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Computer Networks Group Universität Paderborn Ad hoc and Sensor Networks Chapter 9: Localization & positioning Holger Karl.

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Presentation on theme: "Computer Networks Group Universität Paderborn Ad hoc and Sensor Networks Chapter 9: Localization & positioning Holger Karl."— Presentation transcript:

1 Computer Networks Group Universität Paderborn Ad hoc and Sensor Networks Chapter 9: Localization & positioning Holger Karl

2 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning2 Outline Introduction AHLoS Ad-Hoc Localization Systeme and overview Atomic Multilateration Iterative Multilateration Collaborative Multilateration Performance evaluation Conclusion

3 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning3 Introduction What is Localization A mechanism for discovering spatial relationships among objects

4 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning4 Introduction here, It is Location discovery for nodes Given a network of sensor nodes where a few nodes know their location how do we calculate the location of the nodes? Known Location Unknown Location

5 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning5 Introduction Why need this kind of localization? Motivation Support Location Aware Applications Track Objects Report event origins Evaluate network coverage Assist with routing, GF Support for upper level protocols. GPS is not practical Not work Indoors or if blocked from the GPS satellites Spends the battery life of the node Issue of the production cost factor of GPS Increase the size of sensor nodes

6 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning6 Introduction Two phases Location discovery approaches consist of two phases : Ranging phase, Estimation phase Ranging phase (distance estimation) Each node estimate its distance from its neighbors Estimation phase (distance combining) Nodes use ranging information and beacon node locations to estimate their positions

7 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning7 Introduction phases 1: Ranging phase Distance measuring methods Signal Strength Uses RSSI readings Time based methods ToA, TDoA Used with radio, acoustic, ultrasound Angle of Arrival (AoA) Measured with directive antennas or arrays

8 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning8 Introduction phases 2: Estimation phase Hyperbolic Trilateration Triangulation Multi-lateration Considers all available beacons A B C a b c Sines Rule Cosines Rule

9 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning9 Introduction Related work Outdoor Automatic Vehicle Location (AVL) Determine the position of police cars Use ToA, Multi-lateration Global Positioning System (GPS) & LORAN GPS:24 NAVSTAR satellites LORAN: ground based beacons instead of satellites Time-of-flight, trilateration Mobile phone position Cellular base station transmits beacons Use TDoA, Multi-lateration

10 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning10 Introduction Related work Indoor RADAR system Track the location of users within a building RF strength measurements from three fixed base stations Build a set of signal strength maps Mathing the online readings from the maps Cricket location support system Use Ultrasound from fixed beacons Multi-lateration The Bat system Node carries an ultrasound transmitter Multi-lateration

11 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning11 Introduction Ranging characterization Received Signal Strength RF signal attenuation is a function of distance Inconsistent Model because of environment fading and shadowing effects and the altitude of the radio antenna A Model is derived by obtaining a least square fit for each power level

12 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning12 Introduction Ranging ToA using RF and Ultrasound The time difference between RF and ultrasound To estimate the speed to sound, perform a best line fit

13 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning13 Introduction Discussion Does ToA suffer from the environment changes? Obstacles, interference to ToA? Extra work to identify the pairs of Radio Signal and Ultrasound pulse. Constraints: Ultrasound range on the Medusa nodes used is about 3 meters (11-12 feet), the ultra-range of second generation of Medusa is about meters, far less than the communication radius (30-100m) Any other comments?

14 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning14 Outline Introduction AHLoS Ad-Hoc Localization Systeme and overview Atomic Multilateration Iterative Multilateration Collaborative Multilateration Performance evaluation Conclusion

15 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning15 AHLoS Ad-Hoc Localization Systeme Ranging phase (distance estimation) ToA Estimation phase (distance combining) Multilateration

16 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning16 AHLoS Overview Some percentage of nodes knows their positions Beacon nodes Nodes with known positions Broadcast their locations to their neighbors Unknown nodes Nodes with unknown positions Use ranging information and beacon node locations to estimate their positions Once knows its location, becomes a beacon node Atomic, Iterative, and Collaborative Multilateration

