Enabling Bit-by-Bit Backscatter Communication in Severe EnergyHarvesting Environments

Pengyu Zhang, Deepak Ganesan{pyzhang, dganesan}@cs.umass.eduUniversity of Massachusetts Amherst

AbstractMicro-powered wireless sensors present new challengesdue to the severe harvesting conditions under which theyneed to operate and their tiny energy reservoirs. How-ever, existing low-power network stacks make a slewof design choices that limit the ability to scale downto such environments. We address these issues withQuarkNet, a backscatter-based network stack that is de-signed to enable continuous communication even if theremay be only enough harvested energy to transmit a fewbits at a time while simultaneously optimizing through-put across a network of RFID-scale devices. We de-sign and implement QuarkNet on a software radio basedRFID reader and the UMass Moo platform, and showthat QuarkNet increases the communication distance by3.5× over Dewdrop, 9× over Buzz, and is within 96%of the upper bound of achievable range. QuarkNet alsoimproves the communication throughput by 10.5× overEPC Gen 2 for tag-to-reader communication and by 1.5×for reader-to-tag communication. Its throughput benefitis 5.79× over Dewdrop, and 3.3× over Flit.

1 Introduction

The idea of networks of perpetual self-powered sens-ing, communication and actuation devices that can flyin swarms, swim through the bloodstream, and navigatethrough pipes and debris has propelled the imaginationof science fiction writers for decades, but reality is fi-nally catching up. Among the key technology trendsenabling this vision is advances in micro-harvesters thatscavenge energy from light, electro-magnetic waves, vi-brations, temperature, and other sources [5]. Such micro-harvesters enable platforms to cut their reliance on storedenergy in batteries, thereby enabling true miniaturizationand perpetual operation [20, 21].

While self-powered devices present an exciting op-portunity, they present tremendous challenges due to the

amount of energy they harvest and the sizes of their en-ergy reservoirs. The amount of harvested energy usinga micro-energy harvester is of the order of µWatts tonanoWatts, which is three to six orders of magnitudelower than the average power draw of a Mote. At firstglance, this seems to suggest that if we wait long enough,the device can trickle charge to accumulate sufficient en-ergy to operate similar to a battery-powered device. Butthere are three problems. First, long delays before per-forming useful work are often unacceptable, particularlyfor continuous sensing and communication. Second, thevoltage from the incoming energy source is often low,therefore accumulating energy into an energy reservoirrequires boosting voltage which is wasteful compared toincoming energy (imagine pumping water up a hill tostore for future use). Third, micro-powered platformsoften have small energy reservoirs since the raison d’treof micro-harvesters is to reduce form factor. For exam-ple, the Michigan Micro Mote (M3) [11] has an energyreservoir that is six orders of magnitude smaller than acoin cell, and an Intel WISP [3] has a reservoir that isfour orders of magnitude smaller.

The dual limitations of low harvesting rates and tinyenergy reservoirs has profound implications on the de-sign of a network stack for micro-powered devices. Ev-ery communication task needs to be small enough to fitwithin the available energy in the reservoir. Enablingcommunication despite such minuscule energy budgetsis akin to working on a micro-sculpture — optimizationsat the granularity of individual instructions, bits, on-offtransitions, and analog-to-digital conversions are needed.To compound matters, small short-term variations in har-vesting conditions that typically would be smoothed outby a larger energy reservoir begin to impact system op-eration, and can cause an order of magnitude variation inavailable energy for a task.

Recent systems such as MementOS [14] and Dewdrop[6] tackle this problem in different ways. Both thesesystems use backscatter — a communication technology

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tailor-made for micro-powered systems since the devicedoes not need to actively transmit, rather, the reader gen-erates a signal which is modulated and reflected to trans-fer data. But how can a backscatter communication stackbe made to fit within the energy budget? MementOSintroduces checkpoints within computation tasks suchthat it can recover from outages and continue execution.Dewdrop continually adapts task execution to harvestingconditions such that the efficiency of execution is opti-mized. To evaluate the ability of these systems to scaledown, we consider two harvesting conditions — stronglight (2000 lux) and natural indoor light (200 lux), bothof which should, in principle, provide enough energy tooperate a RFID-scale sensor. But while both Mementosand Dewdrop operate under strong light, they are inoper-able under natural light.

The inability of current systems to scale-down illus-trates the central challenge in designing a network stackfor micro-powered devices. A wireless network stack in-volves a variety of tasks that are simply too large to fitinto the extreme energy constraints of this regime. Eventhe core primitive of a network stack — packet transfer— can involve hundreds of instructions and bits. In thiswork we ask the following question — what are the gen-eral principles that we, as systems designers, should useto enable these micro-powered platforms to communi-cate continuously despite trickles of energy, tiny energyreservoirs, and dynamic harvesting conditions?

We present QuarkNet, a network stack that embodiesa simple but powerful abstraction — by fragmenting abackscatter network stack into its smallest atomic units,we can enable the system to scale down to resource-impoverished regimes. The fundamental building blockof QuarkNet is the ability to dynamically fragment alarger packet transfer into µframes that can be as smallas a single bit under severe energy constraints, and aslarge as the whole packet when sufficient energy is avail-able. On top of this abstraction, we design a varietyof innovative techniques to handle dynamic frames thatcan be abruptly terminated in low energy settings, max-imize throughput by tracking harvesting dynamics in alow-overhead manner, interleave µframes across tags tomaximize throughput despite different harvesting rates,and minimize overhead across the entire stack.

Our results on a USRP reader and UMass Moo tagsshow that:I The maximum communication distance achieved by

QuarkNet is 21 feet, 3.5× longer than Dewdropand 4.2× longer than EPC ID transfer. QuarkNetachieves close to the maximum achievable range,beyond which decoding even a single bit fails.

