1. Location-sensing using the IEEE 802.11 Infrastructure and the Peer-to-peer Paradigm for Mobile Computing Applications Anastasia Katranidou Supervisor: Maria Papadopouli Master Thesis, University of Crete & FORTH-ICS, Hellas 20 February 2006

2. Master Thesis, University of Crete & FORTH-ICS, Hellas 2 Overview Location-sensing Motivation Proposed system - CLS Evaluation of CLS Conclusions Future work

3. Master Thesis, University of Crete & FORTH-ICS, Hellas 3 Pervasive computing century Pervasive computing  enhances computer use by making many computers available throughout the physical environment but effectively invisible to the user

4. Master Thesis, University of Crete & FORTH-ICS, Hellas 4 Why is location-sensing important ? Navigation systems Locating people & objects Wireless routing Smart spaces Supporting location-based applications  transportation industry  medical community  security  entertainment industry  emergency situations

5. Master Thesis, University of Crete & FORTH-ICS, Hellas 5 Location-sensing properties Metric (signal strength, AoA, ToA, TDoA) Techniques (triangulation, proximity, scene analysis) Multiple modalities (RF, ultrasound, infrared) Limitations & dependencies (e.g., infrastructure vs. ad-hoc) Localized or remote computation Physical vs. symbolic location Absolute vs. relative location Scalability Cost Specialized hardware Privacy

6. Master Thesis, University of Crete & FORTH-ICS, Hellas 6 Related work GPS satellite localization, absolute, outdoors Active Badge infrared, symbol, absolute, special hardware APS with AoA RF, ultrasound, physical, relative, special hardware Cricket ultrasound, RF from IEEE 802.11 Savarese et al. ad-hoc networks RADAR IEEE 802.11 infrastructure, physical absolute, triangulation Ladd et al. IEEE 802.11 infrastructure, physical, relative

7. Master Thesis, University of Crete & FORTH-ICS, Hellas 7 Motivation Build a location-sensing system for mobile computing applications that can provide position estimates:  using the available communication infrastructure  within a few meters accuracy  without the need of specialized hardware and extensive training  operating on indoors and outdoors environments Use  peer-to-peer paradigm  knowledge of the environment and mobility

8. Master Thesis, University of Crete & FORTH-ICS, Hellas 8 Design goals Robust to tolerate network failures, disconnections, delays due to host mobility Extensible to incorporate application-dependent semantics or external information (e.g., floorplan, signal strength map) Computationally inexpensive Scalable Use of cooperation of the devices and information sharing No need for extensive training and specialized hardware Suitable for indoor and outdoor environments

9. Master Thesis, University of Crete & FORTH-ICS, Hellas 9 Thesis Implementation of the Cooperative Location System (CLS) Extension of the CLS design  signal strength map  information about the environment (e.g., floorplan)  heuristics based on confidence intervals Extensive performance analysis  range error  density of hosts  mobility Empirical study of the range error in FORTH-ICS

10. Master Thesis, University of Crete & FORTH-ICS, Hellas 10 Cooperative Location System (CLS) Communication Protocol  Each host estimates its distance from neighboring peers refines its estimations iteratively as it receives new positioning information from peers Voting algorithm  accumulates and evaluates the received positioning information Grid-representation of the terrain

11. Master Thesis, University of Crete & FORTH-ICS, Hellas 11 CLS beacon  neighbor discovery protocol with single-hop broadcast beacons  respond to beacons with positioning information (positioning entry & SS) CLS entry  set of information (positioning entry & distance estimation) that a host maintains for a neighboring host CLS update messages  dissemination of CLS entries CLS table  all the received CLS entries Peer idPositionTimeRangeWeightDistanceVote A(x A,y A )tntn RARA wAwA (d u,A - e, d u,A + e)Positive C(x C,y C )tktk RCRC wCwC (R C,  )Negative CLS table of host u Positioning entry Distance estimation CLS entries Communication protocol

12. Master Thesis, University of Crete & FORTH-ICS, Hellas 12 Voting algorithm Grid for host u (unknown position)  Corresponds to the terrain  Peer A has positioned itself  Positive votes from peer A The value of a cell = sum of the accumulated votes The higher the value of a cell, the more hosts agree that this cell is likely position of the host  Peer B has positioned itself  Positive votes from peer B  Negative vote from peer C

