AN OPEN-LOOP ENERGY NEUTRAL POWER MANAGER

FOR SOLAR HARVESTING WSN

Andrea Castagnetti, Alain Pegatoquet, C´ecile Belleudy and Michel Auguin

University Nice Sophia Antipolis, LEAT-CNRS, 250 Rue Albert Einstein, 06560 Valbonne, France

Keywords:

Wireless Sensor Networks, Solar Energy Harvesting, Power Management.

Abstract:

Energy harvesting Wireless Sensor Networks are receiving increasing interest due to their potential to extend

system lifetime. Because environmental energy availability is highly variable, an efﬁcient power management

is required. An energy harvesting power manager must adapt to different situations that depends on the energy

that can be harvested from the environment. In this paper we propose a generic model for solar energy

harvesting wireless sensor node, that we validate on real hardware. A novel power management architecture is

then proposed. Simulation results show that up to 30% performance improvement can be achieved compared

to a state of the art power management algorithm.

1 INTRODUCTION

Wireless Sensor Networks (WSNs) consists of dis-

tributed autonomous sensors that can operate with-

out a pre-established infrastructure. WSNs are used

for monitoring environment (temperature, humidity,

etc.), ﬁre surveillance, high precision agriculture and

many other applications. Due to their physically em-

bedded nature, WSNs are well suited for energy-

harvesting. This offers an alternative solution to the

problem of the energy limitation that arise from the

fact that sensor nodes are equipped with small bat-

teries. More often, due to the fact that nodes are de-

ployed in harsh environment, batteries cannot be re-

placed. Virtually, the lifetime of an energy-harvesting

WSN node is only limited by the hardware reliabil-

ity. However we argue that the potential of an energy-

harvesting WSNs node cannot be exploited without

an efﬁcient power management system.

In battery-powered system, the goal of a power

manager is to minimize the power consumption in

order to maximize the lifetime of the battery. At

the opposite energy-harvesting systems can experi-

ence peak of energyavailability. During those periods

the system can provide peak quality of service (QoS)

only using the energy coming from the environment.

Those periods are interleaved with intervals in which

the environment provides only small amount of en-

ergy, or even nothing. When an energy-harvesting

node operates on battery, performance must be ad-

justed to extend the lifetime of the node until sufﬁci-

ent energy will again be provided by the environment.

From the situations sketched above it becomes

clear that different power management techniques can

be used depending on environmental conditions. As

an example, consider the case in which a sufﬁcient

amount of energy can be harvested from the environ-

ment. The power manager can then decide to operate

the system in energy neutrality, that is balancing the

energy consumed (to perform the task) and the har-

vested energy.

In this paper we address the problem of power

management for energy-harvesting WSNs starting

from a modeling perspective. We propose a generic

model that permits to describe a solar energy-

harvesting node in a compact form. The derived

model is validated on a recent solar energy harvest-

ing sensor platform. Based on this framework, we

propose a novel architecture, called Open-Loop en-

ergy neutral Power Manager (OL-PM). This power

manager is validated in simulation. We have also

adapted the power manager presented in (Kansal

et al., 2007) to our model and we compared its per-

formance against our power manager. Experimen-

tal results are very encouraging as up to 30% data

throughput improvement can be achieved. Moreover

we have observed that the battery is never completely

discharged using our power manager, that leads to an

improvement on system reliability. Finally the im-

pact of the power management reactiveness on the

system’s performance has been studied.

352

Castagnetti A., Pegatoquet A., Belleudy C. and Auguin M..

AN OPEN-LOOP ENERGY NEUTRAL POWER MANAGER FOR SOLAR HARVESTING WSN.

DOI: 10.5220/0003942703520358

In Proceedings of the 2nd International Conference on Pervasive Embedded Computing and Communication Systems (PECCS-2012), pages 352-358

ISBN: 978-989-8565-00-6

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

2 RELATED WORK

In recent years several energy harvesting technology

have emerged. Besides solar panels (Nelson, 2003)

and wind generators, vibration scavengers, thermal-

to-electricity (Toriyama et al., 2001) and electromag-

netic converters (Yi et al., 2007) have also been pro-

posed. Many energy harvesting systems also need en-

ergy storage because they have to continue operation

even when no energy is available from the environ-

ment (i.e. during night for a solar harvesting system).

Nowadays rechargeable batteries are used for long-

term energy storage. However new technologies are

emerging like supercapacitors (Shin et al., 2011).