I The minimum illuminance required for QuarkNetto operate is 150 lux, which is 13× lower than the2000 lux budget of 12 byte EPC ID transfer. This

transmit antenna current

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Figure 1: Backscatter signaling at PHY.

suggests that µframe can operate when a deviceis powered by natural indoor illuminance, dramat-ically increasing utility of RFID-scale tags for prac-tical deployments

I The average throughput of QuarkNet is 18 kbps,10.5× higher than EPC Gen 2, 5.8× higher thanDewdrop, and 3.3× higher than Flit. In fact,a micro-powered tag running QuarkNet performssubstantially better than all alternate approacheseven when they operate on charged batteries.

I For reader to tag communication, we obtainthroughput of 1.5 kbps, 2× higher than a battery-assisted device which runs EPC Gen 2 write com-mand.

I When ten tags transmit simultaneously to a reader,we achieve a throughput of 16.48 kbps as a resultof variability-aware scheduling and interleaving ofµframes, which is 5.4× higher than the throughputwhen devices are inventoried individually. Flit andEPC Gen 2 obtain zero throughput in this case.

2 Case for µframes

A backscatter radio is designed to both provide powerto a passive device as well as to enable communica-tion. As shown in Figure 1, the reader provides a car-rier wave, which can be reflected by a passive deviceback to the reader with its own information bits. Thismakes backscatter a considerably more energy-efficientcommunication mechanism compared to active radios,and ideally suited to the constraints of micro-poweredsystems. The Intel WISP [3] and UMass Moo [23] areexamples of sensor platforms that rely on backscatter ra-dio.

Despite the energy benefits of backscatter radios, ex-isting network stacks achieve only short communicationrange and low throughput. We make an empiric argu-ment these limitations are, in part, due to the design ofthe network stack. To do this, we compare the range andthroughput of existing network stacks versus achievableperformance. Our experiment uses a UMass Moo [23]

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Table 1: EPC Gen 2 vs Achievable Performance.Range (feet) Throughput (kbps)

EPC Gen 2 3.57±0.81 1.69±0.26Optimal 18.55±3.32 21.69±3.74

and a USRP reader [7]. Since the power output of theUSRP reader is only a quarter of that achievable by com-mercial readers, we augment the Moo with a small solarpanel. We vary the distance from the reader by smallsteps, and at each step, we vary RF power from 17dBmto 26dBm, while not changing the light levels (naturalindoor light).

To measure the achievable range, we look at the rawbackscattered signal at the reader, and find the distanceat which the reader is unable to decode even a single bit.This would be the edge of the communication range forour hardware platform.

Measuring the maximum achievable throughput isharder since it is influenced by several system parame-ters including voltage at the energy reservoir when com-munication starts, the length of each transmission unit,and control overheads associated with the protocol. Webrute-force search across all possible voltages and packetlengths to find the setting that results in the maximumnumber of transistor flips at the tag. We then convert thetransistor flips to a maximum number of bits transmittedusing the default Miller-4 encoding scheme, and assumezero control overhead for each packet, which gives us anestimate of the maximum throughput.

Table 1 shows the range and throughput while execut-ing the EPC Gen 2 stack (used in Mementos [14], Dew-drop [6], and Blink [24]) versus achievable limits. Wesee that the achievable range is 18.55 feet, which is over5× longer than the communication range of EPC Gen 2.Similarly, we see that the achievable throughput is 21.69kbps, whereas EPC Gen 2 achieves barely 1.69 kbps, anorder of magnitude difference.

We now investigate the fundamental factors underly-ing this performance gap, and outline the core challengesthat need to be addressed to bridge the gap.

Challenge 1: Variable energy per transmission Akey challenge in designing a backscatter network stack ishandling variability in the amount of energy accumulatedin the energy reservoir. To understand the reasons, let uslook at how micro-powered devices work. As shown inFigure 3, micro-powered devices operate in a sequenceof charge-discharge cycles since there is too little energyto continually operate the device. The device sleeps for ashort period during which it harvests energy and chargesa small energy reservoir, and then wakes up and transmitsa packet during which the reservoir discharges.

There are several reasons why it is difficult to antici-

voltage

charging

discharging

packet TX

failed packet TX due to power outage

uframe

packet

uframe

Figure 3: Energy harvesting systems.

pate how much energy will be available in each dischargecycle. First, if harvesting conditions are too low, it isoften too expensive to push more energy into a reser-voir due to the inefficiencies of stepping up the volt-age. As a result, the maximum amount of energy thatcan be accumulated depends on current harvesting con-ditions. Second, RF energy harvested by a tag dependson how much energy is output by the reader. When areader is doing nothing, the RF output power is constantto a first-order approximation. However when a readeris communicating, this RF carrier wave is being modu-lated which changes the amount of harvested energy. Ina multi-tag network, the reader is communicating withdifferent tags, therefore harvesting rates continually varyat each tag. Third, even if the node were to wait until ithas a certain amount of energy prior to communication,this requires measurement of energy levels using analog-to-digital conversions (ADC). Each ADC operation con-sumes 327 uJ on Moo platform [23], which is equal tothe energy budget for transferring 27 bits of data. Suchoverhead is far too substantial on a micro-powered plat-form.

While choosing a smaller transmission unit mightseem like a straightforward solution to this problem, thisover-simplifies the design challenge. As the distance be-tween the tag and reader increases to the limit of theachievable range in Table 1, the number of bits that canbe successfully transmitted reduces. Thus, we need touse frames that may be as small as one or a few bitsin size when the energy levels are low, which requiresa network stack that can scale down to unprecedentedlevels. But such scale down often comes at the expenseof throughput, which suffers due to the overheads associ-ated with each transmission, including preambles, head-ers, and hardware transition overheads. To simultane-ously optimize throughput, it is important to transmit aslarge a transmission as is possible given available energy.Thus, the problem faced by a tag is that it needs to scaledown its transmission unit to the bare minimum underpoor harvesting conditions, while scaling up to improvethroughput when the conditions allow.

