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Wireless Sensor Networks Positioning Algorithms & Energy Management

Wireless Sensor Networks Positioning Algorithms & Energy Management. Sherry Adair Beaux Sharifi CS526 Spring 2005. Agenda. Motivation Positioning Algorithms Energy Management References. Example Applications. Example Applications (cont). UC Berkeley Biology Research.

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Wireless Sensor Networks Positioning Algorithms & Energy Management

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  1. Wireless Sensor NetworksPositioning Algorithms & Energy Management Sherry Adair Beaux Sharifi CS526 Spring 2005

  2. Agenda • Motivation • Positioning Algorithms • Energy Management • References CS526 WSN Adair/Sharifi

  3. Example Applications CS526 WSN Adair/Sharifi

  4. Example Applications (cont) UC Berkeley Biology Research CS526 WSN Adair/Sharifi

  5. Research Focus • Positioning Algorithms • Energy Management Positioning Algorithms are distributed heuristic algorithms used to determine the local or global coordinate positions of nodes in an ad-hoc wireless sensor network. • Most applications implicitly require positioning information • Most research topics are focused on methods for saving energy CS526 WSN Adair/Sharifi

  6. Positioning Agenda • Background • 3 Different Algorithms • Simulation Results • Conclusion CS526 WSN Adair/Sharifi

  7. Positioning Background • 2D Trilateration • 3D Trilateration a b b c d a c CS526 WSN Adair/Sharifi

  8. Positioning Background (cont) • Two Major Difficulties to Positioning: • Sparse Anchor Node Problem • Range Error Problem CS526 WSN Adair/Sharifi

  9. Positioning Algorithms • ABC – Assumption Based Coordinate [Savarese, Rabaey, Beutel, 2001] • TERRAIN - Triangulation via Extended Range and RedundantAssociation of Intermediate Nodes [Savarese, 2002] • Hop-TERRAIN[Savarese, 2002] • Two-Phase[Savarese, 2002] • First Phase: Hop-TERRAIN • Second Phase: Refinement CS526 WSN Adair/Sharifi

  10. 1 3 4 4 2 3 TERRAIN Algorithm ABC Algorithm n0 = (0,0) n1 = (r01, 0) n2 = (r012 + r022 – r122) , r022 – x22 ) 2 (6,6) (3,5) 2r01 3 (1,3) (3,2) (5,2) 1 = 8.5 = sqrt(62 + 62) 1 (0,0) (5,1) 2 = 4.3 (3,0) 3 = 1.2 (18, 24) CS526 WSN Adair/Sharifi

  11. 3 3 2 3 2 3 2 1 2 3 1 3 1 2 3 1 2 Hop-TERRAIN Algorithm • Binary nature provides following benefits: • No compounding of errors at each hop • Provides consistent results • Scales to much larger networks 2 3 1 1 = 3 * Hop Metric = 6 = 4 2 3 = 2 (18, 24) CS526 WSN Adair/Sharifi

  12. Two-Phase Refinement Algorithm • First Phase: Hop-TERRAIN • Detects Edge Independence (for poor topologies) • Second Phase: Refinement • Iterative improvement of positions via ranges until position converges • Uses Confidence Metrics (for convergence) CS526 WSN Adair/Sharifi

  13. Simulation ResultsTERRAIN vs. Hop-TERRAIN Range Error Sensitivity of Hop-TERRAIN and TERRAIN (nodes = 40, anchors = 4, range = 10, grid = 30x30) CS526 WSN Adair/Sharifi

  14. Simulation Results (cont)Hop-TERRAIN vs. Refinement Average Position Error After Refinement (5% Range Errors) Average Position Error After Hop-TERRAIN (5% Range Errors) CS526 WSN Adair/Sharifi

  15. Simulation Results (cont)Hop-TERRAIN vs. Refinement Range Error Sensitivity between Hop-TERRAIN and Refinement (10% Anchors, 12 Nodes Connectivity) CS526 WSN Adair/Sharifi

