On the Limits of EffectiveHybrid Micro-Energy Harvesting on Mobile CRFID Sensors

Jeremy Gummeson, Shane S. Clark, Kevin Fu, Deepak GanesanUniversity of Massachusetts Amherst, Dept. of Computer Science

{gummeson,ssclark,kevinfu,dganesan}@cs.umass.edu

ABSTRACTMobile sensing is difficult without power. Emerging Compu-tational RFIDs (CRFIDs) provide both sensing and general-purpose computation without batteries—instead relying onsmall capacitors charged by energy harvesting. CRFIDshave small form factors and consume less energy than tra-ditional sensor motes. However, CRFIDs have yet to seewidespread use because of limited autonomy and the propen-sity for frequent power loss as a result of the necessarily smallcapacitors that serve as a microcontroller’s power supply.Our results show that hybrid harvesting CRFIDs, which usean ambient energy micro-harvester, can complete a varietyof useful workloads—even in an environment with little am-bient energy available.

Our contributions include (1) benchmarks demonstratingthat micro-harvesting from ambient energy sources enablesgreater range and read rate, as well as autonomous operationby hybrid CRFIDs, (2) a measurement study that stressesthe limits of effective ambient energy harvesting for diverseworkloads, (3) application studies that demonstrate the ben-efits of hybrid CRFIDs, and (4) a trace-driven simulator tomodel and evaluate the expected behavior of a CRFID withdifferent capacitor sizes and operating under varying condi-tions of mobility and solar energy harvesting. Our resultsshow that ambient harvesting can triple the effective com-munication range of a CRFID, quadruple the read rate, andachieve 95% uptime in RAM retention mode despite longperiods of low light.

Categories and Subject DescriptorsC.4 [Performance of Systems]: Measurement Techniques

General TermsDesign, Experiment, Measurement, Performance

KeywordsRFID, Solar, Energy Harvesting, Sensing

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1. INTRODUCTIONAn emerging model of RFID requires tags that go be-

yond mere identification, also carrying out sensing, com-putation, and storage duties. Such Computational RFIDs(CRFIDs) [14] come equipped with ultra low-power micro-controllers and a suite of low-power sensors such as thosefound in the Intel Wireless Sensing Platform (WISP) [18].A CRFID resembles a sensor mote stripped of its batteryand active radio, but augmented with an RFID front endfor RF energy harvesting and backscatter communication.

CRFIDs offer several advantages over battery-powered em-bedded sensor platforms [3, 14]. Because CRFIDs are ex-pected to be continually charged by a remote energy source,they can rely on small capacitors rather than large batteriesfor energy storage. This difference in the size and weightof the energy store makes CRFIDs more amenable to SoCintegration and low-cost manufacturing. For instance, thelightweight SoCWISP has been implanted in the wing mus-cles of a hawkmoth [12]. In addition, capacitors are cheaperand more environmentally friendly than batteries—makingthem better suited for large-scale deployments. Finally, ca-pacitors can endure a large number of recharge cycles—important for the frequent, bursty charge cycles commonto energy harvesting.

CRFIDs also offer extremely low power profiles, consum-ing roughly an order of magnitude less energy than motes formany operations. Communication, in particular, is very en-ergy efficient in CRFIDs, as they leverage backscatter com-munication rather than using active radio transmitters [1].In addition, CRFIDs are optimized to exploit limited har-vested energy rather than continuous long-term operation;hence they use far fewer hardware components and conse-quently consume less power than mote-class devices.

Limitations. Despite the advantages of CRFIDs, severalchallenges remain before they are suitable for widespreaduse in ubiquitous computing and sensing applications. Akey limitation of CRFIDs is the reliance on a dense deploy-ment of RFID readers. CRFIDs must be placed such thateach device harvests enough energy to compute and sense.A CRFID cannot sustain operation for long outside of theeffective harvesting range of a reader; this lack of autonomynecessitates carefully planned deployments of RFID readersrelative to tags, making such networks expensive to deployand maintain. A CRFID’s lack of autonomy is exacerbatedby the observation that its performance varies with distancefrom a reader. For example, our experiments show that anIntel WISP [18] within a few feet of an RFID reader receivessufficient energy to sample and transmit hundreds of times

per second, whereas one that is near its maximum reliabledistance (a few meters) may be able to sample and trans-mit only a few times per minute. This rapid performancedegradation limits the efficacy of CRFIDs for applicationsrequiring complex processing or frequent sensing.

Approach. Our work exploits supplemental energy har-vested using a miniature solar panel attached to the IntelWISP (Figure 2) to make the WISP more autonomous andtherefore more useful for mobile sensing and computation.Even a tiny amount of hybrid harvesting enables a CRFIDto better tolerate power interruptions. Our measurementsshow that the SolarWISP prototype is able to retain mem-ory state in all but the most difficult lighting conditionsthat we encountered. Modest modifications to the WISPenable ambient energy harvesting and provide the platformwith new capabilities, but there are many issues associatedwith batteryless harvesting. The specific challenges raisedare (1) how a small amount of ambient energy can be usedmost effectively to enable autonomous operation and (2) howto select appropriate workloads. To determine appropriatecomponent sizes and applications, we performed an applica-tion study and built a trace-driven software simulator thatpredicts the behavior of a CRFID’s capacitor under an as-sortment of sensing and computation workloads.

1.1 Design ConsiderationsThere are several design considerations for CRFIDs that

leverage hybrid energy harvesting. In this section, we presentsome of these considerations, as well as the key questionsthat motivate the approaches in this paper.

Using limited energy. A central challenge in design-ing hybrid harvesting CRFIDs is dealing with the limitedamount of harvested energy. CRFIDs have smaller foot-prints than motes, restricting harvesting units to only a fewcubic centimeters in size. Micro harvesters of this size typ-ically generate very little power. For example, our exper-iments show that solar harvesting provides power rangingfrom less than a µW to a few mW depending on the panelsize and lighting conditions. Thus, the first question thatwe ask is:Question 1: What are the lower limits of usable energy forcurrent CRFIDs?

Handling harvesting variability. Ambient energy har-vesting exhibits spatial and temporal variability, sometimesover very short time scales. Harvesting output can be rel-atively predictable for a static outdoor deployment, for ex-ample, but unpredictable and bursty when CRFIDs are at-tached to mobile objects or persons constantly moving pastocclusions and altering the panel’s orientation. Because CR-FIDs use capacitors with potentially low storage capacity,even a few seconds without any incoming energy can havedisastrous consequences. The second question that we ad-dress is then:Question 2: What impact do dynamics in the ambient en-ergy source have on hybrid harvesting CRFIDs?

Making component choices. Capacitor size plays animportant role in the design of a CRFID system. Largercapacitors are desirable because they can more effectivelybuffer excess harvested energy, allowing a CRFID to surviveperiods when there is no power from a light source or reader.Smaller capacitors have the advantage of short charge times.This leads to our third question:

Question 3: What capacitor size maximizes CRFID per-formance for a given application?

Choosing suitable workloads. While CRFIDs haveunique advantages for some applications, they have no clearbenefit for others. Past work has focused almost entirelyon applications requiring low throughput communication ornear-constant reader interactions [4, 25]. No work has yetbeen done with applications specifically suited to hybrid har-vesting CRFIDs. The final question is then:Question 4: For what applications are hybrid harvestingCRFIDs most suitable?

Motivated by the questions above, we aim to quantify theperformance of hybrid harvesting CRFIDs and determine anappropriate set of environmental conditions and use caseswhere these devices prosper while passive CRFIDs (such asWISPs) and active battery-powered sensors (such as motes)may be less appropriate.