Challenge 2: Variable harvesting rate The energyharvesting rate has significant impact on the communi-cation throughput, since higher harvesting rate meansthat more energy can be used for data transfer. Whileenergy harvesting rate might seem like a characteristic

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antenna

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Figure 2: Factors that impact communication throughput.

of the harvesting source, system parameters have a sur-prisingly high impact. Figure 2(a) shows the empiricallymeasured harvesting rate as we vary the amount of timefor which the tag replenishes energy between two trans-missions. The results are counter-intuitive — while onemight expect more energy to be harvested over time, theharvesting rate drops to zero for longer sleep durations.

This observation can be explained analytically bylooking at how capacitors buffer energy. The charg-ing process of a capacitor follows its charging equationV = Vmax(1− e−ts/τ), where ts is the sleep time, τ is theRC circuit time constant, and Vmax is the maximum volt-age to which the capacitor can be charged under the cur-rent harvesting conditions. Its energy harvesting rate fol-lows the equation: H = CV 2

maxτ

(1− e−ts/τ)e−ts/τ . Whenthe harvesting conditions are constant (i.e. Vmax and τ

are fixed), H is a concave function of ts, which is shownboth analytically and empirically in Figure 2(a). Whenharvesting conditions change, both Vmax and τ change,therefore the maximum operating point changes as well.Thus, to optimize throughput, it is important to adapt tocurrent harvesting conditions, and continually track themaximum harvesting point.

One factor that should not be overlooked is keepingthe overhead of adaptation low. Most methods to trackthe charging rate of batteries and capacitors use analog-to-digital conversions to obtain the voltage at the energyreservoir. This overhead is minuscule for most platforms,but a significant part of the harvested energy in our case.Thus, it is important to minimize such overheads whileadapting to harvesting conditions.

Challenge 3: Time-decaying SNR A peculiar aspectof backscatter communication is that the signal to noiseratio (SNR) of the received signal at the reader degradessteadily as the size of the transmission unit increases.Figure 2(b) shows that the signal strength of a tag re-sponse decreases gradually from 0.18 at 1.5ms to 0.05at 8ms during the transmission process. While decodingthe initial part of the transmission is straightforward dueto high SNR, it becomes much more challenging after

about 8ms since the SNR is too low for reliable decod-ing, resulting in packet losses.

In order to understand why this happens, let us lookat how a backscatter radio works. A backscatter radioprovides power to a passive device and enables commu-nication. The reader provides a carrier wave, which canbe reflected by a passive device back to the reader withits own information bits. The modulation is achieved bytoggling the state of the transistor of a backscatter de-vice shown in Figure 2(c). Since the same RF powersource is shared by different system components, the redcurve shows that some fraction of the incoming poweris used to operate the micro-powered device while therest is reflected back to the reader for communication.The exact fraction depends on the state of the energyreservoir C and the state of the matching circuit, whichis designed to charge the energy reservoir C when thevoltage is low. Therefore, when the transmission begins,C is fully charged, the antenna resistance is mismatchedwith the resistance of other hardware components of thesystem. As a result, most of the incoming power willbe reflected back to the reader, which receives a strongsignal that can be easily decoded. As the transfer pro-gresses, C slowly discharges, and the antenna resistancematches the resistance of the system load. Therefore,most of the incoming power is harvested to operate thesystem, and less RF power is reflected. This leads todecreased backscatter signal strength at the reader, andconsequently, packet losses. Thus, to ensure that packetsare received successfully, the tag needs to adapt the sizeof each packet such that the SNR at the tail of the packetis higher than the minimum decoding requirement.

Challenge 4: Energy-induced reader to tag lossesWhile time-decaying SNR only presents a problem whena tag communicates with a reader, reader to tag commu-nication presents other challenges. The central issue isthat that the energy level on the receiving tag might dipbelow the low watermark at any point during the recep-tion, at which point the tag has to shut off its RF circuitand go to sleep to recharge. The reader, however, does

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not know that the tag has gone to sleep, and only realizesthis fact after a timeout.

While such losses can be attributed to small energyharvesting variations at longer ranges, we observed toour surprise that such losses occur even when a tag isplaced relatively close to the reader — 40% at 2 ft. Thereason for this behavior is that data transfer from thereader to tag comes at the expense of RF power beingtransmitted to the tag. Since the reader is actively trans-mitting to the tag, the carrier wave from the reader totag is intermittent, causing substantial variations in RFenergy harvesting and consequently variations in energylevels at the tag.

The energy dynamics at the tag makes it difficult to usereader-side estimation to identify the best transmissionunit to communicate with a tag. In addition, explicitlyproviding information to the reader about the current en-ergy level has considerable overhead, in addition to notbeing robust to dynamics. Thus, the challenge we face isthat the reader needs to have a way of knowing the instan-taneous energy state at the tag, and detecting its shut-offpoint despite without using cumbersome protocol-levelmechanisms to enable this information exchange.

3 Fragmenting packets into µframes

At the heart of QuarkNet is a simple hypothesis —by breaking down packet transmission into its smallestatomic units, which we refer to as µframes, we can en-able the system to scale down to severely limited harvest-ing regimes. We address the challenges in enabling suchextreme fragmentation both for tag-to-reader and reader-to-tag communication.

3.1 Fragmentation at bit boundariesThe first question we ask is: what are the practical con-siderations that determine how we can dynamically frag-ment a logical transmission unit (packet) into µframes?Ideally, we would want to insert fragment boundaries atarbitrary positions within a packet so that we can makeµframes as small or large as needed, however, this makesdecoding extremely error-prone.

To understand where to place fragmentation bound-aries, we need to give some more detail about howbackscatter modulation works. Figure 4 shows a se-quence of backscatter pulses that compose bits in apacket. Backscatter modulation uses On-Off-Keying(OOK), therefore each bit is composed of a sequence ofon and off pulses. As can be seen, the template for a’0’ pulse and ’1’ pulse differ only slightly in the phaseinformation of the pulses within the bit.

