ARTICLE IN PRESS. Sustainable Computing: Informatics and Systems xxx (2012) xxx xxx. Contents lists available at SciVerse ScienceDirect

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1 Sustanable Computng: Informatcs and Systems xxx (2012) xxx xxx Contents lsts avalable at ScVerse ScenceDrect Sustanable Computng: Informatcs and Systems jou rn al h om epage: QuARES: A qualty-aware renewable energy-drven sensng framework Nga Dang, Elaheh Bozorgzadeh, Naln Venkatasubramanan Computer Scence Department, Unversty of Calforna, Irvne, USA a r t c l e n f o Artcle hstory: Receved 9 February 2012 Accepted 17 August 2012 Keywords: Energy harvestng Wreless sensor network a b s t r a c t Renewable energy technologes have become a promsng soluton to reduce energy concerns that arse due to lmted battery n wreless sensor networks. Whle ths enables us to prolong the lfetme of a sensor network (perpetually), the realzaton of sustanable sensor platforms s challengng due to the unstable nature of envronmental energy sources. In ths paper, we propose an adaptve energy harvestng management framework, QuARES, whch explots an applcaton s tolerance to qualty degradaton to adjust data collecton qualty based on energy harvestng condtons. The proposed framework conssts of two phases: an offlne phase whch uses predcton of harvested energy to allocate energy budget for tme slots; and an onlne phase to tackle fluctuatons n the tme-varyng energy harvestng profle. We formulate the energy budget allocaton problem as a lnear programmng problem and mplemented heurstcs to mnmze error n data qualty at runtme. Our technques are mplemented n a network smulator, QualNet. In comparson wth other approaches (e.g., [8]), our system offers mproved sustanablty (low energy consumpton, no node deaths) durng operaton wth data qualty mprovement rangng from 30 to 70%. QuARES s currently beng deployed n a campus-wde pervasve space at UCI called Responsphere [11] Elsever Inc. All rghts reserved. 1. Motvaton In energy harvestng systems, energy s derved from envronmental sources such as sunlght, wnd and heat. Renewable energy sources allow us to contnuously harvest energy from the envronment, provdng a constant and perpetual source of energy to the systems they drve. Despte ts low effcency, renewable energy technology s a vable and promsng soluton for low power wreless sensor network systems (WSN). WSNs have found applcaton n areas rangng from envronmental montorng, health montorng, and smart buldng to mltary applcatons (see Akyldz et al. [29] for a survey of WSN applcatons). We are partcularly nterested n data collecton applcatons here, sensors collect nformaton about ther surroundng envronments; update ths nformaton at a server va a base staton through sngle-hop or mult-hop adhoc networks and respond to frequent or sporadc montorng requests from users. It s desrable that data collecton process satsfes the qualty (accuracy, tmelness) and relablty needs (no dead nodes) of the applcaton at hand. Scavengng energy from surroundng envronments permts such smart sensor nfrastructures to functon ndefntely by elmnatng the need for frequent battery changes; n the long term, ths enables the deployment of sustanable and manageable Correspondng author. Tel.: E-mal addresses: ntdang@cs.uc.edu, ngad@uc.edu (N. Dang), el@cs.uc.edu (E. Bozorgzadeh), naln@cs.uc.edu (N. Venkatasubramanan). nfrastructures. Recent research prototypes have been developed that couple wreless sensor motes wth solar cells enablng them harvest energy from surroundng envronments for contnuous operaton [1 3]. Energy-harvestng capable wreless sensor networks have obvous advantages. They are self-sustanable, perpetually powered by replenshable energy sources from the surroundng envronment. The systems hence become autonomous and lfe-tme of the system s only lmted by the robustness of the underlyng hardware. Another beneft s the low mantenance cost assocated wth managng the nfrastructure especally when the sze of sensor network can span several orders of magntude, rangng from hundreds to thousands or even mllons of nodes [29]. In some cases, t s nfeasble to change batteres on sensor networks deployed n naccessble locatons such as volcanoes or battlefelds. However, the ablty to leverage renewable energy effectvely depends on a varety of factors. Renewable energy sources exhbt both temporal and spatal varatons. There could be low or no avalablty of replenshed energy for extended perods of tme, e.g., nght tme for solar energy. Envronmental condtons dctate the avalablty of energy sources (e.g., harvested solar energy on a sunny day s much hgher than t s on a cloudy or rany day). Fg. 1a depcts the solar energy profle n Los Angeles durng a week n September, 2011 [20]. Fg. 1b [31] llustrates wdely dfferent solar rradance levels at locatons wthn close proxmty of each other. In addton to varaton n harvestng capabltes, there s also varaton n sensng needs (due to events and actvtes) and applcaton requrements (accuracy of sensng requred for the target applcaton). Queres mght arrve n bursts, events occur randomly and /$ see front matter 2012 Elsever Inc. All rghts reserved.

2 2 N. Dang et al. / Sustanable Computng: Informatcs and Systems xxx (2012) xxx xxx What s unque about our approach s the fact that we consder applcatons wth tolerance to qualty degradaton and explot data qualty-energy tradeoffs n desgnng energy-neutral approaches for managng WSNs. Refs. [21,22,37] attempt to maxmze or farly assgn data rate for data collecton n wreless sensor networks; the data rate per se does not drectly reflect data qualty; changng data rates wthout beng aware of data qualty requrements mght lead to unsatsfyng results. Technques for energy-effcent data collecton [14,32,33] optmze energy consumpton to prolong lfetme of fnte charge batteres and as a result, lfe-tme of the whole sensor network. Han et al. [14] utlze an applcaton s tolerance to qualty degradaton to mnmze energy consumpton by mantanng lowest possble data qualty. Ths approach s desgned for and suted to tradtonal contnuously dschargng battery systems. In contrast, our work, explots an applcaton s tolerance to qualty degradaton to adapt the system to the fluctuatons of renewable energy sources whle smultaneously optmzng data qualty. Specfc contrbutons nclude: Fg. 1. (a) Temporal varaton and (b) spatal varaton n solar energy profle [20,31]. not evenly dstrbuted n space and applcaton requrements mght change based on context. Ths dynamcty of energy supples and heterogenety of applcaton needs create new and exctng research problems n harvestng-capable wreless sensor networks. In partcular, t creates a shft n research focus from energy-effcent to energyneutral approaches,.e., from optmzng energy consumpton and prolongng battery lfe-tme to optmally adaptng systems to deal wth unstable energy sources. Desgnng a sustanable wreless sensor network (wth replenshable but fluctuatng energy supply) whle optmzng the data qualty of applcatons usng the sensed nformaton s a formdable challenge. To tackle ths challenge s the man target of our work. In ths paper, we propose QuARES, qualty-aware renewable energy-drven sensng framework for data collecton applcatons n energy harvestng wreless sensor networks. To the best of our knowledge, ths work s the frst attempt to jontly use both applcaton data qualty and harvestng ablty to perform energy management for such systems. Our framework ncludes two phases: an offlne slot-based energy budget allocaton algorthm and an onlne adaptaton strategy. The offlne phase explots predctablty of the energy harvestng sources and data qualty-energy tradeoff to allocate slot-based energy budgets n a harvestng perod (e.g., hours n a day or days n a week). In the onlne phase, onlne adaptaton polces are proposed to guarantee tmely responses to queres n spte of the tme-varyng characterstcs of harvested energy. Energy-neutral desgn n tself s not a new concept; ths approach has been proposed n lterature to cope wth the tmevaryng characterstcs of the energy harvestng profle. Many of these efforts adopt a cross-layer desgn approach;.e., consderng energy status at battery layer and adaptng system at other layers. At the node layer, Hsu et al. [4] adapt duty cyclng of systems to the changes n renewable sources. Hasenfratz et al. [10,35,36] modfy routng