Leveraging application context for efficient sensing Jinseok Yang ECE, UCSD

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1 Leveragng applcaton context for effcent sensng Jnseok Yang ECE, UCSD Tajana Smunc Rosng CSE, UCSD Sameer Tlak Calt2, UCSD Abstract Today s platforms for long-term envronmental montorng (e.g. buoys or towers) typcally host large solar panels and batteres. Ideally, mnaturzed platforms could be used nstead, so state of the art power management technque that takes nto account battery levels and harvested energy to provde unform samplng rate. However, the fxed pre-defned ntervals s not desrable. The state-of-art adaptve samplng mechansm, optmal adaptve samplng algorthm (OSA) uses data uncertanty and past measurements to determne the optmal samplng rate at the cost of hgh computatonal complexty O(n 3 ), thus dranng the batteres even further. Even f the samplng were done optmally, there are stll sgnfcant challenges wth data transmsson. The state of the art approach for determnng optmal transmsson polcy offers lmted control over the energy-delay tradeoff and s not sutable to support wde range of applcatons rangng from real-tme and delay-tolerant. To address these challenges, we have developed a novel power management framework that adapts samplng and transmsson rates based on battery level, energy harvestng level and applcaton-context (e.g. characterstcs of the gathered data). Our framework s optmal n terms of energy effcency wth low computatonal complexty. We evaluate the performance of the proposed framework usng datasets from two real-world deployments. Our results show that our approach saves sgnfcant amounts of energy (between 20% to 60%) by avodng oversamplng when the applcaton does not need t and uses ths saved energy to support samplng at hgh rates to capture event wth necessary fdelty when needed. I. INTRODUCTION A typcal envronmental montorng system conssts of two components (1) feld deployed sensor nodes (2) data center. The sensor nodes are hosted on a platform (e.g. buoys or towers), whch has solar panels, batteres and an embedded computer to whch multple sensors (order of 30) are connected ether va seral or Bluetooth lnk. The computer runs a lowpower operatng system and s equpped wth one or more network modaltes (e.g, WF, cellular, and satellte). Fgure 1 shows a buoy that we deployed n collaboraton wth ecologsts at a lake n Northern Wsconsn, USA. The system samples key lmnologcal varables such as water temperatures at varous depths, dssolved oxygen level, conductvty, and salnty along wth meteorologcal data from a co-located weather staton. Energy-constrants mposed by solar panels and batteres, lmt the duraton of the deployment. Scentsts often have to accept low samplng and transmsson rates to meet the power budgets. There are sgnfcant lmtatons n the exstng research on energy management, adaptve samplng and transmsson for such deployed systems. A good example of a far energy aton algorthm s Progressve Fllng [11] that takes nto account battery levels and harvested energy to provde unform samplng rates [3]. However, samplng the envronment at fxed pre-defned ntervals s not desrable, as usng a sngle preset samplng rate forces that rate to be relatvely hgh n an attempt to capture an mportant event wth the desred fdelty. If those events are rare, then a lot of energy s wasted at such hgh samplng. However, f samplng s lower, then there s a hgh chance of mssng an event of nterest. The state-of-art adaptve samplng mechansm, optmal adaptve samplng algorthm (OSA) [15] uses data uncertanty and past measurements to determne the optmal samplng rate at the cost of hgh computatonal complexty O(n 3 ), thus dranng the batteres even further. Fgure 1. Buoy deployed n late located n northern Wsconsn, USA measurng several key lmnologcal varables Even f the samplng were done optmally, there are stll sgnfcant challenges