The key observation is that placing boundaries at cer-tain points in a packet can be done without disrupting the

0 01 1

positions that can be inserted with sleep gaps

Figure 4: Sleep gaps can be inserted into backscatterpulses at various position (lines with dots).

phase information required for decoding, whereas otherboundaries would disrupt decoding. For example, sup-pose that a fragment boundary is inserted between twoadjacent bits, the phase information of each bit is main-tained, thereby not impacting the ability to match thetemplate to the bit. On the other hand, suppose that afragment boundary is inserted within a single bit, thephase information within the bit is disrupted, therebycausing a mismatch at the decoder between received bitpulses and its template.

This leads us to a general principle for fragmentinga packet into µframes — µframe boundaries can be in-serted between bits but not within a bit. The ability tofragment at any bit boundary gives us the requisite com-bination of fine-grained fragmentation as well as low de-coding error.

3.2 Tuning inter-µframe gapWe now have a method for fine-grained fragmentation oflarger packets, but how do we use this to dynamicallyfragment packets? How do we decide the length of eachµframe and the sleep gap between µframes where thetag replenishes energy?

We first answer this question for tag-to-reader com-munication. In this case, we need to address two of thechallenges discussed in §2: a) how to optimize through-put by operating at the optimal harvesting rate, and b)how to ensure the tail of each µframe transmitted from atag has sufficiently high SNR to be decoded at the reader.

Gradient descent algorithm As can be seen in Fig-ure 2(a), the harvesting rate curve is a concave functionof the gap between µframes (under constant harvestingconditions). A fast and effective method for convergingto the optimum of a concave function is to use gradi-ent descent [1]. The gradient descent algorithm works asfollows: first, we start with an initial guess about the op-timal sleep gap. Second, we compute the gradient at thispoint, and look for the direction of the positive gradient.Third, we take a step along the direction of the positivegradient with step size proportional to the gradient. Werepeat this process until convergence (i.e. step is smallerthan a threshold). The algorithm takes large steps whenthe gradient is steep (i.e. point is far from optimal), andsmall as the gradient reduces (i.e. point is near optimal).

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What if the harvesting conditions change and the curveitself shifts to create a new optimal harvesting point? Ourgradient descent-based sleep gap adaptation algorithmoperates continually — once it converges to the optimal,it periodically probes the gradient at the current optimal,and moves along the positive gradient if the optimal har-vesting rate changes. In this manner, the algorithm seam-lessly adapts to such dynamics.

Handling time-varying SNR We need to add anotherconstraint to to the gradient descent algorithm — theSNR at the tail of the frame should be higher than thedecoding threshold at the reader, otherwise the framecannot be decoded. This constraint is easy to add sinceit simply translates to a bound on the maximum lengthof the inter-µframe gap. Since the length of the gapdirectly impacts the length of the µframe, capping theinter-µframe gap ensures that the length of each µframeis lower than the decoding threshold. The only changeto the gradient descent algorithm is that a step cannotexceed the maximum inter-µframe as determined by theSNR constraint.

Duty-cycling the radio One important aspect of theinter-µframe gap is that we shut off the tag’s RF cir-cuit for this length of time. In a multi-tag environment,the reader is constantly talking to other tags, so leavingthe RF circuit on results in substantial reception over-head since backscatter is a broadcast-based protocol, andwakes up every node that has its radio circuit turned on.To avoid these costs, we turn off the RF circuit during therecharge cycle. Once the tag has slept for the intendedduration, it switches on its RF circuit. One side-effect ofour decision to turn off the RF circuit during gaps is thatthe reader now has to be more careful to avoid transmit-ting to a tag or scheduling a tag for transmission while itis inactive. We return to this question in §4.2.

3.3 Remote interruptsWe now turn to µframe adaptation for communicationfrom a reader to a tag. As described in §2, the key chal-lenge is that the reader cannot detect when a tag’s energylevel drops below a low watermark, and it should stoptransmitting. Similarly, once a tag has gone to sleep,a reader does not know when it will wake up for thenext µframe. Given these constraints, how can we en-able reader-to-tag communication?

Estimating µframe length Our idea is to use a re-mote interruption mechanism, where a tag issues an in-band interrupt during reader transmission, and informsthe reader that it has reached a low-energy state. Thisremote interrupt is generated by toggling its transistorwhile receiving the current frame. In other words, theremote interrupt is a signal that is overlaid on the same

Amplitude

reader's messages

remote interruptions

Figure 5: In-band remote interruptions from tags.

time-slot and frequency signal as the message from thereader to tag.

How can the reader decode an in-band interrupt fromthe tag? The key insight is that the reader modulates thecarrier by toggling the carrier wave whereas the tag com-municates back to the reader by changing the amplitudeof the backscattered signal. In other words, both can oc-cur simultaneously! Thus, when the reader is sendingan ON pulse, the amplitude of the backscattered signalthat it receives depends on whether the state of the tran-sistor at the tag is ON or OFF — the amplitude is higherwhen the tag’s transistor is ON and lower when it is OFF.When the carrier is OFF at the reader, then the state of thetag’s transistor does not matter since there is no backscat-tered signal. The reader can detect the remote interruptby looking for a large signal variance in the carrier wavewhen the reader has the carrier wave turned on.

Figure 5 shows an example signal where toggling thetransistor causes a large variance on the carrier wave,which is monitored by a reader and can be identified bytracking the signal variance within a reader pulse. How-ever, the signal variance is detected only when the carrierwave is on. As shown in the figure, a reader cannot ob-serve the large signal variance when the carrier wave isoff. Fortunately, the carrier wave is on for 50% of thetime when the reader transmits 0s and 75% for 1s. Thus,as long as a remote interrupt is longer than 50% of thelength of a ’0’ bit from a reader, it can reliably detect theinterrupt and pause its transfer.