13. Master Thesis, University of Crete & FORTH-ICS, Hellas 13 Voting algorithm termination Set of cells with maximal values defines possible position A cell is a possible position If the num of votes in a cell is above ST and the num of cells with max value below LECT  terminate the iteration process  report the centroid of the set as the host position u

14. Master Thesis, University of Crete & FORTH-ICS, Hellas 14 Evaluation of CLS Impact of several parameters on the accuracy  ST and LECT thresholds  range error  density of hosts and landmarks Simulation testbed  100x100 square units in size  Randomly placed nodes (10 landmarks + 90 nodes) in the terrain  Location & range errors as % of the transmission range (R=20 m)

15. Master Thesis, University of Crete & FORTH-ICS, Hellas 15 Impact of range error  avg connectivity degree = 10  avg connectivity degree = 12

16. Master Thesis, University of Crete & FORTH-ICS, Hellas 16 Impact of connectivity degree & percentage of landmarks For low connectivity degree or few landmarks  the location error is bad For 10% or more landmarks and connectivity degree of at least 7  the location error is reduced considerably 5% range error

17. Master Thesis, University of Crete & FORTH-ICS, Hellas 17 Extension of CLS Incorporation of:  signal strength map  information about the environment (e.g., floorplan)  confidence intervals  topological information  pedestrian speed

18. Master Thesis, University of Crete & FORTH-ICS, Hellas 18 Signal strength map Training phase:  each cell & every AP  60 measured SS values  1 signal strength (SS) value / sec  95% - confidence intervals Estimation phase :  SS measurements in 45 cells  if LB i [c] ≤ ŝ i ≤ UB i [c] cell c accumulates vote from APi  final position: centroid of cells with maximal values

19. Master Thesis, University of Crete & FORTH-ICS, Hellas 19 CLS with signal strength map 95% - confidence intervals  no CLS : 80% hosts ≤ 2 m  extended CLS : 80% hosts ≤ 1 m

20. Master Thesis, University of Crete & FORTH-ICS, Hellas 20 Impact of mobility Movement paths Speed Frequency of CLS runs Simulation setting  10 landmarks, 10 mobile and 80 stationary nodes  transmission range (R) = 20 m  range error = 10% R

21. Master Thesis, University of Crete & FORTH-ICS, Hellas 21 Impact of movement paths Simulation setting  10 different scenarios  max speed = 2 m/s  time= 100 sec Mean location error [%R] Simulation time (sec)

22. Master Thesis, University of Crete & FORTH-ICS, Hellas 22 Impact of the speed Simulation setting  6 times the same scenario  fixed initial and destination position of each node at each run  time = 100 sec Simulation time (sec) location error [%R]

23. Master Thesis, University of Crete & FORTH-ICS, Hellas 23 Impact of the frequency of CLS runs Simulation setting  6 times the same scenario  every 120, 60, 40, 30, 20 sec  CLS run = 1, 2, 3, 4, 6 times  speed = 2 m/s  time = 120 sec Tradeoff accuracy vs. overhead  message exchanges  computations Simulation time (sec) location error [%R]

24. Master Thesis, University of Crete & FORTH-ICS, Hellas 24 Evaluation of the extended CLS under mobility Incorporation of:  topological information  signal strength map  pedestrian speed Simulation setting  5 landmarks, 30 mobile and 15 stationary nodes  speed = 1m/s  R = 20 m  range error = 10% R  sim time = 120 sec  CLS every 10 sec

25. Master Thesis, University of Crete & FORTH-ICS, Hellas 25 Use of topological information mobile node cannot:  walk through walls  enter in some forbidden areas negative weights CLS under mobility:  80% of hosts ≤ 90% location error (%R) CLS & topological information :  80% of hosts ≤ 60% location error (%R)

26. Master Thesis, University of Crete & FORTH-ICS, Hellas 26 Use of signal strength map CLS & topological information & SS map :  80% of hosts ≤ 30% location error (%R)

27. Master Thesis, University of Crete & FORTH-ICS, Hellas 27 Use of the pedestrian speed pedestrian speed: 1 m/s  time instance t1 : at point X  after t sec : at any point of a disc centered at X with radius equal to t meters CLS & topological information & SS map & pedestrian speed :  80% of hosts ≤ 13% location error (%R )

