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CS-541 Wireless Sensor Networks - uoc.gr
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CS-541Wireless Sensor Networks
Spring Semester 2017-2018
Prof Panagiotis Tsakalides, Dr Athanasia Panousopoulou, Dr Gregory Tsagkatakis
Lecture 13: Localization in WSNs
Spring Semester 2017-2018CS-541 Wireless Sensor Networks
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Overview
Geometric-based
Learning-based
Hardware-based
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Location information and network services
• Routing
• Topology Control
• Coverage
• Boundary Detection
• Clustering
• Medical care
• Smart space
• Logistics
• Environment monitor
• Mobile P2P
• Social Networks
• Gaming
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Localization steps
• Distance/angle estimation: estimating information about the distances and/or angles between two nodes.
• Position computation: computing a node’s position based on available information concerning distances/ angles and positions of anchor nodes.
• Localization algorithm: determines how the available information will be manipulated in order to allow the nodes of a WSN to estimate their positions.
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Global Positioning System (GPS)
Usage of Global Positioning System (GPS) devices
• The satellites carry synchronized atomic clocks
• GPS satellites continuously transmit their
current time and position
Why not is WSN?
• High cost of the device • value/energy/computation power/space
• Unavailability/poor precision of the service• Indoors
• Underground
• Non line-of-sight
• Relying on DoD (ESA Galileo)
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Taxonomy
Range vs. Range-free (connectivity) based• Received Signal Strength Indicator (RSSI)
• Time/Angle of Arrival (ToA & AoA)
• Hop-Counting Techniques
Anchor-based vs. Anchor-free • Mobile source/sink
• # of anchors (beacons)
Centralized vs. Distributed• # Hops
• Hybrid schemes
Parametric vs. Non-parametric• Model and learning
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Challenges
Sources of uncertainties in location sensing:• Multipath,
• No-line-of-sight (NLOS)/blockage,
• Interference,
• Measurement noise,
• System/hardware limitations
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Distance estimation - RSSI
Received Signal Strength (RSS)
• Measure the power of the signal at the receiver.
• Based on the known transmit power, the respective propagation loss can be calculated.
• Theoretical or empirical models are used to translate this loss into a distance estimate.
This method has been used mainly for RF signals.
Free Space Equation
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Distance estimation - RSSI
• Radio Signal Strength (RSS): noisy but cheap
Received power at distance 𝑑
Received power at some reference distance 𝑑0
path-loss exponentfading effects
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“Cheap and UbiquitousReceived-Signal-Strength” ?
nonlinear noisy
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Timing-based approaches
• Time based methods (ToA, TDoA) record the time-of-arrival (ToA) or time-difference-of-arrival (TDoA).
• Propagation time can be directly translated into distance, based on the known signal propagation speed.
• Active vs. Passive
• One-way propagation time and roundtrip propagation time measurements
• Types of signals• RF (wireless)
• Acoustic
• Infrared (Ultrasound)
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TDoA: better resolution but costly
Need line-of-sight conditions
Uncertainties: temperature, humility, synchronization & delay.
TDoA Distance estimation
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Angle-based approaches
Angle-of-Arrival (AoA): estimate the angle at which signals are received and use simple geometric relationships to calculate node positions
localization accuracy <-> measurement accuracy
Requires multiple antennas
• Antenna array
• Sensor array
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Hop-count
Anchors • flood network with known position
• flood network with avg hop distance
Nodes• count #hops to anchors
• multiply with avg hop distance
The expected number of neighbors
Number of hops Communication range
D = ℎ𝑖𝑗 ∗ 𝑑ℎ𝑜𝑝CS-541 Wireless Sensor Networks
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A
B
1
1
1
1
2
2
2
2
2
3
3
3
4
4
D(A-B) = 3x4= 12
hAB:3 hops
dhop: avg hop distance : 4
D = ℎ𝑖𝑗 ∗ 𝑅
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Failed in anisotropic network !