  16. (< 40%) Positioning Conclusion CS526 WSN Adair/Sharifi

  17. Future Positioning Research • Total Least Squares Algorithm • Hop-Refinement CS526 WSN Adair/Sharifi

  18. Energy Agenda • Importance of Energy Management • Sources of Wasted Energy • Methods of Reducing Energy Consumption • Future Research • Conclusions CS526 WSN Adair/Sharifi

  19. Importance of Energy Management • Thousands of motes • Not feasible to access them because of location, or quantity • Reliability of application depends on motes continuing to operate • Required to operate for many years CS526 WSN Adair/Sharifi

  20. Source of Wasted Energy • Transmissions • Collisions • Overhearing • Control packet overhead • Idle listening • Lossy links CS526 WSN Adair/Sharifi

  21. Methods of Reducing Energy Consumption • Algorithms designed with power consumption in mind • Special MAC protocols (S-MAC, B-MAC) • Active/Sleep periods • Decreasing the sensing coverage area • Data Reduction • Shorter, more reliable links • Scavenging Power from solar, vibration using custom IC CS526 WSN Adair/Sharifi

  22. Special MAC Protocols • Needed to focus on energy management • Based on 802.11 protocol • Use active/sleep schedule • Collision Avoidance • Increase latency • Reconfigure network based on current load (B-MAC) CS526 WSN Adair/Sharifi

  23. Example of Energy Saved by Sleeping • System Components: • StrongArm SA-1110 microprocessor • Sensor • Radio CS526 WSN Adair/Sharifi

  24. Mica2 sleep savings Full operation of the sensor requires about ~15ma of current AA batteries supply ~1800 ma which would last about 120 hours or 5 days CS526 WSN Adair/Sharifi

  25. Shorter, more reliable links CS526 WSN Adair/Sharifi

  26. Energy Scavenging CS526 WSN Adair/Sharifi

  27. Energy Scavenging (cont) CS526 WSN Adair/Sharifi

  28. Energy Scavenging PicoRadio Meso-scale radio CS526 WSN Adair/Sharifi

  29. Moore’s Law • Capabilities increasing • Costs staying the same • Power consumption staying the same • Reduced power consumption for special purpose nodes CS526 WSN Adair/Sharifi

  30. Future Research • Renewable sources of energy • MAC protocols designed especially for WSN • Custom low power ICs CS526 WSN Adair/Sharifi

  31. Energy Conclusions • Much energy is spent in the communication task of the mote, with almost as much energy required to listen as to send • Special MAC protocols are required to address the special needs of WSN such as conserving power and adjusting to the changing network topology • Active/sleep schedule is a common method used to conserve energy. Tradeoff is latency in packet delivery • Possibility of extending the lifetime of motes using renewable energy sources such as solar and vibration CS526 WSN Adair/Sharifi

  32. References • http://bwrc.eecs.berkeley.edu/People/Faculty/jan/presentations/AmbientIntelligence.pdf • Jason Hill, Mike Horton, Ralph Kling, Lakshman Krishnamurthy. The Platforms Enabling Wireless Sensor Networks. Communications of the ACM June 2004/ Vol47. No. 6. p 41-46. • C. Savarese, “Robust Positioning Algorithms for Distributed Ad-Hoc Wireless Sensor Networks”, Masters Thesis, 2002. • C. Savarese, J. Rabaey, and J. Beutel, “Locationing in Distributed Ad-hoc Wireless Sensor Networks,” in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pages 2037-2040, Salt Lake City, UT, May 2001 • Eugene Shih, Seong-Hwan Cho, Nathan Ickes, Rex Min, Amit Sinha, Alice Wang, and Anantha Chandraskasan. Physical Layer Driven Protocol and Algorithm Design for Energy-Efficient Wireless Sensor Networks. CS526 WSN Adair/Sharifi

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