wth data transmsson. The state of the art approaches for determnng optmal transmsson polcy nclude control-lmt polcy [2] and MDP based approaches [4]. The control-lmt polcy does not consder heterogeneous sensors. The MDP based approach [4] requres complete knowledge of transton probablty of the harvestng level and channel condton varatons, whch s unrealstc. In addton, both offer lmted control over the energy-delay tradeoff and are not sutable for real-tme and delay-tolerant applcatons. We desgn a novel data transmsson polcy to addresses the lmtatons of the prevous work. Our polcy s optmal n terms of energy effcency whle ts computatonal complexty s same as the exstng control-lmt polcy-based approach [2]. Another key challenge s that most sensor power management frameworks use models to estmate the current battery level, but those models are often not accurate. One key source of naccuracy s due to thermal varatons. Sensor nodes are often deployed n physcally harsh envronments where temperature can vary dramatcally. For example, n buoy deployment shown n Fgure 1 the temperature can vary from 15 C to 30 C. These varatons can sgnfcantly lower the battery capacty and reduce the sensor network lfetme [20]. We use publcly avalable data from NASA Ames Prognostcs Data Repostory [13] that ncludes battery capactes of 2Ah L-on batteres at dfferent temperatures and dscharge currents to evaluate avalable battery models. Our results ndcate that battery model accuracy has hgh varablty. Our results

2 ndcate that the accuracy of models has hgh varablty. We can observe that at 4 C, the battery capacty estmaton error vares sgnfcantly from approxmately from 1.26% to 26.7%. Our work addresses all these challenges wth the followng: (1) We develop a novel and modular Advanced Power Management Framework (APMF) that for the frst tme ntegrates together adaptve samplng (AS) and transmsson polcy (TP). To the best of our knowledge, APMF s the frst comprehensve such framework. (2) Our AS algorthm has sgnfcantly lower computatonal complexty, O(n 2 ), vs. state of the art research that has O(n 3 ) complexty. (3) We propose optmal transmsson polcy that supports heterogeneous sensors. Typcal sensng envronments use multple types of sensors. (4) We evaluate the effect of ambent temperature on accuracy of the state of the art battery model [8][9] wth NASA Ames Prognostcs Data Repostory [13] dataset. (5) We evaluate the performance of the proposed approach usng datasets from two real-world deployments. Ths paper s organzed as follows. In Secton II, we brefly present the system archtecture for the proposed power management framework (APMF). In Secton III, we descrbe n detal varous components of APMF. In addton, we evaluate the accuracy of the state-of-art battery capacty estmaton algorthm. In Secton IV, we evaluate performance of the proposed approach wth the state of the art approaches for adaptve samplng and transmsson rate estmaton. II. SYSTEM ARCHITECTURE Fgure 2 descrbes the system archtecture for tradtonal Power Management Framework (PMF) as well as the proposed APMF. The tradtonal PMF ncludes three modules (ref. Fgure 2) namely, battery manager, green energy predctor, and energy aton algorthm. Fgure 2. System archtecture for PMF and APMF The battery manager typcally estmates the avalable battery capacty wth one of the well-establshed capacty estmaton algorthms [8][9][19], and provdes ths nformaton to the energy aton algorthm. Based on the nformaton from battery manager and green energy predctor modules, the energy aton algorthm calculates/ates the optmal energy for each tme slot (ref. Equaton (4)). The state-of-art energy aton algorthm, Progressve Fllng (PF) [11], only consders battery level and harvested energy as optmzaton parameters, so the samplng rate s calculated only based on energy nformaton. In contrast to the tradtonal PMF, the APMF (ref. Fgure 2) conssts