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Dynamic Fine-Grained Localization in Ad-Hoc Networks of Sensors

Dynamic Fine-Grained Localization in Ad-Hoc Networks of Sensors. Andreas Savvides, Chin-Chieh Han and Mani B. Strivastava University of California, Los Angeles ACM SIGMOBILE ’ 01 Presented by Kisuk Kweon. Contents. Introduction Background Related work Ranging Characterization

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Dynamic Fine-Grained Localization in Ad-Hoc Networks of Sensors

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  1. Dynamic Fine-Grained Localization in Ad-Hoc Networks of Sensors Andreas Savvides, Chin-Chieh Han and Mani B. Strivastava University of California, Los Angeles ACM SIGMOBILE ’01 Presented by Kisuk Kweon Computer Architecture Lab

  2. Contents • Introduction • Background • Related work • Ranging Characterization • Localization Algorithms • Experimental Setup and Results • Centralized vs. Distributed • Conclusions

  3. Introduction • Sensor Networks and Location Discovery • A new form of distributed information exchange • Various Applications : environmental and natural habitat monitoring, home networking and smart battlefields • Physical location of sensor in space • 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

  4. Introduction • AHLoS (Ad-Hoc Localization awareness) • Low cost, work indoors, not expensive infrastructure • Limited fraction of the nodes knows their exact location • Nodes dynamically discover their location through a two-phase process : Ranging and Estimation phase • Ranging phase • Each node estimate its distance from its neighbors • Estimation phase • Nodes use ranging information and beacon node locations to estimate their positions

  5. Background • Location discovery approaches consist of two phases : distance estimation, distance combining • Methods for estimating the distance • Received Signal Strength Indicator (RSSI) • Time based methods (ToA, TDoA) • Angle-of-Arrival (AoA) • Methods for combining phase • Hyperbolic tri-lateration • Triangulation (using the direction of the node) • Maximum Likelihood (ML) estimation

  6. Hyperbolic tri-lateration • Triangulation • ML Multilateration

  7. Related Work • Outdoor • In 1970s, the automatic vehicle location (AVL) • Determine the position of police cars • In 1993, the Global Positioning System (GPS) • Based on the NAVSTAR satellites (24 satellites) • LORAN • Use ground based beacons instead of satellites • Indoor • The RADAR system • Use RF strength from three base stations • The Cricket location support system • Use Ultrasound from fixed beacons • The Bat system • Node carries an ultrasound transmitter

  8. Ranging Characterization • Received Signal Strength • RF signal attenuation as a function of distance • For signal strength measurements use WINS nodes • 200MHz processor, 128 KB RAM, 1MB Flash • 15 transmission power levels : 0.12 to 36.31 mW • A pair of RSSI(Received Signal Strength Indicator) registers • Inconsistent Model because of Multipath, 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

  9. Received Signal Strength Figure 1 Distance (m) Figure 2 Figure 1 : WINS Sensor Node Figure 2 : Radio Signal Strength Radio Characterization using WINS Nodes (power level P = 7, 13) Figure 3 : RSSI Ranging Model Parameters for WINS nodes Figure 3

  10. Ranging Characterization • ToA using RF and Ultrasound • The time difference between RF and ultrasound • For ToA measurements use Medusa nodes • AVR 8535 processor 8 KB Flash, 512 Bytes SRAM and EEPROM • DR3000 radio module : two data rates (2.4 and 19.2 kbps) • Six pairs of 40 KHz ultrasonic transducers • The ultrasound range is about 3 meters • To estimate the speed to sound, perform a best line fit using linear regression • For this model S = 0.4485, k= 21.485831

  11. Figure 2 Figure 1 Figure 1 : Medusa node Figure 2 : Distance measurement using ultrasound and radio signals Figure 3 : Ultrasound Ranging Characterization MCU time measurement Distance (cm) Figure 3

  12. Signal Strength vs. ToA ranging • ToA is more reliable than received signal strength • Signal strength is greatly affected by amplitude variations • ToA raging only depends on time difference • AHLoS chose ToA raging A comparison of RSSI and ultrasound ranging

  13. Localization Algorithms • 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

  14. Atomic Multilateration • Requirement 1 • Atomic multilateration can take place if the unknown node is within on 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 which atomic multilateration can be applied

  15. The difference between the measured distance and estimated Euclidean distance (Equation 1) A Maximum Likelihood estimate of the node’s position can be obtained by taking The minimum mean square estimate (MMSE) of equations By setting = equation 1, squaring and rearranging term Solve using the matrix solution for MMSE

  16. Iterative Multilateration • Use atomic multilateration • Repeats until the positions of all the nodes that eventually can have three or more beacons are estimated Iterative Multilateration Algorithm as it executes on a centralized node

  17. Collaborative Multilateration • Used when atomic multilateration requirement is not met • Use of location information over multiple hops • Ad-hoc network to be G = (N,E) • Beacon nodes are denoted by a set B • The set of unknown nodes is denoted by U • Our goal is to solve for xu, yu ⊆ U by minimizing

  18. Collaborative Multilateration • Definition 1 • A node is a participating node if it is either a beacon or if it is an unknown node with at least three participating neighbors • Definition 2 • A participating node pair is a beacon-unknown or unknown-unknown pair of connected nodes where all unknowns are participating

  19. Collaborative Multilateration • A sensor field of 100 by 100, sensor range of 10 300 nodes Percent resolved nodes Percent beacons 200 nodes Percent resolved nodes Percent beacons

  20. Experimental Setup and Results • Testbed • 9 Medusa nodes and Pentium II 300MHz PC • All nodes perform ranging and transmit to PC that runs the localization algorithm

  21. Centralized vs. Distributed Byte Transmitted Byte Transmitted Network Size Network Size Traffic in distributed and centralized With 10 % beacons Traffic in distributed and centralized With 20 % beacons

  22. Centralized vs. Distributed Energy per node (J) Energy per node (J) Network Size Network Size Average energy spent at a node during localization 10% beacons, 20% beacons

  23. Conclusions • The use of ToA ranging is a good for fine-grained localization • Fine-grained localization scheme should operate in distributed fashion • Future Work • For ranging phase we will use the combination of ultrasonic ToA and received signal strength RF

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