Transcript Document

Location-sensing using the IEEE 802.11
Infrastructure and the Peer-to-peer Paradigm
for mobile computing applications
Anastasia Katranidou
Supervisor: Maria Papadopouli
Master Thesis, University of Crete – ICS-FORTH Hellas
20 February 2006
Overview
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Location-sensing
Motivation
Proposed system (CLS)
Evaluation of CLS
Comparison with related work
Conclusions - Future Work
Master Thesis, University of Crete – ICS-FORTH, Hellas
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Pervasive computing century
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Pervasive computing
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enhances computer use by making many computers available
throughout the physical environment but effectively invisible to
the user
Master Thesis, University of Crete – ICS-FORTH, Hellas
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Why is location-sensing important ?
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Mapping systems
Locating people & objects
Wireless routing
Smart spaces
Supporting location-based applications
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transportation industry
medical community
security
entertainment industry
emergency situations
Master Thesis, University of Crete – ICS-FORTH, Hellas
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Location-sensing properties
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Metric (signal strength, direction, distance)
Techniques (triangulation, proximity, scene analysis)
Multiple modalities (RF, ultrasonic, infrared)
Limitations & dependencies (e.g., infrastructure vs. ad hoc)
Localized or remote computation
Physical vs. symbolic location
Absolute vs. relative location
Scale
Cost
Hardware availability
Privacy
Master Thesis, University of Crete – ICS-FORTH, Hellas
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Related work
GPS
satellite localization, absolute, outside
buildings only
Active Badge
infrared, symbol, absolute, extensive
hardware
APS with AoA
RF, ultrasound, physical, relative, extensive
hardware
RADAR
IEEE 802.11 infrastructure, physical
absolute, triangulation
Ladd et al.
IEEE 802.11 infrastructure, physical, relative
Cricket
ultrasound, RF from IEEE 802.11
Savarese et al.
ad hoc networks
Master Thesis, University of Crete – ICS-FORTH, Hellas
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Motivation
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Build a location-sensing system for mobile computing
applications that can provide position estimates:
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within a few meters accuracy
without the need of specialized hardware and extensive training
using the available communication infrastructure
operating on indoors and outdoors environments
using the peer-to-peer paradigm, knowledge of the environment
and mobility
Master Thesis, University of Crete – ICS-FORTH, Hellas
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Design goals
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Robust to tolerate network failures, disconnections, delays
due to host mobility
Extensible to incorporate application-dependent semantics
or external information (floorplan, signal strength maps)
Computationally inexpensive
Scalable
Use of cooperation of the devices and information sharing
No need for extensive training and specialized hardware
Suitable for indoor and outdoor environments
Master Thesis, University of Crete – ICS-FORTH, Hellas
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Thesis contributions
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Implementation of the Cooperative Location System (CLS)
protocol on a different simulation platform (ns-2)
Extensive performance analysis
Extension of CLS
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signal strength map
information about the environment (e.g., floorplan)
Study the impact of mobility
Extension of CLS algorithm under mobility
Study the range error in ICS-FORTH
Master Thesis, University of Crete – ICS-FORTH, Hellas
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Cooperative Location System (CLS)
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Communication Protocol
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Voting algorithm
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Each host
 estimates its distance from neighboring peers
 refines its estimations iteratively as it receives new
positioning information from peers
accumulates and evaluates the received positioning information
Grid-representation of the terrain
Master Thesis, University of Crete – ICS-FORTH, Hellas
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Communication protocol
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CLS beacon
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CLS entry
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set of information (positioning entry & distance estimation) that a host maintains
for a neighboring host
CLS update messages
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neighbor discovery protocol with single-hop broadcast beacons
respond to beacons with positioning information (positioning entry & SS)
dissemination of CLS entries
CLS table
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all the received CLS entries
Positioning entry
CLS entries
Distance estimation
Peer id
Position
Time
Range
Weight
Distance
Vote
A
(xA,yA)
tn
RA
wA
(du,A- e , du,A+ e)
Positive
C
(xC,yC)
tk
RC
wC
(RC, )
Negative
CLS table of host u with entries for peers A and C
Master Thesis, University of Crete – ICS-FORTH, Hellas
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Voting algorithm
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Grid for host u (unknown position)
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Corresponds to the terrain
Peer
itself
PeerBAhas
haspositioned
positioned
itself
Positive
peer
B A
Positivevotes
votesfrom
from
peer
Negative vote from peer C
A cell is a possible position
The value of a cell = sum of the accumulated
votes
The higher the value of a cell, the more hosts
agree that this cell is likely position of the host
Master Thesis, University of Crete – ICS-FORTH, Hellas
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Voting algorithm termination
Set of cells with maximal values defines possible position
If there are enough votes (ST) and the precision is acceptable
(LECT)
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Report the centroid of the set as the host position
Master Thesis, University of Crete – ICS-FORTH, Hellas
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Evaluation of CLS
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Impact of several parameters on the accuracy:
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ST and LECT thresholds
Range error
Density of peers and landmarks
Master Thesis, University of Crete – ICS-FORTH, Hellas
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Impact of range error
