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Wireless Sensor Networks Positioning Algorithms & Energy Management

This document explores the motivation, background, and techniques associated with positioning algorithms and energy management in wireless sensor networks (WSNs). It discusses heuristic methods for determining node positions within ad-hoc networks, emphasizing the critical role of energy consumption management to ensure reliable operations. Key algorithms examined include ABC, TERRAIN, and Hop-TERRAIN, along with their effectiveness in maintaining network performance. The research underlines future directions, including renewable energy sources and tailored MAC protocols for sensor networks.

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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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