Data treatment

1. Data treatment • Collect 257 days, (2010/12~2011/1, 2011/3/15~2011/9) • Outage raw data 475 times • Case I: timer setting => 253 times • Case II: insufficient radiation => 73 times • Case III: system/ human error => 149 times • Determine whether interpolation or not • Data treatment algorithm

2. Data treatment algorithm Tstart: start time of outage, Tend: end time of outage, Tdur=Tstart - Tend T={12:00, 13:00, 5:00, 7:00}, τ1= 10min, τ2=5min Voff: the last voltage before outage Von: the first voltage after outage Rd1<-- 1 day total radiation, Rd2 <-- 2 day total radiation S(AB) <-- △VoltageAB/ △tAB, S(BC) <-- △VoltageBC/ △tBC ======================================== Case1 IfTstart = T{i} ± τ 1 ANDTdur < τ2 then interpolate Included; Case2 if {Voff <11.6 AND Von>12.0 AND date < 2010/3/14} OR {Voff <11.2 AND Von>11.5 AND date > 2010/3/14} if 4.53<Rd1<14 AND 12.61<Rd2<23.71, then discard else Included ; Case 3 if |S(AB)-S(BC)|>0.015, then discard elseifTstart ∩ {t| 7pm<t<6pm} ≠ ΦOR Tend ∩ {t| 7pm<t<6pm} ≠ Φ, then discard else interpolate Included; outage V B C A D t

3. Data treatment summary

4. Modeling • B(t) = B(t-1) + λ(t) – μ(t) ,if λ(t) – μ(t) ＜24 Bmax, drop(t) = λ(t) – μ(t)-24 ≧ 0 • B(t): energy in battery • λ(t): energy from radiation • μ(t): energy consumption • Bmax: energy in fully charged battery B(t) λ(t) μ(t)

5. Derive [ λ(t) – μ(t)] • Collect continued 2 days CASEII outage: 35 • Lifetime vs Radiation w/o battery effect • Lifetime(hour) = 1.8516x(Radiation) + 0.7029

6. Derive Bmax • Collect outage day due to insufficient radiation & work over last night : 18 • Expected lifetime(hr) by radiation • B(t-1) = actual lifetime – {λ(t) – μ(t)} < 15.78

7. example

8. Testing Bmax =15.78 • Collect outage day after continuous days which past nights • 6 data sets (4/4, 4/17, 5/13, 5/24, 5/28, 9/21, )

9. Lifetime(hour) = 1.8516x(Radiation) + 0.7029 • Sufficient for past one day : radiation > 12.58 • Outage data 18 + past w/ insufficient radiation 31 = 49 day

11. Used Slide (backup)

12. Derive B(t) • Collect CaseII outage and occur in night: • Record Lifetime vs Battery voltage(V) • Lifetime = 30.896V2 -727.51V + 4284

13. Modified Modeling • B(t) = B(t-1) + λ(t) – μ(t) ; if B(t-1) + λ(t) – μ(t)<Bmax Bmax, drop(t) = λ(t) – μ(t); if B(t-1) + λ(t) – μ(t) ≧ Bmax • Bmax = 25hr

14. One day radiation vsλ(t) – μ(t)

15. Radiation vs life time

16. Outage case

17. Preliminary analysis • Case I (253)=> interpolate 108 +81days • Case II (73)=> discard 18 days • Data ambiguous • High amount radiation but short life time • Case III (149)=> discard 8+9 days • Empty:2 +3 (12/29 30, 8/5 8 9 ) • manual:3 (6/7, 7/16, 8/18) • unknown:1 (5/11) • no log:4 (12/10 14, 1/7, 3/14) • Keep 224 out of 257 days for modeling

18. Data treatment algorithm Tstart: start time of outage, Tend: end time of outage, Tdur=Tstart - Tend T={12:00, 13:00, 5:00, 7:00}, τ1= 10min, τ2=5min Voff: the last voltage before outage Von: the first voltage after outage Rd1<-- 1 day total radiation, Rd2 <-- 2 day total radiation S(AB) <-- △VoltageAB/ △tAB, S(BC) <-- △VoltageBC/ △tBC ======================================== IfTstart = T{i} ± τ 1 ANDTdur < τ2 then interpolate else if {Voff <11.6 AND Von>12.0 AND date < 2010/3/14} OR {Voff <11.2 AND Von>11.5 AND date > 2010/3/14} if 4.53<Rd1<14 AND 12.61<Rd2<23.71, then discard else if |S(AB)-S(BC)|>0.015, then discard elseifTstart ∩ {t| 7pm<t<6pm} ≠ ΦOR Tend ∩ {t| 7pm<t<6pm} ≠ Φ, then discard else interpolate outage V B C A D t