protocols at the MAC layer to explot both temporal and spatal varatons of renewable energy and maxmze data delvery rate for sensors. Vogt et al. [7] adapt LEACH, a clusterbased routng protocol for sensor networks to take advantage of energy harvestng. At operatng system layer, Moser et al. [6] and Lu et al. [5] propose task schedulng technques for energy harvestng systems. At the applcaton layer, Wang et al. [9] use an adaptve technque to turn on and off storage servces based on dfferent energy thresholds. Ravnagarajan et al. [12] adapt task utlty of structural health montorng applcatons to maxmze accuracy of tasks whle sustanng the system. (1) Explotaton of applcaton tolerance to qualty degradaton to adapt the sensor data collecton process under unstable energy harvestng condtons (Secton 2). (2) Desgn of the QuARES framework, an energy harvestng management framework wth 2 phases (onlne and offlne) that utlzes energy harvestng predcton and knowledge of data qualty energy cost to mantan system sustanablty and optmze data qualty (Secton 4). (3) Deployment of an energy-harvestng sensor network test-bed at UCI to carry out a measurement study for ndoor energy harvestng wreless sensor networks (Secton 5) (4) Evaluaton of QuARES n comparson to other state-of-the-art offlne/onlne strateges (fxed-error-margn, mnmum varance [8]) under dfferent applcaton and energy harvestng scenaros. QuARES s mplemented n QualNet smulator (here, the battery model was modfed to smulate energy harvestng). Results show that our framework can tolerate lower error margns (.e., hgher data accuracy) of 30 70%, ensure a response to all queres; addtonally, sensors do not have to shut down to replensh (Secton 6.2). (5) Illustraton of our smulator as a valuable tool for desgners to explore the desgn space by applyng QuARES for ndoor energy harvestng sensor network scenaros (Secton 6.3). 2. The QuARES approach In ths secton, we wll frst descrbe our wreless sensor network system and then the observatons leadng to the desgn of the complete QuARES framework. Our system conssts of a wreless sensor network (WSN) deployed n an nfrastructure for montorng purposes. The components of our systems are a set of n sensor nodes {s 1, s 2,..., s n } and a base staton(s) B as shown n Fg. 2. Each sensor node has a processor wth lmted memory, an embedded sensor(s), an analogto-dgtal converter and a rado crcutry. Sensor node s collects nformaton about ts surroundng envronment by readng a value Fg. 2. Data collecton n a wreless sensor network.

3 N. Dang et al. / Sustanable Computng: Informatcs and Systems xxx (2012) xxx xxx 3 Fg. 3. Data qualty energy cost tradeoff. v from ts embedded sensor and perodcally sends an update to the base staton(s). v s a property of the envronment, e.g., temperature, humdty or sound, that the applcaton needs to collect to montor the envronment through our WSN. In ths study, we assume that all sensor nodes are equpped wth a harvestng crcutry. Harvested energy s accumulated n an energy buffer that supples power for a sensor node s operaton. A base staton B resdes at a node wth unlmted resources, e.g., power, storage, computaton. It collects data from sensor nodes and stores them n a cache. The cache contans an approxmaton range [l, u ], a range based representaton for each sensor node s. The base staton B s connected to a montorng applcaton on the user sde. The applcaton perodcally polls sensor nodes through the base staton(s) for the montored phenomenon. When necessary, the applcaton can ask for sporadc nformaton. In partcular, the applcaton sends a query Q j to the base staton each tme t needs data from a sensor node or a set of sensor nodes. Each query Q j contans data qualty constrants. If the approxmaton range [l, u ] for sensor s n the cache satsfes these constrants, base staton B returns an approxmated value to the montorng applcaton. Otherwse, B sends an update request to retreve latest value v from sensor s and reples query Q j wth exact value. Our approach to desgnng effcent data collecton n energyharvestng sensor networks s based on 2 man observatons of ths data collecton applcaton and the harvestng systems. Frst, we notce that there exsts a tght couplng between the ablty of the system to harvest energy and the consequent data qualty,.e., n systems wth energy harvestng capabltes, sensor nodes only sense and communcate when there s suffcent harvested energy. Intutvely better harvestng leads to better data qualty; poor harvestng condtons mply loss of accuracy. Fg. 3 [17] llustrates an example of how data qualty (measured as error margn) needs can dctate energy cost. The fgure shows the total energy cost whch s the sum of 2 components, source-ntated cost and consumer-ntated cost. We later (n Secton 3) wll explan the concept of error margn, ts relaton to data qualty and the cost components. The man observaton s that n the useful range of data qualty [0, W*], the smaller the error margn s, the hgher data qualty and the cost are. Such graph and observaton can be generated for all range-based/approxmated data collecton applcatons [17]. Note that the total energy cost curve has a global mnmum W*. For tradtonal battery systems n whch the battery charge keeps decreasng untl replacement, ths mnmum total energy cost pont s the optmal operatng pont because t consumes least energy, prolongs battery and system lfetme whle mantanng reasonable data qualty. Ths s the man dea behnd energy effcent approaches for approxmated data collecton applcatons [14,32,33]. However, from the standpont of an energy harvestng system, ths s not the optmal operatng pont. Instead, the system should scale data qualty (n ths case, error margn) to match energy harvestng supply. Such scalng s possble snce many applcatons can tolerate certan loss/reduce n data qualty n favor of other features, such as longer system lfetme or system sustanablty. In short, our goal s to take advantage of the data qualty-energy tradeoff and to adapt data qualty gven energy harvestng constrants. The second observaton s that despte varaton, renewable energy sources (e.g., solar energy) have repettve patterns (day nght, season) and these patterns can be exploted to predct future energy harvestng capablty. The goal of our work s not to desgn new energy harvestng predcton technques; nstead, we am to use exstng predcton algorthms for energy harvestng to optmally plan the energy budget data qualty for the next harvestng perod. In our proposed scheme, a harvestng perod s dvded nto tme slots of equal length. We propose technques to allocate energy budgets for each slot that wll maxmze overall data qualty whle ensurng that nodes wll not shutdown due to lack of energy. If hgh data qualty s requred for a slot wth low energy harvestng capablty, the energy supply wll not meet the energy demand and the system mght run out of battery. In ths case, a node must shut down to replensh, suspendng montorng actvtes. Suspended nodes may cause crtcal events (often unpredctable) to be mssed. On the other hand, f low data qualty/low energy budget s assgned to a slot wth predcted hgh energy harvestng, the harvested energy s not utlzed fully and mght be wasted due to energy overflow once energy accumulaton exceeds buffer capacty. To solve ths problem, we must leverage both data qualtyenergy tradeoff and energy harvestng predcton to optmally plan ahead. Another challenge for slot-based budget allocaton scheme arses due to fluctuatons n the energy harvestng profle. Fnegran varaton n energy harvestng profle s unknown untl real-tme; they are not captured by predcton algorthms and cannot be dealt wth durng an offlne phase of budget plannng. Ths mples that we need an onlne phase to contnuously montor changes n energy harvestng profle and adapt the energy budget as well as data qualty accordngly at runtme. In summary, a hgh level defnton of the problem s as follows: gven predctons of energy harvestng and data qualty requrements, the goal s to keep all nodes of the system operatonal whle optmzng data qualty. Ths can be done by optmally allocatng energy budget n the offlne phase and adaptng data qualty based on the changes n energy profle n the onlne phase. We propose a framework called QuARES, qualty-aware renewable energy-drven sensng framework to address ths problem. QuARES s a cross-layer energy management framework consstng of 2 phases, an offlne phase and an onlne phase. Fg. 4 depcts our framework and ts phases. Offlne phase s executed durng a harvestng perod to plan energy budgets and data qualty for the next harvestng perod. In ths phase, the base staton runs a predcton algorthm to estmate energy harvestng avalablty n the next perod. The predcted nformaton and the knowledge of data accuracy-energy cost are nputs to an optmzaton algorthm usng a lnear programmng formulaton (step 1, Fg. 4). The optmzaton algorthm allocates energy budgets for each tme slot n the next perod to acheve optmal overall data qualty. The results are then sent to sensor nodes (step 2) and each sensor node stores the energy budgets n ts memory. Onlne phase s executed durng each harvestng perod. At the begnnng of each tme slot, the energy budget calculated n the offlne phase for ths perod s retreved from memory and correspondng data qualty s assgned (step 3). Sensor nodes and the base staton exchange messages accordng to ther protocol to