Since the remote interrupt is an unencoded signal,a noise spike in the frequency band could be mis-interpreted by the reader as a remote interrupt. How-ever, this would only result in the reader pausing its trans-mission earlier than necessary, and would not impact theability of the protocol to make forward progress.

An auxiliary benefit of the remote interrupt is that itacts as an inexpensive µframe ACK from the tag, whichobviates the need for more explicit protocol-level mech-anisms and reduces our overhead.

Estimating inter-µframe gap We now have a way forthe tag to interrupt a reader when it needs to replenishenergy, but how long should the reader wait before ini-

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tiating the next µframe transfer? Clearly, this durationshould be at least as long as the inter-µframe gap that thetag is using, otherwise the reader might be trying to com-municate to a tag that has its RF circuit turned off. Weaddress this by using a simple probing-based approachat the reader — for each µframe gap that the reader se-lects, it knows whether the frame was received or not bychecking the presence of a remote interrupt. If no re-mote interrupt is received, the reader knows the tag doesnot receive the frame properly. The reader continuallyadjusts the gap to minimize missed frames at the tag.

4 QuarkNet for multi-tag networks

So far, we have focused on communication between asingle tag and reader. We now turn to the case wherethere are several tags in the vicinity of a reader. The keydifference between a single tag and multi-tag setting isthat in the former, the reader stays idle during times whenthe tag is asleep to replenish energy, whereas in the lat-ter, these inter-µframe intervals present an opportunity toschedule another tag’s µframe transfer, thereby ensuringthat throughput is maximized.

4.1 Design Options

Before launching into the details of our design, lets stepback and look at the design options. Co-ordinationmechanisms for backscatter networks are more restric-tive than typical active radio-based networks for two rea-sons: a) nodes cannot overhear each other’s transfer,hence carrier sense-based approaches are infeasible, andb) the stringent resource constraints of tags render ap-proaches that require complex coding and synchroniza-tion infeasible. As a result, existing proposals have fo-cused on two classes of techniques — EPC Gen 2 andvariants which use a sequence of random-access slots,and rateless transfer where nodes transfer concurrently,and the reader simultaneously and successively decodesall transmissions.

While the deficiencies of EPC Gen 2 for severely en-ergy constrained regimes have been detailed earlier inthis paper, other alternatives and enhancements are sur-prisingly poor in dealing with this regime as well. In par-ticular, consider two prominent recent techniques — Flit[8] and Buzz [19]. Our earlier work, Flit, re-purposesEPC Gen 2 slots for bulk transfer, thereby amortizingoverhead, but it assumes that nodes are able to sustain along stream of transfer, which we realized was not thecase in severe harvesting conditions. Buzz uses ratelesscodes, but in-order to get these codes to work, it has touse synchronous single-bit slots across tags. Each single-bit slot incurs substantial overhead due to slot indicators,

and turning on and off the radio, which dramatically im-pacts performance. Given that existing approaches arenot well-suited to our nodes, the question is what proto-col to use for co-ordinating tags.

4.2 Variability-aware tag schedulingOur scheduler is designed to interleave µframes fromdifferent tags, thereby fully utilizing the inter-µframegaps. The reader divides time into variable-sized µslots,during which it explicitly schedules a single tag to trans-mit its µframe. The length of each µslot depends on thesize of the µframe — a tag-to-reader µframe terminateswhen the tag reaches its low watermark energy level andthe reader ACK is received, and a reader-to-tag µframeterminates when the tag issues a remote interrupt. In bothcases, there is a maximum bound on the µframe size todeal with tags that have plentiful energy.

While the µslot mechanism appears relativelystraightforward, the main challenge is handling the factthat tags turn off their RF circuit when they are asleep.As a result, if a tag is scheduled too early by the reader,then it may not be awake to utilize the slot, but if it isscheduled too late, then it is not operating at its maximalharvesting rate.

To handle this, we use a token-based scheduler to dealwith the stochastic nature of harvesting conditions, whileoptimizing throughput. For each tag, the scheduler main-tains a running estimate of the gap between µslots as-signed to a specific tag, and whether the µslot resultedin a successful transfer. It uses the distribution to selectthe inter-µframe gap that ensures a high likelihood ofobtaining a tag response (greater than 90% in our imple-mentation).

The reader’s estimate of the inter-µframe gap is usedas input to a token bucket scheduler, which assigns to-kens to tags at a rate inversely proportional to its inter-µframe gap. Once a tag has accumulated sufficient to-kens, it is likely to have woken up after sleep, thereforethe reader places the tag into a ready queue since it isready to be scheduled. The ready tags can be scheduledbased on a suitable metric — for example, the highestthroughput tag may be selected from the queue to max-imize throughput, or the tag that has received least slotsmay be selected for fairness.

5 Implementation

In this section, we describe key implementation detailsnot covered in eariler sections. We use the USRP readerand UMass Moo tag for our instantiation of QuarkNet.

USRP Reader QuarkNet is built based on the USRPsoftware radio reader developed by [7] with a ANT-

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NA-2CO antenna [2]. We modify the signal process-ing pipeline to enable variable sized µframe decoding,harvesting-aware tag scheduling, and detection of in-band remote interrupts. The one drawback of our plat-form is that the USRP RF daughterboard (RFX900) isonly able to issue 200mW of power, compared to 1Wby a commercial reader. Thus, while the range thatwe achieve with QuarkNet is of the order of 21ft witha USRP reader, these will increase substantially with acommercial reader.

Backscatter Tag The UMass Moo tag is a passivecomputational RFID that operates in the 902MHz ∼928MHz band. Perhaps the most challenging aspect ofour implementation is debugging under extreme low en-ergy conditions. Traditional methods for debugging em-bedded systems, such as using JTAG, supply power tothe tag and change its behavior. Instead, we instrumentthe Moo to toggle GPIO pins at key points during its ex-ecution, and a logic analyzer to record what the toggleevents that happened. In many cases, however, it is dif-ficult to insert sufficient instrumentation to have visibil-ity while still working with tiny energy harvesting levels.Thus, intuition and experience is particularly importantin designing systems for these regimes.