of the followng fve modules: (1) battery manager, (2) green energy predctor, (3) Transmsson Polcy (TP), (4) Adaptve Samplng (AS) and (5) Energy aton algorthm. The battery manager, green energy predctor, and the energy aton modules are dentcal n PMF and APMF, whle APMF also ncludes adaptve samplng (AS) and transmsson polcy (TP) modules to ncrease the energy effcency by consderng the applcaton context. The AS module takes the energy budget calculated by the energy aton module as ts nput and calculates the samplng rate at each decson epoch by capturng varatons n phenomenon. AS and TP modules work n tandem. After AS determnes samplng for the current decson epoch, the TP module uses ths nformaton to determne the optmal transmsson polcy so that both the transmsson cost and the loss of data freshness (an applcatonspecfc metrc) are mnmzed. III. ADVANCED POWER MANAGEMENT FRAMEWORK A. Transmsson Polcy module (TP) TP s goal s to postpone data delvery n order to save energy whle maxmzng the data freshness. At each decson epoch, the TP module takes samplng rates, R samplng, from the Adaptve Samplng (AS) module, and decdes whether to transmt buffered packets or contnue samplng them. We assume exponental dstrbuton for nter arrval tme of decson epoch as dd work presented n [2]. The mean of nterarrval tme s µ. The montorng system conssts of m dfferent sensors. Each measurement has a dfferent lfetme, specfed as the maxmum delay of data arrvng to the data center whle stll meetng applcaton-specfc data freshness requrements [5]. Fgure 3. Illustraton of decson process of TP We leverage [2] to defne the reward functon (Equaton (1)) as a product of energy gan and data freshness loss. Note that energy gan s a monotoncally ncreasng functon, whereas the data freshness s a monotoncally decreasng functon. Ths reward functon allows applcatons to easly tune the delay-data freshness tradeoff. 1 r ( t ) exp( - t ) g( t ) = exp( - t ) g( t ) d d d Lfetme d d = a (1) The r (t d) denotes the reward of sensor node wth delay t d. The g(t d) s the energy gan acheved by decreasng transmsson events by t d, and exp(-α t d) ndcates the freshness loss wth the delay t d. Any functon what has monotoncty and non-decreasng characterstc wth delay can be used as g(t d). For example, n [2], author uses the number of buffered samples as energy gan. Fgure 4. Dfferent dscount factors, α, result n dfferent reward We defne α as a dscount factor, whch vares based dfferent level of allowable latency of applcaton. Note that the control-

3 lmt and MDP based approaches assume that all sensors have same dscount factor, α, and therefore, they cannot support a wde-range of applcatons. In contrast, our approach allows an applcaton to set the value of α on per sensor bass. Fgure 4 shows the effect of the varyng the delay on reward for varous dscount factors. To show the effect of dscount factor and delay, we set the ntal value of reward (.e. Equaton (1)) when t d =0 to 1. The real tme applcaton should set α to have a large dscount factor, so the TP transmts buffered packet wth shorter delay to maxmze rewards (e.g., when α s 1, t d s 3 mnutes the reward s 0.1). In other words, delay-tolerant applcatons can set α to have small delay, so TP can acheve maxmum reward wth a certan delay (e.g., when α s 0.1, t d s 3 mnutes the reward s 0.7). The proposed TP module extends the control-lmt polcy (ref. Algorthm 1) to account for dfferent values of α. We now show that ths polcy s optmal (ref. Theorem 1). Theorem 1. We assume the system conssts wth m sensors, where each sensor has ts own reward functon (ref. Equaton (1)). Ths functon has a fnte value whch monotoncally decreases wth delay (ref. Assumpton 2.2 [2]). Each sensor has α as ts dscount factor. The value of nter-arrval tme of decson epoch s δw, so mean nterval arrval tme s µ=1/ δw. * s s the optmal number of samples that maxmze reward for sensor (ref. Equaton (2)). Then, the total number of optmal number of samples (s * * ) s the sum of s (ref. Equaton (3)). We set current samplng rate as R samplng. -adw * é E[ Xe ] ù é Rsamplng m ù s = + 1 = ê ú (2) adw ê1 - E[ e ] ú êa ( a + m) ú m * å s = 1 * s = (3) Proof: We need to show that sum of optmal number of samples for each sensor s also optmal. Ths problem s same as weghted nterval schedulng problem [22]. The schedulng problem can be formulated as follows. Assume a system wth multple requests, where each request has a start tme and a fnsh tme. The system can operate only one request at a tme. A certan reward s returned when the system fnshes a request. The goal of schedulng problem s to schedule requests such that the sum of rewards s maxmzed whle ensurng that the scheduled requests do not overlap. It has been proved [22] that the schedulng problem has maxmum value when all requests are mutually compatble whch means they do not overlap. In our case, snce all the sensors can be sampled ndependently, they are mutually compatble, so the maxmum value of s * n Equaton (3) s ndeed optmal. Algorthm 1 descrbes the operatonal detals of TP. At every decson epoch, TP decdes what to do wth the buffered packets n the followng manner. If number of buffered packets s larger than s *, then TP decdes to transmt all the buffered packets. If t s smaller than s *, then t delays the transmsson untl the next decson epoch. The proposed Transmsson Polcy (TP) provdes controls for tunng the energy-delay and applcaton data lfetme tradeoffs. For example, a delay-ntolerant applcaton mght set the data lfetme to a large value to ensure that data freshness maxmzed at the expense of more frequent transmsson. On the other hand, a delay-tolerant applcaton mght tolerate loss n data freshness by transmttng less frequently thereby savng more energy. The exstng MDP [4] based approach does not allow applcatons to tune ths tradeoff. The control-polcy approach [2] consders tradeoff between energy gan and delay of buffered data because a system can save energy whle decreasng the number of transmsson events. To characterze the tradeoff they defne reward as a functon of energy gan and delay, and show that ncreasng delay ncreases reward untl a certan pont and after the pont reward start decreasng. ALGORITHM 1.TRANSMISSION POLICY MODULE Input: Rsamplng: calculated samplng rate at AS, Start at each decson epoch BufLength = total number of buffered packets For every sensors s * = equaton (2) wth Rsamplng s * = Equaton (3) wth all s * IF BufLength > = s * Transmt all buffered packets DECIDE next decson epoch based on based on dstrbuton of δw B. Adaptve Samplng module (AS) The exstng research on adaptve samplng assumes a dscrete and bounded samplng rate and calculates optmal samplng rates over K tme slot by applyng task-aton algorthm (.e. Hungaran method [18]). The authors [15] use a lnear programmng approach wth ntal battery capacty before the frst tme slot as ts constrant. They mnmze dstance between approxmated sampled data and real data. Based on past measurements, samplng rates for K future tme slots are predcted. However, ths optmal adaptve samplng algorthm (OSA) requres hgh computatonal complexty O(n 3 ). The AS module estmates the samplng rate usng the ated energy from the energy aton module as ts nput. As we descrbed n Secton III.A, TP has a varable length decson epoch, thus, the AS calculates optmal samplng rate at every decson epoch. TP then uses ths as ts nput. In Algorthm 2, The E () s the ated energy by the next decson epoch, and β denotes the factor reducton n energy by down samplng. An applcaton sets β so that down samplng saves energy, whle meetng ts requrements n terms of data fdelty. The set of samplng rates of sensors are R=[r 1,,r M], and r 1 and r M as ts lower and upper bounds respectvely. AS calculates the qualty of samples for all values of E and R and then