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Localization in wireless sensor ad-hoc networks

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Localization in wireless sensor ad-hoc networks. Xiaobo Long ECSE 6962 course presentation. Introduction. What is localization Determine node locations in ad-hoc sensor networks Distributed Without relying on external infrastructure Without base stations, satellites, etc.

Localization in wireless sensor ad-hoc networks

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Localization in wireless sensor ad-hoc networks

Xiaobo Long

ECSE 6962 course presentation

- What is localization
- Determine node locations in ad-hoc sensor networks
- Distributed
- Without relying on external infrastructure
- Without base stations, satellites, etc.
- GPS: too expensive
- not suitable for low-cost, ad-hoc sensor networks

- Determine node locations in ad-hoc sensor networks
- Why need localization
- Routing techniques require knowledge of location
- Sensing tasks require knowledge of location

- Truly distributed
- employed on large-scale ad-hoc sensor networks

- do not depend on global infrastructure

- be tolerant to node failures and range errors

- require little computation and communication

- Nodes are randomly distributed
- 2-D environment
- Static network
- Nodes don’t move

- Anchor nodes
- Have a priori knowledge of their own position
- with respect to some global coordinate system

- Have a priori knowledge of their own position

- Range errors
- describe accuracy of the distance measurements
- effect accuracy of localization algorithms

- i.e., the average number of neighbors

- some anchor nodes have a priori knowledge of their own position

- Distance to anchors
- Determine the distances between unknowns and anchor nodes
- starting at the anchor nodes, measure distance to neighbors
- distance information is flooded into the network
- flooding limit
- three algorithms
- Sum-dist
- DV-hop
- Euclidian

- Determine the distances between unknowns and anchor nodes

- Derive for each node a position from its anchor distances
- Lateration
- Min–max

- Refine the node positions
- using information about the range (distance) to, and positions of, neighboring nodes

- Sum-dist
- adding the ranges at each hop during flooding
- anchors nodes:
- send a message
- identity, position, and a path length set to 0

- send a message
- receiving node:
- adds the measured range to the path length
- forwards (broadcasts) the message
- if the flood limit allows
- if the current path length is less than the previous one

- result
- each node have stored the position
- minimum path length

- drawbacks
- range errors accumulate when distance information is propagated over multiple hops
- error is significant for large networks with few anchors and/or poor ranging hardware

- anchors nodes:

- adding the ranges at each hop during flooding

- DV-hop
- use topological information instead of summing the (erroneous) ranges.

- counting the number of hops
- calibration: convert hop counts into distances
- multiplying the hop count with an average hop distance
- average hop distance obtained by anchors

- drawback
- fails for highly irregular network topologies
- where the variance in actual hop distances is very large

- Euclidean
- based on the local geometry of the nodes around an anchor
- anchors: initiate a flood
- receiver:
- receive messages from two neighbors that:
- know their distance to the anchor
- know their distance to each other

- calculate the distance to the anchor

- receive messages from two neighbors that:
- result
- two possible distance to anchor
- solution
- neighbor vote: a third neighbor n3 connected to either n1 or n2. replace n1 or n2 with n3

- based on the local geometry of the nodes around an anchor

- Nodes determine their position
- based on the distance estimates to a number of anchors
- provided by one of the three Phase 1 alternatives
- Sum-dist, DV-hop, or Euclidean

- Using:
- the estimated distances (di)
- known positions (xi; yi)

- Methods
- Lateration
- Min–max

(1) unknown position is denoted by (x; y).

(2) Linear the system by subtracting the last equation from the first n-1 equations.

(3) Reordering the terms gives a proper system of linear equations in the form Ax = b

(4) The system is solved using a standard least-squares approach:

(5) additional sanity check by computing

the residue between the given distances di and the distances to the location estimate of x

(6) exceptional cases: the matrix inverse can not be computed and Lateration fails.

* quite expensive in the number of floating point operations that is required.

