Location-sensing using the IEEE 802.11 Infrastructure and the Peer-to-peer Paradigm for mobile computing applications

1
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

2
Overview

• Location-sensing
• Motivation
• Proposed system (CLS)
• Evaluation of CLS
• Comparison with related work
• Conclusions - Future Work

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Pervasive computing century

• Pervasive computing
• enhances computer use by making many computers
  available throughout the physical environment but
  effectively invisible to the user

4
Why is location-sensing important ?

• Mapping systems
• Locating people objects
• Wireless routing
• Smart spaces
• Supporting location-based applications
• transportation industry
• medical community
• security
• entertainment industry
• emergency situations

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Location-sensing properties

• 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

6
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
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Motivation

• Build a location-sensing system for mobile
  computing applications that can provide position
  estimates
• 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

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Design goals

• 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

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Thesis contributions

• Implementation of the Cooperative Location System
  (CLS) protocol on a different simulation platform
  (ns-2)
• Extensive performance analysis
• Extension of CLS
• 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

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Cooperative Location System (CLS)

• Communication Protocol
• Each host
• estimates its distance from neighboring peers
• refines its estimations iteratively as it
  receives new positioning information from peers
• Voting algorithm
• accumulates and evaluates the received
  positioning information
• Grid-representation of the terrain

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Communication protocol

• CLS beacon
• neighbor discovery protocol with single-hop
  broadcast beacons
• respond to beacons with positioning information
  (positioning entry SS)
• CLS entry
• set of information (positioning entry distance
  estimation) that a host maintains for a
  neighboring host
• CLS update messages
• dissemination of CLS entries
• CLS table
• all the received CLS entries

Positioning entry
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 entries
CLS table of host u with entries for peers A and C
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Voting algorithm

• Grid for host u (unknown position)
• Corresponds to the terrain
• Peer A has positioned itself
• Positive votes from peer A

• Peer B has positioned itself
• Positive votes from peer B

• 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

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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)
• Report the centroid of the set as the host
  position

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Evaluation of CLS

• Impact of several parameters on the accuracy
• ST and LECT thresholds
• Range error
• Density of peers and landmarks

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Impact of range error

• Simulation setting (ns-2)
• 10 landmarks 90 stationary nodes
• avg connectivity degree 10
• transmission range (R) 20m
• avg connectivity degree 12

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Impact of connectivity degree percentage of
landmarks
5 range error

• For low connectivity degree or few landmarks
• the location error is bad
• For 10 or more landmarks and connectivity degree
  of at least 7
• the location error is reduced considerably

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Extension of CLS

• Incorporation of
• signal strength maps
• information about the environment (e.g.,
  floorplan)
• confidence intervals
• topological information
• pedestrian speed

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Signal Strength map

• training phase
• each cell every AP
• 60 measured SS values
• (one SS value per sec)
• estimation phase
• SS measurements in 45 different cells
• 95 - confidence intervals
• If LBic si UBic the cell c accumulates a
  vote from APi
• final position centroid of all the cells with
  maximal values

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CLS with signal strength map

• 95 - confidence intervals
• no CLS 80 hosts 2 m
• extended CLS 80 hosts 1 m

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Impact of mobility

• Movement of mobile nodes
• Speed of the mobile nodes
• Frequency of CLS runs

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Impact of movement of mobile nodes

• Simulation setting
• 10 different scenarios
• 10 landmarks, 10 mobile, 80 stationary nodes
• max speed 2m/s
• time 100 sec

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Impact of the speed of the mobile nodes

• Simulation setting
• 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

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Impact of the frequency of CLS runs

• Simulation setting
• 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
• message exchanges
• computations

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Evaluation of the extended CLS under mobility

• Incorporation of
• topological information
• signal strength maps
• pedestrian speed
• Simulation setting
• 5 landmarks, 30 mobile, 15 stationary nodes
• Speed 1m/s
• range error 10 R
• R 20 m
• time 120 sec
• CLS every 10 sec

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Use of topological information

• mobile node cannot walk through walls and cannot
  enter in some forbidden areas (negative weights)
• a mobile node follows some paths (positive weight)

• 'mobile CLS' 80 of the nodes have 90 location
  error (R)
• 'extended mobile CLS with walls' 80 of the
  nodes have 60 location error (R)

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Use of signal strength maps

• 'extended mobile CLS with walls SS'
• 80 of the nodes have 30 location error (R)

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Use of the pedestrian speed

• pedestrian speed 1 m/s
• 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'
• 80 of the nodes have 13 location error (R)

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Estimation of Range Error in ICS-FORTH

• 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 erroric maxd(i,c) - d(i,c),
• ? c' such that CIicnCIic ? Ø

• 90 cells 4 meters range error (10 R)
• Maximum range error due to the topology 9.4
  meters

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Conclusions

• Evaluation and extension of the CLS algorithm
• Evaluation of the system under mobility
• Good accuracy with mobility without additional
  hardware, training and infrastructure

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Future work

• 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

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RADAR vs. CLS

• RADAR
• 3 APs
• 90 hosts 6 m
• sampling density 1 sample every 13.9 m2
• Extended static CLS
• 5 APs
• 90 hosts 2 m
• sampling density 1 sample every 14.8 m2

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Ladd et al. vs. CLS

• Static localization Ladd et al.
• 9 APs
• 77 of hosts 1.5 m
• Extended static CLS
• 5 APs
• 77 of hosts 0.8 m
• Static fusion Ladd et al.
• 9 APs
• 64 of hosts 1 m
• Extended mobile CLS
• 5 APs
• 45 of hosts 1 m

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Savarese et al. vs. CLS

• Savarese et al.
• better with very small connectivity degree (4) or
  less than 5 landmarks
• Extended static CLS
• better with connectivity degree of at least 8 and
  10 or more landmarks

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Impact of ST and LECT thresholds

• Terminate the iteration process
• ST the num of votes in a cell must be above it
• LECT the num of cells with max value must be
  below it

• LECT
• Host h defined solely from host g
• not acceptable the possible cells of host h
  correspond to a ring

• ST
• eventually each host will receive votes from
  every landmark and every other host (CLS updates)
• wall_landmarks wall_hosts

• Host h defined from host g and k
• 1 case not acceptable
• 2 case location errormax
• v Dmax2 (Dmin e)2

• Host h defined from host g, k and m
• Possible area (2 e 1)2
• location errormax v(2 e 1)2 / 2

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ST and LECT

• Simulation setting
• 10 landmarks and 90 nodes
• avg connectivity degree 10
• range error 10 R
• Best values
• ST 800
• LECT 5