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CLIPS: Infrastructure-free Collaborative Indoor Positioning for Time-critical Team Operations. Youngtae Noh (Cisco Systems) Hirozumi Yamaguchi (Osaka University, Japan) Prerna Vij ( Adobe Systems) Uichin Lee ( KAIST, Korea) Joshua Joy (UCLA) Mario Gerla (UCLA). Motivation.

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clips infrastructure free collaborative indoor positioning for time critical team operations

CLIPS: Infrastructure-free Collaborative Indoor Positioningfor Time-critical Team Operations

Youngtae Noh (Cisco Systems)HirozumiYamaguchi (Osaka University, Japan)PrernaVij (Adobe Systems)Uichin Lee (KAIST, Korea)Joshua Joy (UCLA)

Mario Gerla (UCLA)

motivation
Motivation
  • Navigating a team of first responders in shopping centers/ buildings in case of emergency
  • However, location of APs is unknown, and they may not be working due to power failure or network failure
  • hard for first responders to locate themselves on the map
objective and assumptions
Objective and Assumptions
  • to locate a team of wireless nodes on a floormap without
  • infrastructure support (such as WiFiAPs)
  • prior-learning / on-site training
  • Assumptions:
    • each node can (i) sense RSS of the neighboring nodesand (ii) obtain its movement trace
    • a roughly-drawn floormap and a wireless signal simulator are available as prior-knowledge and an offline tool, respectively
clips architecture
CLIPS Architecture

Offline simulation result

of Pathloss on floormap

  • Before the team mission
    • offline pathloss simulationand map installation on nodes
  • In the team mission
    • RSS measurement amongwireless nodes and localization

preliminarily-installed

RSS measurement

wireless nodes

of a team

how it works 1 offline simulation1
How it works (1) offline simulation

set N grid points on the map

how it works 1 offline simulation2
How it works (1) offline simulation

1

2

3

70dB

130dB

N

Generate a pathloss map (or matrix)

using signal propagation simulator

n x n pathloss matrix example
N x N Pathloss Matrix Example

Destination Point

Source Point

Each node installs this matrix before it starts the mission

how it works 2 localization
How it works (2) Localization
  • Each node measures RSS and estimates pathloss values from all reachable members

55dB

node A

node B

90dB

50dB

node C

node D

how it works 2 localization1
How it works (2) Localization

55dB

B

A

90dB

50dB

Each node finds matching between measurement and matrix to identify its coordinates

C

D

how it works 2 localization2
How it works (2) Localization

90dB

55dB

55dB

B

A

50dB

90dB

50dB

Each node finds matching between measurement and matrix to identify its coordinates

C

D

how it works 2 localization3
How it works (2) Localization

node A

90dB

55dB

55dB

B

A

50dB

90dB

50dB

Each node finds matching between measurement and matrix to identify its coordinates

C

D

how it works 2 localization4
How it works (2) Localization
  • Problem Formulation and Complexity

Node B

55

Node A

50

Node C

90

Node D

Complete Graph of Npoints

(with pathloss values as edge weights)

Graph of M Nodes with Star Topology

(with pathloss values as edge weights)

Pathloss matrix (map)

Measurement

how it works 2 localization5
How it works (2) Localization
  • Problem Formulation and Complexity

70

Node B

70

55

Node A

150

M-1

nodes

93

50

N-1

points

Node C

91

90

bipartite matching

of O(|M ||N|)

Node D

52

Totally O(|M ||N|2)

localization result of node a if node a is lucky
Localization Result of Node A(if node A is lucky)

(node D)

node A

True Position

of Node A

(node C)

(node B)

f easible coordinates are not unique
Feasible coordinates are not unique

node A

node A

node A

node A

node A

node A

node A

node A

About 20% of N coordinates were feasible in out field test

how it works 3 removing invalid coordinates by trace
How it works (3) Removing Invalid Coordinates by trace

Trace by DR

Use dead reckoning to obtain

user traces and perform trace-map matching

how it works 3 removing invalid coordinates by trace1
How it works (3) Removing Invalid Coordinates by trace

Trace by DR

Use dead reckoning to obtain

user traces and perform trace-map matching

how it works 3 removing invalid coordinates by trace2
How it works (3) Removing Invalid Coordinates by trace

Trace by DR

Use dead reckoning to obtain

user traces and perform trace-map matching

how it works 3 removing invalid coordinates by trace3
How it works (3) Removing Invalid Coordinates by trace

I am here now!

Use dead reckoning to obtain

user traces and perform trace-map matching

dr design step stride profiling
DR design: step stride profiling
  • Average step stride (by statistics)
    • Men : 0.415 * height
    • Women : 0.413 * height
  • We may calculate distance by
    • step stride * step count
  • However:
    • step stride should be profiled in more details
    • walking speed also plays a crucial role in calculation of step stride

Stride Length (m)

Step Speed (mph)

By training, we provide 4 “gender x height” profiles with different step speeds

dr design example profile
DR design: example profile
  • Calculate the distance covered by person by statistics
    • Average step size
      • Men : 0.415 * height
      • Women : 0.413 * height
  • Walking speed also plays a crucial role in calculation of step stride.
  • Target application will be more accurate by taking speed into account
  • With this the Distance can be calculated as:
    • Distance = Step count * Stride

distance error (m) with 100m trace

field experiment settings for offline process
Field Experiment Settings(for offline process)

3D modeling of UCLA

CS building floor

RF Simulator:

Qualnet 4.5 + Wireless Insite

field experiment settings for localization process
Field Experiment Settings(for localization process)
  • We have implemented the following CLIPS components on Android phones
    • WiFi beaconing & RSS scanning module
    • pathloss matching module
    • dead reckoning module
    • trace-map matching module
  • We have tested CLIPS with 2-9 nodes & three routesscenarios
pathloss matching hit ratio probability to contain true coordinate
Pathloss Matching: Hit Ratio (probability to contain true coordinate)

Matching Hit Ratio

Slack value a (in matching algorithm: +/- a dB)

measured pathlossm is matched with simulated pathlosssiffm in [s-a, s+a]

pathloss matching feasible coordinate ratio fcr
Pathloss Matching: Feasible Coordinate Ratio (FCR)

Feasible Coordinate Ratio

e.g. 14% FCR with 8 members & a=9

Slack value (in matching algorithm: +/- a dB)

convergence ratio
Convergence Ratio
  • shows the convergence ratio using two different DR mechanisms (statistics-based and step profiling)
  • step profiling provides 100% ratio in Route 1
  • but slightly degraded performance in Route 3
overhead of three modules of clips
Overhead of three modules of CLIPS
  • time taken to converge to a unique point with step profiling in the three routes
  • Wi-Fi scanning and matching takes almost constant time
  • difference comes from the fact that users are traveling different routes

Convergence Time (sec)

why we need both pathloss and trace matching modules
Why we need both pathloss and trace matching modules?
  • traveled distance to converge to the unique point
    • w/ or w/o RSS (i.e. pathloss matching)
  • shows why we need pathloss matching modules (traveled distance differs 14 - 38m)

Traveled Distance (m)

conclusion and future work
Conclusion and future work
  • Conclusion
    • CLIPS can quickly remove invalid candidate coordinates and converge to a user’s current position via RSS matching and dead reckoning over a floorplan
  • Future work
    • Use of Path-loss simulation on Random coordinate (instead of grids)
    • Aggressive coordinates information sharing: sharing the feasible coordinates among the team members
    • Robust dissemination: piggybacking discovered coordinates in a packet can be eventually disseminated to the entire team members