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Simulation setting (ns-2)
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10 landmarks + 90 stationary
nodes
avg connectivity degree = 10
transmission range (R) = 20m
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avg connectivity degree = 12
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Master Thesis, University of Crete – ICS-FORTH, Hellas
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Impact of connectivity degree & percentage of landmarks
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For low connectivity degree
or few landmarks
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5% range error
the location error is bad
For 10% or more landmarks
and connectivity degree of at
least 7
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the location error is reduced
considerably
Master Thesis, University of Crete – ICS-FORTH, Hellas
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Extension of CLS
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Incorporation of:
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signal strength maps
information about the environment (e.g., floorplan)
confidence intervals
topological information
pedestrian speed
Master Thesis, University of Crete – ICS-FORTH, Hellas
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Signal Strength map
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training phase:
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estimation phase:
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each cell & every AP
60 measured SS values
(one SS value per sec)
SS measurements in 45 different
cells
95% - confidence intervals
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If LBi[c] ≤ ŝi ≤ UBi[c]: the cell c
accumulates a vote from APi
final position: centroid of all the
cells with maximal values
Master Thesis, University of Crete – ICS-FORTH, Hellas
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CLS with signal strength map
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95% - confidence intervals
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no CLS: 80% hosts ≤ 2 m
extended CLS: 80% hosts ≤ 1 m
Master Thesis, University of Crete – ICS-FORTH, Hellas
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Impact of mobility
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Movement of mobile nodes
Speed of the mobile nodes
Frequency of CLS runs
Master Thesis, University of Crete – ICS-FORTH, Hellas
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Impact of movement of mobile nodes
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Simulation setting
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10 different scenarios
10 landmarks, 10
mobile, 80 stationary
nodes
max speed = 2m/s
time= 100 sec
Master Thesis, University of Crete – ICS-FORTH, Hellas
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Impact of the speed of the mobile nodes
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Simulation setting
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6 times the same
scenario
fixed initial and
destination position of
each node at each run.
10 landmarks, 10
mobile, 80 stationary
nodes
time = 100 sec
Master Thesis, University of Crete – ICS-FORTH, Hellas
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Impact of the frequency of CLS runs
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Simulation setting
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6 times the same scenario (every
120, 60, 40, 30, 20 sec)
CLS run = 1, 2, 3, 4, 6 times
speed = 2m/s.
10 landmarks, 10 mobile, 80
stationary nodes
time = 120 sec
Tradeoff accuracy vs. overhead
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message exchanges
computations
Master Thesis, University of Crete – ICS-FORTH, Hellas
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Evaluation of the extended CLS under mobility
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Incorporation of:
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topological information
signal strength maps
pedestrian speed
Simulation setting
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5 landmarks, 30 mobile, 15 stationary nodes
Speed = 1m/s
range error = 10% R
R = 20 m
time = 120 sec
CLS every 10 sec
Master Thesis, University of Crete – ICS-FORTH, Hellas
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Use of topological information
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'mobile node
mobile
CLS':cannot
80% ofwalk
the
through
nodes
have
walls
90%
and
location
cannot
enter (%R)
error
in some forbidden
areas (negative weights)
a
mobile node
follows
some
'extended
mobile
CLS with
paths
weight)
walls':(positive
80% of the
nodes
have 60% location error
(%R)
Master Thesis, University of Crete – ICS-FORTH, Hellas
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Use of signal strength maps
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'extended mobile CLS
with walls & SS':
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80% of the nodes have
30% location error (%R)
Master Thesis, University of Crete – ICS-FORTH, Hellas
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Use of the pedestrian speed
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pedestrian speed: 1 m/s
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time instance t1: at point X
after t sec: at any point of a
disc centered at X with
radius equal to t meters
'extended mobile CLS with
walls & SS, pedestrian':
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80% of the nodes have
13% location error (%R)
Master Thesis, University of Crete – ICS-FORTH, Hellas
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Estimation of Range Error in ICS-FORTH
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50x50 cells, 5 APs
For each cell we took 60 SS values
95% confidence intervals (CI) for each
cell c and the respective APs I
Range errori[c] = max{|d(i,c) - d(i,c’)|},
 c' such that: CIi[c]∩CIi[c’] ≠ Ø
90% cells ≤ 4 meters range error
(10% R)
Maximum range error due to the
topology ≤ 9.4 meters
Master Thesis, University of Crete – ICS-FORTH, Hellas
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Conclusions
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Evaluation and extension of the CLS algorithm
Evaluation of the system under mobility
Good accuracy with mobility without additional hardware,
training and infrastructure
Master Thesis, University of Crete – ICS-FORTH, Hellas
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Future work
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Incorporate heterogeneous devices (e.g, RF tags, sensors) to
enhance the accuracy
Provide guidelines for tuning the weight votes of landmarks
and hosts
Incorporate mobility history
Employ theoretical framework (e.g., particle filters) to support
the grid-based voting algorithm
Master Thesis, University of Crete – ICS-FORTH, Hellas
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