Besides the problem of designing an energy har-

vesting system that effectively extract and store en-

ergy the issue of an efﬁcient power management pol-

icy must be faced. Research on energy harvesting

power management has gained lot of attention in re-

cent years mainly in the ﬁeld of wireless sensor net-

works. One of the earliest work on energy harvest-

ing power management has been presented in (Kansal

et al., 2004). Duty-cycling is used to adjust the power

consumption of a WSN node. During an initializa-

tion phase the characteristics of the energy source are

learned. Then a ﬁxed duty-cycleis applied. The effec-

tiveness of this solution depends on the variability of

the energy source. A more principled algorithm for

dynamically adapting the the duty-cycle of a sensor

node is discussed in (Kansal et al., 2007). The authors

assume that the energy source is periodic. A period is

then divided into intervals of equal duration. An es-

timate of the energy input, that is assumed to be con-

stant over the course of a single block, is computed

from historical data. Duty-cycle is then set for each

block based on an initial estimate. Online changes

are applied if there is a mismatch between the actual

energy received and the expected energy computed

by the model. The reactiveness of this power man-

agement solutions, that is the ability to adapt to en-

vironmental variations, mainly depends on the initial

choice of intervals duration. In the work of (Kansal

et al., 2007) this duration was ﬁxed to 30 minutes.

The authors also present the notion of energy neutral

operation, that is the ability to operate such that the

energy used is always less than the energy harvested.

They use this concept to develop the energy neutral

power manager discussed above.

The work of (Kansal et al., 2007) is extended in

(Vigorito et al., 2007). Here the authors use tech-

niques from adaptive control theory to increase the

adaptivity of an energy harvesting power manager.

This power manager only uses the current battery

level of the node to make duty-cycling decisions. This

approach is then model-free with respect to the energy

source. The powermanager is validated in simulation.

Experimental results show that an average 16% per-

formance improvement can be achieved compared to

the power manager proposed by (Kansal et al., 2007).

3 SYSTEM MODEL

In this Section we present the system model that is

composed of three main components: the platform

load, the energy harvesting and the battery.

3.1 A Task Level Platform Load Model

The Figure 1 represents a formalization of the plat-

form load. As shown, the platform load is ﬁrst char-

Figure 1: Formalization of the platform load.

acterized by a wake-up period T

. During this period,

the platform is ﬁrst active, then inactive. An active pe-

riod may be composed by several activities that cor-

respond to different current consumption. A typical

active period is composed of Sensor, CPU and RF ac-

tivities. However, in order to get a platform model

as much general as possible, different activities hav-

ing their own period that can be different from the

wake-up period can be deﬁned. For example, in Fig.

1 two different kinds of activities have been deﬁned:

sensing and transmission. The sensing activity is ex-

ecuted every wake-up period (T

), while the trans-

mission occurs with a period T

. During the inactive

period the system enters sleep state for power saving.

sense

is the charge delivered by the battery when the

sensing task is executed, which accounts for the cur-

rent consumed by the microcontroller and the sensor.

When the transmission task is also executed, there is

an increase in the current consumption and the battery

delivers supplementary Q

Ampere-hour of charge

compared to previous case. The rate at which the bat-

tery is discharged depends on the average discharge

current. This parameter can be computed using our

model and it is represented by the α factor that is de-

ﬁned as follows:

α =

(1)

AN OPEN-LOOP ENERGY NEUTRAL POWER MANAGER FOR SOLAR HARVESTING WSN

353

α denotes the charge consumed by the platform over

a wake-up period. In other words α is the average

current delivered by the battery during a wake-up pe-

riod. This parameter can be computed independently

for each activity (e.g. sensing, RF transmission) en-

abling a modular and accurate characterization of the

application.

3.2 A Battery and Energy Harvesting

Integrated Model

We model the solar panel and the recharge circuit us-

ing a parameter, called β, which is a function of the

light intensity. The β parameter models both the ef-

ﬁciency of the solar panel and the efﬁciency of the

voltage regulator and the charge circuit that are used

to recharge the battery. This parameter is expressed in

Ampere and indicates the rate at which the solar panel

can recharge the battery under a ﬁxed light intensity.

Figure 2 shows the β parameter as a function of the

Figure 2: β for different light conditions.

light intensity expressed in Lux using a logarithmic

scale. The curve was extracted through experimen-

tations for a 2.25in x 2.25in solar panel and a 100

µAh Li-Ion battery. More parameters can be taken

into account in order to reﬁne the model, e.g. the type

of light (ﬂuorescent, incandescent, sun light) and its

spectral characteristics, the quantum efﬁciency of the

solar cell and more. Although simple, our model pro-

vides a good approximation of the energy that can be

harvested by the system and help deﬁning the appli-

cation load requirements and the power management

policies.

3.3 A Battery State of Charge Model for

Periodic Workload

Using the α and β parameters introduced in Sections

3.1 and 3.2, the state of charge (SoC) of a battery for

the next n wake-up periods (Twi) can be computed

using equation 2:

SoC(t + nT

) = SoC(t) + [β− (α

+ α

)]nT

− K

leak

(2)

Where α

and α

are the current consumption for

sensing and transmission respectively. The Eq. 2 is

true if the conditions 3, 4 and 5 are respected.

SoC(t = 0) = SoC

max

(3)

SoC

min

≤ SoC(t + nT

) ≤ SoC

max

(4)

β, α

and α

are constants on [t, t + nT

] (5)

SoC

max

represents the capacity of the battery. We con-

sider that the battery is fully charged at t = 0 (3).

