Localization Techniques in Wireless Networks

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Localization Techniques in Wireless Networks

• ECE 591

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Instructions

• Print my last name WANG in the STUDENT NAME box.
  There is NO need to fill in the corresponding
  ovals.
• Print course and section number 59102 (for
  ECE591) in the first 5 positions of the STUDENT
  ID NUMBER box. There is NO need to fill in the
  corresponding ovals.
• Queries on the Questionnaire are matched to the
  numbers on the Answer Sheet.

E Strongly Agree D Agree C Neutral B
Disagree A Strongly Disagree
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Motivation

• Technology trends creating cheap wireless
  communication in every computing device
• Radio offers localization opportunity in 2D and
  3D
• New capability compared to traditional
  communication networks

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Research Challenge

• General purpose localization analogous to general
  purpose communication.
• Work on any wireless device with little/no
  modification
• Supports vast range of performance
• Device always knows where it is
• Lost --- no longer a concern
• Use only the existing communication
  infrastructure?

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Background Localization Strategies

• Scene matching
• The best match on a previously constructed radio
  map
• A classifier problem best spot that matches
  the data
• Lateration and Angulation
• Use distances, angles to landmarks to compute
  positions

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Scene Matching

• Build a radio map
• X,Y,RSS1,RSS2,RSS3
• Training data
• Classifiers
• Bayes rule
• Max. Likelihood
• Machine learning (SVM)
• Slow, error prone
• Have to change when environment changes

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Lateration and Angulation
D1
D4
D3
D2
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Observing Distances and Angles

• Received Signal Strength (RSS) to Distance
• Path loss models
• In absence of noise
• RSS to Angle of Arrival (AoA)
• Directional antenna models
• Time-of-Flight to distance(ToF)
• Speed of light

signal that has strength A at a unit distance
from the source. Suppose the signal strength at a
distance from the source is s. ß is the path
loss coefficient
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RSS to Angle
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Results Overview

• Last 6 years --- many, many varied efforts
• Most are simulation, or trace-driven simulation
• Scene matching
• 802.11, 802.15.4 Room/2-3m accuracy Elnahrawy
  04
• Need lots of training data
• Lateration and Angulation
• 802.11, 802.15.4 Room/3-4m accuracy
• Real deployments worse than theoretical models
  predict (1m)

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• Network Localization Algorithm

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Motivations

• Location-aware computing
• Resource Selection (server, printer, etc.)
• Location aware information services (web-search,
  advertisement, etc.)
• Sensor network applications
• Inventory management, intruder detection, traffic
  monitoring, emergency crew coordination,
  air/water quality monitoring, military/intelligenc
  e apps
• Geographic routing in ad hoc networks
• Scalable, lightweight, fault-tolerant protocols
• Current location more important than identity

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Geographical Routing

• Each node only needs to keep state (positions) of
  its neighbors
• each node broadcasts its MAC and position

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GeoRouting Greedy Distance
S
D

• Find neighbors who are the closest to destination

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Localization Problem
Given Set of n points in the plane,
Distances between m pairs of points. Find
Positions of all n points
Illustration
node with unknown position
distance measurement
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Localization problem rephrasing
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2
3
1
0
4
5
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Remove one edge
and the problem becomes unsolvable
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Two Cases
2
x1, x2, x3
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d14, d24, d34
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1
4
1
3
3
In general, this graph is uniquely realizable.
Case 1
first case x4 second case ???
4
1
2
?
1
3
2
3
?
Case 2
In degenerate case, it is not
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Network Localization Problem
Given Set of n points in the plane,
Positions of k of them, Distances between m
pairs of points. Find Positions of all n
points.