chooses the samplng rate. Total Devaton (TD) [15] s a dstance between predcted and real sample values. A larger value of TD mples lower qualty of samples, so we calculate the largest TD (ref. calculatetd n Algorthm 2) for dfferent samplng rates for a gven energy lmt. I TD n Algorthm 2 s a 2-dmensonal matrx that saves the correspondng TD entry for each samplng rate and avalable energy combnaton. After calculatng TDs for all elements n E and R, Algorthm 2 derves the samplng rate as the rate whch has the smallest uncertanty & hghest qualty. Its computatonal complexty s O(n 2 ), but wth small set of E and R, t can be readly mplemented on a resource-constraned smart phone. ALGORITHM 2. ADAPTIVE SAMPLING MANAGER Input: E = [E/β E()]], β>1, = 0, R = [r1,, rm] : set of avalable samplng rates

4 Output: Rsamplng: optmal samplng rate of current decson epoch For p=1 to length(r) For q=1 to length(e) ITD (p,q) = calculatetd(r(p), E(q)) Rsamplng = fnd the smallest value n ITD C. Green Energy Predctor In case of perodc or partally perodc renewable energy such as solar energy, exstng research has shown that the state of the art energy predctors such as Weather-Condtoned Movng Average, WCMA can be used to accurately predct the amount of harvestng energy [14]. Therefore, n ths paper we use WCMA algorthm for solar energy predcton. D. Energy Allocaton Algorthm The state of the art energy aton algorthm (e.g. Progressve Fllng (PF)) takes nto account only current battery level and harvestng energy and farly ates as much energy as possble along the tme dmenson. PF s only connected to battery manager and green energy predctor components (ref. Fgure 2). It uses a dscrete-tme model n whch dvdes the tme nto K slots, and ates energy to each tme slot by solvng Equaton (4) to nteract wth TP and AS whch have dynamc length of decson epochs as descrbed n Fgure 3. E () s energy ated to decson epoch as shown n Equaton (4). We use the followng utlty functon U(E ())= E () snce any monotoncally ncreasng functon can be used (.e. U(E ())= U( E ())) [2]. B() s battery levels at the th decson epoch and B max s the maxmum level of battery level. K E å - 1 max ( ) = 0 s. t. B( ) B( -1) + H ( -1) - E E U ( E ( )) ( ) B( ), B( ) B max ( -1), B( ) ³ 0, E ( ) ³ 0 " E. Battery manager The capacty of battery s determned by several factors, such as dschargng current, battery age, and temperature. Peukert s equaton [10] provdes an ntutve nsght nto the relatonshp between battery capacty and dschargng current. Suppose I d s dschargng current, C s rated capacty (.e. 100 Ah), and R s the battery hour ratng. The rated capacty s specfed n the battery manufacture s datasheet and n most cases hour ratng s 20h [9]. Wth these factors, Peukert s equaton (ref. Equaton 5) can derve battery operaton tme T wth constant dschargng current I d, where n denotes Peukert s exponent that depends on the battery chemstry. The typcal value of n for lead-acd s 1.15 and lthum ron phosphate batteres s 1.05 [9]. C T = I n d æ C ö ç è R ø The frst term explans the relatonshp between rated capacty (.e. Ah) and dschargng current. The second term mtgates the error whle handlng battery hour ratng. Further detals are avalable n [10]. By rearrangng Equaton (5), we can derve estmated battery capacty, C est, of Coulomb countng method [8] as descrbed n Equaton (6). n-1 (4) (5) n-1 æ C ö C ç est = T I d = C (6) è I d R ø The Coulomb Countng Method (CCM) estmates the battery capacty usng dschargng current and operaton tme [8]. The lmtaton of CCM s the fact that t does not consder effect of temperature. Sensors are typcally embedded deeply wthn a harsh physcal envronment where temperature has hgh varablty. For example, durng our deployment (ref. Fgure 1) the temperature vared from -16 C to 30 C. Such temperature varatons can sgnfcantly lower the battery capacty. It s well known that battery capacty (Amp-hours t can