1.2 Goals & ContributionsOur research seeks to discover how to design hybrid energy

harvesting CRFID platforms that match hardware choiceswith application workloads. Thus, our work aims to:

• Determine the boundary between useful and unwork-able energy for hybrid CRFIDs.

• Explore the design tradeoffs in hardware componentsthat influence CRFID performance.

• Understand the effectiveness of CRFIDs for an appro-priate set of applications.

Our contributions that address these goals include:

Tools. We created two tools to assist in the evaluationof CRFIDs. Our software contribution is the trace-drivenCRFID Crash Test Simulator (CCTS) that models the ex-pected behavior of a CRFID’s capacitor under varying work-loads with solar harvesting. Our hardware contributions arethe SolarWISP and FrankenWISP. The SolarWISP allows usto test the effectiveness of miniature solar panels for CRFIDenergy harvesting and the FrankenWISP passively measuresthe capacitor voltage of a CRFID during application execu-tion.

Measurements. A comprehensive measurement study isimportant in determining how miniature solar panels can beused to improve the performance of passive CRFIDs. We useour software and hardware tools to evaluate how solar har-vesting improves CRFID performance by: (a) demonstrat-ing that energy harvested from ambient lighting is sufficientfor essential device operations, (b) quantifying how solarharvesting improves the communication range of a conven-tional WISP, (c) comparing SolarWISP performance againsta micro-harvesting, mote-class platform under low-light con-ditions, (d) determining how capacitor size impacts platformresponsiveness and survivability, and (e) showing how designdecisions may be quickly evaluated using CCTS for a varietyof gathered light traces.

Applications. We present an end-to-end study of twoapplications that are enabled by ambient solar harvesting.Path Reconstruction is an application that needs to com-municate frequently with readers while harvesting minimalamounts energy from dynamic, indoor lighting sources. Green-house Monitoring is a sensing application that relies on the

MCU

RF Harvester Sensors

NonvolatileStorage

Regulator

Supervisor

Figure 1: A CRFID strives to combine the versa-tility of a microcomputer with the energy efficiencyof an RFID. With sensing capabilities, CRFIDs en-able a host of new ubiquitous applications that gobeyond mere identification.

predictable nature of static, outdoor deployments to bufferlarge amounts of energy in order to survive long harvest-ing outages. These applications are designed to stress twoextremes along the spectrum of possible lighting variations.We use a combination of empirical and simulation results toshow that CCTS can be used as a guide to size componentsfor hybrid-powered CRFIDs given a particular applicationand expected lighting environment.

2. A CRFID PRIMERTraditional passive RFIDs use small amounts of harvested

RF energy from reader infrastructure to report static identi-fiers. Computational RFIDs, or CRFIDs, use the same basicoperating principles, but provide general-purpose computa-tion. The core components of CRFIDs are as follows (seeFigure 1 for a block-level illustration):

• RF harvester: CRFIDs harvest small amounts ofRF energy provided by an RFID reader. The energyis rectified to produce DC voltage and boosted to anappropriate level by a charge pump The RF harvesteralso includes an analog comparator circuit to decodereader transmissions.

• Backscatter circuit: To transmit data, a CRFIDtoggles the state of a transistor to detune its antennaand reflect a different signal back to the reader. Thismethod of transmission requires little energy from thetag when compared to active communication circuits.

• Storage capacitor: Energy harvested from a readerneeds to be accumulated before any computation orsensing can begin. A CRFID uses a small storage ca-pacitor for this purpose.

• MCU: A microcontroller is used to participate in theRFID protocol, as well as to perform arbitrary compu-tation. Ultra low-power MCUs are limited in capabil-ity but provide fine-grained duty-cycling options withlow power state transition costs.

• Supervisor: The microcontroller does not know thestate of its capacitor, thus additional hardware is re-quired for energy awareness. A supervisor circuit gen-erates an interrupt or releases the MCU from a resetstate after climbing above a preset voltage threshold.

Figure 2: The WISP 4.1 with attached solar panel.

• Regulator: The voltage stored on a CRFID’s capac-itor is highly variable. A regulation circuit is used toprovide a stable supply voltage for the MCU.

• Sensors: Sensors give a CRFID awareness of the worldaround it, unlike a traditional RFID. Sensors that areconsidered low power for other devices can dominatethe power budget of a CRFID.

• Nonvolatile storage: Frequent power failure is com-mon for CRFIDs, making nonvolatile storage a neces-sity. MCUs typically contain a small amount of on-board storage, but this may be augmented with off-chip storage.

3. TOOLS AND TRACESIt is challenging to debug CRFID applications and evalu-

ate their performance. Traditional methods of logging plat-form behavior are ineffective, as current CRFIDs possess noserial port for debugging and insufficient nonvolatile storageto create local logs of any significant length. Any attemptto actively log performance metrics will of course affect theenergy consumption as well, which is unacceptable givenCRFIDs’ typically tiny energy store. We have developedtwo key tools that allow us to evaluate the effectiveness ofambient energy harvesting for CRFIDs. First, we presentthe FrankenWISP, a tool used to measure energy consump-tion and production of CRFIDs in several different hard-ware and software configurations. We also present CCTS,a trace-driven simulation tool that allows a system designerto quickly test a variety of hardware and software configura-tions against real harvesting traces. Finally, we present theilluminance traces gathered to carry out application studies.

3.1 The FrankenWISPTo overcome the introspective deficiencies of CRFIDs, we

constructed the FrankenWISP (see Figure 3), which consistsof a WISP observed by a TelosB mote. The TelosB, withthe addition of a simple high-resistance voltage divider cir-cuit, allows measurement of a WISP’s capacitor voltage asa target application executes. The TelosB’s 1MB of flashmemory allows time-stamped voltage readings to be gath-ered at a frequency of 20 Hz for a duration of 2.5 hours, orat a lower rate for longer intervals. Depending on the size ofthe capacitor being measured, an appropriate sampling ratecan be determined. The ability to gather runtime voltagetraces during the deployment of an application is invaluable— these traces allow us to evaluate applications using differ-ently sized capacitors and determine the length and number

Figure 3: The FrankenWISP measures the sup-ply voltage of a WISP during application execu-tion without affecting the outcome or excessivelyrestricting device mobility and deployability.

of power outages experienced by the WISP without affect-ing the outcome. We chose to construct the FrankenWISPin order to have a platform small enough for mobile testdeployments. Oscilloscopes and Data Acquisition Units aretoo large to allow for realistic mobile testing.

3.2 Trace-Driven SimulatorWe created the CRFID Crash Test Simulator (CCTS) in

order to more fully explore the space of possible parametersand applications for CRFIDs — particularly the points offailure. It is difficult to profile a platform with power produc-tion and consumption values as small as those of CRFIDs.The difference of a microwatt is significant in many cases,leaving little room for error. Additionally, solar-powereddevices that use capacitors for energy storage require themodeling of non-linear solar panel and capacitor outputs.Some platforms also use linear regulators, yet another non-linear component to model. With these modeling difficul-ties in mind, the goal for CCTS is to track the charge anddischarge of the storage capacitor closely enough to informplatform provisioning decisions.

CCTS requires a number of parameters to adequately de-scribe the platform and application to evaluate:

I A recorded or generated illuminance trace

I Maximum and minimum VI curve approximations

I Capacitor size and initial voltage

I Application power consumption characteristics

With the exception of a recorded illuminance trace, eachof these inputs can be found in a datasheet or measuredempirically with little more than a multimeter.

