Location-sensing using the IEEE 802.11 Infrastructure and the Peer-to-peer Paradigm for Mobile Computing Applications

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. Overview • Location-sensing • Motivation • Proposed system - CLS • Evaluation of CLS • Conclusions • Future work

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. Why is location-sensing important ? • Navigation systems • Locating people & objects • Wireless routing • Smart spaces • Supporting location-based applications • transportationindustry • medical community • security • entertainment industry • emergency situations

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. Related work

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. 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. 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. 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. Communication protocol • 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 Positioning entry Distance estimation CLS entries CLS table of host u

12. Voting algorithm • Grid for host u (unknown position) • Corresponds to theterrain • PeerA has positioned itself • Positive votes from peer A • PeerB has positioned itself • Positive votes from peer B • Negative vote from peer C • 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

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. 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. Impact of range error • avg connectivity degree = 10 • avg connectivity degree = 12

16. Impact of connectivity degree & percentage of landmarks 5% range error • 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

17. Extension of CLS • Incorporation of: • signal strength map • information about the environment (e.g., floorplan) • confidence intervals • topological information • pedestrian speed

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 LBi[c] ≤ ŝi ≤ UBi[c] cell c accumulates vote from APi • final position: centroid of cells with maximal values

19. CLS with signal strength map • 95% - confidence intervals • no CLS: 80% hosts ≤ 2 m • extended CLS: 80% hosts ≤ 1 m

20. Impact of mobility • Movement paths • Speed • Frequency of CLS runs • Simulation setting • 10landmarks, 10mobile and 80 stationary nodes • transmission range (R) = 20 m • range error = 10% R

21. Impact of movement paths • Simulation setting • 10 different scenarios • max speed = 2m/s • time= 100 sec Mean location error [%R] Simulation time (sec)

22. Impact of the speed • Simulation setting • 6 times the same scenario • fixed initial and destination positionof each node at each run • time = 100 sec location error [%R] Simulation time (sec)

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 = 2m/s • time = 120 sec • Tradeoff accuracy vs. overhead • message exchanges • computations location error [%R] Simulation time (sec)

24. Evaluation of the extended CLS under mobility • Incorporation of: • topological information • signal strength map • pedestrian speed • Simulation setting • 5 landmarks, 30 mobile and15 stationary nodes • speed = 1m/s • R = 20 m • range error = 10% R • sim time = 120 sec • CLS every10 sec

25. Use of topological information • mobile nodecannot: • 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. Use of signal strength map • CLS & topological information& SS map: • 80% of hosts ≤30% location error (%R)

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 atX with radius equal to t meters • CLS & topological information & SS map & pedestrian speed: • 80% of hosts ≤ 13% location error (%R)

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

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. 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. Publications • Under preparation for submission to the Mobile Computing and Communications Review (MC2R) journal

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

33. APPENDIX • Appendix

34. RADAR vs. CLS RADAR: • 3 APs • 90% hosts≤ 6 m • sampling density: 1 sample every 13.9 m2 Extended static CLS: • 5 APs • 90% hosts≤2 m • sampling density: 1 sample every 14.8m2

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. 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. 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 • notacceptable: the possible cells of host h correspond to a ring • ST • eventually each host will receive votes from every landmark and every other host (CLS updates) • wall_landmarks +wall_hosts Host h defined from host gand k • 1 case: not acceptable • 2 case: location errormax = √[Dmax2– (Dmin + e)2 ] Host h defined from host g, kand m • Possible area: (2· ε +1)2 • location errormax: √[(2· ε +1)2 / 2]

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. Interpolation methods • Cubic interpolation • Least squares • Linear interpolation

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. Grid size • 100x100: reasonable choice

42. Message exchanges

43. Movement example • Random waypoint model • Max speed • Pause time