1. 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

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 E residual : current residual energy in battery T full (E residua l ):expected time when battery is full C: battery capacity P solar : average power charging rate by solar panel P sys : average power consumption rate by system

10. Method How to get E residual threshold if B(E residua l )=0? B(Eresidual): the expected duration of blackout time 1.E residual = P sys * T full (E residual ) at least 2. 3.E residual threshold = C*(P sys /P solar ) △ E: energy allocated for enhancing data reliability(if Eresidual ≧ C*(Psys/Psolar) ) △ E = E residual - C*(P sys /P solar )

11. Method S residual : current residual storage space left △ S: storage surplus R: expected data sensing rate M: expected time from now to the next upload opportunity △ S= S residual - 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 does not depend on the initial states

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

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 does not 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