17 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning17 Outline Introduction AHLoS Ad-Hoc Localization Systeme and overview Atomic Multilateration Iterative Multilateration Collaborative Multilateration Performance evaluation Conclusion

18 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning18 AHLoS Atomic Multilateration Requirement Atomic multilateration can take place if the unknown node is within one hop distance from at least three beacon nodes. The node may also estimate the ultrasound propagation speed if four or more beacons are available Topology for atomic multilateration 1 d1x X X One beacon, location X is not unique 1 d1x X X d2x 2 Two beacons, location X is not unique 1 X d1x d2x 2 3 d3x Three beacons, location X is unique 1 d1x X X d2x 2 d3x 3 Three lined beacons, location X is not unique

19 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning19 What we know: 1. The location of Three or more beacons N1,N2,N3, … … 2. T i0, the time from beacon Ni to unknown node 0 for ultrasound propagation What we want to get: The location of the unknown node 0 How to get the location: Make the difference between the measured distance and estimated Euclidean distance to be as small as possible. Method used: The minimum mean square estimate (MMSE), let F to be as small as possible (Equation 3) AHLoS Atomic Multilateration (Equation 4)

20 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning20 The goal is let F(X0,Y0,S) in equation 4 to be as small as possible Here, equation 5 is generated by setting = 0 AHLoS Incorrectness 1 in Atomic Multilateration (Equation 4) (Equation 5) We should have So it has (Equation 40) If equations 5 have solutions, they are solutions to equation 4. BUT equations 5 may not have solutions, because T i0 is a measured value, equations 5 can not be guaranteed to have solutions on the measured values T i0.

21 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning21 Look at the solution of the system of equations AHLoS Incorrectness 2 in Atomic Multilateration (Equation A) How to get it? In the process, one important assumption is (Equation B) Ifdoesnt exist. We can not use the method,

22 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning22 3 beacons are not enough to get a unique solution with unknown speed s. AHLoS Incorrectness 3 in Atomic Multilateration In the left figure, d1x, d2x, d3x are distance But in the equations, distance is unknown, Another variable is introduced, the ultrasound Propagation speed s. There are only 3 equations with x, y square factors and unknown s. 3 beacons are not enough to get a unique location solution with unknown speed s. 1 X d1x d2x 2 3 d3x

23 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning23 EXAMPLE AHLoS Atomic Multilateration Example 1 N1(0,1) N2(0,-1) N0(1,0) N3(2,0)1 Conditions: Three beacons N1(0,1),N2(0,-1),N3(2,0) One unknown node N0 The time of the ultrasound propagation: From N1 to N0, it is sqrt(2) s From N2 to N0, it is sqrt(2) s From N3 to N0, it is 1 s Test: Using the algorithm on the paper to see if we can get the coordinates of N0 or some other interesting results.

24 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning24 EXAMPLE AHLoS Atomic Multilateration Example 1 N1(0,1) N2(0,-1) N0(1,0) N3(2,0)1 From equation above, we have Equation N1 Equation N2 Equation N3 N1 – N3 and N2 – N3, we have

25 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning25 EXAMPLE AHLoS Atomic Multilateration Example 1 N1(0,1) N2(0,-1) N0(1,0) N3(2,0)1 We can not directly use the solution provided by the paper.

26 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning26 EXAMPLE AHLoS Atomic Multilateration Example 1 N1(0,1) N2(0,-1) N0(1,0) N3(2,0)1 From equations above, we have Equation e1 Equation e2 Equation e3 Equation e4 Equation e5 From Equation e4,e5, we have Equation e6 From Equation e5,e6, we haveEquation e7 From Equation e3,e5,e6 we have Equation e8 Eliminating

27 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning27 EXAMPLE AHLoS Atomic Multilateration Example 1 N1(0,1) N2(0,-1) N0(1,0) N3(2,0)1 Equation e6 Equation e7 Equation e8 From Equation e6,e7,e8, we have 2 sets of results OR

28 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning28 AHLoS Atomic Multilateration Example 1 N1(0,1) N2(0,-1) N0(1,0) N3(2,0)1 OR N0(7,0) Taking the algorithm on the paper 3 beacons are not enough to get a unique solution with unknown speed s.