5.1 Trimming OverheadsAnother important aspect of our system is careful mea-surement and tuning of all overheads, which impacts ourability to scale-down to severe harvesting conditions.

Radio transition overhead: An important source ofoverhead is transition times for turning on or off the ra-dio. Fortunately, since hardware timers are responsiblefor generating the pulses on the backscatter radio, sleepgaps can be inserted by clearing the hardware timers andputting micro-controller into low power mode. Theseoperations are inexpensive energy-wise, and consumeroughly the same amount of energy as a data frame ofsize 3 bits. Note that this observation does not hold formore complex active radios — for example, a WiFi radiotakes 79.1ms to be on, and 238.1ms to be turned off [9],which is five orders of magnitude higher than the corre-sponding numbers for a backscatter radio.

Pilot tone and preamble: Each backscatter frame canpotentially include two additional components beyondthe payload: pilot tone and preamble. The pilot tone isused when a tag can change its baud rate [15] — we fo-cus on a minimalist protocol that uses fixed baud rate,therefore we remove the pilot tone. The frame preambleis a known sequence of bits at the start of a frame, and isused for frame synchronization and channel estimation.We performed extensive experiments to tune the size ofthe preamble, and use a 4 bit preamble instead of 6 bits

used in EPC Gen 2 without loss of synchronization accu-racy at the USRP reader. In sum, the total overhead perµframe is 4 bits, in contrast to the overhead of EPC Gen2 (and variants such as Flit [8]) which is 22 bits.

Probing energy state: As mentioned earlier, analog-to-digital conversions are expensive, and should beavoided while tracking the maximum harvesting rate.Our key insight is that rather than measure the voltageon the tag, we can leverage the existing low watermarkthreshold detector that is already present on most suchtags. QuarkNet gets an interrupt both when the voltagecrosses above the threshold, as well as when it drops be-low the threshold, and uses this information as a one-bitproxy for the actual voltage. This information is inputto a sleep time tracker, which determines how long towait after crossing the threshold in the upward directionbefore initiating transfer. Our approach is 100× less ex-pensive than an ADC conversion.

5.2 QuarkNet Protocol

For completeness, we provide a brief overview of theQuarkNet protocol, starting with the point when a tagenters the vicinity of a reader. We assume that each taghas a unique ID (similar to an EPC ID).

Discovery phase To discover tags and to initiate trans-fers, the reader uses a periodic discovery phase, duringwhich tags are assigned a short SessionID to reduce theoverhead of addressing nodes. This phase is very similarto a typical EPC Gen 2 singulation phase — the readersends a Discovery Pulse indicating the beginning of thephase. A tag can choose a random slot to respond withits ID. During the responded slot, a tag sends its ID in asequence of µframes since an ID may be tens of byteslong and not fit in a single µframe. The reader waitsuntil all µframes are obtained, and responds with a Ses-sionID. The discovery phase is infrequent, therefore wedo not attempt to optimize it significantly.

Scheduling phase The reader schedules transmissionand reception of data as a sequence of variable lengthrounds. Each round is initiated by a short reader pulsefollowed by the sessionID of the tag that is being ad-dressed, as well as a one-bit flag indicating whetherthe slot is for transmission (tag-to-reader) or reception(reader-to-tag). For transmission, the tag transmits avariable-sized µframe, following which the reader re-sponds with a short pulse indicating successful recep-tion, and optionally piggybacks a new SNR-based framelength constraint to the tag. If its a reception, the µframeterminates upon a remote interrupt or when the µframelength exceeds a pre-determined threshold.

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6 Evaluation

The evaluation consists of three parts: 1) demonstratingthe range and throughput benefit of µframe transmission,2) benchmarking the performance of our reader-to-tagcommunication, and 3) evaluating the benefit of inter-leaving µframes from multiple tags.

6.1 Benefit of µframesIn this section, we validate our claim that the ability tobreakdown packets into µframes that can be as small asa single bit can allow us to operate under lower energyconditions and achieve higher operating range. To focuson the effect of the choice of frame size, we strip off over-heads (slot indicators, handshakes, etc) for all protocolsthat we compare.

Minimum operating conditions We look at two har-vesters — RF and solar — and ask what is the minimumpower requirements for different approaches. We findthat the minimum illuminance required for a 1 bit µframeis 150 lux, which is 13× lower than the 2000 lux bud-get of 12 byte packet transmission (used by EPC Gen 2,Dewdrop, Flit, etc). To translate from lux to the typicalenergy available from indoor energy sources, we mea-sure the natural indoor illuminance in 30 positions in aoffice room. We find that 92% indoor illuminance is be-tween 150 lux and 1000 lux. This suggests that µframescan operate in most of natural indoor illuminance condi-tions while a canonical 12 byte transfer scheme can al-most never operate under natural indoor light.

The minimum RF power required for 1 bit µframe is13dBm, which is 20× smaller than the 26dBm budget of12 byte packet transmission that is the minimum neededfor EPC Gen 2 and its variants to operate. Both experi-ments illustrate the benefits of using tiny µframes.

Increased operational range Our second claim is thatwe can improve operational range by using µframes.Figure 6 shows the maximum communication distancethat is achieved by QuarkNet with 1 bit µframes, EPCGen 2 with fixed 12 byte packets, Dewdrop with fixed 12byte packets, Buzz with two slot choices, and a battery-assisted tag which represents the best-case scenario. Thecommunication distance is measured when we adjustthe RF power of the USRP RFID reader from 17dBmto 25.7dBm — our choices are guided by the range ofRF powers that can be generated by the USRP RFX900daughterboard.