hold) decreases wth decrease n temperature [23]. Ths happens because the cold temperature ncreases the nternal resstance and dmnshes the battery capacty. For example, batteres that would provde 100 percent capacty at 27 C wll typcally delver only 85 percent at 0 C. More specfcally, each decrease n temperature by 1 C, results n lowerng the battery capacty by 0.6% [23]. Therefore, we can descrbe mpact of temperature wth varable on battery capacty as: α temp=1-((27-current temperature)*0.006) (ref. Equaton (7)). C est _ temp n-1 æ ( atemp C) ö = ( a ) ç temp C (7) è Id R ø Snce APMF uses current battery level as one of ts nput, we evaluated the mpact of ambent temperature varatons on accuracy of exstng battery models n Secton IV.E. IV. EXPERIMENTAL RESULTS Our expermental results are based on two real-world data sets: (1) The deployment on a lake n Northern Wsconsn (ref. Fgure 1) from Aug.8 to Oct We use the measured temperature and humdty. (2) The second data set s from North temperate lake ecologcal study [16], wth samplng from June 26 to Nov For solar energy predcton we use data from USCRN [17] database for Necedah, Wsconsn locaton snce t s the closest locaton to our deployment. Table 1 s the power specfcaton of a subset of sensors and the smartphone from our deployment (.e. dataset 1) [7][12]. The smartphone acqures data from all the sensors and then transmts t to a data center hosted on Amazon-EC2 cloud platform. The phone s equpped wth W-F and cellular rados. In our deployment the phone transmtted data over cellular (3G) network. In Table 1, suspend means that the applcaton processor s dle, whle the communcatons processor performs a low level of actvty, as t must reman connected to the network to be able to receve messages, etc. Idle state means that all components are n a low-power state wthout applcaton operaton. Table 1. Power consumpton specfcaton for our deployment [7][12] Current (ma) Power (mw) Tme (s) Wnd Speed (m/s) Wnd drecton ( ) Barometrc pressure(hpa) Ar temperature ( C) Relatve humdty (%) Templne Androd phone - Suspend Androd phone Idle Androd phone Tx+Rx

5 We assume mean decson epoch nterval of 13 mnutes, same as the smulaton setup n [2]. Thus, we restrct maxmum samplng nterval to 13 mnutes. We model data lfetme n terms of a sensor s samplng nterval wth three dfferent values: 1, 5, and 10. In the remander of ths paper, we use x1, x5, and x10 to represent these lfetme factors. We defne data freshness loss as the percentage of data whch stays longer than ts lfetme n the local buffer. For example, when the samplng nterval s 1 mnute and lfetme factor s set to 10, the sample does not lose ts data freshness for 10 mnutes. Our proposed power management framework s mplemented n Matlab. A. Performance of AS wth real world data sets We compare AS wth optmal adaptve samplng algorthm (OSA) [15] n terms of calculated average samplng rate over expermental perod. The dfference between OSA and AS s length of tme slot: the OSA consders fxed length of tme slot, and AS determnes samplng rate at each decson epoch. In addton, AS dscretze allowable energy (.e. vector E n Algorthm 2) nto a set of fnte sze. The Table 2 shows that the dfference between OSA and AS s less than 1 mnute. AS samples slghtly at hgher rates than OSA, whle sgnfcantly reducng the computatonal overhead. Table 2. Average samplng rate wth real data sets Temperature Humdty chlorde AS 4.5 ± ±0.6 6±3.6 OSA 5.17 ±1.6 5± ±6.4 B. Study of energy-delay tradeoff for varous lfetme factors In ths study, we descrbe the mpact of varyng lfetme factors on relatve power consumpton (ref. Table 3) and data freshness loss (ref. Table 4). To check the performance of TP, we consder the scenaro where each of these sx sensors unformly selects ts samplng nterval between 0 to a predefned upper bound durng the ntalzaton phase. We vary upper among 3, 5, 10, 13 mnutes. In Table 3, the relatve power