4 4 N. Dang et al. / Sustanable Computng: Informatcs and Systems xxx (2012) xxx xxx Fg. 4. QuARES framework. mantan applcaton s data qualty (steps 4 and 5). Onlne adaptve heurstcs montor the energy buffer status and the actual harvested energy rate (step 6) to adjust the data qualty accordngly (step 7). Adaptaton allows the system to cope wth varaton n renewable energy sources and mantan system operaton. In addton, energy harvestng statstcs are sent to the base staton (together wth messages n step 4) to enhance the energy harvestng predcton for the next harvestng perod (step 8). 3. Energy harvestng and data qualty models In ths secton, we defne system parameters, energy harvestng and data qualty models. Ths secton s prelmnary explanaton for the algorthms used n each phase of our framework n the next secton Energy harvestng predcton model Renewable energy sources such as solar energy show predctable patterns that are exploted n several coarse-gran (or slot-based) harvested energy predcton algorthms [4,8,15,16,34]. These predcton algorthms use estmaton technques such as Exponental Weghted Movng Average [4] or Weather-Condton Movng Average [34]. Sharma et al. [30] leverage weather forecast to mprove ts predcton model. Harvested energy predcton then can be used for plannng actvtes of nodes n a sensor network to keep them functonal, yet stll powered. In ths paper, we assume an deal predcton algorthm for slot-based energy harvestng estmaton. Fg. 5 shows our energy harvestng predcton model. We assume that each sensor has an energy buffer, wth capacty C, to store harvested energy. Energy accumulated beyond capacty C wll be dscarded (energy overflow). Let E 0 ntal be the avalable energy at the begnnng of the harvestng perod. Let E mn be the mnmum energy to be reserved at the end of the harvestng perod. We denote the length of a harvestng perod as T. A harvestng perod s then dvded nto equal-length ntervals or tme slots. Let N be the number of slots n a harvestng perod T. The values of T and N are defned by the energy harvestng predcton algorthms on the bass of renewable energy source, the nature of sensor nput and query model. Our framework, however, s ndependent of these parameters. For each tme slot, the predcton algorthm provdes E harvest whch s the amount of harvested energy n that slot. Usng harvested energy predcton, our framework can plan energy budget for future slots E budget n order to sustan system operaton and maxmze data qualty based on qualty-energy tradeoff presented n Secton 3.2 below. Some exstng works explot patterns n renewable energy profle to predct future harvested energy and to plan energy budget accordngly. Noh et al. [8] propose a mnmum varance slot-based energy budget allocaton for systems whch prefer steady level of operaton. Gorlatova et al. [28] propose several tme-far energy budget plannng algorthms. These solutons do not consder data qualty requrements and possble changes n such requrements. Hence t s not sutable for systems whose level of operaton s dctated by applcaton and user constrants that vary. Furthermore, snce data qualty s not the goal, t wll not be optmzed. Moser et al. [13] allocate energy budget for tme slots to maxmze qualty of servce for general systems. However, gven the fluctuaton of energy harvestng wthn each tme slot, an offlne budget allocaton methodology cannot always guarantee system operaton and certan data qualty. Therefore, onlne phase wth adaptaton s mportant and necessary n an energy harvestng management framework. Both the offlne budget plannng and onlne adaptaton needs a formal way to express data qualty needs and data qualty-energy cost trade-off n order to allocate energy budget and leverage data qualty. Ths model of data qualty s essental to our work and wll be present n the next secton Data qualty model Our work focuses n data accuracy, the most mportant aspect of data qualty. Data accuracy requrement can be expressed usng Fg. 5. Our energy harvestng predcton model.

5 N. Dang et al. / Sustanable Computng: Informatcs and Systems xxx (2012) xxx xxx 5 error margn of an actual value v, e.g., v ± 10 or v ± 10%. Such error tolerance n data qualty requrement can be exploted whle tunng the system. Error margns can be ncreased or decreased to meet both data accuracy constrants and system constrants, such as varyng energy supply. Our energy harvestng management framework explots ths error tolerance to adapt the system to the avalablty of renewable energy sources. We model data accuracy n terms of error margn. Error margn ı s the bounded dfference between the sensng value v at the sensor node s and the approxmated response r j to a query Q j,.e., rj v ı. The approxmaton range [l, u ] n the base staton s cache where l s the lower bound and u s the upper bound for sensng value v must satsfes u l = 2ı. Ths error margn s the constrant of the applcaton and s not the measurement error or senstvty of physcal sensors. We provde the followng defntons: Defnton 1. Consstent state refers to the state of cached entry on the base staton B. If the approxmaton range [l, u ] satsfes u l = 2ı and sensng value v satsfes l v u, the cache entry s n a consstent state. Defnton 2. Inconsstent state refers to the state of cached entry on the base staton B n whch v falls outsde the approxmaton range [l, u ] or u l /= 2ı. When sensor node s reads a new value v, t checks f the cache s stll consstent. If the cache s nconsstent, a new approxmaton range [v ı, v + ı ] s sent to the base staton to update the cache. Ths process s called source update. If the sensor node does not update the base staton, e.g., runnng out of battery for sensng and communcaton, the cache entry s n nconsstent state. The number of source updates essentally reflects the physcal characterstcs of montored phenomenon. We assume the samplng rate of sensors s suffcent to detect changes n the montored envronment and tme to send update message s neglgble. When the base staton B receves a new query Q j, t frst checks f the current cache entry satsfes query s data accuracy constrant A j,.e., ı A j. If t s satsfed, the base staton mmedately responses to the query wth value (u + l )/2. Otherwse, the base staton sends a request to s for current sensng value v. The sensor node reples wth the updated approxmaton range [v ı, v + ı ], the base staton updates ts cache entry and sends v to the applcaton. Ths process s called consumer update. The number of consumer updates reflects the nature of query model both n term of query frequency and assocated accuracy constrants. The smaller the error margn s, the more source updates and less consumer updates are. On the other hand, the larger the error margn s, the less sources updates and more consumer updates are. The two extremes are ı = 0 (only source updates) and ı = (only consumer updates). The total energy cost conssts of both energy cost for source updates and energy cost for consumer updates. Fg. 2 s one example. Accordng to Olston et al. [17], the total energy cost for a gven error margn can be expressed as E(ı) = Cost source update K1 ı 2 + Cost consumer update K2. where K1 and K2 are parameter defned by applcaton and query model. The value for ı such that the total energy cost s mnmum s ı = (2 Cost source update/cost consumer update K1/K2) 1/3 We call ths error margn as ı max. Beyond ths pont, both the error margn and energy consumpton s hgher, hence t s not worth consderng error margn > ı max. Therefore, our adaptaton choose only error margn n the range [0, ı max ]. Problem Formulaton Gven nput: Harvestng perod T, N tme slots 0 Battery capacty C 0 and Intal battery 0 E ntal Mnmum battery remaned after T: E mn 0 Energy harvestng predcton of each tme slot E harvest 0 N Error margn and energy cost vectors: ( 1,,. 