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

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

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  1. 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)

  2. 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

  3. 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

  4. 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

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

  6. How it works (1) offline simulation set N grid points on the map

  7. How it works (1) offline simulation 1 2 3 70dB 130dB N Generate a pathloss map (or matrix) using signal propagation simulator

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

  9. 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

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

  11. 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

  12. 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

  13. 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

  14. 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)

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

  16. 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

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

  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

  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

  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

  21. 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

  22. 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

  23. Field Experiment Settings(for offline process) 3D modeling of UCLA CS building floor RF Simulator: Qualnet 4.5 + Wireless Insite

  24. 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

  25. 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]

  26. 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)

  27. 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

  28. 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)

  29. 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)

  30. 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

  31. Thank you! Q&A 31

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