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 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 2

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 to locate a team of wireless nodes on a floormap without infrastructure support (such as WiFi APs) prior-learning / on-site training 3

CLIPS Architecture Before the team mission ▫offline pathloss simulation and map installation on nodes In the team mission ▫RSS measurement among wireless nodes and localization 4 RSS measurement preliminarily-installed Offline simulation result of Pathloss on floormap Offline simulation result of Pathloss on floormap wireless nodes of a team wireless nodes of a team

acquire a floor map How it works (1) offline simulation 5

6 set N grid points on the map

7 Generate a pathloss map (or matrix) using signal propagation simulator Generate a pathloss map (or matrix) using signal propagation simulator How it works (1) offline simulation 130dB 70dB N N

N x N Pathloss Matrix Example Source Point Destination Point 8 Each node installs this matrix before it starts the mission

How it works (2) Localization 9 Each node measures RSS and estimates pathloss values from all reachable members 55dB 50dB node A node B node C node D 90dB

10 How it works (2) Localization 55dB 50dB B 90dB A D C Each node finds matching between measurement and matrix to identify its coordinates

11 50dB 55dB 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

12 How it works (2) Localization 55dB 50dB B 90dB A D C node A 50dB 55dB 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 A Node B Node C Node D Measurement Pathloss matrix (map)

How it works (2) Localization Problem Formulation and Complexity Node A Node B Node C Node D N-1 points bipartite matching of O(|M ||N|) bipartite matching of O(|M ||N|) M-1 nodes Totally O(|M ||N| 2 )

15 (node B) node A (node C) (node D) Localization Result of Node A (if node A is lucky) True Position of Node A True Position of Node A

node A 16 Feasible coordinates are not unique node A About 20% of N coordinates were feasible in out field test

17 How it works (3) Removing Invalid Coordinates by trace Use dead reckoning to obtain user traces and perform trace-map matching Use dead reckoning to obtain user traces and perform trace-map matching Trace by DR

18 How it works (3) Removing Invalid Coordinates by trace Trace by DR Use dead reckoning to obtain user traces and perform trace-map matching Use dead reckoning to obtain user traces and perform trace-map matching

19 How it works (3) Removing Invalid Coordinates by trace Trace by DR Use dead reckoning to obtain user traces and perform trace-map matching Use dead reckoning to obtain user traces and perform trace-map matching

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

DR design: step stride profiling Average step stride (by statistics) ▫ Men : * height ▫ Women : * 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) Stride Length (m) By training, we provide 4 “gender x height” profiles with different step speeds 21

DR design: example profile ▫ Calculate the distance covered by person by statistics  Average step size  Men : * height  Women : * 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 22 distance error (m) with 100m trace

Field Experiment Settings (for offline process) RF Simulator: Qualnet Wireless Insite RF Simulator: Qualnet Wireless Insite 3D modeling of UCLA CS building floor 3D modeling of UCLA CS building floor 23

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 routes scenarios 24

Pathloss Matching: Hit Ratio (probability to contain true coordinate) Slack value  (in matching algorithm: +/-  dB) Matching Hit Ratio 25 measured pathloss m is matched with simulated pathloss s iff m in [s- , s+  ]

Slack value (in matching algorithm: +/-  dB) Feasible Coordinate Ratio e.g. 14% FCR with 8 members &  =9 26 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 27

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) 28

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 m) Traveled Distance (m) 29

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 30

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Why we need RSS and matching modules? 32

RSSI ▫Vary over time To improve ▫Use sniffers ▫Offline training (off-line computation), ▫Averaging multiple measurements Using anchors to localize mobile nodes is popular schemes (Skyhook, Google Latitude, MobileMe, etc) Neither Instant nor infrastructure-free 33

(N x (N-1)) Pathloss Matrix Source PointDestination PointPathloss (dB) NN-390 NN-2120 NN

Simulation settings RSS & PDR (pedestrian dead reckoning) simulation ▫Qualnet 4.5. Wireless signal propagation was computed by Wireless Insight with 3D model of BH (by CoCreate Modeling Software). ▫point interval = 2m x 2m ▫Measured RSS was randomly generated from the simulated RSS with Gaussian Error N(0dB,5dB) (mean 0 and variance=5dB) ▫default slack value = 10dB ▫Matching Algorithm was written by Ruby 1.9 ▫dead reckoning is implemented assuming perfect compass and step detector (i.e. no DR errors were considered) Mobility ▫Random Room Search : initially each node is at randomly chosen position. He/she chooses a destination room randomly and moves toward there. When he/she reaches the room, he/she immediately picks a next destination. ▫node speed = 2m/s 35

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(m) (m) 42

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RF Simulator: Qualnet Wireless Insite 44

true positions (points with ID) - propagation (lines) 45