Internally, CCTS models the platform and mobility in thefollowing logical order:

Illuminance trace. CCTS illuminance traces are time-stamped lists of lux readings used to estimate solar paneloutput. The simulator looks up the most recent illuminancemeasurement as time progresses and provides this as inputto the simulated solar panel.

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Figure 4: Example solar panel VI curves. Note thatboth curves are non-linear and that the relationshipbetween the two varies greatly with voltage output.

A potential limitation with the use of illuminance tracesas input is that illuminance is weighted toward portions ofthe spectrum perceptible by humans using the luminosityfunction. Irradiance, measured in W/m2, would be theideal metric for light intensity but we did not have readyaccess to any compact device capable of measuring irra-diance. In order to keep conversions from illuminance topower output from being overly optimistic, we make the as-sumption that there is very little light available outside ofhumans’ perceptible range. This assumption makes conver-sions from sources such as fluorescent bulbs the most accu-rate, while underestimating the power available from sourceswith greater bandwidth, such as incandescent bulbs or thesun. In practice, this inaccuracy may not have much of animpact because the efficiency of a given solar cell also varieswith wavelength and it is impractical to model this efficiencycurve for a large subset of those available.

Harvesting unit. The harvester is characterized usingmaximum and minimum functions to approximate the so-lar panel’s family of VI curves. Given an illuminance valueand voltage, CCTS interpolates between the two curves andestimates a current output value. We chose this approachbecause of the relationship between a solar panel’s voltageand current output. As the load resistance of the platformincreases, the solar panel’s voltage output approaches itsmaximum and the current output approaches its minimum.A different VI curve exists for each light level that the panelcould possibly observe and the curves do not simply scalewith incident radiation. Their shapes change as well. SeeFigure 4 for an example of two curves from the same fam-ily. This behavior makes it clear that simply scaling a singlecurve is insufficient, but it is also infeasible to measure aninfinite family of curves. To balance accuracy and usabil-ity, CCTS makes the simplifying assumption that a solarpanel’s output will scale linearly between that predicted bya minimum and maximum curve.

Storage capacitor. The energy output by the solar panelis next applied to the storage capacitor. The capacitor is themost difficult component to model accurately because of itsdynamism and complex behavior. Changes in voltage ona capacitor depend on present voltage, effective resistanceof the load, incoming energy, capacitor size, and internalleakage. None of these factors are modeled precisely in our

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Figure 5: The two series in this plot are of a So-larWISP (as measured by the FrankenWISP) anda voltage trace produced by CCTS using a con-currently gathered illuminance trace. Notice thatCCTS consistently overestimates the voltage, butthe shape of the curve is similar. We believe thatCCTS represents an upper-bound on empirical per-formance.

discrete-time simulator. In order to approximate continuousbehavior, the simulator re-calculates the voltage on the ca-pacitor at a rate of 10 kHz using the standard equation forvoltage at time i given a load R (which scales with presentvoltage) applied for t seconds:

V (i) = Vf +Ae−t/RC

t is equal to ∆i so that all changes in load are taken into ac-count. The effective resistance of the load, R, is calculatedusing the voltage at time i− ∆i and the difference betweenincoming and outgoing current. The constant A is similarlyrecalculated based on conditions at time i − ∆i. Internalcapacitor leakage is difficult to even approximate, as it de-pends on factors including: temperature, present voltage,time stable at present voltage, storage capacity, and capac-itor type (ceramic, electrolytic, etc.). CCTS does not yetmodel internal leakage.

Platform power states. Finally, CCTS removes energyfrom the storage capacitor based on the platform’s consump-tion. Platform power state information is a time-stampedlist of operations used in the application and their asso-ciated consumption data. At runtime, CCTS applies theappropriate current drain for the given operation. Model-ing the WISP, for example, required that we measure EEP-ROM operations, computation, and two low-power modes.Unfortunately the WISP uses a linear voltage regulator,which changes current consumption dependent upon volt-age. Rather than using a single consumption number, wechose to measure current consumption at a variety of volt-ages and to use a regression function to scale the simulatedconsumption with voltage.

3.2.1 Simulator ValidationIn order to gauge the accuracy of CCTS, we compared

simulated versus actual SolarWISP performance. As pre-viously noted, CCTS does not attempt to achieve perfectaccuracy. The goal for CCTS is to produce results accu-rate enough to inform platform provisioning decisions. The

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Figure 6: Mobile indoor environments are challeng-ing for hybrid harvesting CRFIDs. Illuminance lev-els vary rapidly because light sources are localizedand the mean illuminance is much lower than thattypically observed outdoors (see Figure 7).

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Figure 7: Outdoor environments exhibit diurnal andweather-induced variations in light level, but aregenerally much more amenable to energy harvestingthan indoor environments. The main challenge foroutdoor deployments is surviving the night, whichis ∼11.5 hours in length in this trace.

comparison was performed using a FrankenWISP to mon-itor a SolarWISP continuously while it sat stationary andthe lights were turned on and off to vary incoming energy.While this trace was captured, a TelosB mote was positionedas close as possible without shading the WISP’s solar paneland set to sample illuminance continuously. Despite beingplaced close together, it is difficult to measure the illumi-nance at exactly the same angle and position as the solarpanel. Other potential sources of error include the accuracyof the TelosB’s illuminance sensor. We chose to use a 100 µFcapacitor for this experiment.

As Figure 5 shows, CCTS consistently overestimates thevoltage on the capacitor but stays within ∼0.5 V of theempirical result. Because CCTS consistently overestimatesthe voltage, we believe that it is an upper-bound for anempirical deployment.

3.3 Real-World Illuminance TracesTo ensure that our platform benchmarks and simulation

results translate into real-world performance, we gathered aset of illuminance traces from diverse environments, includ-ing mobile indoor and outdoor deployments from all timesof day. These traces allow us to calculate the amount ofsolar energy available to a CRFID in each scenario. Eachtrace was captured using a TelosB’s photodiode to take pe-riodic illuminance measurements. For mobile traces, theTelosB was carried on the shoulder. To accurately capturemobility-induced dynamics, all mobile traces were collectedat a sampling rate of 20 Hz, providing a maximum trace

Environment Trace length Mean / Std. dev.

office1 2.5 hours 241.3 / 78.82 luxoffice2 2.5 hours 34.71 / 24.25 lux

residential1 2.5 hours 49 / 62.91 luxresidential2 2.5 hours 124 / 161 lux

campus 15 minutes 3200 / 97.62 luxyard 15 days 1076 / 1407 lux

Table 1: The length, mean, and standard deviationfor each illuminance trace. The standard deviationvaries widely based on mobility and diurnal cycles.See Section 3.3 for more detailed discussion.

length of 2.5 hours given TelosB flash memory constraints.The static outdoor trace was collected using a sampling pe-riod of 30 seconds; this is suitable because of slower dynam-ics and allowed the capture of a trace 15 days in length. SeeTable 1 for a summary of the traces, with mean and stan-dard deviation values for each illuminance trace. The tracesfall into a few rough qualitative categories.

Indoor office traces. The first set of traces, office1 andoffice2, represents two different office buildings. In each case,the TelosB was carried during daylight hours. These mobilescenarios illustrate how changes in panel orientation and lo-cation affect harvestable light. Figure 6 shows a timeseriesplot of the trace office1. The dataset shows short-lived illu-minance peaks as high as 700 lux, with long-lived smoothervariations between 200–300 lux. There are also several out-age periods during which no significant amount of light isavailable. On average, this environment provides 241 lux.Dynamics for the office2 trace were similar, although theoverall illuminance was much less — on average, only 35 luxwas available with peaks as high as 294 lux. The office2trace also has a much lower standard deviation (see Table 1),making it a much more consistent environment, though thereis little available light.

