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Localization in Wireless Sensor Networks. Shafagh Alikhani ELG 7178 Fall 2008. Outline. Wireless Sensor Networks Localization – What? Why? Classification of Localization Algorithms Examples of Localization Techniques. Wireless Sensor Networks. a large number of self-sufficient nodes - PowerPoint PPT Presentation
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Localization in Wireless Sensor Networks
Shafagh Alikhani
ELG 7178Fall 2008
Outline
Wireless Sensor Networks Localization – What? Why? Classification of Localization Algorithms Examples of Localization Techniques
Wireless Sensor Networks
a large number of self-sufficient nodes nodes have sensing capabilities can perform simple computations can communicate with each other
Environments of Deployment
Indoor vs outdoor
Stationary vs mobile
2D vs 3D
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
Problem Formulation
Defining a coordinate system
Calculating the distance between sensor nodes
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
Classifications of Localization Methods
Centralized vs Distributed Anchor-free vs Anchor-based Range-free vs Range-based Mobile vs Stationary
Centralized vs Distributed
Centralized– All computation is done in a central server
Distributed– Computation is distributed among the nodes
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
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
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)
Phase 1: Calculating Distance to Anchor Nodes
Three algorithms– Sum-dist– DV-Hop – Euclidean
Anchors– flood network
with their own position
Anchors– flood network with own
position
Nodes– add hop distances– requires range
measurement
Sum-dist Phase 1:
C
A
B
A: 8
8
B: 10+6 = 16
10
6
C: 7+8+6 = 21
87
Anchors – flood network with
own position– flood network with avg hop distance
Nodes– count number
of hops to anchors– multiply with avg hop
distance
DV-hop Phase 1:
C
A
B
1
1
1
1
22
2
3
3
4
4
A-B: 153 hops
avg hop: 5
Anchors– flood network with
own position
Nodes– determine distance by
1. range measurement2. geometric calculation
EuclideanPhase 1:
C
A
B
Euclidean Phase 1:
Needs high connectivity Error prone (selecting wrong distance) Perfect accuracy possible
Phase 2:Determining 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
A
B
C
a b
cB A
C
Phase 2:Min-max
Distance to anchors determines a bounding box
Center of box estimates node position
A
B
C
Phase 3: Iterative refinement
Node obtains initial position (phase 1 and 2)
Node broadcasts its position
Position is refined iteratively using:– distances to neighbours– node’s previous positions
Phase 3:Iterative refinement
1. Initial estimate
A
2. Receive neighbour positions
4. Broadcast new position to neighbors
3. Local lateration
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, thepossible location set from the previous time step, and the new observations.
Phase 1: Initialization
Initialization: Node has no knowledge of its location. L0 = { set of N random locations in the deployment area }
Node’s actual position
Phase 2: Prediction & Filtering
Node’s actual position
Prediction: Node predicts its new possible locations based on previous possible locations and given maximum velocityFiltering: Samples inconsistent with observations are filtered out
Anchor node: Knows its own location and transmits it
r
Observations
Indirect AnchorIf node does not hear an anchor,
but one of its neighbors does, node must be within distance (r, 2r] of
that anchor’s location.
Direct AnchorIf node hears an anchor,
the node must lie on a circle with radius r of
the anchor’s location
S
S
r
2r
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 anchor’s location. If node does not hear an anchor, but one of its neighbors does, node must be within distance (r, 2r] of that anchor’s location.
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.