Hop-count - Limitation
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Comparative Study & Future work
Promising technique: UWB and Chirp Spread SpectrumCS-541 Wireless Sensor Networks
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Comparison
•Range measurement• Very accurate: Euclidean• Reasonable: Sum-dist• None / very bad: DV-hop
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Possible Implementations/ Computation Models
1. CentralizedOnly one node computes
2. Locally Centralized Some of unknown nodes compute
3. (Fully) DistributedEvery unknown node computes
Computing Nodes
• Each approach may be appropriate for a different application
• Centralized approaches require routing and leader election
• Fully distributed approach does not have this requirementCS-541 Wireless Sensor Networks
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Localization of Nodes
Anchor Nodes: These are nodes that know their coordinates a priori and are used to calculate global coordinates in Anchor-based systems
1. Determine the distance between unknown and anchor nodes
2. Derive the position of each node from its anchor distances
3. Refine the node positions using information about the range and positions of neighbouring nodes
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Calculating Distance to Anchor Nodes
Anchors: Prelocalized nodes
Relative vs. Absolute localization
• (N+1) non-collinear beacon nodes required to define a global coordinate system in N dimensions
There are three algorithms
• Sum-Dist (DV-Distance)
• DV-Hop
• Euclidean
Anchors flood the network with their position
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Example of lateration
To obtain localization a node has to have at least 3 neighbors with known position
1 2 3
D A
E
F
B
CD A
E
F
B
CD A
E
F
B
C
D A
E
F
B
C
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Area Based Estimation
Single Reference Area Estimation
• Distance, angle, etc.
Compute the intersection of
overlapping regions -> centroid
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Trilateration in practice
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Ideal scenario Realist scenario
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Multilateration
The distances from an unknown node to several references constrain the presence of this node
Hx = z
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Min-Max (Bounding box)
Distance to anchors determinesa bounding box
Center of box estimates nodeposition
Main idea:
construct a bounding box foreach anchor using its positionand distance estimate
determine the intersection ofthese boxes.
The position of the node is setto the centre of the intersectionbox.
A
B
C
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Centroid-based Localization
Nodes localize themselves to the centroid of their proximate reference points
• Simple to implement
• Assume perfect spherical radio propagation.
• Assume Identical transmission range for all radios.
• Every anchor beacons location information (Xi,Yi)
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Xi1,Yi1
Xi,Yi
Xi2,Yi2
..
.Xik,Yik
x
..
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Multi-Dimensional Scaling (MDS)
MDS maps objects from a high-dimensional space to a low-dimensional space, while preserving distances betweenobjects.
similarity between objects <-> coordinates of points
Classical metric MDS:
• Proximities are treated as distances in an Euclidean space
Optimality:
• Exact reconstruction if the proximity data are from anEuclidean space
Efficiency:
• Singular Value Decomposition
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Applying Classical MDS
1. Create a proximity matrix of distances D
2. Convert into a double-centered matrix B
3. Take the Singular Value Decomposition of B
4. Compute the coordinate matrix X
U
NIDU
NI
11
2
1-B 2
NxN matrix of 1s
NxN matrix of 1sNxN identity matrix
TVAVB 2
1
VAX
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The basic MDS-MAP algorithm:1. Compute shortest paths between all pairs of nodes.2. Apply classical MDS to construct a relative map.
3. Given sufficient anchor nodes, transform the relative map to an absolute map.
Example: Localization Using Multidimensional Scaling (MDS) (Yi Shang et. al)
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RECURSIVE POSITION ESTIMATION
• A node determines its reference nodes.
• The node estimates its distance to these reference nodes.
• The node computes its position using trilateration (becoming a settled node).
• The node becomes a reference node by broadcasting its newly estimated position to its neighbors.
When a node becomes a reference, it can assist other nodes in computing their positions as well.
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Learning Based Approaches
• Employ a metric for location determination (e.g. RSS)
• localization is usually divided into : training and serving (testing)
• Training• involve the site survey process in which engineers record the RSS
fingerprints at every position of an interesting area and build a fingerprint database
• Serving• user sends a location query with its current RSS fingerprint,
localization algorithms return the matched locations
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RSS FingerprintingTraining
Signature map
Runtime
TAP1,Cell1TAP1,Cell2
… TAP1,CellD
?
Runtime measurements
RAP1RAP2
RAP3
Compare
Location Estimation
Localization Server (LS)
MD RSS measurements are collected for each position.