Indoor residential. The second set of traces, residen-tial1 and residential2, gauges available light in a residentialenvironment. The same experimental setup was used as inthe office traces. This environment is of interest becausethere is little light available from incandescent bulbs butample light available from windows. The residential1 traceshows illuminance peaks of 1091 lux when the FrankenWISPapproached windows, but a mean of only 49 lux over theduration of the deployment. Periods of insignificant avail-able light were also commonplace, indicating a challengingharvesting environment. The residential2 trace has roughlydouble the mean illuminance.

Outdoor mobile. There is one mobile trace gatheredoutdoors — labeled as campus. The trace chronicles a 15-minute walk, which includes periods when the sensor wasobstructed by occlusions such as trees and buildings. Of par-ticular interest is the availability of much more intense lightavailable outdoors. The photodiode observed illuminancepeaks of up to 3361 lux and a mean of 3200 lux — an orderof magnitude greater than any of the indoor deployments.The second point of interest is the fact that mobility dynam-ics are smoothed out by large amounts of available diffuselight. The standard deviation is only 97.62 lux, among thelowest in any trace.

Outdoor static. The final trace, yard, uses a static sen-sor with a low sampling rate placed outdoors. It is in-tended to capture dynamics primarily induced by diurnaland weather variations. To assist in correlating light vari-ations with weather patterns, we took note of conditionsduring the deployment. Our data represent days rangingfrom cloudy to sunny as well as several rainstorms. Nights,as defined by periods with no measurable illuminance, av-erage ∼ 11.5 hours in this trace. In this variety of lightingconditions, Figure 7 shows an average of 1076 lux with peaksas high as 3445 lux. The standard deviation in this trace isby far the highest at 1407 lux.

4. EVALUATIONWe now evaluate our hybrid CRFID prototype using plat-

form benchmarks, real-world illuminance traces, and twoapplication studies supplemented with CCTS simulation re-sults. The first part of our evaluation explores several newcapabilities enabled by ambient energy harvesting and quan-tifies them with platform benchmarks. Our measurementsshow that solar harvesting extends a WISP’s effective com-munication range from 2 m to more than 7 m. We alsoshow that the SolarWISP prototype can achieve perpetualwith local timekeeping at an illuminance of 35 lux, while asolar harvesting mote prototype requires 200 lux to achieveperpetual operation. Next, we look at the implications ofharvesting dynamics on platform performance with a simu-lation study. Our trace-driven simulation results show thatthe SolarWISP, even with the smallest capacitor size, canachieve nearly 95% uptime in RAM retention mode duringa period of low light, while a standard WISP would fail com-pletely in fewer than 15 seconds in the absence of an RFIDreader. After examining the impact of harvesting dynam-ics, we provide a table to help platform designers predictresponse time and read rate. Finally, we present two casestudies to validate our recommendations.

4.1 Is Micro-Harvesting Sufficient for CRFIDs?Harvesting energy from an ambient source, such as in-

door lighting, is fundamentally different than intentionallydelivering RF energy to a conventional RFID tag. Mostimportant to consider when assessing the viability of solarharvesting for a CRFID are scenarios where a CRFID isdisconnected from reader infrastructure.

Energy harvesting CRFIDs, such as the SolarWISP, mustprovide a reasonable level of performance when operatingautonomously and must simultaneously be energy efficientenough to survive short-lived interruptions to harvestableenergy. To assess whether the SolarWISP is viable for typ-ical sensing and computational workloads, we present mi-crobenchmarks that quantify the performance of critical sys-tem components. Additionally, we show that solar harvest-ing increases the effective communication rate and range be-yond that of a conventional tag, thus improving performancefor basic identification applications. Finally, we present acomparison demonstrating that the SolarWISP can achievehigher uptime and communication rates than a mote proto-type that uses a battery for energy storage.

4.1.1 Micro-Energy Harvester BenchmarksTo evaluate the effectiveness of solar harvesting for CR-

FIDs, we first benchmarked the SolarWISP’s small photo-voltaic cell (see Table 2). The particular panel used for

Conditions Illuminance Harvested power

Full shading 28 lux 6.6 µWPartial shading 85 lux 35.9 µW

Diffuse 340 lux 62.5 µWDirect 1300 lux 192.0 µW

Table 2: The amount of actual harvested powerdepends greatly on illuminance. A fully-shaded11.4 cm2 solar panel produces 29 times less powerthan the same panel under bright indoor lightingconditions.

Power state Power draw Energy consumption

Active 467.1 µW -LPM3 4.5 µW -

ADC read - 0.244 µJEEPROM read - 0.216 µJEEPROM write - 0.125 µJ

Table 3: WISP power consumption benchmarks.LPM3 is the lowest power mode that allows theWISP to maintain a persistent clock. The WISPcan achieve a long lifetime despite limited energyreserves by leveraging low-power states.

this measurement study is optimized for artificial, indoorlight sources. The power harvested varies widely with illu-minance. In an indoor setting, the panel produces 6.6 µWof power while under full shading (28 lux) and 192.0 µW ofpower while under bright indoor light (1300 lux).

4.1.2 Computation, Sensing and Storage BenchmarksMicro-power harvesting and capacitor-based energy stor-

age together provide small amounts of buffered energy. Thisharvested energy is primarily used for three CRFID tasks:computation, sensing and storage. To evaluate the costs ofthese tasks, we benchmarked computation cost in terms ofplatform power consumption while in different power statesand evaluated sensing and storage in terms of the energyrequired for an individual operation. A summary of thesebenchmarks appears in Table 3. The WISP consumes 4.5 µWin the lowest power mode that allows internal clocks (LPM3).This power state is sufficiently low to achieve perpetual oper-ation while harvesting at a light level of 28 lux. The platformconsumes 467 µW of power while in a fully active state. Bycombining low-power states with periods of active operation,the platform can achieve duty-cycles of 0.5% to 40.5% forilluminance values ranging from 28 to 1300 lux. Sensor read-ings require the WISP to sample the MCU’s 12-bit ADC,with each read costing 0.244 µJ. Storage is accomplished bywriting data over the MCU’s I2C bus to an off-chip EEP-ROM, with reads and writes costing 0.216 and 0.125 µJrespectively. These measurements agree with our claim thatmicro-power harvesting is sufficient for performing severalcomputation, sensing, and storage operations on a CRFID.

4.1.3 Exploiting Hybrid-Harvesting for Improved Tag-Reader Interactions

CRFIDs must be able to communicate data to reader in-frastructure in order to function effectively as autonomous

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Figure 8: The number of successful tagID reads/secindoors at a variety of distances with and withouta 11.4 cm2 solar panel. Note that the SolarWISP’senergy harvesting gives it a consistent advantage atmost ranges whereas the non-solar WISP encountersread rates of nearly zero beyond two meters.

sensors. We now consider computation and sensing in con-junction with communication and determine whether hybridharvesting can improve performance while a CRFID inter-acts with a reader.

A CRFID generates responses to reader queries by mod-ulating and reflecting a carrier waveform. Because the tagsthemselves do not generate the RF signal, wireless commu-nication can occur at extremely low energy cost relative toan active radio circuit. Interestingly, the range at whicha passive tag’s RF circuit can correctly decode messages islonger than the range at which a tag harvests sufficient en-ergy to generate a reply [5]. This imbalance suggests thata passive tag harvesting relatively continuous solar powerhas the potential for significantly improved communicationranges.

