CLIPS: Infrastructure-free Collaborative Indoor Positioningfor 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•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

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Objective and Assumptions

•Assumptions:▫each node can (i) sense RSS of the

neighboring nodes and (ii) obtain its movement trace

▫a roughly-drawn floormap and a wireless signal simulator are available as prior-knowledge and an offline tool, respectively

to locate a team of wireless nodes on a floormap without• infrastructure support (such as WiFi

APs)• prior-learning / on-site training

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CLIPS Architecture•Before the team mission

▫offline pathloss simulationand map installation on nodes

•In the team mission▫RSS measurement among

wireless nodes and localization

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RSS measurementpreliminarily-installed

Offline simulation resultof Pathloss on floormap

wireless nodesof a team

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acquire a floor map

How it works (1) offline simulation

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How it works (1) offline simulation

set N grid points on the map

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Generate a pathloss map (or matrix)using signal propagation simulator

How it works (1) offline simulation

130dB70dB

1 2 3

N

N x N Pathloss Matrix Example

1 2 3 ... N

1 0 90dB 65dB ... 180dB

2 90dB 0 120dB ... 160dB

3 65dB 120dB 0 ... 140dB

... ... ... ... ... ...

N 180dB 160dB 140dB ... 0

Source Point

Destination Point

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Each node installs this matrix before it starts the mission

How it works (2) Localization

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•Each node measures RSS and estimates pathloss values from all reachable members 55dB

50dB

node A node B

node Cnode D

90dB

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How it works (2) Localization

55dB

50dB

B

90dB

A

D C

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

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50dB55dB

90dB

How it works (2) Localization

55dB

50dB

B

90dB

A

D C

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

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How it works (2) Localization

55dB

50dB

B

90dB

A

D C

node A

50dB55dB

90dB

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

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

Complete Graph of N points(with pathloss values as edge weights)

Graph of M Nodes with Star Topology(with pathloss values as edge weights)

Node ANode B

Node C

Node D

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55

50

90

Measurement Pathloss matrix (map)

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

Node ANode B

Node C

Node D

N-1points

bipartite matching of O(|M ||N|)

M-1nodes

Totally O(|M ||N|2)

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55

50

90

70701509391

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(node B)

node A

(node C)

(node D)

Localization Result of Node A(if node A is lucky)

True Positionof Node A

node A

node A

node A

node A

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Feasible coordinates are not unique

node A

node A

node A node A

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

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How it works (3) Removing Invalid Coordinates by trace

Use dead reckoning to obtain user traces and perform trace-map matching

Trace by DR

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How it works (3) Removing Invalid Coordinates by traceTrace by DR

Use dead reckoning to obtain user traces and perform trace-map matching

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How it works (3) Removing Invalid Coordinates by traceTrace by DR

Use dead reckoning to obtain user traces and perform trace-map matching

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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

• 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

Step Speed (mph)

Stri

de L

engt

h (m

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

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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

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distance error (m) with 100m trace

Field Experiment Settings(for offline process)

RF Simulator: Qualnet 4.5 + Wireless Insite

3D modeling of UCLA CS building floor

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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

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Pathloss Matching: Hit Ratio (probability to contain true coordinate)

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

Mat

chin

g H

it R

atio

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measured pathloss m is matched with simulated pathloss s iff m in [s-a, s+a]

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

Feas

ible

Coo

rdin

ate

R

atio

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

𝐹𝐶𝑅=¿𝑜𝑓 h𝑀𝑎𝑡𝑐 𝑖𝑛𝑔𝑂𝑢𝑡𝑐𝑜𝑚𝑒𝑠

¿𝑜𝑓 𝐴𝑙𝑙𝐶𝑜𝑜𝑟𝑑𝑖𝑛𝑎𝑡𝑒𝑠(48 𝑥 48)

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Pathloss Matching: Feasible Coordinate Ratio (FCR)

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

𝐶𝑅 (𝑝𝑒𝑟 𝑟𝑜𝑢𝑡𝑒)=¿ 𝑜𝑓 𝐶𝑜𝑛𝑣𝑒𝑟𝑔𝑒𝑛𝑐𝑒𝑑𝐶𝑎𝑠𝑒𝑠20𝐶𝑎𝑠𝑒𝑠

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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

Con

verg

ence

Tim

e (s

ec)

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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)

Trav

eled

Dis

tanc

e (m

)

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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

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