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Energy M anagement in Wireless Sensor Networks

Energy M anagement in Wireless Sensor Networks. Mohamed Hauter CMPE257 University of California, Santa Cruz. Outline. Wireless S ensor Networks Energy and Wireless Sensor Networks Paper1 Paper2 Paper3 Conclusion. Wireless Sensor Network.

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Energy M anagement in Wireless Sensor Networks

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  1. Energy Management in Wireless Sensor Networks Mohamed Hauter CMPE257 University of California, Santa Cruz

  2. Outline Wireless Sensor Networks Energy and Wireless Sensor Networks Paper1 Paper2 Paper3 Conclusion

  3. Wireless Sensor Network Consists of spatially distributed autonomous sensors. Monitors physical or environmental conditions (i.e. temperature, pressure, etc.) Cooperates to pass data through network to main location

  4. Energy and Wireless Sensor Networks Usually deployed in remote regions Energy consumption vs. battery life Energy harvesting

  5. Energy aware efficient geographic routing in lossy wireless sensornetworks with environmental energy supply BY:Kai Zeng Kui Ren Wenjing Lou Patrick J. Moran

  6. Basic Idea! Combine the efficiency of Geo-Aware routing and energy harvesting techniques.

  7. Proposal • Geographic Routing with Environmental Energy Supply (GREES) • Packets are delivered through low cost links • Balances residual energy on nodes using environmental energy supply • Two protocols are proposed: • GREES-L • GREES-M

  8. Related Work • Battery technology has been unchanged for many years • Former energy aware routing protocols: • Batteries have limited/fixed capacity • Decisions are made based on energy consumption • Energy scavengers: • Harvests small amounts of energy from ambient sources • Solar-aware routing protocols: • Must have a global knowledge of the whole network

  9. Protocol Description • Maintain one-hop neighbor’s information: • Location • Residual energy • Energy harvesting rate • Energy consumption rate • Wireless link quality

  10. Protocol Description (Cont.) • To balance the geographical advance efficiency per packet transmission and the energy availability on receiving nodes: • GREES-L - uses linear combination • GREES-M – uses multiplication

  11. GREES

  12. GREES (Cont.)

  13. GREES (Cont.)

  14. Simulation Results

  15. Simulation Results

  16. Conclusions • Strengths: • Maintains a higher mean residual energy on nodes • Achieves better load balancing • Small standard deviation of residual energy on nodes • Does not compromise the end-to-end throughput performance • Weaknesses: • Exhibits graceful degradation on end-to-end delay • What happens when energy harvesting fails?

  17. Minimum-Energy Asynchronous Dissemination to Mobile Sinks in Wireless Sensor Networks BY:HyungSeokKim Tarek F. Abdelzaher Wook Hyun Kwon

  18. Basic Idea • Achieve energy savings in wireless sensor networks by: • Optimizing communications between sensor nodes and sinks • Tradeoff? • Increase in path delay. • Is the tradeoff a good one? We’ll see…

  19. Related Work • Overlay Multicasting • Uses sinks as intermediate nodes in the tree • Uses flooding to disseminate information • Flooding is energy-intensive

  20. Proposal • SEAD – Scalable Energy-efficient Asynchronous Dissemination protocol • Stationary sensor node takes the mobile sink’s place • Build an optimal dissemination tree (d-tree) • Select dissemination paths to stationary sensor nodes • Stationary sensor nodes forward data • Minimize energy cost • As sink moves, forward delay increases (tradeoff) • Reconfigure d-tree when needed

  21. SEAD Tree Model in Wireless Sensor Networks

  22. SEAD Sink Search

  23. SEAD Sink Search

  24. SEAD Sink Search

  25. SEAD Sink Search

  26. Results

  27. Results

  28. Results

  29. Results

  30. Results

  31. Conclusion • Strengths: • SEAD saves energy • Strikes a balance between end-to-end delay and power consumption • Power savings are favored over delay minimization • Weaknesses: • Affects the lifetime of the access node • Not robust in high density networks

  32. Meeting Lifetime Goals with Energy LevelsBY:Andreas LachenmannPedro Jos´e Marr ´onDaniel MinderKurt Rothermel

  33. Basic idea • Levels : an abstraction for energy-aware programming of wireless sensor networks. • Goal is to meet the user-defined lifetime goals while maximizing application quality • Applied in applications with: • Known lifetime • No redundant nodes

  34. How does it work? Define energy levels Measure energy consumption of each level (using an energy profiler) Decide level of functionality to meet lifetime goal Maximize performance within allowed energy level Maintain network connectivity Maintain optimal application quality

  35. Example • ZebraNet monitoring system • Gathers GPS traces • If a node fails due to energy drought, what happens? • Lost track of at least one animal • Possible network disconnection • Solution ???

  36. Solution • A node can: • Stop forwarding data from other nodes • Decrease energy-intensive radio communications • Stop storing other nodes’ data (avoid flash memory access) • Decrease queries of GPS position • …

  37. Benefits to developer Eliminates low energy-levels issues Ensures reaching targeted lifetime Low overhead

  38. Design Considerations Single application running on each sensor node Periodic behavior It is possible to simulate output behavior, thus acquire energy consumption statistics Use voltage sensors Investing time to define energy levels

  39. Design Goals Provide a programming abstraction and runtime support that helps to meet the user’s lifetime goals by deactivating parts of the application if necessary

  40. How to achieve goals? • Divide into sub goals: • Follow definition of optional functionality • Make it easy to use • Minimum overhead • Provide good application quality • Low runtime • Robust with inaccurate energy estimates

  41. Notice Levels approach follows the well-known model predictive control (MPC) schemes

  42. Combining Energy Levels

  43. Code Example for Energy Levels

  44. Computing the Energy Consumption of a Code Block

  45. Special Cases Energy consumed by lower level energy_level(1) = total_energy_consumed – energy_estimated_all_other_levels Energy consumption that depends on some state of the hardware of software Example: attempting to turn on an active device. No energy consumed, thus adjust estimates.

  46. Battery Discharge Characteristics (from three experiments)

  47. Results

  48. Runtime Overhead

  49. Conclusion Helps meet user-defined lifetime goals Requires small code modifications Low overhead Maximize performance within allowed energy level Maintain network connectivity Maintain optimal application quality

  50. Questions ????

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