1 K K) and ( E cos t,.. E cos t ) Objectve: Mnmze, the average actual error margn, n a harvestng perod T Fg. 6. Problem formulaton. Defnton 3. Baselne average error margn denoted as ı,base s the average error margn predcted n the offlne phase, representng the predcted average data accuracy of data collected from sensor node s. Defnton 4. Actual average error margn denoted as ı s the average error margn mantaned by sensor s n a harvestng perod T, representng the actual average data accuracy of data collected from sensor node s. Our framework, QuARES, optmzes ı,base n the offlne phase and uses onlne adaptaton to mantan ı close to ı,base durng onlne phase. A more formal characterzaton/formulaton of the problem s gven n Fg. 6 where our goal s to mnmze the actual average error margn. In the next secton, we descrbe our algorthms n each phase to acheve ths goal. 4. The QuARES framework: algorthms In ths secton, we wll present our lnear programmng soluton to the offlne budget plannng problem, consderng data qualtyenergy tradeoff to optmze data qualty whle regulatng energy usage to match energy harvestng supply. We then propose several onlne polces to cope wth fluctuatons n energy profle at runtme. We assume to have a vector of error margn levels (ı 1,..., ı K ) and a correspondng vector energy consumpton (Ecos 1 t,..., Ecos K t ) where E j cos t s the energy requred to mantan error margn ı j n a tme slot. K s the number of dscreet error margn levels Offlne phase: energy budget allocaton and data qualty assgnment In the offlne phase, we solve a lnear optmzaton problem (see Fg. 7) to allocate energy budget and assgn a correspondng baselne error margn for each tme slot. The lnear optmzaton problem for each sensor node s solved by a lnear solver on the base staton. System parameters (T, N, battery capacty C, ntal battery E 0 ntal, E mn ), harvested energy predcton (E harvest ), and error margn-energy cost (ı j E j cost ) n Fg. 6 are the nputs to ths optmzaton problem. Let E ntal denote the energy n the battery at the begnnng of tme slot. Snce the battery cannot store more than ts capacty C, E ntal must be constraned by ths upper bound (constrant 1, Fg. 7). Any excess energy s dscarded (energy overflow). Let E budget denote the energy budget for slot whch could only be drawn from the avalable energy n the battery at the begnnng of the slot and the energy harvested durng ths slot (constrant 2). Vce versa, the energy n the battery at the begnnng of a slot s lmted by the amount of energy avalable n the prevous slot subtracted by ts energy consumpton (constrant 3).

6 6 N. Dang et al. / Sustanable Computng: Informatcs and Systems xxx (2012) xxx xxx Offlne phase at Base Staton B 1. Run a lnear solver to solve ths lnear programmng for each sensor node s N base Objectve Mnmze base 0 N Subject to constrants: Input and Constrants n Fgure 3 E ntal C (1) E budget Ental E harvest (2) E 1 ntal Ental E harvest Ebudget (3) 0 <= q[,j] <= 1 0 N,0 j K N : N : K j 0 q[,j] base K j q[,j]*e cost j 0 N base K 1 j 0 E q[, j]* budget max j (4) (5) : (7) 2. Send energy budget allocaton and baselne data qualty assgnment to sensor s Onlne Phase at Sensor node s 1. Receve energy budget allocaton and baselne data qualty from base staton 2. Save n memory for run-tme use n the next harvestng perod Fg. 7. Offlne phase: energy budget allocaton. The baselne error margn for each slot s assgned based on the allocated energy budget. Let q[, j] = 1 f error margn ı j s assgned to slot, q[, j] = 0 otherwse. Each slot could only be assgned one base lne error margn (constrant 4) and ths error margn s computed n constrant 5. In addton, ts energy budget must be suffcent to mantan ths error margn,.e., at least E j cos t (constrant 6). Let ı max be the maxmum tolerated error margn of the applcaton n slot and also the upper bound for the assgned base lne error margn of slot (constrant 7). Many applcatons could have tme-based qualty constrants such as montorng closely durng day-tme than nght-tme or vce versa. Under ths constrant, the QuARES framework makes sure the data accuracy s never degraded beyond applcaton needs. The objectve of ths optmzaton problem s to maxmze the baselne average error margn, ı base. Next, we descrbe our onlne phase 2 of our framework, consstng of dynamc adaptaton polces whose goal s to mantan the baselne error margn and guarantee contnuous system operaton Onlne phase: dynamc adaptaton polces Durng onlne phase, data collecton protocol runs on both sensor and base staton to keep the cache n consstent state and respond to montorng queres. However, there could be fluctuatons n energy harvestng profle and actual harvestng rate could be lower than the predcted average rate. The system thus needs onlne adaptaton polces to tackle energy supply fluctuatons, mantan system operaton and data accuracy constrants. Our onlne adapton runnng on sensor node s a heurstc whch keeps track of current harvestng rate and battery status to adjust the error margn, guarantee system operaton and consstency of cached entry on base staton. We develop 2 dynamc adaptaton polces: nter-frame adaptaton and ntra-frame adapton (see Fg. 8). (6) Polcy 1: Inter-frame onlne adaptaton (slot ) 1. buffer = current energy n the buffer 2. offset = Ental - buffer 2. f( offset > epslon ) then #adjust budget of future slots 3. E = budget E - offset budget 4. j = fnd_qualty_level( E ) budget 5. = j 6. update_server(s, v -, v + ) Polcy 2: Intra-frame onlne adaptaton (slot ) 1. h = current_harvestng_rate; 2. f ( h - h old < epslon) then return 3. else h old = h 4. buffer = current energy n the buffer 5. reserve = buffer energy reserved for future sub-slots 6. l = length of a sub_slot 7. supply = h*l + (buffer-reserve) #energy supply for ths sub-slot 8. f (supply < E /number_of_subslots ) budget 9. then = fnd_qualty_level (supply*number of subslots) 10. else base 11. f changes then 12. update server(s, v -, v + ) Fg. 8. Onlne phase: dynamc adaptaton polces. (1) Inter-frame adapton s trggered at the begnnng of each tme slot. The harvested energy often does not come at a constant rate; n partcular, when harvested energy s abundant and the energy buffer s almost full, energy overflow happens. The harvested energy thus could be less than what s predcted and the system needs to adapt ts energy budget plan and base lne error margn. The nter-frame polcy keeps track of ths energy dscrepancy and dstrbutes the energy offset among current and future slots energy budgets. Our nter-frame adaptaton algorthm s summarzed n Polcy 1, Fg. 8. (2) Intra-frame adaptaton on the other hand s trggered more often every sub-slot wthn a tme slot to quckly adapt to fluctuatons of the renewable energy source. The length of a sub-slot depends on the energy source, whch s the nterval of tme that the harvested energy rate remans farly stable, e.g., 1 5 mn. Every sub-slot, ntra-frame polcy wll check f the current harvestng rate s sgnfcantly less than the predcted average harvestng rate and adjust error margn n the current sub-slot accordngly. In the next sub-slots, f the harvestng rate ncreases above the expected average rate, the polcy restores the baselne error margn. Our nter-frame adaptaton algorthm s summarzed n Polcy 2, Fg Data collecton protocol Base staton and server both run a data collecton protocol to communcate wth each other (see Fg. 9). When recevng updates from sensor nodes, base staton wll refresh ts cached entry wth the new approxmaton range (lne 2). If ths s the response to a prevous query, a response message s sent back to the montorng applcaton (lnes 3 and 4). When base staton receves new query (lne 5), t frst checks f the current approxmaton range satsfes the query constrants. If the current approxmaton s suffcent, base staton responses mmedately (lnes 6 and 7). Otherwse, t sends a request message to sensor node and wat for reply from sensor node (lne 8).