To investigate this performance enhancement, we mea-sured the communication range of an RF harvesting WISPrunning the default firmware provided by Intel, as well asan identical SolarWISP. To find the maximum reliable com-munication range, we recorded the read rate for each deviceat a series of distances progressively farther from the reader.Figure 8 shows that read rates for a conventional WISP startat ∼150 reads/second, but quickly reduce to 0 at a distanceof 2 m, with constructive multipath interference allowingintermittent communication at greater ranges. This is notthe case for the SolarWISP, which sustains 23 reads/secondat a distance of 7.1 m while observing an illuminance of∼300 lux. According to our measurements, the SolarWISPis able to sustain more than four times the read rate of astandard WISP at any range greater than 2.5 m.

The SolarWISP has more than triple the effective commu-nication range of a standard WISP, providing the followingbenefits for potential applications:

1. Fewer readers required to cover a given area.

2. More communication opportunities for mobile CRFIDs.

4.1.4 Hybrid CRFIDs vs. MotesAs an emerging research platform, it is important to ex-

plain how a SolarWISP-like approach to sensing differs froma similar mote-class platform. We now present several per-formance benchmarks that compare the SolarWISP with

SolarWISP EZ430

Minimal illuminance 35 lux 200 luxMaximal read rate 4.2 reads/sec 0.1 reads/sec

Table 4: The SolarWISP in timekeeping mode cansurvive extremely poor lighting conditions com-pared to the EZ430-RF2500-SEH. The SolarWISPalso achieves a higher read rate under identical light-ing conditions because of the EZ430’s limited cur-rent output.

the EZ430-RF500-SEH [20] development tool in a controlledlighting environment.

The EZ430 is similar to the SolarWISP in that it harvestssolar energy with an indoor-optimized solar panel and usesan MSP430 microcontroller. The primary differences be-tween the two platforms are the use of an active radio andbattery on the EZ430. These design decisions have a majorimpact on how the devices are best used.

The EZ430 uses a thin film battery optimized for deviceswith a SolarWISP-like power budget. While more efficientthan conventional batteries for ultra low-power operationthin film batteries still suffer from the same fundamentallimitation as other batteries: a fixed number of availablecharge cycles. The sensitivity of batteries to charge anddischarge power conditions limits the ways in which theycan be used.

Stable charge voltage. Batteries store energy chem-ically and have charge voltage requirements that must befollowed to ensure longevity. The EZ430 meets these re-quirements by using a large 32.5 cm2, 2-cell solar panel inconjunction with a boost-converter to provide a stable bat-tery input voltage. This circuit causes the EZ430 to incuroverhead associated with voltage conversion and thus in-creases the minimal amount of energy required for perpetualoperation. To find the minimum stable operating point ofthe EZ430, we measured battery charge voltage at varyinglight intensity levels in a well-controlled lighting environ-ment, using a halogen bulb as the light source. We foundthat the minimum illuminance level required to activate thecharging circuit and provide a stable battery voltage is ap-proximately 200 lux (refer to Table 4), which agrees withthe EZ430 datasheet. In the same lighting environment, theSolarWISP only requires 35 lux to operate perpetually whiletime-keeping. It is important for solar harvesting platformsto achieve energy neutrality. The device must use no morepower than it receives via harvesting, lest the energy bufferexhaust. In Section 3 we presented several light traces fromindoor environments. Trace office2 illustrates the impor-tance of energy neutral operation in low-light environments;99% of the light readings are less than 200 lux, while only45% of the readings are below 35 lux. The gap is similarfor the other traces, indicating that a CRFID like the Solar-WISP may be much better suited for mobile applications inlow-light, indoor environments.

Limited discharge rate. Another drawback of batteriesis the limited rate at which stored energy can be extractedand used. While thin film batteries offer vast amounts ofenergy storage compared to similarly sized capacitors, therate at which energy can be used is much lower than thatrequired for an active radio. The maximum possible rate of

packet transmission on the EZ430 is thus dictated by bat-tery discharge rate, not the amount of buffered energy. InTable 4, we show that the CC2500 is only capable of send-ing packets at a rate of 0.1 per second. The SolarWISP usesless energy per transmission and is not limited by batterydischarge rates. Under the same lighting conditions as theEZ430 in the previous experiment (minimum light level forEZ430), we found that the SolarWISP is capable of achiev-ing a read rate of 4.2 reads/second when harvesting 200 luxof light at a distance of 5 m from the reader. At this distancethe SolarWISP must rely on harvested solar energy becauseit receives negligible energy from the reader (see Figure 8),but still maintains a communication rate an order of mag-nitude greater than that of the EZ430.

4.2 Capacitor Sizing for CRFIDsCapacitor size is an essential design parameter for CRFIDs

and capacitors have two key characteristics that must betaken into account during selection.

1. Exponential charge curve. Capacitors reach a use-ful voltage level slowly. As discussed in Section 3.2, thecharge curve is described by an exponential function.This is in contrast to batteries, which reach nearlymaximal voltage early in their charge cycles. Hard-ware solutions, such as boost converters, could poten-tially reduce this problem but have the potential to sig-nificantly increase quiescent current consumption. Alow-input boost converter from TI, for example, con-sumes 5 µA of current while boosting voltages as lowas 0.9 V [21]. This would more than double the time-keeping current consumption of a WISP.

2. Low energy densities. Capacitors, including super-capacitors, have lower energy densities than batteries.Due to size and weight limits, we only consider capac-itors which will provide sufficient energy for at mosthours of operation without incoming energy.

These characteristics create a tradeoff between responsive-ness and survivability. Responsiveness is how quickly a ca-pacitor can reach a suitable voltage to respond to readercontact events. Survivability is how long a capacitor cansustain operation without incoming energy. To demonstratehow capacitor size impacts responsiveness, we look at twobenchmarks. First, we look at single capacitors’ charge anddischarge times. Next, we determine the performance of theSolarWISP in terms of the number of reads at different dis-tances from a reader and for differently sized energy buffers.

Impact on tag-reader interactions. To demonstratethe effect that capacitor size has on aggregate read rate,we benchmarked tag performance in an environment similarto that which produced Figure 8. The SolarWISP was pro-grammed with an application that ignores any reader querieswhen its voltage is below a threshold of 2 V and responds tothe reader otherwise. Intuitively, a SolarWISP should gen-erate a burst of reads when voltage surpassed this threshold,followed by a period of time spent recovering when it crossedbelow.

Figure 9 shows the average number of reads per secondfor several different capacitors at varying distances. Thesmallest capacitor size (10 µF) and the highest capacitor size(9400 µF) have the two worst read rates among all capacitor

1.5 3 4.5 6 7.50

5

10

15

20

Distance (meters)

Rea

ds p

er s

econ

d

10 µF100 µF1000 µF9400 µF

Figure 9: At distances greater than 3 meters, readrate becomes more dependent on harvestable solarenergy than harvestable RF energy. A 100 µF ca-pacitor provides the maximal read rate in this ex-periment.

sizes. The intermediate sizes perform better with 100 µFperforming the best of the lot.

This behavior results from the combination of two factors.At one end of the spectrum, larger capacitors take more timeto charge and therefore spend long periods of time with volt-age below 2 V, thereby missing many read opportunities. Atthe other end of the spectrum, a small capacitor size trig-gers pathological behavior wherein a SolarWISP attemptsto wake prematurely, starving itself of energy. An interme-diate capacitor size (100 µF) works best since it balancesthe two factors.

Figure 10 provides further insight into our results. Thefigure shows the average burst size and recovery time for allfour capacitance values at a distance of 5 meters. The burstsize monotonically increases with capacitor size because ofthe increasing amount of energy available to the platformduring one burst interval. Response time generally increaseswith capacitance, as it takes more energy to reach the samevoltage.