29 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning29 EXAMPLE AHLoS Atomic Multilateration Example 2 N1(0,1) N2(0,-1) N0(1,0) N3(2,0)1 Conditions: Three beacons N1(0,1),N2(0,-1),N3(2,0) One unknown node N0 The time of the ultrasound propagation: From N1 to N0, it is sqrt(2) ms From N2 to N0, it is sqrt(2) ms From N3 to N0, it is 1 ms Test: Using the standard MMSE method to see if we can get the coordinates of N0 or some other interesting results.

30 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning30 EXAMPLE AHLoS Atomic Multilateration Example 2 N1(0,1) N2(0,-1) N0(1,0) N3(2,0)1 select Taking the algorithm on MMSE 3 beacons are not enough to get a unique solution with unknown speed s.

31 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning31 AHLoS Conclusion in Atomic Multilateration With the unknown speed of ultrasound pulse or other efficient constraints, generally, it is impossible to get a unique location of one unknown node only depending 3 un-lined beacons Other constraints, such as a roughly scope of ultrasound speed, angle, etc, must be added to make the solution determined. Or 4 un-lines beacons determine one unknown nodes location The computation process on the paper is not robust. In the algorithms later, we assume the speed of ultrasound is known

32 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning32 Outline Introduction AHLoS Ad-Hoc Localization Systeme and overview Atomic Multilateration Iterative Multilateration Collaborative Multilateration Performance evaluation Conclusion

33 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning33 Outline Introduction AHLoS Ad-Hoc Localization Systeme and overview Atomic Multilateration Iterative Multilateration Collaborative Multilateration Performance evaluation Conclusion

34 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning34 AHLoS Collaborative Multilateration One node estimates its position by considering use of location information over multiple hops How it works For one node, to decide which nodes should be in its participating node set S For node, i is connected to u, and node u is an unknown node, the goal function is the same as that of the atomic multi-lateration, to minimize the

35 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning35 Outline Introduction AHLoS Ad-Hoc Localization Systeme and overview Atomic Multilateration Iterative Multilateration Collaborative Multilateration Performance evaluation Conclusion

36 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning36 Performance evaluation What kind of performance evaluation do we need for the localization? What do we care most about the localization? Accuracy Scalability Cost ……

37 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning37 Performance evaluation Accuracy Only Iterative Multilateration is included as we talked earlier. What is the behind: 1.How many steps are there for accumulated error? 2.How beacons are deployed? 3.Small scale

38 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning38 Performance evaluation cost 117 nodes/10,000m 2 Uniformly distributed, Range = 10

39 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning39 Performance evaluation cost

40 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning40 Outline Introduction AHLoS Ad-Hoc Localization Systeme and overview Atomic Multilateration Iterative Multilateration Collaborative Multilateration Performance evaluation Conclusion

41 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning41 Papers: 1. Dynamic Fine-Grained Localization in Ad-Hoc Networks of Sensors 2. Distributed Fine-Grained Localization in Ad-Hoc networks 3. Localization in Ad-Hoc Sensor Networks Slides: 1. Dynamic Fine-Grained Localization in Ad-Hoc Networks of Sensors presented by Kisuk Kweon 2. LOCALIZATION presented by Lewis Girod 3.Survey of Estimation of Location in Sensor Networks Presented by Wei-Peng Chen 4.Dynamic Location Discovery in Ad-Hoc Networks presented byAndreas Savvides, Boulis and Mani B. Srivastava 5. Distributed localization in wireless ad-hoc sensor network presented by Vaidyanathan Ramadurai References

42 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning42 Goals of this chapter Means for a node to determine its physical position (with respect to some coordinate system) or symbolic location Using the help of Anchor nodes that know their position Directly adjacent Over multiple hops Using different means to determine distances/angles locally

43 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning43 Overview Basic approaches Trilateration Multihop schemes

44 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning44 Localization & positioning Determine physical position or logical location Coordinate system or symbolic reference Absolute or relative coordinates Options Centralized or distributed computation Scale (indoors, outdoors, global, …) Sources of information Metrics Accuracy (how close is an estimated position to the real position?) Precision (for repeated position determinations, how often is a given accuracy achieved?) Costs, energy consumption, …