7 N. Dang et al. / Sustanable Computng: Informatcs and Systems xxx (2012) xxx xxx 7 Protocol for Base Staton B 1. f receve update_server(s, u, l ) then 2. update database wth new range [l, u ] 3. f ths s a consumer_ntated_update and query Q j watng 4. then responsequery(( l +u )/2) 5. f receve query Q j(s, j C j) then #external queres 6. f C j > (u j l j )/2 #Q j requres lower qualty of data 7. then responsequery((l j +u j)/2) 8. else querysensor(s j ) Protocol for Sensor node (s, tme t) 9. f (current_tme t at begnnng of a new slot ) then 10. ; update base E budget 11. nter_frame_adaptaton() 12. f (current_tme t at begnnng of a new sub-slot) then 13. ntra_frame_adaptaton() 14. v = readsensorvalue(t); 15. f (v [l, u ] or receve querysensor(s ) ) then 16. l = v u = v update_server(s, u, l ) Fg. 9. Data collecton protocol. On sensor node sde, at the begnnng of a new harvestng slot, the allocated budget and base lne error margn saved n memory s used to confgure the data collecton protocol (lne 10). Lnes are calls to onlne adaptaton polces descrbed n the prevous secton. The sensor node keeps readng sensng value and checks t aganst the cached range on the base staton (lne 14). If the new value falls out of ths range or f the sensor node receves an update request (lne 15), a new approxmaton range s computed (lnes 16 and 17) and s sent to the base staton (lne 18). 5. Indoor energy harvestng: a measurement study In ths secton, we explore the opportunty of harvestng energy ndoor. We show that these energy sources can sustan low power system such as wreless sensor networks n a sensorzed nfrastructure. There are varous ways of harvestng energy ndoor from dfferent energy sources. The most accessble are lght n offces and hallways whch can be readly harvested by solar panels. Hande et al. [23] devse a system to harvest lght from fluorescent lght n hosptal hallways to support routng of patent data usng Mcaz motes. Tan and Panda [25] desgned a hybrd energy harvestng system consstng of both ndoor ambent lght and thermal energy harvestng crcuts. Refs. [26,27] present a prototype and analyss of hybrd solar and RFID harvestng sensor mote. Gorlatova et al. [28] carres out ndoor energy harvestng measurement over 16 months at dfferent settngs. Ths work also descrbes several desgn space dmensons and proposes several algorthms to acheve tme-far resource allocaton at several workng ponts of the desgn space. Other energy sources such as knetc, vbraton, magnetc can also be harvested. ndoor Energy Harvester s a project at NYU by Zollnger [24] that bulds an add-on for hnged doors n order to convert knetc energy from openng and closng a door to electrcty for other grd uses. We carry out measurement of ndoor energy harvestng n several settngs n such nfrastructure. In Secton 6, experments wth measurement data from dual energy harvestng sources and varous types of sensors are executed n QuARES system smulaton. The result shows that QuARES s able to explot ndoor energy harvestng avalablty to manage system effcently whle helpng desgner to explore the desgn space and choose optmal solar sze. We are deployng a test bed of energy harvestng wreless sensor network n Responsphere [11]. Fg. 10 shows our Responsphere nfrastructure and prototype of our energy harvestng platform, ncludng a Crossbow sensor (temperature, lght and acoustc sound), two solar panels and 2 AA batteres. The frst step to explore opportunty of ndoor energy harvestng for wreless sensor network s to measure energy harvestng avalablty n dfferent settng and collect data about sensor and sensor mote energy consumpton. We want to answer three essental questons: Queston 1: How much energy avalable to harvest n an ndoor scenaro? Queston 2: Is t possble to use ths energy to support ndoor sensor network? And to what extent? Queston 3: If t s possble, what s optmal data qualty, battery sze and optmal solar panel sze? Wth a focus on lght sources, we dentfy two promsng sources of ndoor energy harvestng: lght bulbs nsde offces, along hallways and solar lght through wndows around the buldng. They are often avalable for extended perod of tme n all buldngs. Table 1 s the summary of our ndoor settngs for measurement. We carred out an extensve lght measurement study at Unversty of Calforna, Irvne for dfferent types of lght bulb. We measure the harvestng power output of a solar panel n perpendcular to the lght source at the dstance of 10 cm (solar-made, 9.6 cm 6.4 cm). Table 2 shows the results of our measurement. We experment wth dfferent possble types of bulb used for lghtng n offces and at home. We observe that the hgher the energy a lght bulb consumes, the hgher the harvestng power t provdes as the rradance ncreases. However harvestng power s not proportonally to lght output whch s measured n Lux whch ndcates how human eyes perceve the lght but not the energy the lght carres. For example, soft whte ncandescent bulb has slghtly lower Lux than compact fluorescent bulbs but sgnfcant hgher harvestng power output. Among these types of bulbs, soft whte compact fluorescent lght bulb has a nce balance between ts energy consumpton and energy harvestng power t can provde. Solar panel set up on wndow panels s another source of energy for ndoor applcatons. Table 3 shows our comparson of 2 ndoor wndow set-ups and 2 outdoor locatons measured at noon. Result shows that fltered lght carres less energy by one order of magntude, from 4 to 10 tmes less compared to outdoor sunny locatons but can be comparable to outdoor shadowy locatons. Longer-term measurement of these wndow energy harvestng set-ups reveals not only temporal but also spatal characterstcs of lght harvestng. Fg. 11 shows the power output of the solar Table 1 Summary of ndoor measurement settngs. Locaton tag Locaton descrpton Quantty Measurement duraton L-1 Dfferent ndoor lght bulbs n lab wthout wndow 6 30 mn to 1 h each type of bulb L-2 On wndow panel, facng South-West drecton 1 3 days L-3 On wndow panel, facng South-East drecton 1 3 days

8 8 N. Dang et al. / Sustanable Computng: Informatcs and Systems xxx (2012) xxx xxx Fg. 10. System prototype. Table 4 Sensor power consumpton. Temperature 9.35 mw Acceleraton 10 mw Humdty 3 15 mw Heart rate/ecg 19.8 mw Low-power mage 80 mw Acoustc 540 mw Magnetc 1.5 W Moton detecton 4 W Low-power GPS 5 W Fg. 11. Indoor energy harvestng wndow setup. panels nstalled on wndow panels at locatons L-2 and L-3 on a same day. The average energy harvestng power n ths perod at L-2 s 18 mw whle at L-3 s 28 mw. Both energy harvestng profles have one peak pont but each peak pont occurs at dfferent tme. One peak pont (L-2) s at 11 am whle another (L-3) s at 5 pm, shftng by an amount of tme depends on the spatal locaton of the solar panels wth respect to buldng and the sun s drecton. The energy harvestng power reaches ts peak at a gven locaton when the solar panel s drectly proportonal to the angle of the sun lght and under no shadow condton. Beng aware of these spatal and temporal varatons s very mportant n buldng models, plannng deployment and schedule actvtes for nodes n an energy harvestng wreless sensor network. Our study has answered queston 1. To answer queston 2, we need to know how much energy sensors requre to sustan. There are varous types of sensors beng used n the buldng; we classfy them nto three groups: low-power sensors (temperature, acceleraton, humdty, heart rate/ecg sensors); medum-power sensors (low-power mage, acoustc and magnetc sensor); hgh-power sensors (moton detecton, low-power camera, GPS, vson or flud ndcator). Table 4 summarzes our study of dfferent types of sensors n the market and ts correspondng typcal power consumpton. Ths Table 2 Indoor energy harvestng lght bulbs. Type of bulb Energy used (W) Lght output (Lux) Harvestng power (mw) Daylght compact fluorescent Brght whte compact fluorescent Round back lght Max Soft whte compact fluorescent Halogen lght Soft whte ncandescent bulb Table 3 Indoor energy harvestng wndow panels. Locaton L2 L3 Outdoor (under shadow) Outdoor (sunny locaton) Voltage (V) Current (ma) Avalablty h h h h