Impact on survivability. While larger capacitors areless responsive to rapid changes in energy, they provide greatersurvivability, The larger the capacitor, the longer a Solar-WISP can survive. As shown in Table 3, the SolarWISPconsumes only a few µW for basic timekeeping operation,hence even the energy in a reasonably sized supercapacitorcan last a long time. In our experiments, we find that thesmallest capacitor (10 µF) lasts only seconds whereas thelarge capacitor (9400µF) lasts ∼11.5 hours. Thus, for theSolarWISP, a modestly sized supercapacitor is sufficient as apower source for hours or even tens of hours while leveraginglow-power states.

4.3 Coping with Harvesting DynamicsWhile we have addressed the feasibility of autonomous

operation, we have yet to account for the important issueof energy harvesting dynamics. Mobile devices that rely onharvested power for continuous operation are at the mercy ofharvesting dynamics while trying to achieve robustness. En-ergy harvested by the SolarWISP varies greatly with timeand movement. Indoors, this can be attributed to light-ing distribution and short-term changes in panel orientation.Outdoors, variations are due mainly to temporary occlusionsand diurnal variations.

To illustrate the impact of harvesting dynamics on the op-eration of a CRFID, we performed a simulation study usinga 30-minute portion of the office2 trace with little available

Response Time (seconds) Number of Reads per Burst10

−1

100

101

102

10µF100µF1000µF9400µF

Figure 10: Progressively larger capacitors allow forincreasingly large bursts of reads at the cost of re-sponsiveness. We attribute the poor response timefor the 10 µF capacitor to a pathology where theWISP attempts to wake prematurely, thus starvingitself of energy.

22000 400 800 1200 1600

240

0

40

80

120

160

200

Time (seconds)

Illum

inan

ce (l

ux)

Figure 11: Time series plot of the office2 trace ex-cerpt used to illustrate dynamism tradeoffs. Themean illuminance is only 24 lux, but the maximumilluminance is more than 200 lux and the minimumis zero.

light and significant dynamics (see Figure 11). The traceexcerpt stresses the ability of the SolarWISP to cope withdynamics and limited harvesting rates. The simulation alsouses the WISP’s standard 10 µF storage capacitor, whichcan only buffer enough energy for a several seconds of oper-ation. As a result, harvesting dynamics are a major factor.

We considered several workloads for the SolarWISP. Thelowest power state is RAM retention mode, which is onlysufficient for refreshing SRAM between reader contact. Aslightly higher power mode is RAM retention with periodicEEPROM writes every 5 minutes. Next, timekeeping re-quires waking up every ten seconds to bump a counter inorder to maintain a persistent clock. We also simulateddifferent duty-cycles for the SolarWISP, where the deviceperiodically transitions from the RAM retention state to anactive state. For a 1% duty-cycle, for example, the Solar-WISP is up for 100 ms out of every 10 s. A summary of theCCTS results appears in Table 5.

The results show that the SolarWISP achieves an uptimeof more than 90% in all cases, whereas a standard WISPwould fail completely in fewer than 15 seconds away froman RFID reader. In this particular trace, writing data toEEPROM memory every five minutes has no impact on theWISP’s time of death and saves most of the data recorded.This behavior is due to the 10 µF capacitor’s high respon-siveness. It typically achieves maximal voltage within the

Workload # Failures % UptimeRAM retention 1 99.19

RAM retention + record 1 99.19Timekeeping 1 98.51

1% Duty-cycle 1 98.142% Duty-cycle 174 95.145% Duty-cycle 169 91.69

Table 5: The total number of outages predicted byCCTS for the office2 excerpt. “Record” is the pro-cess of writing one byte to EEPROM. The numberof outages in these CCTS results highlight how littleexcess energy the SolarWISP can store in a 10 µFcapacitor.

five-minute duration between EEPROM writes. The WISPis then able to perform the energy-expensive EEPROM writeswithout failing. The performance remains similar up to aduty-cycle of 2%, at which point performance degrades sig-nificantly. In terms of uptime, performance reduces onlyby about 3%, but the SolarWISP fails more far more fre-quently (174 failures in 30 minutes). Finally, a duty-cycleof 5% results in slightly fewer failures than 2% because oflonger individual failures, but the overall uptime decreasesas expected.

The simulation study has important implications for de-signing systems using SolarWISPs. Despite the fact thatthe trace represents bad lighting conditions with heavy dy-namics, the SolarWISP achieves uptimes of close to 100%across a range of workloads. This result suggests that it isfeasible to design systems that can exploit the SolarWISPsability to do useful work between reader contact events. Adesigner may even be able to assume that the device rarelyloses power or state between reader contacts. However, theresults also show that it is difficult to guarantee that a So-larWISP will stay awake. For example, it is not possible forthe device to survive the full 30 minutes — even if it remainsin the lowest power mode at all times. Thus, it is critical todesign under the assumption that, even with ambient har-vesting, a small number of failures are inevitable and willneed to be somehow mitigated.

While our study focused on a small capacitor size, dynam-ics are less of a concern with larger capacitor sizes. As men-tioned in Section 4.2, larger capacitors can sustain a CRFIDfor more than 10 hours, but there is a price to be paid in re-sponsiveness. For some applications a large capacitor will al-low a CRFID to ignore harvesting dynamics, but for others,the rate of change in energy availability largely determinesthe minimal appropriate capacitor.

4.4 Application StudiesWe now present case studies that validate our recommen-

dations for balancing responsiveness and survivability. Eachapplication stresses a different aspect of the responsivenessvs. survivability tradeoff. The first mobile application in-teracts with readers in frequent bursts of activity, makingresponsiveness critical. For the second application, a Solar-WISP is statically deployed in an outdoor environment. Thelong harvesting outages caused by night make survivabilitythe critical metric for success. We evaluate each applicationusing empirical measurements of a deployment. For one ap-plication, we supplement the results with CCTS predictions

to quantify the expected performance with a minimal ca-pacitor size during a long deployment. For example, CCTSpredicts that a SolarWISP can achieve 84% uptime in theworst case scenario that occurs in our traces.

4.4.1 Path ReconstructionA mobile RFID tag or CRFID that periodically visits a

set of networked readers generates data as a collection ofindividual read events. Unfortunately, these individual readevents can be quite noisy. For example, Welbourne et al.report less than a 40% median success rate in detecting con-tact events using carefully placed readers and a variety oftag positions [23]. Heavy post processing was required inorder to achieve reasonable inference accuracy for path re-construction applications.

An attractive alternative to inferring tag behavior by ex-amining reader logs is to directly report the desired applica-tion level measurement. Because CRFIDs can execute arbi-trary instructions and store arbitrary state in memory, theyare capable of reporting a result directly to a reader. In thecase of path reconstruction, a single CRFID can report atimestamped list of all encountered readers. Providing mo-bile CRFIDs the ability to directly report pathing informa-tion is attractive because this removes the burden from thebackend to infer paths based on large databases of tag vis-itations. Additionally, security may be added by reportinga cryptographically secure hash of path information insteadof reporting a list of visitations in plain text.

To benchmark this application we implemented local pathreconstruction on the SolarWISP. For tags to distinguishbetween readers and correctly store pathing information inmemory, we embed a unique ID in the session field of theEPC Class 1 Generation 2 query command. Unfortunately,this protocol field contains 2 bits of information, thus placinga cap of 4 readers on our deployment experiments. A moreflexible solution would be to make use of the write command,allowing a reader to report an arbitrary ID in memory, butthe existing WISP firmware only supports the query andread commands.

The reader testbed consisted of 4 Impinj Speedway readersdeployed in offices and labs around a research building. Eachreader was programmed to use a unique session value foridentification purposes.