28. Master Thesis, University of Crete & FORTH-ICS, Hellas 28 Estimation of Range Error in FORTH-ICS 50x50 cells, 5 APs For each cell we took 60 SS values 95% confidence intervals (CI) for each cell c and the respective APs i Range error i [c] = max{|d(i,c) - d(i,c’)|},  c' such that: CI i [c]∩CI i [c’] ≠ Ø 90% cells ≤ 4 meters range error (10% R) Maximum range error due to the topology ≤ 9.4 meters

29. Master Thesis, University of Crete & FORTH-ICS, Hellas 29 Conclusions Evaluation and extension of the CLS algorithm  80% of hosts ≤ 0.8 m  estimations from peers give better accuracy than SS measurements Evaluation of CLS under mobility  80% of hosts ≤ 2.6 m  great impact of frequency of CLS runs Comparison with related work  static RADAR: 80% ≤ 4.5 m  mobile RADAR: 80% ≤ 5 m

30. Master Thesis, University of Crete & FORTH-ICS, Hellas 30 Future work Incorporate heterogeneous devices (e.g, RF tags, sensors) to enhance the accuracy Employ theoretical framework (e.g., particle filters) to support the grid-based voting algorithm and mobility models Provide guidelines for tuning the weight votes of hosts Use more sophisticated radio propagation model

31. Master Thesis, University of Crete & FORTH-ICS, Hellas 31 Publications Under preparation for submission to the Mobile Computing and Communications Review (MC 2 R) journal

32. Location-sensing using the IEEE 802.11 Infrastructure and the Peer-to-peer Paradigm for Mobile Computing Applications Anastasia Katranidou Supervisor: Maria Papadopouli Master Thesis, University of Crete & FORTH-ICS, Hellas 20 February 2006 THANK YOU!

33. Master Thesis, University of Crete & FORTH-ICS, Hellas 33 APPENDIX Appendix

34. Master Thesis, University of Crete & FORTH-ICS, Hellas 34 RADAR vs. CLS RADAR : 3 APs 90% hosts ≤ 6 m sampling density: 1 sample every 13.9 m 2 Extended static CLS: 5 APs 90% hosts ≤ 2 m sampling density: 1 sample every 14.8 m 2

35. Master Thesis, University of Crete & FORTH-ICS, Hellas 35 Ladd et al. vs. CLS Static localization Ladd et al.  9 APs  77% of hosts ≤ 1.5 m Extended static CLS  5 APs  77% of hosts ≤ 0.8 m Static fusion Ladd et al.  9 APs  64% of hosts ≤ 1 m Extended mobile CLS  5 APs  45% of hosts ≤ 1 m

36. Master Thesis, University of Crete & FORTH-ICS, Hellas 36 Savarese et al. vs. CLS Savarese et al. better with very small connectivity degree (4) or less than 5 landmarks Extended static CLS better with connectivity degree of at least 8 and 10% or more landmarks

37. Master Thesis, University of Crete & FORTH-ICS, Hellas 37 Impact of ST and LECT thresholds Terminate the iteration process  ST: the num of votes in a cell must be above it  LECT: the num of cells with max value must be below it LECT Host h defined solely from host g  not acceptable: the possible cells of host h correspond to a ring Host h defined from host g and k  1 case: not acceptable  2 case: location error max = √ [D max 2 – (D min + e) 2 ] Host h defined from host g, k and m  Possible area: (2· ε +1) 2  location error max : √ [(2· ε +1) 2 / 2] ST  eventually each host will receive votes from every landmark and every other host (CLS updates)  w all_landmarks +w all_hosts

38. Master Thesis, University of Crete & FORTH-ICS, Hellas 38 ST and LECT Simulation setting  10 landmarks and 90 nodes  avg connectivity degree = 10  range error = 10% R Best values  ST = 800  LECT = 5

39. Master Thesis, University of Crete & FORTH-ICS, Hellas 39 Interpolation methods Cubic interpolation Least squares Linear interpolation

40. Master Thesis, University of Crete & FORTH-ICS, Hellas 40 Impact of connectivity degree under mobility Simulation setting  5 landmarks  30 mobile nodes  15 stationary nodes Simulation setting  5 landmarks  5 mobile nodes  5 stationary nodes

41. Master Thesis, University of Crete & FORTH-ICS, Hellas 41 Grid size 100x100: reasonable choice

42. Master Thesis, University of Crete & FORTH-ICS, Hellas 42 Message exchanges

43. Master Thesis, University of Crete & FORTH-ICS, Hellas 43 Movement example Random waypoint model  Max speed  Pause time