9 N. Dang et al. / Sustanable Computng: Informatcs and Systems xxx (2012) xxx xxx 9 Fg. 12. Inputs to our smulator. study gves us an understandng the range of power demand each class of sensors requres and roughly estmates whether our solar harvestng are good potental energy sources for these sensor systems. At the frst glance, by matchng power consumpton of sensor and power generated by our ndoor solar panel, t could support low power sensors. However, ths s power consumpton of sensor alone and has not taken nto consderaton power consumpton of sensor node/board whose processng unt, memory and rado component also draw sgnfcant porton of overall power consumpton. In addton, energy management plays an mportant role n plannng, adaptng and thus sustanng the system. In Secton 6.3, we wll apply the real data we present n ths secton to evaluate the feasblty of ndoor energy harvestng for sensor network and evaluate QuARES performance. In short, the study n ths secton explores ndoor energy harvestng and the ntal measurement results shows potentals of ndoor energy harvestng sensor network. We wll use QuARES to evaluate ths opportunty n the next secton to fully answer questons 2 and Evaluaton In ths secton, we frst explan the expermental setup to evaluate the effectveness of our proposed framework QuARES and then we compare the results wth other exstng polces n terms of error margn (data accuracy), responsveness to queres, and energy consumpton. Based on measurement study from Secton 5, we wll also evaluate QuARES by applyng ths framework to ndoor sensng scenaros (Secton 6.3) Expermental setup We ntally mplemented the approxmated data collecton applcaton and QuARES framework n QualNet network smulator [18]. The smulator s confgured to smulate a sensor network of Mca motes wth ZgBee standard specfcaton. Power consumpton of sensor node s set accordngly to Mca-2 [19]. Sensor data are generated randomly from the range [ 150, 150]. The samplng rate s 100 Hz, each samplng ether ncreases or decreases the prevous value by an amount randomly chosen n the range [0.5, 1.5]. Fg. 12b gves an example of randomly generated sensor data for smulated tme of 6 h. Perodc queres arrve every 100 ms. Sporadc queres are modeled by Posson dstrbuton wth mean nterval = 100 ms. Each query s assocated wth an error tolerance of mean = 20 and devaton = 1. Energy harvestng profle s retreved from Natonal Renewable Energy Lab webste [20]. Solar proflng for a day s shown n Fg. 12a. The data s average solar rradance (mw/m 2 ) at a specfc locaton every 5 mn. The rradance s converted to harvested energy by lnear converson consderng solar panel sze 9.6 cm 6.4 cm, solar cell effcency 10% and harvestng effcency 80%. We modfy QualNet battery model to charge battery every 1 mn. We assume a perfect solar energy predcton algorthm whch gves accurate slot-based predcton to our offlne phase. We choose T = 1 day and N = 48 slots. We profle error margn vs. energy cost by smulatng applcaton for dfferent error margns n the range [0, ı max ] (see Fg. 12c). Snce t s mpossble to profle every value n ths range, we choose to profle dscreet values every nterval r = 0.1. For each error margn ı profle, we fx the baselne error margn for all slots (ı base = ı profle ) and set energy buffer always full. We run the smulaton for a smulated tme of one day and dvde the total energy consumpton by the number of tme slots N to obtan average energy cost per slot at error margn ı profle. For the gven query model and sensor nput, we fnd that ı max Expermental results To evaluate the effectveness of our proposed QuARES framework, we have mplemented several offlne or onlne polces for energy management durng data collecton. We evaluate the polces n terms of ther data accuracy (error margn), system sustanablty, and energy consumpton. The mplemented polces are as follows: FIX ERROR (ı = 8.0) and FIX ERROR (ı = 0.5): FIX ERROR s an offlne polcy and has no onlne adaptaton. A fxed baselne error margn s assgned to all tme slots. GREEDY ADAPT: greedy adaptaton s a completely onlne protocol wthout offlne energy budget assgnment. It sets an error margn at the begnnng of each tme slot accordng to the amount of avalable energy n the buffer. MIN VAR: adopted from [8], t allocates energy budget for slots n the offlne phase wth mnmum varance. Its goal s to mantan steady operaton for the system. It does not have onlne adaptaton. QuARES: as presented n ths paper, our qualty-aware framework mnmzes error margn both n offlne and onlne phases. Table 5 shows results of QuARES n comparson wth other approaches. FIX ERROR (ı = 8.0) has a very hgh error margn and consumes mnmum energy. Due to hgh error margn, source updates s very low compared to consumer updates; ths scheme s thus very close to push-based data collecton. The system responds to all queres at the trade-off of hgher error margn. Ths s the extreme case where energy savng s a domnant requrement compared to data accuracy and s sutable for tradtonal dschargng battery systems [14]. The second column FIX ERROR (ı = 0.5) s another fxed error rate polcy whch attempts to mantan a lower error margn ı = 0.5 by explotng energy harvestng. Due to very low error margn, source updates s very hgh compared to consumer updates; ths scheme thus s very close to pull-based data collecton. However, t fals snce the sensor node cannot mantan