consumpton decreases wth the ncrease n the lfetme factor. Ths s because the system can buffer the samples for longer duraton as the lfetme factor ncreases thereby reducng the number of transmssons (ref. Equaton (1)). For example, TP wth lfetme factors 1 (.e. TP (x1)) consumes 1.25 tmes and 1.4 tmes more energy than TP (x5) and TP (x10) polces respectvely. In Table 4, TP (x10) polcy has between 1.3 to 3.3 tmes more loss data freshness than TP (x5) polcy. Table 3. Impact of varaton of lfetme factor on relatve power consumpton (%) for dfferent samplng upper bounds 3mn 5mn 10mn 13mn TP (x1) TP (x5) TP (x10) Table 4. Impacts of dfferent lfetme factor to freshness loss (%) for dfferent samplng upper bound 3mn 5mn 10mn 13mn TP (x1) TP (x5) TP (x10) C. Study of energy-delay tradeoff for In ths study, we descrbe the mpact of varous transmsson polces on relatve power consumpton (ref. Table 5) and data freshness loss (ref. Table 6). The energy consumpton of TP wth 5x lfetme s consstently 20% lower than the MDP based approach (ref. Table 5). However, because TP (x5) polcy uses large lfetme factor, ts data freshness (ref. Table 6) loss s hgher (between 2% to 7%) as compared to the MDP based approach. Table 5. Impacts of dfferent transmsson polces on power consumpton (%) for dfferent samplng ntervals 5mn 10mn 15mn 20mn MDP [4] TP (x5) Table 6. Impacts of dfferent transmsson polces to freshness loss (%) for dfferent samplng nterval 5mn 10mn 15mn 20mn MDP [4] TP (x5) D. Study of PF, AS and TP wth real data sets In ths secton, we compare APMF wth PMF [11] (ref. In Table 7). APMF results n 27% to 72% energy savngs n comparson wth PMF. We also selectvely dsable TP and AS modules to evaluate ther mpact on overall performance of APMF. We can see the TP domnates energy savngs because transmsson cost s 25x hgher than the sensng cost. Table 8 shows that APMF has a neglgble data estmaton error as compared to PMF. Therefore, APMF results n hgher energy savngs wth no loss n accuracy. The low level of estmaton error s because measured temperature and humdty are slowly varyng (.e. at most 4.2 C and 4.5% varance respectvely per day). We use another set of measurement from North template lake ecologcal study [16], whch nvolves samplng from 6/26/2008 to 11/4/2008. Ths data ncludes a lmnologcal varable that vares more quckly than temperature. From Table 9, we can observe that APMF consumes 62% less energy than PMF. Table 7. Relatve energy consumpton n comparson wth data set 1 vs. PMF Humdty Temperature APMF 27% 32% APMF w/o TP 62% 72% APMF w/o AS (5mn) 48% 53% APMF w/o AS (10mn) 46% 42% Table 8. Estmaton error n comparson wth data set 1 vs. PMF Humdty Temperature APMF APMF w/o TP APMF w/o AS (5mn) APMF w/o AS (10mn) Table 9. Relatve energy consumpton comparson wth data set 2 Vs. PMF Energy consumpton (%) APMF 62% APMF w/o TP 88% APMF w/o AS (5mn) 74% APMF w/o AS (10mn) 69%

6 E. Impact of Ambent Temperature Varatons on CCM To test the accuracy of CCM [8] battery model, we used NASA Ames Prognostcs Data Repostory [13] dataset. Ther set up nvolves dschargng/chargng of 2Ah L-on batteres at dfferent temperatures (.e. 4 C and 24 C) and constant dschargng currents. The dataset s based upon experments that end when a battery drans to 30% of ts ntal rated capacty (.e. from 2Ah to 1.4Ah). We evaluated the accuracy of CCM under dfferent temperature regmes. Table 10 summarzes the battery capacty estmaton error of CCM and CCM wth α temp (ref. Equaton (7)) under 4 C. The low temperature results n the battery capacty estmaton error vares sgnfcantly from approxmately from 1.26 % (Battery 56) to 26.7 % (Battery 53). We can observe that average estmaton accuracy of CCM wth 2 tmes hgher than CCM. However, the accuracy of temperature aware CCM s not always hgher as dfferent batteres can have dfferent temperature factor, α temp. To the best of our knowledge, ths s the frst paper that