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TAP1,Cell1TAP1,Cell2
… TAP1,CellD
TAP2,Cell1TAP2,Cell2
… TAP2,CellD
TAP3,Cell1TAP3,Cell2
… TAP3,CellD
TAP2,Cell1TAP2,Cell2
… TAP2,CellD
TAP3,Cell1TAP3,Cell2
… TAP3,CellD
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Issues with RSSI
wall-penetrating effect• signals may encounter a considerable abrupt change
while passing through a wall• RSS of a same AP can vary significantly in two rooms
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Runtime Phase
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Sparse vector: b
0
1
.
.
.
0
D x 1
D cells
User’sLocation
The location of mobile device is sparse in space.
0 0 0 0 0
1 0 0 0 0
0 0 0 0 0
0 0 0 0 0
0 0 0 0 0
Space Vectorization
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Localization based on Jointly Sparsity
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MotivationExploit correlation structures Reduce the amount of RSS exchanged dataKey idea
Jointly Compressed Sensing Multiple collection points capture related phenomena
joint structureApproach Decentralized Localization
1b
2b
3b
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Training Phase
• Construction of the signature map
• Construction of the measurement matrix
• Each BS contracts a random measurement matrix Φ
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Tim
e
Location
Signature map for BS1
1,1R
1,tR
,1JR
,J tR2Ψ
1Ψ
JΨ
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Decentralized Localization
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• Runtime phase
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Decentralized Localization
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• Runtime phase
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Decentralized Localization
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• Runtime phase
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Localization performance
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Multi-modal localization
•Site Survey• time-consuming• labor-intensive• easily affected by environmental dynamics
•Exploiting user motions from mobile phones
•Tri-axial accelerometers• obtain user movements and utilizes moving traces to
assist localization• explore reachability between different areas
•Achieve competitive room level accuracy
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WILL: Wireless Indoor LocalizationWithout Site Survey
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Room-level localization
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UltraWide Band
• UWB is a technology developed to transfer large amounts of data wirelessly over short distances over a very wide spectrum of frequencies in a short period of time
• This technology operates at a level that most systems interpret as noise and, as a result, does not cause interference to other radios such as cell phones, cordless phones or broadcast television sets
• UWB will be ideally suited for transmitting data between consumer electronics (CE), PC peripherals, and mobile devices within short range at very high speeds while consuming little power
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FrequencyModulation
2.4 GHz
Nar
row
ban
dC
om
mu
nic
atio
n
0 1 0 1
Time-domain behavior Frequency-domain behavior
ImpulseModulation
3 10 GHzfrequency
Ult
raw
ideb
and
C
om
mu
nic
atio
n
time
1 0 1
(FCC Min=500Mhz)
What is UltraWideBand?
•Communication that occupies more than 500 MHz of spectrum•Communication with fractional bandwidth of more than 0.2
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UWB in WSNs
IEEE 802.15.4 (2003) low data rate communications systems (Zig-Bee)
The 802.15.4a (2007) support higher data rates and accurate ranging capability
Benefits for WSNs
• Trade power for bandwidth vs. power
• Excellent robustness against fading
• Limited interference from concurrent transmitters
• Resistant to jamming
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• LRT = TRR − TSB
• LTA = TSF − TRR
• FRT = TRF − TSR
• FTA = TSR−TRB
UWB based localization
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Evaluation of IR-UWB for localization
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Reading List
• Wireless Sensor Networks by Akyildiz, Vuran, Chapter 12 Localization
• Han, Guangjie, Huihui Xu, Trung Q. Duong, Jinfang Jiang, and TakahiroHara. "Localization algorithms of wireless sensor networks: a survey."Telecommunication Systems 52, no. 4 (2013): 2419-2436.
• Boukerche, Azzedine, H. A. B. Oliveira, Eduardo F. Nakamura, andAntonio AF Loureiro. "Localization systems for wireless sensornetworks." wireless Communications, IEEE 14, no. 6 (2007): 6-12.
• Material Used
Slides prepared by Lanchao Liu and Zhu Han, ECE Department, University of Houston, Mar. 2010
CS-541 Wireless Sensor NetworksUniversity of Crete, Computer Science Department
50Spring Semester 2017-2018