Tag–reader interactions. To accurately reconstruct apath, at least one successful read must be recorded perreader contact event. The additional range and higher readrates enabled by ambient energy harvesting give the So-larWISP a higher chance of being read when passing by areader.

Table 6 shows a comparison between the standard and So-larWISP in terms of successful reads per contact event. Theexperimental setup consisted of a mobile WISP moving pasteach deployed reader 30 times. Note that the SolarWISP canachieve up to four times the number of reads as compared toa standard WISP. Also of significance is that the SolarWISPperforms consistently better at each reader location, in spiteof different multipath environments, due to its greater en-ergy availability. Contact duration is quantified in Table 7.Again, the SolarWISP consistently achieves longer contactdurations than a conventional WISP because of its longerread range, typically by a factor of four or greater.

Responsiveness. The responsiveness of a tag to readerqueries is key to the performance of path reconstruction.

Reader Reads (WISP) Reads (SolarWISP) Improvement

0 25.9 31.8 1.23x1 7.2 18.3 2.54x2 7.7 31.5 4.09x3 6.9 26.6 3.86x

Table 6: The average number of times a mobile tag can be read increases by as much as a factor of four whenharvesting energy from ambient indoor lighting.

ReaderContact duration Contact duration Improvement

(WISP) (Solar WISP)

0 0.863 seconds 4.711 seconds 5.96x1 0.539 seconds 2.978 seconds 5.53x2 1.724 seconds 4.585 seconds 2.66x3 1.124 seconds 5.211 seconds 4.64x

Table 7: The reader contact duration of a mobile WISP increases when ambient energy is available becauseof the greater maximum range that hybrid harvesting provides.

0 20 40 60 80 1000

0.5

1

Number of Reads per Visit

Fra

ctio

n of

Rea

der

Vis

its

10 µF1000 µF

Figure 12: Large capacitors allow many reads pervisit, but reduce responsiveness. A SolarWISPequipped with a 1000 µF capacitor misses 16% ofreader visitations in this experiment.

Choosing an excessively large capacitor will result in largerecovery times after responding to a burst of queries. If theserecovery times become too large, it is possible that readervisitations could be missed because of insufficient voltage onthe SolarWISP’s capacitor.

Figure 14 illustrates the substantial effect that reader con-tact has on capacitor voltage. The time series depicts thevoltage over time stored by a mobile SolarWISP with a10 µF capacitor. The capacitor is pre-charged to a stablevoltage by indoor lighting before logging begins. The testbegins with the WISP in range of a reader—the large volt-age swings represent individual reader transactions. Next,the SolarWISP moves out of reader range, resulting in thesmooth variations observed in capacitor voltage. These vari-ations are caused by movement under individual lightingunits and slight variations in panel orientation during tran-sit.

To test responsiveness, we moved a SolarWISP throughthe field of a reader ∼50 times with two different sized ca-pacitors. Figure 12 shows how the number of reads pervisitation contributes to the fraction of total reader visita-tion events. A SolarWISP equipped with the stock 10 µFcapacitor was read 2–7 times per visitation. This small ca-pacitor size results in few reads per visit, but no visitations

Office1 Office2 Residential10

20

40

60

80

100

Trace

% U

ptim

e

RAM retentionTimekeeping

Figure 13: The percent uptime for a SolarWISPwith two different workloads as predicted by CCTS.There are no outages predicted for the office1 trace,but there are for the others. The percent differ-ence in performance between the RAM retentionand timekeeping workloads is ∼2%.

are missed completely. The SolarWISP augmented with a1 mF capacitor is read 0–558 times per visit. This capacitorsize, however, takes longer to charge and causes the Solar-WISP to miss 16% of reader visitations.

Based on this experiment, it is clear that dense readerdeployments or high rates of mobility require careful consid-eration of capacitor size. An excessively large capacitor willcause a CRFID to miss reader visitations. A capacitor thatis too small will result in few reads per visitation, but mayrespond more consistently.

Survivability. Survivability is also desirable for path re-construction, but is not essential since readers could supplytimestamps while reader IDs may be potentially stored inEEPROM. Whether a CRFID keeps a local clock or relieson reader infrastructure, the power consumption of this ap-plication is minimal in between reader contact events. Inthe worst case, the application only needs to increment atimestamp once every 10 seconds and retain the state ofSRAM, which contains past pathing data. These application

0 50 100 150 2001

2

3

4

5

Cap

acito

r V

olta

ge

Time (seconds)

Figure 14: The capacitor is pre-charged by the at-tached solar panel; the large variations in voltageare caused by initial communication to a reader,while the smooth variations indicate lighting dynam-ics caused by mobility while disconnected.

characteristics suggest that a very small capacitor should besufficient in many cases.

To gauge the performance of a large variety of capacitorsizes for disconnected operation in the path reconstructionapplication, we implemented the application in CCTS andtested it against the indoor illuminance traces discussed inSection 3.3. To test the application fully, we implementedboth the version we measured empirically which records timelocally, and the simple RAM retention version which as-sumes timestamps from the reader. Figure 13 shows themost interesting results from these simulation runs.

The only capacitor sizes to experience any outages were10 µF and 100 µF, with the 10 µF performance being worsein all cases. Because these runs represent the worst perfor-mance, we present the percent uptimes for a 10 µF Solar-WISP in Figure 13. For the office1 simulation, there areno predicted outages for either workload. For the residen-tial1 simulation, the predicted percent uptime is between92% and 95%. Finally, the predicted performance for theoffice2 trace is significantly worse—with only 85% and 84%uptime for the timekeeping and RAM retention workloadsrespectively.

4.4.2 Greenhouse MonitoringThe temperature, relative humidity, and incident radia-

tion observed in a greenhouse over time are of great interestto scientists studying biological responses to these factors.The sensing rate used for the experiment is 0.1 Hz. Thissampling rate was suggested by a biologist who monitorsgreenhouse conditions.

From a sensing perspective, greenhouses are an environ-ment of interest for solar-assisted CRFIDs because they canharvest light for long durations during the day and little tono artificial light at night. The sampling rates are also lowenough that a mote-class device is greatly over-provisionedfor the task. At the same time, greenhouse monitoring is anapplication for which a perpetual deployment is desirable,making batteryless computation a concrete maintenance ad-vantage.

The SolarWISP contains an onboard temperature sensor,allowing the platform to achieve some of the functionalityrequired by biologists. We implemented an application thatrecords a 1-byte ADC sample once every 8 seconds and storesthe result to a 1kB EEPROM. This limited amount of non-volatile storage only provides sufficient space for 2.8 hoursof temperature readings at this sampling rate, but this does

0 .5 1 1.50

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Days

Cap

acito

r V

olta

ge

0.1F0.22F

Figure 15: Supercapacitors of varying sizes storesufficient energy during daytime periods to pre-vent outages during night intervals. On an overcastsnowy day, the SolarWISP still manages to storeenough energy to survive.

not change the accuracy of the application power profile. Anext generation platform could easily select a larger EEP-ROM while maintaining a similar form factor.

To survive the long power outages caused by night, weaugment the SolarWISP with a supercapacitor. We alsoleverage the FrankenWISP to record the supercapacitor volt-age once every 30 seconds to test survivability. Figure 15shows voltage traces for a two-day application deploymentusing two different supercapacitor sizes. While both the100 mF and 220 mF capacitor survive this deployment, theydo decline to ∼2 V at night.