19. 2 continuous day outage(35 days)

20. III outage • Occur 42 days in 165 days • Reboot: 6 • Error: 3 • Empty: 13 • Manual: 3 • Unknown: 4(5/7, 9, 10, 11) • No log: 12

21. 8 days filtered in 42 days • Empty:2(12/29 30), • manual:1(6/7), • unknown:1(5/11), • no log:4(12/10 14, 1/7, 3/14) • 10,14,29,30 Dec., 7 Jan., 14 Mar., 11 May, 7 Jun.

22. 開始 III類斷電 斷電前後電壓是否符合控制器設定 抓出每天斷電資料 No 斷電前後斜率差是否<0.015 No Yes 斷電時間是否符合timer 設定 No Yes II類斷電 斷電時間是否跨越晚上(7:00pm~6:00am) Yes Yes No 前一天日照累積是否>4.53 I類斷電 No Yes 不能採用的資料discard 前二天日照累積是否<12.61 No interpolation interpolation Yes 不能採用的資料discard 可以採用的資料

24. 0< 有電壓值斜率差 <0.015 0.000315<III類斷電斜率差 <1.4365

25. smooth最大斜率差0.015 =>只要斜率差>0.015,挑出來,不能用

26. Related works • Design, modeling and capacity planning for micro-solar power sensor networks, IPSN08 • Cloudy computing: leverage weather forecasts in energy harvesting sensor systems, secon10 • Solarstore: enhancing data reliability in solar-powered storage-centric sensor network, mobisys09 • Steady and Fair rate allocation for rechargeable sensors in perpetual sensor networks, Sensys08 • Networking low-power energy harvesting devices: measurements and algorithm, infocom11

27. Mobisys09 • 計算是否有多餘電量來讓系統備份data • If 目前電池電量>(平均消耗功率/平均充電功率)x電池容量, 就可以保持供電 • Evaluation data loss (無備份,always備份,電池滿才備份)

28. Sensys 08 • 用太陽能收到多少來決定data collection rate • 讓node都不會因為傳太快而沒電 • Centralized/distributed algorithm

29. Infocom11 • 室內光產生電源,實際測量8個環境 • Model 分三類:可預測,半預測,隨意型 • Ep(i) = a x E(i-1) +(1-a)Ep(i-1), 0<a<1 • 但誤差還是很大 • 所以在某些環境採用SECON10 天氣model,誤差比較小

30. IPSN08 • Solar Model • Network & HW design • 19nodes for collect/receive 溫濕度 • Sizing and selection • 實際去量測每個component profile • 比較用不同電池壽命多久 • Evaluation for two test deployments • 市區佈建 VS 森林佈建

31. Secon10 • 分析一整年的資料,搭配氣象局資料 • 日照輻射(W/m2)與充電功率(W)=>線性關係 • Solar power = 0.0444*radiation -2.65 • 每日充電能量(W) vs時間(hr)=>2次方程式 • Power = a*(Time+b)^2 + c

32. Solarstore: enhancing data reliability in solar-powered storage-centric sensor network, mobisys09 • Networking low-power energy harvesting devices: measurements and algorithm, infocom11

33. BSpower consumption in one hr

34. BS 電壓與溫度相關性

35. Outage III algorithm • if (abs(mAB-mBC)>Threshold) || (abs(mBC-mCD)>Threshold) filter out Threshold = 0.019 • else if(13V<斷電前電壓) &&(duration > 19600 ) filter out • else if(12V<斷電前電壓<12.5V) &&(duration > 10800 ) filter out • else if (斷電前電壓<12V) &&(duration > 3600) filterout

36. III類挑出不能用 • 12/14 • 12/29

37. How to filter for III類 • 一階微分用斜率來判斷 • |mAB-mBC|< δ • |mBC-mCD|< δ • 二階微分 • |F’’ABC- F’’BCD| < δ D C B A C B D A