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Brian 2009/8/17

SolarStore : Enhancing Data Reliability in Solar-powered Storage-centric Sensor Networks Yong Yang , Lili Wang, Dong Kun Noh, Hieu Khac Le and Tarek F. Abdelzah e Mobisys 2009. Brian 2009/8/17. Outline . Introduction Method Hardware system Implementation Performance Result

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Brian 2009/8/17

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  1. SolarStore: Enhancing Data Reliability in Solar-poweredStorage-centric Sensor NetworksYong Yang, LiliWang, Dong Kun Noh, HieuKhacLe and Tarek F. AbdelzaheMobisys 2009 Brian 2009/8/17

  2. Outline • Introduction • Method • Hardware system • Implementation • Performance • Result • Conclusion

  3. Introduction • WSN in habitat and environment monitoring • Sensors are deployed in remote locales • Limited connectivity • Data need to be stored in the network • Long-term running • SolarStore • Energy adaptive • Storage reliability mechanism

  4. Motivations • Energy • How to estimate redundancy energy to enhance the reliability? • Storage • How to use the redundancy energy to enhance the reliability?

  5. Implementation • 9 nodes in the farm of the University of Illinois at Urbana-Champaign (40.1N, 88.20W) • 12V, 98AH • 120Watts

  6. Hardware • EEE PC :10~15Watts (0.8~1.2A for 12V), 18GB • Linksys WRT54GL : 2.4Watts • >3Mbps transmission by 50m outdoor • Phidget voltage sensor:0.06V resolution

  7. Architecture of SolarStore • Repository: a piece of storage space on the solid state disk managed by the operating system • Replicator: reads data blocks from Repository and encodes them into data chunks • Receiver:receives the encoded data chunks from other nodes and stores them into Repository

  8. Architecture of SolarStore

  9. Method • Eresidual: current residual energy in battery • Tfull(Eresidual):expected time when battery is full • C: battery capacity • Psolar: average power charging rate by solar panel • Psys: average power consumption rate by system

  10. Method • How to get Eresidual threshold if B(Eresidual)=0? B(Eresidual): the expected duration of blackout time • Eresidual= Psys*Tfull(Eresidual) at least • Eresidualthreshold = C*(Psys/Psolar) • △E: energy allocated for enhancing data reliability(if Eresidual ≧ C*(Psys/Psolar) ) △E = Eresidual- C*(Psys/Psolar)

  11. Method • Sresidual: current residual storage space left • △S: storage surplus • R: expected data sensing rate • M: expected time from now to the next upload opportunity △S=Sresidual - R*M

  12. Data coding and Reliability level • Fountain coding for replication • partitions a data block into k chunks and generates k’ (k’ ≧ k) encoded chunks, eg. k=8, k’=12 • Scatter out to each neighbor k’/(g+1) chunks, g= amount of neighbors(eg. g=8) • Reliability level : α=k’/h • h:the number of data chunks stored on the node that were generated from this data block

  13. Voltage charging characteristic • Charging on from 6AM~7PM • 14.0V as 100% • 11.0V as 0%

  14. Performance evaluation • Charging current from Oct.21~Nov.4 2008 • Emulation

  15. Three Experiments • Under different energy states • Adaption to other environment • Comparison to three other schemes

  16. Under different energy states • Residual energy • the behavior of SolarStore in a long run doesnot depend on the initial states

  17. Under different energy states • Residual storage and storage surplus • Surplus remain constant Node 9 Node 2

  18. Adaption to other environment • Enlarge charging current by 3 times for one day every 3 three days • The other two days multiply 0.2

  19. Comparison to three other schemes • 0-Reliable • no data replication at all and uses all energy and storage space for data sensing • 1-Reliable • always replicates data to maximize data reliability • full-Reliable • only starts data replication when the battery is nearly full (99%) because the energy charged from solar panels will be wasted if not used.

  20. Comparison to three other schemes • Data loss • Data sensing during energy blackout • Node failure • 0-Reliable :worst at node failure • 1-Reliable: best at recovering • full-Reliable : at least 58% data loss

  21. Conclusion • the behavior of SolarStore in a long run doesnot depend on the initial states • SolarStore can dynamically responds to variations in the environment • leads to more retrievable data under different node failure scenarios, compared to three other schemes

  22. Pros • Adaptive to control energy and storage effectively • Cons • Not consider the severe weather deeply • How to coordinate energy sharing between Replicator and Receiver?

  23. Thank you

  24. Reliability level of node 9

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