The results show that the communication distance ofQuarkNet is always longer than other schemes across allRF power levels. At the lowest power level (17.5dBm),µframes do not improve range since the tag is not ableto decode the reader signal beyond 5ft. But as the RF

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level increases, the operational range increases dramati-cally, with about 4× longer than EPC Gen 2 at the high-est power. In fact, the performance of 1 bit µframe trans-fer while using harvested energy almost matches the per-formance of a battery-assisted tag, which shows that weare able to reach the ceiling of operational range despiteoperating on micro-power.

Figure 6 also shows that Buzz [19] performs poorlycompared to other schemes. This can be attributed to thefact that each one-bit slot in Buzz has substantial over-head — the reader sends a pulse, followed by one bitfrom the tag, random number generation for decidingwhether to transfer in the next slot, and a recharge pe-riod. Thus, while Buzz has high range in some settings,the overhead is too high to scale gracefully.

6.2 Benefits of µframe adaptation

We now turn to the benefits of adapting the inter-µframegap to maximize throughput.

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Figure 7: Benefit of µframe adaptation.

Convergence of gradient descent How well does thegradient-ascent algorithm learn the optimal harvestingrate? Figure 8 shows the results for a tag placed in threeRF+light harvesting combinations that include short andmedium range, and low and medium light. In all cases,we see convergence to close to the optimal point — thebest inter-µframe gap ranges from 0ms for case (a) sincethere is enough power to continuously operate the tag,4ms when the tag is moved to 6ft, to 12ms when thelight conditions dip further. In all cases, our trackingalgorithm converges in very few steps (≤ 4).

Throughput benefits We now know that QuarkNetpicks close to the optimal harvesting rate, but what arethe benefits in terms of throughput? To understand this,we place a tag 3 feet from a reader, and vary RF powerfrom 17dBm to 26dBm in small steps of 0.3dBm. Fig-ure 7(a) shows the throughput achieved by EPC Gen 2,Dewdrop, Flit, and QuarkNet. We find that the through-put achieved by QuarkNet is higher than EPC Gen 2,Dewdrop and Flit across all RF power levels. The aver-age communication throughput of QuarkNet is 18kbps,10.5× higher than EPC Gen 2, 5.8× higher than Dew-drop, and 3.3× higher than Flit. While not shown inthe graph, Figure 6 shows that the throughput obtainedby Buzz is less than a 1 kbps since the per-slot over-head dominates. The lowest slot size of our current re-implemention of Buzz is 3ms, which gives us 0.3 kbpsthroughput.

The previous experiments were done by varying theRF power level. To be sure that these results translate tothe case where nodes are placed at different locations infront of a reader, we measure the throughput achieved byEPC Gen 2, Dewdrop, Flit, and QuarkNet at 30 differentrandomly chosen locations between 2 to 13 ft in front ofa reader. Figure 9 shows that the throughput achieved byQuarkNet is higher than the other three schemes acrossall locations. The average throughput of QuarkNet is7.8× higher than EPC Gen 2, 6.4× higher than Dewdrop,and 4.4× higher than Flit. In particular, QuarkNet con-tinues to operate in many locations where other schemes

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cease to operate.

Breaking down the benefits QuarkNet has a varietyof optimizations including reduced overheads, variable-sized µframes, and the SNR adaptation. To under-stand the contributions of these techniques to through-put, we start with the default implementation of Dew-drop, which is only an adaptation algorithm but uses theEPC Gen 2 MAC layer, and add one optimization at atime: a) Dewdrop+µframe MAC, which replaces Dew-drop’s MAC with our low-overhead version, b) Dew-drop + µframe MAC + adaptive frame, which includesvariable-length µframes with the low-overhead MAC,and c) Dewdrop + µframe MAC + adaptive frame +SNR adaptation which includes the SNR adaptation. Fig-ure 7(b) shows the throughput achieved by the four vari-ants of Dewdrop vs QuarkNet. Clearly, each of the opti-mizations plays a major role in the throughput improve-ments observed by QuarkNet. The average communica-tion throughput of µframe is 18kbps, 5.79× higher thanDewdrop, 3.26× higher than Dewdrop+µframe MAC,1.37× higher than Dewdrop+µframe MAC+adaptiveframes, and 1.14× higher than Dewdrop+µframeMAC+adaptive frames+SNR adaptation. The final stepis solely a result of a better adaptation algorithm, andelimination of ADC conversions.

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QuarkNet vs battery-assisted alternatives Anotherinteresting question is how QuarkNet performs whencompared to battery-assisted versions of the other proto-cols (excluding Dewdrop + battery, which is identical toEPC Gen 2 + battery). Some protocols, such as Flit [8],improve in performance when there is more energy sincethere is more opportunity for bulk transfer. Would theseoutperform QuarkNet in battery-assisted scenarios? Fig-ure 7(c) shows that throughput achieved by QuarkNet isconsistently better. The average throughput of QuarkNetis 18kbps, 3.75× higher than EPC Gen 2 + battery, and1.87× higher than Flit + battery. This result shows thebenefit of reducing per-frame overheads in QuarkNet.

Handling harvesting dynamics Finally, we look athow well QuarkNet adjusts to harvesting dynamics. Fig-ure 10 shows the throughput achieved by QuarkNetover time when we adjust the RF power following thepattern 26dBm-24dBm-21dBm-26dBm-24dBm-21dBm.When the RF power is 26dBm, the achieved throughputis 8.7kbps. The throughput automatically decreases to8kbps when a 24dBm RF power is used, and 4.5kbps for21dBm RF power. The results suggest that µframe auto-matically handles the dynamics of energy harvesting.

6.3 Reader-to-tag communicationWe now turn to an evaluation of reader-to-tag communi-cation. We begin by looking at the effectiveness of re-mote interrupts. We find that remote interrupts are ex-tremely reliable — the reader detects remote interruptswith 100% accuracy across all distances where the tagcan communicate with the reader, and detection rate di-rectly drops to 0% at roughly 19 – 20 feet where the tagcannot detect the signal sent by the reader.

