1 Empirical-based Analysis of a Cooperative Location-Sensing System 1 Institute of Computer Science, Foundation for Research & Technology-Hellas (FORTH) 2 Department of Computer Science, University of Crete K. Vandikas 1 L. Kriara 1,2 T. Papakonstantinou 1 A. Katranidou 1 H. Baltzakis 1 Maria Papadopouli 1,2 This research was partially supported by EU with a Marie Curie IRG and the Greek General Secretariat for Research and Technology.

2

3 Overview Motivation Taxonomy of location-sensing systems Collaborative Location Sensing (CLS) Performance analysis Conclusions Future work

4 Motivation Emergence of location-based services in several areas transportation & entertainment industries emergency situations assistive technology → Location-sensing is critical for the support of location-based services

5 Taxonomy of location-sensing systems Modalities Dependence on & use of specialized infrastructure & hardware Position and coordination system description Cost, accuracy & precision requirements Localized or remote computations Device identification, classification or recognition Models & algorithms for estimating distances, orientation & position Radio (Radar, Ubisense, Ekahau), Infrared (Active Badge) Ultrasonic (Cricket) Bluetooth Vision (EasyLiving) Physical contact with pressure (smart floor) or touch sensors

6 Cooperative Location-Sensing (CLS) Enables a device to determine its location in self-organizing manner using the peer-to-peer paradigm Employs a grid-based representation of the physical space → can incorporate contextual information to improve its estimates Uses a probabilistic-based framework  Each cell of the grid has a value that indicates likelihood that the local device is in that cell  These values are computed iteratively using distance between peers and position predictions

7 Classifying CLS Modalities Dependence on & use of specialized infrastructure & hardware Position and coordination system description Cost, accuracy & precision requirements Localized or remote computations Device identification, classification or recognition Models & algorithms for estimating distances, orientation & position Radio and/or Bluetooth Can be extended to incorporate other type of modalities Grid representation of the space Transformation to/from any coordination system Position: a cell in the grid Objective: 0.5 to 2.5 m (90%) Computations can be performed remotely or at the device depending on the device capabilities  Does not perform any of these functionalities Statistical analysis and particle filters on signal strength measurements collected from packets exchanged with other peers No need for specialized hardware or infrastructure Can use only IEEE APs, if necessary

8 Example of voting process (1/2) Accumulation of votes on grid cells of host at different time steps

9 Example of voting process (2/2) Host C votes Most likely position x x Peers A, B, C have positioned themselves Host A Host B votes x

10 Voting algorithm 1. Initialize the values of the cells in the grid of the local device 2. Gather position information from peers 3. Record measurements from these received messages 4. Transform this information to probability of being at a certain cell of its local grid 5. Add this probability to the existing value that this cell had from previous steps 6. Assess if the maximal value of the cells in the grid is sufficient high to indicate the position of the device

11 Example of training & run-time signature comparison AP 1 AP 2 Signal-strength measurements per AP cell weight of that cell Run-time signatureTraining-phase signature comparison

12 Position estimation (at peer A) 1. Initialize the values of the cells in the grid of the local device 2. Training phase: Build a signal-strength map of the space (training-phase signatures) 3. Run-time phase: Build signal-strength signature of the current position 4. Compare the run-time and training phase signatures 5. For each new peer that sends its position estimation (e.g., peer B) I. Position B on the local grid of A based on B’s estimation II. Determine their distance based on signal-strength signature III. Infer likely positions of A IV. Update the value of the cells accordingly 6. Assess maximal weight of the cells, accept or reject the solution Landmarks vote Non-landmark peers vote

13 Signature based on confidence interval of signal-strength values Weight of cell c assigned as: total number of APs run-time confidence interval of i-th AP training confidence interval of i-th AP

14 Example of confidence interval-based comparison AP 1 AP 2 Signal-strength measurements per AP cell weight of that cell Run-time signatureTraining-phase signature T2T2 T1T1 - T1T1 + T2T2 +- T1T1 - T1T1 + T2T2 T2T2 +- R R [ T -, T + ] confidence interval based on signal strength measurements from an AP 1 R2R2 R2R2 +- … …

15 Distance estimation between two peers entries of training seti th distance from training set confidence interval of the run-time measurements confidence interval of the i-th entry in the training set

16 Signature based on percentiles of the signal-strength values samples in training set number of percentiles j th run-time percentile j th percentile of ith cell in training set

17 Particle filter-based framework step 1 for L = 1, …, P (L-th particle) Transition: Draw new sample x k (L), P( x k (L) | x k-1 (L) ) Compute weight w k (L) of x k (L), w k (L) = w k-1 (L) * P( y k | x k (L) ), where y k measurement vector: signal strength values end loop Normalize weights Resample Goto step 1

18 Performance evaluation Performance analysis of CLS via simulations [percom’04] Empirical-based measurements in different areas  Various criteria for comparing the training phase and run- time signatures  Particle-filter model  Impact of the number of signal strength measurements  Impact of the number of APs and peers  CLS vs. Ekahau

19 Testbed description Area 7m x Telecommunication and Networks Lab (in FORTH) Each cell of 50cm x 50cm Total 11 IEEE APs in the area 3.5 APs, on any cell

20 CLS variations features variations CriteriaOnly APs vote Peers vote Distance computed CLS confidence interval CLS-p2p confidence interval CLS-percentiles percentiles CLS-particles particle-filter

21 Similarities between CLS & Ekahau v3.1 Use IEEE infrastructure Create map with callibration data Compare trainning & run-time measurements

22 Ekahau vs. CLS no peers only APs participate additional measurements Percentiles capture more information about the distribution of signal strength

23 Impact of number of APs One AP off

24 Impact of peers One extra peer

25 Use of Bluetooth instead of IEEE802.11

26 Conclusions The density of landmarks and peers has a dominant impact on positioning Experiments were repeated at the lab in FORTH and in a conference ACM Mobicom  median location error 1.8 m Incorporation of Bluetooth measurements to improve performance  median location error 1.4 m

27 Discussion & future work (1/2) Reduce training, management & calibration overhead Easily detect changes of the environment  density and movement of users or objects  new/rogue APs  Inaccurate information & measurements Singular spectrum analysis of signal strength  Distinguish the deterministic and noisy components  Construct training and run-time signatures based on the deterministic part

28 Discussion & future work (2/2) Incorporate heuristics  about hotspot areas, user presence and mobility information, and topological information of the area (e.g., existence of walls) Experiment with other wireless technologies  Sensors, cameras, and RF tags I

29 UNC/FORTH Archive  Online repository of models, tools, and traces  Packet header, SNMP, SYSLOG, signal quality  Free login/ password to access it Joint effort of Mobile Computing FORTH & UNC  Thank You! Any questions?

30 Multimedia Travel Journal Tool Novel p2p location-based application for visitors Allow multimedia file sharing among mobile users

31

32

33 Simulations

34 Simulations Simulation setting (ns-2)  10 landmarks  90 stationary nodes  avg connectivity degree = 10  transmission range (R) = 20m 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

35 Bluetooth estimation experiments

36 Bluetooth-only estimation validation experiments

37 J oint IEEE & Bluetooth estimation experiments

38 Joint IEEE & Bluetooth estimation experiments impact of modalities - performance analysis

39 Modality comparison