- For each anchor:
- construct a bounding box
- using its position & distance to estimate
- [xi-di, yi-di] x [xi+di, yi+di]

- Determine the intersection of these boxes
- [max(xi-di), max(yi-di)] x
[min(xi+di), min(yi+di)]

- [max(xi-di), max(yi-di)] x
- Position of the node
= center of the intersection box

- Refine the (initial) node positions computed during phase 2
- not all available information used in the first two phases
- positions are not very accurate, even under good conditions
- (high connectivity, small range errors)

- take into account all inter-node ranges
- nodes update their positions
- a node broadcasts its position estimate
- receives the positions and range estimates from its neighbors
- performs Lateration procedure of Phase 2 to determine its new position
- refinement stops when position update becomes small -> reports the final position

- errors propagate quickly through the network
- a single error from 1 node needs only d iterations to affect all nodes (d: network diameter)

- Ad-hoc positioning by Niculescu and Nath [NN01]
- Robust positioning by Savvides, Langendoen and Rabaey [SLR02]
- N-hop multilateration by Savarese, Park and Srivastava [SPS02]
- compare various alternatives for each phase
- simulation on the same platform

- conclusion
- no single algorithm performs best
- which algorithm be preferred depends on the conditions
- range errors, connectivity, anchor fraction, etc.

- still significant room for improving accuracy & increasing coverage

- insufficient data
- lack of absolute reference points or anchors

- distance measurements are noisy
- creating additional uncertainty

- difficult for scalability
- algorithms that scale linearly with the size of the network are hard to devise
- data must be broadcast through wireless channel
- limited communications capacity.

- data must be broadcast through wireless channel

- algorithms that scale linearly with the size of the network are hard to devise

- Challenges of network localization with noise
- only numerical optimization of distance constraints ---- fails
- knowing the length of each graph edge
---- does NOT guarantee a unique realization

- knowing the length of each graph edge
- need to handle nodes with ambiguous positions
- non-rigid graph
- can be continuously deformed to produce an infinite number of different realizations

- rigid graph
- two kinds of ambiguity
- flip ambiguities
- discontinuous flex ambiguities

- two kinds of ambiguity
- Can NOT be solved by graph rigidity theory or tests
when distance measurements are noisy

- only numerical optimization of distance constraints ---- fails

For (b):

If edge AD is removed, then reinserted, the graph can flex in the direction of the arrow, taking on a different configuration but exactly preserving all distance constraints.

For (a):

Vertex A can be reflected across the line connecting B and C with no change in the distance constraints.

- only localize those vertices that:
- have a small probability of being flip or flex ambiguity

- robust quadrilaterals
- construct robust quadrilaterals regions to locate node
- prevent incorrect realizations of flip ambiguities
- would otherwise corrupt localization computations

- cope with measurement noise in the system
- drawback
- bad performance under low node connectivity

- prevent incorrect realizations of flip ambiguities

- construct robust quadrilaterals regions to locate node

- Define: cluster
- a node and its set of neighbors

- Three phases
- Cluster localization
- Quadrilaterals
- the smallest possible sub-graph that can be unambiguously localized in isolation

- identify all robust quadrilaterals
- find the largest sub-graph
- composed solely of overlapping robust quads

- minimizes the probability of realizing a flip ambiguity

- Quadrilaterals
- Cluster optimization (optional)
- refine the position estimates for each cluster
- using numerical optimization

- refine the position estimates for each cluster
- Cluster transformation
- compute transformations between neighboring clusters
- finding the set of nodes in common between two clusters
- solving for the rotation, translation, and possible reflection that best aligns the clusters

- compute transformations between neighboring clusters

- Cluster localization

- Quadrilaterals:
- knowing the locations of any three vertices
- sufficient to compute the location of the fourth using trilateration

- problem
- but still NOT sufficient to guarantee a unique graph realization
- when distance measurements are noisy

- If the smallest angle θi is near zero, there is a risk that measurement error

- but still NOT sufficient to guarantee a unique graph realization
- solution
- restrict our quadrilateral to be robust
---> only those triangles with a sufficiently large minimum angle as robust

- b is the length of the shortest side and θ is the smallest angle

- use the robust quadrilateral as a starting point
- localize additional nodes by chaining together connected robust quads
- whenever two quads have three nodes in common & the first quad is fully localized
- can localize the second quad by trilaterating from the three known positions

- restrict our quadrilateral to be robust

- knowing the locations of any three vertices

(a) robust four-vertex quadrilateral

(b) decomposition of the robust quadrilateral into four triangles.