Next, we look at the throughput of reader-to-tag com-munication when a tag is placed at different distancefrom the reader. Figure 11 shows the results. We find thatthe throughput achieved by QuarkNet is always higherthan fixed 100 bit transfer across all distances. (We chose

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Figure 11: Throughput of reader-to-tag communication.

100 bits instead of 12 bytes because of the slower baudrate of the reader-to-tag link, as a result of which 12 bytetransfer ceases to operate even when the tag is deployed1 feet from the reader.) The throughput of QuarkNet ishigher than even a battery-assisted EPC Gen 2 due to thereduced overhead. This shows that the benefit of vari-able sized µframes is substantial even for reader-to-tagcommunication.

One trend in the graph that requires a bit more ex-planation is the fact that throughput decreases rapidlywhen the tag is close to the reader (less than 4 feet), andplateaus until about 18 ft after which it quickly dropsto zero. This is because RF-harvesting only works un-til 4ft (because of the limitations of the USRP reader),and beyond this distance, indoor light harvesting playsthe dominant role.

6.4 Evaluating the QuarkNet MAC layer

We now turn to the evaluation of our MAC layerthat includes all components of the protocol includ-ing various co-ordination overheads, frame interleaving,and scheduling. Figure 12 shows the communicationthroughput when we deploy 10 tags in front of the readerand adjust the RF power from 17dBm to 26dBm. We usea throughput-maximizing scheduling policy in this ex-periment. For each RF power level, we plot the averagedthroughput across the ten tags and the 90% confidencewhen they are scheduled in an interleaved manner andwhen they are inventoried individually. The throughputachieved by other MAC layer designs — EPC Gen 2 andFlit — are close to zero, so we do not plot them.

We find that even at the lowest RF power level, almostall nodes get to transmit data to the reader, and the aver-age throughput steadily increases with higher RF power.In addition, the throughput achieved by interleaving the10 nodes is 5.4× higher than the throughput when those10 nodes are inventoried individually. These results showthat our algorithm scales well across a wide dynamicrange of harvesting conditions, and uses gaps between

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Table 2: Overhead of µframe transmission.System overhead (us) µframe overhead (us)TX to inactive 9.9 interrupt config 10.58inactive to TX 47.5 handle interrupt 9.3

RX to TX 4.08 µframe adaptation 24.3sleep to wakeup 9.83 voltage detection 3

µframes efficiently.

6.5 MicrobenchmarksTable 2 shows the overhead incurred by different compo-nents of QuarkNet. The biggest system overhead is theswitch from inactive mode to transmission mode (47.5us), to configure several registers associated with trans-mission, such as the hardware timer register and data reg-ister. The overhead of the entire µframe size and inter-µframe gap adaptation algorithm (47.2us), is compara-ble to the total system overhead, and 40× smaller thanthe cost of an ADC conversion. Overall, the results showthat our performance tuning measures have substantialbenefits — the sum total of these overheads is smallerthan the cost of transmitting 7 bits.

7 Related Work

We have already discussed Dewdrop, Flit, Buzz, andEPC Gen 2, so we focus on other approaches.

Computational RFIDs (CRFIDs) There has been in-creasing emphasis on CRFIDs in recent years givenits potential for battery-less perpetual sensing. Am-bient Backscatter [12] uses backscatter of FM signalsfor short-range communication between tags. This isa severely energy limited platform, and could leverageQuarkNet when harvested energy is low. BLINK [24]is a bit-rate and channel adaptation protocol to maxi-mize communication throughput, which can also lever-age QuarkNet for performance. [16] introduces a power-

optimized waveform which is a new type of multiple-tone carrier and modulation scheme that is designed toimprove the read range and power efficiency of chargepump-based passive RFIDs. [17] presents a system ar-chitecture for backscatter communication which reaches100m communication distance at the cost of slow bit rate(10 bits per second). Such techniques are complemen-tary to QuarkNet — each bit transmitted at slow bit ratecan be fragmented into several segments where the infor-mation within each bit is still preserved. Also of note isMementOS [14], which uses of non-volatile flash storagefor checkpoints within a task such that the it can continueexecution after an outage. Flash checkpointing is usefulfor outage tolerance but is more than the entire cost oftransmitting an entire EPC Gen 2 packet, hence it haslimited utility in our case.

EPC Gen 2 optimizations Much of the work onbackscatter communication is specific to EPC Gen 2tags, for example, better tag density estimation [18], bet-ter search protocols to reduce inventorying time [10],better tag collision avoidance [13], more accurate tagidentification [22], better recovery from tag collisions[4], and more efficient bit-rate adaptation [24]. Noneof these tackle the problem of maximizing range andthroughput from RFID-scale sensors, which have theability to offload sensing data back to a reader.

EPC Gen 2 supports tag user memory operations inaddition to simple EPC queries including the Read andWrite command, however they are second-class citizensin the protocol since the main goal is to inventory tags.As a result, both are inefficient primitives for data trans-fer from tag to reader or vice-versa. In our experiments,we found that the Read and Write commands simply donot work at all under low energy conditions.

8 Conclusion

In this paper, we present a powerful network stack,QuarkNet, that can enable systems to seamlessly scaledown to severe harvesting conditions as well as sub-stantial harvesting dynamics. At the core, our approachdeconstructs every packet into µframes, handle dynam-ics with variable-sized µframes, and maximize through-put via low-cost adaptation algorithms and interleavingof µframes. Results show that our QuarkNet providessubstantial benefits in pushing the limits of operation ofmicro-powered devices, and allow them to perform use-ful work under more extreme environments than previ-ously imagined possible. We believe that our networkstack that works under such conditions can make it valu-able to a wide range of emerging micro-powered embed-ded systems and applications.

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