5. DISCUSSION AND FUTURE WORKMicro-harvesters and small energy buffers. Thekey message of our work is that, despite the use of micro-harvesters and tiny energy buffers, ambient energy harvest-ing has the potential to greatly improve the performanceand usability of CRFIDs. Unexpectedly, the benefits arenot limited to well-lit environments. The SolarWISP wasable to continue sensing or at least retain its RAM con-tents in all but the darkest conditions that we tested. Whilethis demonstrates compelling potential, fully utilizing hybridharvesting CRFIDs requires new systems that can adaptto short-term energy dynamics while achieving applicationobjectives. Existing solutions do not fill this gap — mostharvesting-based systems achieve deployment lifetime ob-jectives by adapting to long term variations in energy avail-ability (e.g. hours, days) using relatively large buffers thatshield applications from unpredictable, short-term dynam-ics. Our work makes a compelling case for a new energymanagement paradigm, in which embedded devices are de-signed to use micro-harvesters and small energy buffers, andto handle short-term energy dynamics.

Alternative hardware designs. Our evaluation wasperformed using specific CRFID hardware (WISP), so onemight ask if an alternative hardware design might changesome of our conclusions. For example, we showed that thereis a tradeoff in terms of responsiveness and survivability dueto the size of the energy buffer. One might ask whetherwe can achieve both objectives simultaneously by pairing alarge and small buffer on a CRFID. While this is a possibil-ity, our comparison between CRFIDs and motes shows thatit is important to keep the hardware minimal in order tofully exploit tiny amounts of harvested energy. Additional

hardware complexity typically results in inefficiency and en-ergy wastage — in addition to introducing new constraintson the operating voltage and current.

Applications. While we have examined two potentialCRFID applications, a broader question remains as to whatclass of applications is suitable for hybrid harvesting CR-FIDs. Hybrid harvesting CRFIDs retain many of the bene-fits of RFIDs such as form factor and energy efficiency, butoffer considerably more flexibility in application possibili-ties. However, CRFIDs may not be suitable for applicationswhere individual points of failure are unacceptable, such asthe detection of rare events. While these tasks cannot besupported reliably, CRFIDs can maintain a reasonable levelof performance according to our measurements and are suit-able for applications in which occasional failures are tolera-ble.

Simulation. We believe that CCTS is sufficiently accu-rate to aid in CRFID provisioning decisions, but there isroom for improvement. The current characterization of so-lar panels, in particular, is both coarse-grained and labor-intensive. Rather than measuring a number of points alongmaximal and minimal VI curves, thus approximating theoutput of the panel, it may be possible to accurately modelthe panel itself in simulation. Other improvements to CCTSare possible and we intend to continue development with thegoal of providing CCTS to the research community.

6. RELATED WORKRFIDs and RFID sensors. As the first example of aCRFID, Intel’s WISP has defined the class of devices [18].Emerging variants on the WISP platform include Duke’sBlue Devil WISP [15] and the UW SoCWISP [12]. Yeager etal. propose the use of supercapacitors to extend the fleetinglifetime of the WISP given a full charge [25]. Buettner etal. demonstrate activity inference enabled by the WISP’ssensors [4]. This body of work is mostly concerned withdesigning RFID sensors, and does not consider how theycan be augmented with ambient energy for autonomy.

Energy harvesting. There has been significant work onenergy harvesting in sensor networks. Recent work [2, 9,10, 17, 19, 22, 7] has explored scenarios in which nodes canharvest energy from their environment (e.g., from the sun)and use it to recharge their batteries. In the absence of suchenergy, nodes can then subsist on their replenished batterysupply. There are also a growing number of solar-poweredsensor network deployments, such as James reserve in Irvine,CA [8], Berkeley Angelo reserve, CA [19], CSIRO’s Flecksensor network in Australia [6], and the LUSTER system inVirginia [17]).

These systems are predominantly of the macro-harvestingtype, wherein solar panel and energy buffer sizes are chosento smooth out the short time scale variations in incomingenergy. This greatly simplifies the design of a harvesting-aware sensor network, and enables a priori provisioning ofresources. In many of these deployments, prior measure-ment studies of incident solar energy at the deployment lo-cation are used to select appropriate solar panel sizes forthe sensor devices and to set system parameters such as theduty-cycling rate [24]. In contrast, our work tackles micro-harvesting, where the device, panels, and buffer are small.As a consequence, small-scale variations in energy conditions

across seconds, minutes, or hours have a significant impacton the design of our system.

More broadly, the viability of various energy harvestingsources for computation and sensing has been consideredmany times in the past [13], but we focus exclusively onRFID harvesters that produce power at the µW scale. Sam-ple et al. have demonstrated a WISP retrofitted with adirectional TV antenna capable of harvesting energy from aTV transmitter over two miles away when positioned care-fully [16]. The energy, however, was used to power a staticload (small thermometer with LCD) rather than a WISP orother computation device.

Energy storage. At the core of our work is an under-standing of the tradeoffs presented by capacitors for hybridharvesting CRFIDs. The use of supercapacitors in sensorplatforms is relatively common. For example, Prometheusis a harvesting-based sensor platform that integrates a con-ventional LiOn rechargeable battery and supercapacitors [9].Capacitors are used in RFID systems as well. However, tothe best of our knowledge, the tradeoff between survivabilityand responsiveness due to the use of capacitors has not beenstudied in any of the prior work.

Energy scheduling. Energy management for harvesting-based sensor networks has been studied in the past. Moseret al. [11] present optimal scheduling algorithms for harvest-ing networks that must meet deadlines; Vigorito et al. [22]present algorithms for adaptive duty-cycling based on har-vested energy. In addition to adaptive duty-cycling, Kansalet al. [10] present a methodology for sizing energy buffers.While our work does not directly relate to such schedul-ing schemes, we believe that our work can inform such ap-proaches. Many existing scheduling techniques are designedwith implicit assumptions about the energy buffer and har-vesting rate in mind. For example, [11] uses predictions ofharvesting rates for its scheduling. Our work shows that formicro-harvesting, one needs to consider extremely small win-dows of time, and cannot assume that harvesting is smoothedby the energy buffer.

7. CONCLUSIONA small amount of ambient energy can grant autonomy to

CRFID sensors. The SolarWISP augments the traditionalWISP with a 11.2 cm2 solar panel to supplement the energyharvested by the RF charge pump. This small change in-creases effective communication range threefold and quadru-ples read rate. The SolarWISP requires only 35 lux to re-main in perpetual timekeeping mode, whereas the EZ430solar mote requires 200 lux. Our trace-driven simulationresults show that the SolarWISP, even with only a 10 µFcapacitor, can achieve nearly 95% uptime in RAM retentionmode during a period of low light, while a standard WISPwould fail completely in less than 15 seconds in the absenceof an RFID reader.

Our tools include a trace-driven simulator and an energymonitoring subsystem. The CRFID Crash Test Simulatorestimates the survivability of a parameterized platform un-der a variety of lighting conditions. The FrankenWISP al-lows real-time monitoring of extremely resource-limited de-vices without greatly disturbing mobility or deployability.We hope that our tools and traces will help developers moreeasily evaluate the design space for future hybrid micro-energy harvesting CRFIDs.

8. ACKNOWLEDGMENTSThis work was funded by a Sloan Research Fellowship

and NSF grants CNS-0923313, CNS-0845874, CNS-0615075,CNS-0627529, CNS-0546177, CNS-0855128, and CNS-0916577.We thank Intel Research Seattle for providing several WISPs;Priyanka Rajendra Iyer for help in performing experiments;Benjamin Ransford for help with simulation and measure-ment; and Negin Salajegheh and Quinn Stewart for feedbackon drafts.

The free CRFID Crash Test Simulator software and en-ergy traces are available from http://www.cs.umass.edu/

~ssclark/crfid/.

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