CLIPS: Infrastructure-free Collaborative Indoor Positioning for Time-critical Team Operations
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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)
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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
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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
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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
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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)
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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)
14
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
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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
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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
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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]
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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)
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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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