10 10 N. Dang et al. / Sustanable Computng: Informatcs and Systems xxx (2012) xxx xxx Table 5 Comparson of 5 dfferent approaches. FIX ERROR (ı = 8.0) FIX ERROR (ı = 0.5) GREEDY ADAPT MIN VAR QuARES offlne QuARES offlne + onlne Average error margn Total energy consumpton (J) Shut down tme for harvestng (mn) Faled responses to queres ths low error margn when the harvested energy s low. The battery s exhausted and system needs to shut down for 45 mn to replensh energy. Ths leads to a very hgh number of faled responses to queres. FIX ERROR approach n general cannot work n dynamc energy harvestng systems. The thrd and the fourth column show results of GREEDY ADAPT and MIN VAR [8]. Both have comparable average error margn and energy consumpton. However, both do not consder fluctuaton of energy harvestng n ts greedy onlne adaptaton or offlne budget allocaton. Therefore, n both cases, sensor node runs out of battery and shuts down for harvestng, leadng to faled responses to queres. It s thus necessary to have onlne adaptaton to handle fluctuaton of renewable energy. In columns 5 and 6 of Table 5, we study the sgnfcance of each phase n the QuARES framework: offlne budget allocaton and onlne adaptaton. We compare our QuARES offlne and the whole QuARES wth both onlne and offlne phases. As seen from Table 5, wthout onlne adaptaton, QuARES (offlne) must shut down the sensor node for 4 mn and thus faled to response to 64 queres. The system often shuts down durng the sunrse when both battery reserves and harvested energy rate s low. Among all approaches, QuARES wth both onlne and offlne phases keeps the system alve for the whole harvestng perod, responds to all queres, mantans low error margn from 30 to 70%, and enables a unform energy usage across nodes. The results show that whle our proposed offlne phase n QuARES maxmze data accuracy, the onlne adapton phase s requred for successful query responsveness Varyng applcaton constrants We evaluated our framework for dfferent applcaton constrant profles. Applcaton constrant profle 1 (AC1) mantans low error margn durng nght tme (ı < 1.5) from 5 pm to 7 am. Applcaton constrant profle 2 (AC2) mantans low error margn durng nght tme (ı < 1.5) from 7 pm to 5 am. do not gve any feedback to desgners when applcaton constrants are nfeasble. Furthermore, the budget s not n accordance wth energy demand n dfferent slots, MIN VAR spends more energy n slots where t should save energy for hgher-demand slots n the future and soon runs out of battery and fals to response to queres Impact of battery capacty We smulate the applcaton wth dfferent battery capactes. Fg. 13a shows average error margn durng day tme and nght tme under dfferent battery capactes on a summer day. As seen from the fgure, battery capacty has neglgble effect on error margn durng the day as the energy supply s abundant. On the other hand, battery capacty has sgnfcant mpact on error margn durng the nght. There s no energy harvested durng ths perod and the capacty of battery lmts energy savng to mantan data accuracy at nght. In addton, we compare results of QuARES on summer days wth results on wnter days (Fg. 13b). Results show that to obtan a same error margn, the battery capacty requred on wnter days s larger than the battery capacty on summer days as wnter nght tme s longer than summer nght tme. For example, to acheve average error margn of 1.0 at nght, system would requre battery capacty C = 950,000 (mj) on a summer day but would requre hgher C = 1,400,000 (mj) on a wnter day Applyng QuARES for ndoor sensng In ths secton, we evaluate QuARES n a realstc settng. In ths settng, we assume Mcaz motes, each wth a sensor (we experment wth all dfferent sensor types presented n Secton 5). Sensor motes are powered by ndoor energy harvestng. We frst show an estmaton of duty cycle such system can sustan gven ndoor energy harvestng. We then show how QuARES can mprove the result, gve the answers to mportant questons we proposed n Secton 5 and also present many parameter optons for system desgners. QuARES (Table 6) satsfes all applcaton constrants at the trade-off of suboptmal energy budget allocaton compared to no constrant case, hgher error margn and less energy utlzaton. Interestngly, the QuARES offlne phase also gves mmedate feedback to system desgners f the gven data accuracy constrants are nfeasble n the next harvestng perod and needed to be adjusted. MIN VAR, on the other hand dstrbutes energy budget among slot wth mnmum varance regardless of applcaton constrants, thus Table 6 Varyng applcaton constrants. App. constr. 1 App. constr. 2 MIN VAR QuARES MIN VAR QuARES Average error margn Energy cons. (J) Shut down tme (mn) Faled responses to queres Fg. 13. Impact of battery capacty on applcaton data accuracy (a) on summer days and (b) on wnter days.

11 N. Dang et al. / Sustanable Computng: Informatcs and Systems xxx (2012) xxx xxx 11 Fg. 14. System operaton tme an estmaton. We do an estmaton of system operaton tme or duty cycle usng ths raw computaton: System operaton tme = maxmum energy consumpton average energy harvestng and apply ths for systems wth Mcaz sensor board connected to dfferent type of sensors. Mcaz board tself has maxmum energy consumpton of 95 mw, typcal power consumpton of dfferent types of sensors was shown n Table 4. Maxmum energy consumpton of the system s the sum of embedded sensor s power consumpton and sensor board s maxmum power consumpton. In the base result and smulaton result that we show later, we assume hybrd energy harvestng sources from one wndow solar panel and one soft whte compact fluorescent bulb. We choose a smple model of the bulb that t s avalable for 10 h per day from 8 am to 6 pm. The average energy harvestng of ths heterogeneous renewable energy source s mw. Fg. 14 shows the estmaton of duty cycle durng whch the system s actve for each type of sensor and board. Ths estmaton s the base for our comparson later. From the fgure we observe that low-power sensors such as temperature, humdty, heart rate needs at least 2 gven solar panels to sustan 100% operaton whle medum power sensor such as acoustc, magnetc sensor mght need more than 16 of such solar panels to sustan % operaton. However, a solar panel of sze 16 tmes of our solar panel could be too ntrusve n an ndoor envronment such as offces or school and t can complete cover the lght sources lke lamps and small wndows. For hgher power sensor such as moton Fg. 15. System operaton tme by QuARES. or GPS locaton, ndoor energy harvestng s currently unable to support. From ths base estmaton, we mght thnk that t s only feasble to use ndoor energy harvestng for low power sensor network. In fact, we run QuARES wth measurement nput of heterogeneous energy harvestng sources consstng of one wndow panel and one lght bulb set-ups. We use a smplfed model for lght bulb energy harvestng n whch we assume that the lght s avalable at constant rate (as measured) for 10 h per day from 8 am to 6 pm. The result of measurng system operaton tme for systems wth dfferent embedded sensors s shown n Fg. 15. Overall there s sgnfcant mprovement n system operaton tme for all sensor types. Wth only 1 solar panel, the energy harvestng system s able to sustan 70 80% operaton tme for low power sensors, mprovng over the base estmaton by 30%. Acoustc sensor needs 9 solar panels to support 100% operaton tme whch s about half of the sze predcted by the base estmaton. QuARES s able to do such mprovement by explotng tradeoff between data qualty and energy consumpton of sensor nodes. The summary of data qualty vs. battery capacty for dfferent types of sensor (low-power and medum-power sensors) s gven n Fg. 16. Usng smulaton result from QuARES, the desgner can explore the desgn space and decde: What s the solar panel sze gven the duty cycle requrement? What s the battery sze requred for a gven data qualty constrant? The frst queston here s answered by tracng the graph of system operaton tme vs. solar panel sze (e.g., Fg. 14) and the second Error margn Temperature 1500K 1550K 1600K 1650K 1700K Ba ery Capacty Error Margn Sp K 2040K 2080K 2120K 2160K 2200K Ba ery Capacty Error Margn ImageSensor 4800K 4850K 4900K 4950K 5000K Error Margn Acous c 1814K 1816K 1818K 1820K Ba ery Capacty Ba ery Capacty Fg. 16. Data qualty for dfferent types of sensor.