If θ3 (smallest)is near zero:

say in edge AD, will cause vertex D to be reflected over this sliver of a triangle

- Goal
- using fewer anchor nodes to derive the locations of the nodes
- even yields relative coordinates when no anchor nodes are available

- using fewer anchor nodes to derive the locations of the nodes

- MDS (multi-dimensional scaling)
- starts with one or more distance matrices
- derived from points in a multidimensional space

- find a placement of the points in a low-dimensional space
- usually two or three-dimensional

- closely related to PCA (principal component analysis)
- types of MDS techniques
- classical metric MDS, replicated MDS, weighted MDS, etc.

- starts with one or more distance matrices

- tolerates error gracefully
- due to the over-determined nature of the solution

- it can be performed efficiently on large matrices
- a closed-form solution

- First step
- estimate distance between each possible pair of nodes
- use shortest-paths algorithm
- shortest path distances are used to construct the distance matrix for MDS

- estimate distance between each possible pair of nodes
- Second step
- apply classical MDS to the distance matrix
- core of classical MDS
- SVD (singular value decomposition)

- result of MDS
- a relative map that gives a location for each node

- core of classical MDS

- apply classical MDS to the distance matrix

- if given sufficient anchor nodes
- transform the relative map to an absolute map
- based on the absolute positions of anchors

- requires centralized computation

- Usually
- mobility make localization more difficult
- none of above mechanism consider mobile nodes and anchors

- mobility make localization more difficult
- Sequential Monte Carlo localization
- take advantage of mobility
- to improve the accuracy of localization
- reduce the number of anchors required

- based on MCL (Monte Carlo Localization)
- used for robots localization

- take advantage of mobility

- Key idea
- estimate the posterior distribution of discrete time dynamic models

- Algorithm
- t: discrete time
- l(t): position distribution of the node at time t
- o(t): observations from anchor nodes received between time t-1 and time t
- p(l(t) | l(t-1)): transition equation
- prediction of node’s current position based on previous position

- p(l(t) | o(t)): observation equation
- describes the likelihood of the node being at the location l(t) given the observations
- filter impossible positions

- estimate recursively in time the filtering distribution p(l(t) | o(0), o(1), …, o(t))
- A set of N samples L(t) is used to represent the distribution l(t)
- recursively computes the set of samples at each time step
- since L(t-1) reflects all previous observations, can compute l(t) using only L(t-1) and o(t).

- Goal
- determine node locations in ad-hoc sensor networks
- can use a small number of anchors

- Three phases
- various alternatives for each phase

- Challenges
- noisy distance measurements
- mere connectivity
- mobility

- Ian F. Akyildiz, Weilian Su, Yogesh Sankarasubramaniam, and Erdal Cayirci, A Survey on Sensor Networks.
- [LR02] Koen Langendoen, Niels Reijers, Distributed localization in wireless sensor networks: a quantitative comparison, Computer Networks, 2003, pp. 499-518.
- [NN01] D. Niculescu, B. Nath, Ad-hoc positioning system, IEEE GlobeCom, 2001.
- [SLR02] C. Savarese, K. Langendoen, J. Rabaey, Robust positioning algorithms for distributed ad-hoc wireless sensor networks, USENIX Technical Annual Conference, 2002, pp. 317–328.
- [SPS02] A. Savvides, H. Park, M. Srivastava, The bits and flops of the N-hop multilateration primitive for node localization problems, in: First ACM International Workshop on Wireless Sensor Networks and Application (WSNA), 2002, pp. 112–121.
- [MLRT04] David Moore, John Leonard, Daniela Rus and Seth Teller, Robust Distributed Network Localization with Noisy Range Measurements, ACM, 2004.
- [SRZ03] Yi Shang, Wheeler Ruml, Ying Zhang, Markus P. J. Fromherz, Localization from Mere Connectivity, MobiHoc, 2003.
- [HE04] Lingxuan Hu, David Evans, Localization for Mobile Sensor Networks, MobiCom, 2004.