Cooperative Location System 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 – ICS-FORTH Hellas 20 February 2006

2. Overview • Location-sensing • Motivation • Proposed system (CLS) • Evaluation of CLS • Comparison with related work • 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 ? • Mapping 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, direction, distance) • Techniques (triangulation, proximity, scene analysis) • Multiple modalities (RF, ultrasonic, infrared) • Limitations & dependencies (e.g., infrastructure vs. ad hoc) • Localized or remote computation • Physical vs. symbolic location • Absolute vs. relative location • Scale • Cost • Hardware availability • Privacy

6. Related work

7. Motivation • Build a location-sensing system for mobile computing applications that can provide position estimates: • within a few meters accuracy • without the need of specialized hardware and extensive training • using the available communication infrastructure • operating on indoors and outdoors environments • using the 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 (floorplan, signal strength maps) • 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 contributions • Implementation of the Cooperative Location System (CLS) protocol on a different simulation platform (ns-2) • Extensive performance analysis • Extension of CLS • signal strength map • information about the environment (e.g., floorplan) • Study the impact of mobility • Extension of CLS algorithm under mobility • Study the range error in ICS-FORTH

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 with entries for peers A and C

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 • A cell is a possible position • 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 • If there are enough votes (ST) and the precision is acceptable (LECT) • Report the centroid of the set as the host position

14. Evaluation of CLS • Impact of several parameters on the accuracy: • ST and LECT thresholds • Range error • Density of peers and landmarks

15. Impact of range error • Simulation setting (ns-2) • 10 landmarks + 90 stationary nodes • avg connectivity degree = 10 • transmission range (R) = 20m • 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 maps • 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 (one SS value per sec) • estimation phase: • SS measurements in 45 different cells • 95% - confidence intervals • If LBi[c] ≤ ŝi ≤ UBi[c]: the cell c accumulates a vote from APi • final position: centroid of all the 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 of mobilenodes • Speed of the mobile nodes • Frequency of CLS runs

21. Impact of movement of mobilenodes • Simulation setting • 10 different scenarios • 10landmarks, 10mobile, 80 stationary nodes • max speed = 2m/s • time= 100 sec

22. Impact of the speed of the mobile nodes • Simulation setting • 6 times the same scenario • fixed initial and destination positionof each node at each run. • 10landmarks, 10mobile, 80 stationary nodes • time = 100 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. • 10landmarks, 10mobile, 80 stationary nodes • time = 120 sec • Tradeoff accuracy vs. overhead • message exchanges • computations

24. Evaluation of the extended CLS under mobility • Incorporation of: • topological information • signal strength maps • pedestrian speed • Simulation setting • 5 landmarks, 30 mobile,15 stationary nodes • Speed = 1m/s • range error = 10% R • R = 20 m • time = 120 sec • CLS every10 sec

25. Use of topological information • mobile nodecannot walk through walls and cannot enter in some forbidden areas (negative weights) • amobile node follows some paths (positive weight) • 'mobileCLS': 80% of the nodes have 90% location error (%R) • 'extended mobile CLS with walls': 80% of the nodes have 60% location error (%R)

26. Use of signal strength maps • 'extendedmobile CLS with walls & SS': • 80% of the nodes have 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 • 'extendedmobile CLS with walls & SS, pedestrian': • 80% of the nodes have 13% location error (%R)

28. Estimation of Range Error in ICS-FORTH • 50x50 cells, 5 APs • For each cell we took 60 SS values • 95% confidenceintervals (CI) for each cell c and the respective APs I • 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 • Evaluation of the system under mobility • Good accuracy with mobility without additional hardware, training and infrastructure

30. Future work • Incorporate heterogeneous devices (e.g, RF tags, sensors) to enhance the accuracy • Provide guidelines for tuning the weight votes of landmarks and hosts • Incorporate mobility history • Employ theoretical framework (e.g., particle filters) to support the grid-based voting algorithm