12 12 N. Dang et al. / Sustanable Computng: Informatcs and Systems xxx (2012) xxx xxx queston can be answered by lookng at the graph of data qualty vs. battery capacty (e.g., Fg. 16). To summarze ths case study of ndoor energy harvestng for wreless sensor network, we have shown an extensve study of energy harvestng power for dfferent types of ndoor lght bulb as well as wndow solar panels. Our smulaton result usng synthetc data shows that QuARES can mprove system operaton (duty cycle) by explotng trade-off between data qualty and energy consumpton. QuARES also helps desgner to explore the desgn space to answer questons such as solar panels sze and battery capacty needed accordng to requrement of applcaton s data qualty. To the best of our knowledge, our work s the frst to explore systems wth dfferent types of sensor and dfferent types of ndoor lghtng bulb for harvestng. 7. Concluson In concluson, ths paper proposes a complete autonomous energy management framework n energy harvestng WSNs whose goal s to optmze data accuracy for approxmated data collecton applcatons and sustan system operaton. The offlne phase explores energy harvestng predcton nformaton to allocate energy budget among tme slots n a harvestng perod and maxmze overall data accuracy. The onlne adaptaton phase mantans the predcted data accuracy whle copng wth harvested energy fluctuaton. Our framework s evaluated extensvely n comparson wth other approaches and consderng dfferent weather condtons, battery capactes and applcaton constrants. Fnally, we carry out a measurement study of ndoor energy harvestng system and evaluate QuARES usng measurement data. QuARES shows that t can mprove system operaton tme for ndoor energy harvestng system whle helpng desgner to explore desgn space such as choosng optmal solar panel sze and battery capacty for ther systems. References [1] K. Ln, J. Yu, J. 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Zollnger, ndoor Energy Harvester [Onlne]. sustanable/ndoor-energy-harvestng [25] Y.K. Tan, S.K. Panda, Energy harvestng from hybrd ndoor ambent lght and thermal energy sources for enhanced performance of wreless sensor nodes, IEEE Transacton on Industral Electroncs 58 (September (9)) (2011) [26] Gummeson J., Clark S.S., K. Fu, D. Ganesan, On the lmts of effectve hybrd mcro-energy harvestng on moble CRFID sensors, n: MobSys 10, [27] M. Gorlatova, Z. Noorbhawala, A. Skolnk, J. Sark, M. Zapas, M. Szczodrak, J. Chen, L. Carlon, P. Knget, I. Kymsss, D. Rubensten, G. Zussman, Prototypng Energy harvestng actve networked tags: phase II mca mote-based devces, n: Mobcom, [28] M. Gorlatova, A. Wallwater, G. Zussman, Networkng low-power energy harvestng devces:measurements and algorthms, n: InfoComm, [29] I.F. Akyldz, W. Su, Y. Sankarasubramanam, E. Cayrc, Wreless sensor networks: a survey, Journal of Computer Networks 38 (December) (2001) [30] N. Sharma, J. Gummeson, D. Irwn, P. Shenoy, Cloudy computng: leveragng weather forecasts n energy harvestng sensor systems, n: Secon, [31] A. Kansal, J. Hsu, S. Zahed, M.B. Srvastava, Power management n energy harvestng sensor networks, ACM Transactons on Embedded Computng Systems 6 (4) (2006). [32] U. Cetntemel, A. Flnders, Y. Sun, Power-effcent data dssemnaton n wreless sensor network, n: MobDE 03, [33] T. He, S. Krshnamurthy, J.A. Stankovc, T. Abdelzaher, L. Luo, R. Stoleru, T. Yan, L. Gu, J. Hu, B. Krogh, Energy-effcent survellance system usng wreless sensor network, n: MobSYS 04, [34] J.R. Porno, C. Bergonzn, D. Atenza, T.S. Rosng, Predcton and management n energy harvested wreless sensor nodes, n: Wreless VITAE, [35] Mkkel K. Jakobsen, Jan Madsen, Mchael R. Hansen, T.U. Denmark, DEHAR: a dstrbuted energy harvestng aware routng algorthm for ad-hoc mult-hop wreless sensor networks, n: WOWMOM, [36] Emanuele Lattanz, Edoardo Regn, Andrea Acquavva, Alessandro Boglolo, Energetc sustanablty of routng algorthms for energy-harvestng wreless sensor networks, Journal of Computer Communcatons 30 (14 15) (2007) [37] V. Pryyma, L. Bolon, D. Turgut, Unform sensng protocol for autonomous rechargeable sensor networks, n: MSWM, Nga Dang s currently a Ph.D student n the Department of Computer Scence at the Unversty of Calforna, Irvne. Her research nterests nclude energy harvestng management for embedded system and wreless sensor networks, software/hardware co-desgn and hardware functonal verfcaton. Nga Dang receved the B.S degree n Computer Engneerng from Natonal Unversty of Sngapore, Sngapore n 2007 and the M.S degree n Computer Scence from Unversty of Calforna, Irvne n Elaheh Bozorgzadeh receved the B.S. degree n electrcal engneerng from Sharf Unversty of Technology, Tehran, Iran, n 1998, the M.S. degree n computer engneerng from Northwestern Unversty, Evanston, IL, n 2000, and the Ph.D. degree n computer scence from the Unversty of Calforna, Los Angeles, n She s currently an Assocate Professor n the Department of Computer Scence at the Unversty of Calforna, Irvne. Her research nterests nclude desgn automaton for adaptve embedded systems, reconfgurable computng, and green computng. Dr. Bozorgzadeh s recpent of NSF CAREER award and the Best Paper award n IEEE FPL 2006.

13 N. Dang et al. / Sustanable Computng: Informatcs and Systems xxx (2012) xxx xxx 13 Naln Venkatasubramanan s currently a Professor n the School of Informaton and Computer Scence at the Unversty of Calforna Irvne. She has had sgnfcant research and ndustry experence n the areas of dstrbuted systems, adaptve mddleware, pervasve and moble computng, dstrbuted multmeda and formal methods and has publshed extensvely n these areas. As a key member of the Center for Emergency Response Technologes at UC Irvne, Naln s recent research has focused on enablng reslent and scalable observaton and analyss of stuatonal nformaton from multmodal nput sources; dynamc adaptaton of the underlyng systems to enable nformaton flow under massve falures and the dssemnaton of rch notfcatons to members of the publc at large. Many of her research contrbutons have been ncorporated nto software artfacts whch are now n use at varous frst responder partner stes. She s the recpent of the prestgous NSF Career Award, an Undergraduate Teachng Excellence Award from the Unversty of Calforna, Irvne n 2002 and multple best paper awards. Prof. Venkatasubramanan has served n numerous programs and organzng commttees of conferences on mddleware, dstrbuted systems and multmeda and on the edtoral boards of journals. She receved a M.S and Ph.D n Computer Scence from the Unversty of Illnos n Urbana-Champagn. Her research s supported both by government and ndustral sources such as NSF, DHS, ONR, DARPA, Novell, Hewlett-Packard and Noka. Pror to arrvng at UC Irvne, Naln was a Research Staff Member at the Hewlett-Packard Laboratores n Palo Alto, Calforna.