mplctly shows the varablty of mpact of temperature on battery capacty estmaton error usng publcly avalable NASA dataset. Incorporatng ths varablty n battery capactes n power management s our future work. In addton, fgurng out such a α temp s also our future work. Table 10. Remanng capacty estmaton error (%) Cest (4 C) Cest_temp (4 C) Battery5, 24 C Battery6, 24 C Battery7, 24 C Battery18, 24 C Battery53, 4 C Battery56, 4 C Battery50, 4 C Battery51, 4 C V. CONCLUSION In ths paper, we presented Advanced Power Management Framework (APMF) that adapts samplng & transmsson rates based on battery capacty level, harvestng energy amount and applcaton-context (characterstcs of gathered data). The adaptve samplng and transmsson polcy manager modules of APMF have low complexty and are sutable for resource-constraned devces. APMF provdes applcatons a fner control over delay-energy tradeoff. We evaluated the performance of our proposed approach usng dataset from two real-world deployments. Our results show that APMF saves 20% to 60% of energy consumpton by avodng oversamplng. ACKNOWLEDGMENTS Ths work has been funded by NSF project, Ctsense grant CNS , NSF OCI Award Award and a grant from the Gordon and Betty Moore Foundaton. Ths work was also supported n part by TerraSwarm, one of sx centers of STARnet, a Semconductor Research Corporaton program sponsored by MARCO and DARPA. REFERENCES [1] A. Manwarng, D. Culler, J. Polastre, R. Szewczyk, J. Anderson, "Wreless sensor networks for habtat montorng," ACM WSNA, 2002 [2] Z. Ye, A. A. Abouzed, J. A, Optmal stochastc polces for dstrbuted data aggregaton n wreless sensor networks, IEEE/ACM Trans. Networkng, vol. 17, 2009 [3] JS. Yang, S. Tlak, T.S. Rosng, An Interactve Context -aware Power Management Technque for Optmzng Sensor Network Lfetme, IEEE ISSNIP, 2013 [4] S. Mao, M.H. Cheung, V.W.S. Wong,"An optmal energy aton algorthm for energy harvestng wreless sensor networks," IEEE ICC, 2012 [5] M. Bouzeghoub, "A framework for analyss of data freshness," ACM IQIS, 2004 [6] GLEON: The Global Lake Ecologcal Observatory Network: [7] A. Carroll, G. Heser, An analyss of power consumpton n a smartphone, ACM USENIX 2010 [8] K. Ng, Y. Huang, C. Moo, Y. Hseh, "An enhanced coulomb countng method for estmatng state-of-charge and state-of-health of lead-acd batteres," INTELEC 2009 [9] B. Aksanl, E. Petts, T.S. Rosng, "Dstrbuted Battery Control for Peak Power Shavng n Data Centers ", IGCC, 2013 [10] Peukert s law: peukert2.html [11] M. Gorlatova, A. Wallwater, G. Zussman, Networkng Low-Power Energy Harvestng Devces: Measurements and Algorthms, INFOCOM 2011 [12] Vasala Weather sataton: /multweathersensors/pages/wxt520.aspx [13] B. Saha and K. Goebel, "Battery Data Set, NASA Ames Prognostcs Data Repostory," 2007 [14] J.R. Porno, C. Bergonzn, and D. Atenza, and T.S. Rosng, Predcton and management n energy harvested wreless sensor nodes, IEEE VITAE 10, 2010, pp [15] J. Kho, A. Rogers, N. R. Jennngs Decentralzed control of adaptve samplng n wreless sensor networks. ACM Trans. on Sensor Network vol. 5, 2009 [16] Noth template lake ecologcal study: [17] US Clmate Reference Network Database(USCRN) [Onlne]. Avalable: [18] Hungaran method: /handouts/assgnment_overheads.pdf [19] D. Rakhmatov, S. Vrudhula, D.A. Wallach, "Battery lfetme predcton for energy-aware computng," ISLPED 2002 [20] K. Kutluay, Y. Cadrc, Y.S. Ozkazanc, I. Cadrc, I., "A new onlne state-ofcharge estmaton and montorng system for sealed lead-acd batteres n Telecommuncaton power supples," Industral Electroncs, IEEE Transactons on, vol.52, no.5, pp.1315,1327, Oct [21] M. L. Puterman, Markov Decson Process: Dscrete Stochastc Dynamc Programmng, New York, NY:Wley, 1994 [22] J. Klenberg, E. Tardos, Algorthm Desgn, Addson-Wesley [23] V. Johnson, A. Pesaran, T. Sack "Temperature-dependent battery models for hgh-power lthum-on batteres", Electronc. Vehcle. Symposum, 2000