© 2007 Sean A. Williams 1 Ecolocation: A Sequence Based Technique for RF Localization in Wireless Sensor Networks Authors: Kiran Yedavalli, Bhaskar Krishnamachari, Sharmila Ravula, Bhaskar Srinivasan Paper Presented By: Sean A. Williams Mobile Computing

© 2007 Sean A. Williams 2 Overview Background on Localization Introduction to Ecolocation –Novelty and Contribution Paper Details Results Conclusions

© 2007 Sean A. Williams 3 Background Localization is process of determining an entity’s spatial coordinates. Advantages of localization for WSNs? –Locating disasters and fires –Locating enemies on battlefields –Other services for rescue and relief Range-based vs. Range-free –Estimating distance btw unknown & reference to determine location –Estimating distance of unknown node w/o reference to determine location

© 2007 Sean A. Williams 4 Background Some Localization Techniques –Proximity Closest reference node = location of unknown node –Centroid Center of all reference nodes in range –Approximate point in Triangle Creates triangle of each 3 anchor combination, location is the intersection of the triangles –Maximum Likelihood Estimation Statistical estimation technique APIT

© 2007 Sean A. Williams 5 Introduction Error COntrolling LOCAlizaTION –AKA: ECOLOCATION Motivation –To provide a localization technique –Outperforms various other localization methods –Robust (fluctuation of Received Signal Strength –RSS)

© 2007 Sean A. Williams 6 Introduction Novelty of Ecolocation –Distance-based ordering of reference nodes creates a unique fingerprint in a region

© 2007 Sean A. Williams 7 Evaluation Scenarios Ideal vs. Real World Scenarios Ideal: –Without multi-path fading and shadowing –Received Signal Strength (RSS) represents distance –Low RSS = Farther away Real: –With multi-path fading and shadowing –Low RSS ~ Farther away

© 2007 Sean A. Williams 8 Ecolocation (Ideal) Location based on unknown nodes constraints and grid-point location constraints Unknown Node constraints determined by: –(RSS) of reference nodes and rank sequentially. –Based on the number of reference nodes and there RSS from the unknown node B:1C:2D:3E:4F:5 R1R2<R1R3<R1R4<R1R5<R1 R3<R2R4<R2R5<R2 R4<R3R5<R3 R5<R4

© 2007 Sean A. Williams 9 Ecolocation (Ideal) Location Grid points constraints –based on the Euclidean Distance to the reference nodes and not the RSS Overall location of unknown node is: –Compare unknown constraints with all location grid point constraints –Grid point that has most matches is LE Given Points: P=(p 1 …p n ) & Q=(q1…q n )

© 2007 Sean A. Williams 10 Ecolocation (Real) LE is effected by shadowing and fading Ecolocation is robust to multi-path effects –Evident in display of various erroneous constraints on an unknown node

© 2007 Sean A. Williams 11 Algorithm Generate constraint matrix A –Based on Euclidean Distance btw grid points and reference nodes Generate constraint matrix B –Based on RSS btw unknown node & reference node If A’s element matches B’s -> increment maxConstraint count Find all grid points where maximum number of constraints are matched LE = centroid of those matching gridpoints

© 2007 Sean A. Williams 12 No Errors

© 2007 Sean A. Williams % Erroneous Constraints

© 2007 Sean A. Williams % Erroneous Constraints X-AXIS (length units) Y-AXIS (length units) Location estimate for P E A1 A2 A4 A7 A3 A9 A5 A8 A6

© 2007 Sean A. Williams % Erroneous Constraints

© 2007 Sean A. Williams 16 Evaluation Simulation model equation Simulation parameters and characteristics 100 Random Trials 10 seeds, 48bit RNG

© 2007 Sean A. Williams 17 SIMULATION RESULTS

© 2007 Sean A. Williams 18 Location Error

© 2007 Sean A. Williams 19 Location Precision Standard Deviation in Location Error

© 2007 Sean A. Williams 20 EXPERIMENTAL RESULTS

© 2007 Sean A. Williams 21 Real World Experiments Parking Lot –11 reference MICA 2 Motes within 1 hop –No NLOS –Motes record RSS of each other that broadcast –Location Estimated and compared to actual Office Building –12 reference MICA 2 Motes within 1 hop –NLOS –Included power attenuation based on walls

© 2007 Sean A. Williams 22 True vs. Ecolocation

© 2007 Sean A. Williams 23 Parking Lot Location Error

© 2007 Sean A. Williams 24 True vs. Ecolocation

© 2007 Sean A. Williams 25 Indoor Location Error

© 2007 Sean A. Williams 26 Summary Localization Techniques are more accurate in: –more open outdoor environments –NLOS Possible to create a hybrid of localization techniques. –Taking advantage of different methods based on RF Techniques –TDOA/AOA, TOA/RSS, TDOA/RSS, RSS/Proximity, etc.

© 2007 Sean A. Williams 27 Critique Strengths –The idea is logical and novel –Evaluation is thorough (Simulation, Indoor, Outdoor) Weakness –Some details are left out, making it unclear –How are % calculated? –Why decrement 1 if not a match? Why not do nothing? –Flow of paper Related work usually in beginning or end

© 2007 Sean A. Williams 28 Relativity Course Relativity –We have discussed many location techniques –Both Range-based and Range-free –Range estimations based on RSS information Project Relativity –We are performing a site survey tool which utilizes the RSS information

© 2007 Sean A. Williams 29 References Kiran Yedavalli, et al. “Ecolocation: a sequence based technique for RF localization in wireless sensor networks”. Fourth Internation Symposium on Information Processing Sensor Networks, Pages Thoedore S. Rappaport, Wireless Communication, Principles & Practice, Prentice hall, ceng.usc.edu/~bkrishna/research/talks/Krishnamachari_AROWorks hop05_Localization.ppt