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Localization Techniques And Extending Network life-time in Sensor Networks

Localization Techniques And Extending Network life-time in Sensor Networks. Archana Bharathidasan October 10, 2002. What are sensor networks?. large number of densely deployed sensor nodes co-operate to carry out some task. Applications. Military Environmental Managing inventory etc.

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Localization Techniques And Extending Network life-time in Sensor Networks

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  1. Localization Techniques And Extending Network life-time in Sensor Networks Archana Bharathidasan October 10, 2002

  2. What are sensor networks? • large number of densely deployed sensor nodes • co-operate to carry out some task

  3. Applications • Military • Environmental • Managing inventory etc.

  4. Challenges • Ad hoc deployment • deployed in regions without infrastructure • Unattended Operation • No human intervention • Untethered • not connected to any energy source • communication dominates processing in energy consumption • Dynamic changes

  5. What is Localization? • Localization refers to the problem of determining the position of a sensor node in some co-ordinate system • Most common technique to determine position -GPS. • Disadvantages: • Cannot work indoors, or underwater or under dense foliage • Power consumption is huge • Expensive • Size of GPS and antenna increases the sensor node size.

  6. Various Localization Techniques • Designate some sensor nodes as "beacons." • Beacons know their positions by some means (maybe GPS). • Other sensor nodes use info from beacons to calculate own position

  7. Various Localization Techniques (cont.) • Timing • calculate the time-of-flight of the communication signal between receiver node and reference point. • Signal Strength • Attenuation of signal proportional to the distance traveled. • Angle of Arrival • Estimate angle of arrival of signals.

  8. Ad Hoc Localization System (AHLoS): • Two phase localization process • 1. Ranging • estimate distance of node from neighbors • 2. Estimation • Use info from the ranging phase to estimate positions

  9. Ranging Characterization • Received Signal Strength • Time Difference of Arrival

  10. . Received Signal Strength • WINS sensor nodes with RSSI resisters • PRSSI = X/rn • PRSSI is the RSSI register reading • r is the distance between 2 nodes • X,n are constants which are a function of distance r

  11. Results • Use of radio signal strength very unpredictable • Suffers from multi-path, fading and shadowing effects • Different nodes exhibit different variations in transmit power for same transmit power level • Accuracy up to a few meters, do not provide accuracy for fine-grained localization • Range same as radio communication range

  12. Time of Arrival (ToA) using RF and Ultrasound • Time difference between two simultaneously transmitted radio and ultrasound signals at the receiver. • Speed of sound characterized in terms of micro-controller timer ticks. • t = sd+k • s is speed of sound in timer ticks • d is estimated distance between two nodes • k is a constant

  13. Results • Accuracy of 2 centimeters for node separations under 3 meters. • Multi-path effects easier to detect. • Range of up to few 10s of meters.

  14. Localization Algorithms for Estimation • Atomic Multilateration • Iterative Multilateration • Collaborative Multilateration

  15. Atomic Multilateration • Unknown node can estimate its location if it can be reached by 3 beacons • Maximum Likelihood estimate of the node's position can be obtained by taking mean square estimate of a system • A set of 3 equations can be constructed and used to determine the (x,y) coordinates • Baseline • Atomic multilateration is possible if the unknown nodes is within one hop distance from at least three beacon nodes

  16. Iterative Multilateration • Atomic multilateration is used a basic primitive. • Determine position of unknown nodes with maximum number of beacons • When location is estimated, the node becomes a beacon • Disadvantage • accumulation of error when unknown nodes which become beacons are used in estimation

  17. Collaborative Multilateration • Position estimation by considering use of location information over multiple hops • Conditions for participation • A node is a participating node if it is either a beacon or if it is an unknown with at least three participating neighbors • A participating node pair is a beacon-unknown or unknown-unknown pair of connected nodes where all unknowns are participating • Can be used to enhance iterative multilateration.

  18. Node and Beacon Placement • Probability of node having a degree d in a binomial distribution is given by • P(d) = PdR. (1-PR)N-d-1. N-1Cd • where, • N is the total number of nodes deployed in a square field of side L • PR, the probability of being in the transmission range is given by, • PR=R2/L2

  19. Implementation details • Medusa node design is used (refer to paper for specifications) • Fitted with ultrasound transceiver • Measurements by node sent to a PC base station using DSDV (Destination Sequenced Distance Vector) protocol • 9 Medusa nodes and PII 300MHz machine • Node positions updated at 5 second intervals on visualization tool

  20. Results

  21. Centralized or Distributed ? • Centralized Solution : Drawbacks • route to central node should be known • time synchronization problem • pre-planning of central node location so that it is easily accessible by other nodes • not robust • data aggregation to conserve bandwidth is not possible.

  22. Centralized, Distributed Tradeoffs • Distributed setup has 6 -10 times less communication overhead than centralized setup. • Network traffic increases in centralized setup as the number of beacons increase • Centralized implementation gives more accurate location estimation

  23. Another goal: • Extending sensor network lifetime!

  24. Sensor nodes need not be turned on all the time .... • User needs to be informed only when a condition is satisfied • Sensor networks can be in • monitoring state • transfer state • Go to active transfer state only when event occurs.

  25. STEM - Sparse Topology and Energy Management • Trade-off between energy and latency and density • A technique to quickly transition to transfer state while making the monitoring state as energy efficient as possible

  26. Basic concept • When there is no traffic to forward, turn on only preprocessing cirtuitry • Main processor awakened when possible event is detected • To be informed that a event has occured even when in sleep state, periodically turn on radio • De-couple transfer and wakeup functionalities.

  27. Initiator • Poll other nodes continuously when event of interest occurs until they wake-up. • To avoid collisions between transfer and wake-up, use two radios.

  28. Operation of STEM-B and STEM-T • STEM-B: • send wakeup signals with both the initiator and destination nodes included in the message • stop polling when destination sends ACK • if collisons occur, all nodes which heard the signal wake up - they go back to sleep if they receive no traffic. • STEM-T: • Same approach as before • Destination node does not send back ACK.

  29. Theoretical Analysis of STEM • Setup latency • STEM-B • Ts = (T + TB)/2 + 2. B1 + B2 - TRx • STEM-T • Ts = T - TRx + 2 . T1 • B1, B2 are transmit duration of beacon and ack • T1 is interval over which channel sensing needs to be performed • TRx is interval over which target node’s radio is on

  30. Simulation Results (1) • Average setup latency per hop Vs. Wakeup period

  31. Simulation Results (2) • Energy Vs. Period for STEM-B.

  32. Simulation Results (3) • Energy Vs. Period for STEM-T

  33. Simulation Results for Energy Study • STEM-T has more energy savings because no ACKs are sent back. • Energy for Transmit+ACK > Energy to transmit a tone

  34. Conclusion • Localization Techniques • Techniques to conserve sensor network energy

  35. Papers • 1. Dynamic fine-grained localization in Ad-Hoc networks of sensors. Andreas Savvides, Chih-Chieh Han, Mani B. Srivastava. MOBICOM 2001: 166-179. • 2. Optimizing Sensor Networks in the Energy-Latency-Density Design Space. Schurgers.C, Tsiatsis. V, Ganeriwal.S, Srivastava.M. IEEE Transactions on Mobile Computing, Jan-Mar 2002.

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