Environments of Deployment

• Indoor vs outdoor
• Stationary vs mobile
• 2D vs 3D

5
Localization

• What?
• To determine the physical coordinates of a group
  of sensor nodes in a wireless sensor network
  (WSN)
• Due to application context and massive scale, use
  of GPS is unrealistic, therefore, sensors need to
  self-organize a coordinate system
• Why?
• To report data that is geographically meaningful
• Services such as routing rely on location
  information geographic routing protocols
  context-based routing protocols, location-aware
  services

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Problem Formulation

• Defining a coordinate system
• Calculating the distance between sensor nodes

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Defining a Coordinate System

• Global
• Aligned with some externally meaningful system
  (e.g., GPS)
• Relative
• An arbitrary rigid transformation (rotation,
  reflection, translation) away from the global
  coordinate system

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Classifications of Localization Methods

• Centralized vs Distributed
• Anchor-free vs Anchor-based
• Range-free vs Range-based
• Mobile vs Stationary

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Centralized vs Distributed

• Centralized
• All computation is done in a central server
• Distributed
• Computation is distributed among the nodes

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Anchor-Free vs Anchor-Based

• Anchor Nodes
• Nodes that know their coordinates a priori
• By use of GPS or manual placement
• For 2D three and 3D four anchor nodes are needed
• Anchor-free
• Relative coordinates
• Anchor-based
• Use anchor nodes to calculate global coordinates

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Range-Free vs Range-Based

• Range-Free
• Local Techniques
• Hop-Counting Techniques
• Range-Based
• Received Signal Strength Indicator (RSSI)
• Attenuation
• RF signal
• Time of Arrival (ToA)
• time of flight
• Time Difference of Arrival (TDoA)
• requires time synchronization
• electromagnetic (light, RF, microwave)
• sound (acoustic, ultrasound)
• Angle of Arrival (AoA)
• RF signal

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Generic Approach Using Anchor Nodes

• 1. Determine the distances between regular nodes
  and anchor nodes. (Communication)
• 2. Derive the position of each node from its
  anchor distances. (Computation)
• 3. Iteratively refine node positions using range
  information and positions of neighboring nodes.
  (Communication Computation)

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Phase 1 Calculating Distance to Anchor Nodes

• Three algorithms
• Sum-dist
• DV-Hop
• Euclidean
• Anchors
• flood network
• with their
• own position

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Sum-dist Phase 1

• Anchors
• flood network with own position
• Nodes
• add hop distances
• requires range measurement

B
C
A
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DV-hop Phase 1

• Anchors
• flood network with
• own position
• flood network with
• avg hop distance
• Nodes
• count number
• of hops to anchors
• multiply with avg hop distance

3 hops
B
avg hop 5
C
A
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EuclideanPhase 1

• Anchors
• flood network with
• own position
• Nodes
• determine distance by
• range measurement
• geometric calculation

B
C
A
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Euclidean Phase 1

• Needs high connectivity
• Error prone (selecting wrong distance)
• Perfect accuracy possible

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Phase 2Determining Position

• Trilateration
• uses multiple distance
• measurements between
• known points
• Must solve a set of
• linear equation
• Triangulation
• Law of sines (sin a)/A(sin b)/B(sin c)/C
• Min-max

C
A
B
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Phase 2Min-max

• Distance to anchors determines a bounding box
• Center of box estimates node position

C
A
B
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Phase 3 Iterative refinement

• Node obtains initial position (phase 1 and 2)
• Node broadcasts its position
• Position is refined iteratively using
• distances to neighbours
• nodes previous positions

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Phase 3Iterative refinement

• 1. Initial estimate

A
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Monte Carlo Localization for Mobile Nodes
Initialization Node has no knowledge of its
location. L0 set of N random locations in
the deployment area Iteration Step Compute
new possible location set Lt based on Lt-1,
the possible location set from the previous time
step, and the new observations.
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Phase 1 Initialization
Nodes actual position
Initialization Node has no knowledge of its
location. L0 set of N random locations in
the deployment area
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Phase 2 Prediction Filtering
Anchor node Knows its own location and
transmits it
Nodes actual position
r
Prediction Node predicts its new possible
locations based on previous possible locations
and given maximum velocity
Filtering Samples inconsistent with
observations are filtered out
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Observations
Direct Anchor If node hears an anchor, the node
must lie on a circle with radius r of the
anchors location
r
S
Indirect Anchor If node does not hear an anchor,
but one of its neighbors does, node must be
within distance (r, 2r of that anchors location.
S
2r
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Questions

• 1- What are the main differences between
  range-free and range-based methods?
• Range-based methods require extra
  hardware therefore have a higher cost but provide
  more accurate distance measurements, whereas
  range-free methods use only connectivity
  information and so are less accurate.
• 2- What are the generic steps in calculating node
  position using anchor nodes?
• 1. Determine the distances between regular nodes
  and anchor nodes.
• 2. Derive the position of each node from its
  anchor distances.
• 3. Iteratively refine node positions using range
  information and positions of neighboring nodes.
• 3- What are the observations used for filtering
  the samples in the MCL algorithm.
• If node hears an anchor, the node must lie on a
  circle with radius r of the anchors location. If
  node does not hear an anchor, but one of its
  neighbors does, node must be within distance (r,
  2r of that anchors location.

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References

• 1 I. Stojmenovic, Handbook of Sensor Networks
  Algorithms and Architectures, Wiley Interscience,
  2005.
• 2 K. Langendoen and N. Reijers, "Distributed
  Localization in Wireless Sensor Networks A
  Quantitative Comparison Computer Networks
  (Elsevier), special issue on Wireless Sensor
  Networks, November 2003.
• 3 E. Stevens-Navarro, V. Vivekanandan, and
  V.W.S. Wong, Dual and Mixture Monte Carlo
  Localization Algorithms for Mobile Wireless
  Sensor Networks, in Proceedings of IEEE Wireless
  Communications and Networking Conference (WCNC),
  pp. 4024 4028, March 2007.
• 4 Y. Shang and W. Ruml, Improved MDS-Based
  Localization, in Proceedings of IEEE INFOCOM,
  2004.
• 5 D. Niculescu and B. Nath, DV Based
  Positioning in Ad hoc Networks, Kluwer Journal
  of Telecommunication Systems. 2003.
• 6 L. Hu, and D. Evans, Localization for Mobile
  Sensor Networks, in Proceeding of Tenth Annual
  International Conference on Mobile Computing and
  Networking (MobiCom 2004), October 2004.
• 7 Y. Shang, W. Ruml, Y. Zhang, M. Fromherz,
  Localization from Mere Connectivity, in
  Proceedings of ACM MobiHoc 2003. June 2003.
• 8 Y. Shang, W. Ruml, Y. Zhang, M. Fromherz,
  Localization from Connectivity in Sensor
  Networks, IEEE Transactions on Parallel and
  Distributed Systems, vol. 15, no. 11, pp.
  961-974, November 2004.
• 9 A. Savvides, W. Garber, S. Adlakha, R. Moses,
  and M.B. Srivastava, On the Error
  Characteristics of Multihop Node Localization in
  Ad-Hoc Sensor Networks, Proceedings of the
  Second International Workshop on Information
  Processing in Sensor Networks (IPSN'03), pp.
  317-332, April 2003.
• 10 A. Savvides, H. Park and M.B. Srivastava,
  "The N-Hop Multilateration Primitive for Node
  Localization Problems,", ACM Mobile Networks and
  Applications (Special Issue on Wireless Sensor
  Networks and Applications), pp. 443-451, 2003.