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

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Presentation on theme: "Energy Management in Wireless Sensor Networks Mohamed Hauter CMPE257 University of California, Santa Cruz 1."— Presentation transcript:

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

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

3 3 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 Wireless Sensor Network

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

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

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

7  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 7 Proposal

8  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 8 Related Work

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

10  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 10 Protocol Description (Cont.)

11 11 GREES

12 12 GREES (Cont.)

13 13 GREES (Cont.)

14 14 Simulation Results

15 15 Simulation Results

16  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? 16 Conclusions

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

18  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… 18 Basic Idea

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

20  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 20 Proposal

21 21 SEAD Tree Model in Wireless Sensor Networks

22 22 SEAD Sink Search

23 23 SEAD Sink Search

24 24 SEAD Sink Search

25 25 SEAD Sink Search

26 26 Results

27 27 Results

28 28 Results

29 29 Results

30 30 Results

31  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 31 Conclusion

32 Meeting Lifetime Goals with Energy Levels BY: Andreas Lachenmann Pedro Jos´e Marr ´on Daniel Minder Kurt Rothermel 32

33  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: 1. Known lifetime 2.No redundant nodes 33 Basic idea

34 1.Define energy levels 2.Measure energy consumption of each level (using an energy profiler) 3.Decide level of functionality to meet lifetime goal 4.Maximize performance within allowed energy level 5.Maintain network connectivity 6.Maintain optimal application quality 34 How does it work?

35  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 ??? 35 Example

36  A node can: 1.Stop forwarding data from other nodes 2.Decrease energy-intensive radio communications 3.Stop storing other nodes’ data (avoid flash memory access) 4.Decrease queries of GPS position 5.… 36 Solution

37 1.Eliminates low energy-levels issues 2.Ensures reaching targeted lifetime 3.Low overhead 37 Benefits to developer

38  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 38 Design Considerations

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

40  Divide into sub goals: 1.Follow definition of optional functionality 2.Make it easy to use 3.Minimum overhead 4.Provide good application quality 5.Low runtime 6.Robust with inaccurate energy estimates 40 How to achieve goals?

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

42 42 Combining Energy Levels

43 43 Code Example for Energy Levels

44 44 Computing the Energy Consumption of a Code Block

45  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. 45 Special Cases

46 46 Battery Discharge Characteristics (from three experiments)

47 47 Results

48 48 Runtime Overhead

49  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 49 Conclusion

50 50 Questions ????

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