45 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning45 Main approaches (information sources) Proximity Exploit finite range of wireless communication E.g.: easy to determine location in a room with infrared room number announcements (Tri-/Multi-)lateration and angulation Use distance or angle estimates, simple geometry to compute position estimates Scene analysis Radio environment has characteristic signatures Can be measured beforehand, stored, compared with current situation

46 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning46 Estimating distances – RSSI Received Signal Strength Indicator Send out signal of known strength, use received signal strength and path loss coefficient to estimate distance Problem: Highly error-prone process – Shown: PDF for a fixed RSSI Distance Signal strength PDF

47 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning47 Estimating distances – other means Time of arrival (ToA) Use time of transmission, propagation speed, time of arrival to compute distance Problem: Exact time synchronization Time Difference of Arrival (TDoA) Use two different signals with different propagation speeds Example: ultrasound and radio signal Propagation time of radio negligible compared to ultrasound Compute difference between arrival times to compute distance Problem: Calibration, expensive/energy-intensive hardware

48 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning48 Determining angles Directional antennas On the node Mechanically rotating or electrically steerable On several access points Rotating at different offsets Time between beacons allows to compute angles

49 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning49 Some range-free, single-hop localization techniques Overlapping connectivity: Position is estimated in the center of area where circles from which signal is heard/not heard overlap Approximate point in triangle Determine triangles of anchor nodes where node is inside, overlap them Check whether inside a given triangle – move node or simulate movement by asking neighbors Only approximately correct

50 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning50 Overview Basic approaches Trilateration Multihop schemes

51 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning51 Trilateration Assuming distances to three points with known location are exactly given Solve system of equations (Pythagoras!) (x i,y i ) : coordinates of anchor point i, r i distance to anchor i (x u, y u ) : unknown coordinates of node Subtracting eq. 3 from 1 & 2: Rearranging terms gives a linear equation in (x u, y u )!

52 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning52 Trilateration as matrix equation Rewriting as a matrix equation: Example: (x 1, y 1 ) = (2,1), (x 2, y 2 ) = (5,4), (x 3, y 3 ) = (8,2), r 1 = , r 2 = 2, r 3 = 3 ! (x u,y u ) = (5,2)

53 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning53 Trilateration with distance errors What if only distance estimation r i 0 = r i + i available? Use multiple anchors, overdetermined system of equations Use (x u, y u ) that minimize mean square error, i.e,

54 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning54 Minimize mean square error Look at square of the of Euclidean norm expression (note that for all vectors v) Look at derivative with respect to x, set it equal to 0: Normal equation Has unique solution (if A has full rank), which gives desired minimal mean square error Essentially similar for angulation as well

55 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning55 Overview Basic approaches Trilateration Multihop schemes

56 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning56 Multihop range estimation How to estimate range to a node to which no direct radio communication exists? No RSSI, TDoA, … But: Multihop communication is possible Idea 1: Count number of hops, assume length of one hop is known (DV-Hop) Start by counting hops between anchors, divide known distance Idea 2: If range estimates between neighbors exist, use them to improve total length of route estimation in previous method (DV-Distance)

57 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning57 Iterative multilateration Assume some nodes can hear at least three anchors (to perform triangulation), but not all Idea: let more and more nodes compute position estimates, spread position knowledge in the network Problem: Errors accumulate

58 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning58 Probabilistic position description Similar idea to previous one, but accept problem that position of nodes is only probabilistically known Represent this probability explicitly, use it to compute probabilities for further nodes

59 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning59 Conclusions Determining location or position is a vitally important function in WSN, but fraught with many errors and shortcomings Range estimates often not sufficiently accurate Many anchors are needed for acceptable results Anchors might need external position sources (GPS) Multilateration problematic (convergence, accuracy)

60 SS 05Ad hoc & sensor networs - Ch 9: Localization & positioning60 Acknowledgements Notes are partly from slides of : Andreas Savvides, Athanassios Boulis and Mani B. Srivastava Networked and Embedded Systems Lab University of California, Los Angeles Yong Chen Department of Computer Science University of Virginia


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