As the battery can neither store more charge than

its capacity nor be discharged too deeply, its SoC is

bounded between SoC

max

and SoC

min

(4). Finally and

for sake of simplicity, Eq. 2 is true if the luminosity

(β) and the average discharge current over a wake-

up period (α) are constants (5). If these parameters

are changing during this period, Eq. 2 can be eas-

ily divided into smaller period intervals. The leakage

current is modeled with the K

leak

parameter. This pa-

rameter models two effects: the battery self discharge

and the current drawn by the platform during inactive

periods (e.g. the low-power mode current consump-

tion).

3.4 Model Validation using the EZ430

Platform

We conducted a set of tests to validate our SoC esti-

mation approach. The Texas Instruments EZ430 solar

energy harvesting platform (TexasInstruments, 2011)

was used as experimental platform. This platform

is equipped with a 2.25in x 2.25in solar panel opti-

mized for operating indoor under low-intensity ﬂuo-

rescent light. The energy harvested by the solar panel

is stored in a 100 µAh Li-Ion rechargeable battery.

The application board is equipped with an MSP430

microcontroller and a CC2500 RF transceiver. The

board is programmed with an End Device (ED) ap-

plication that periodically sends a packet over the air

to a base station. During the idle period both the mi-

crocontroller and the radio chip are put into a sleep

state.

We used Equation 2 to predict the SoC, then the

lifetime (LT) of the system under different light con-

ditions. The estimated and measured life-times (LT)

are compared for different wake-up periods. The re-

sults are shown in Tab. 1. As it can be observed, the

model is accurate as the error always remains below

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354

Table 1: Validation of the SoC estimation technique (LT in

minutes).

0 lux ≃ 200 lux

[sec] α [µA] LT LT LT LT

(exp) (model) (exp) (model)

1 128.52 28 29 50 38

2 64.26 54 59 140 112

3 42.84 83 88 312 332

4 32.13 107 117 / +∞

6 21.42 162 176 / +∞

10 12.85 290 294 / +∞

20 6.43 585 587 / +∞

10% for 0 lux. It is interesting to note that with a

of 20 seconds the tests lasts for 10 hours while

the lifetime prediction error is only 2 minutes. The

prediction error slightly increases for 200 lux, but the

model still gives accurate results. Actually, during ex-

perimentations the luminosity ﬂuctuates around 200

lux. This is why we observe a higher deviation of the

model than without energy harvesting (β = 0). For

a T

higher or equal to 4 seconds the energy har-

vested (β) is higher than the energy consumed (α),

so the system is working in energy neutral conditions.

Therefore the LT is theorically inﬁnite (∞). Despite

our model has been validated on the TI EZ430 plat-

form, it is applicable to a wide range of platforms.

3.5 Range of Applicability of the Model

In this section we discuss how our model can adapt

to different architectures. We ﬁrst analyze the plat-

form load model proposed in Section 3.1. The power

consumption usually changes according to the voltage

supply. Nowadays electronics can be powered with a

wide voltage range. If a switching regulator (e.g. a

buck regulator) is used to provide regulated power to

the load then the current that it draws from the battery

will change according to its input and output voltage.

In this case the α parameter must be rescaled by a fac-

tor that takes into account the efﬁciency of the switch-

ing regulator. At the opposite, if a linear regulator is

used, as in our case, it is reasonable to consider α to

be independent from the battery voltage.

In Section 3.2 we proposed to model the recharge

process with a β parameter, which is a function of the

light intensity. The solar cell and recharge circuit are

then modeled as a simple current generator. Some as-

sumptions must be made in order for this model to be

valid. First of all the operating point of the solar panel

must be forced by a proper circuit (i.e. MPPT control

circuit). Moreover the efﬁciency of the recharge cir-

cuit will vary a lot depending on the output voltage of

the solar cell. Finally, the capacity of the battery will

have a huge impact on the recharge process. In our

model all the variables which have an impact on the

efﬁciency of the recharge process are implicitly in-

cluded in the β parameter. Of course β will change for

a different solar panel and so the model would have to

be re-calibrated.

4 OPEN-LOOP ENERGY

NEUTRAL POWER MANAGER

In this Section we consider the problem of adapting

the performance and the power consumption of the

system to the available energy. Performance scaling

is achieved by varying the wake-up period (T

) of

the sensor node. Duty-cycling between active and

low power states is very effective in reducing the

power consumption. Moreover, almost all the WSNs

nodes features one or more low-power states. Figure

3 shows the architecture of an Open-loop power man-

ager (OL-PM). We use the name open-loop to under-

Energy neutral

Power Manager

Energy

Harvesting

Sensor

Battery

recharge

model

Timer

Figure 3: Open-loop power manager architecture.

line the fact that the decision of the power manager is

based only on the current value of β. The power man-

ager does not use the SoC prediction to compute the

current value of T

. The system is composed of four

main blocks:

Power Manager Timer (T

). The power man-

ager is executed periodically, with a period that is a

multiple of the wake-up period T

. Every time T

changes, this timer is set to n times T

. Limiting the

execution of the power manager reduces the overhead

but it also limits the reactiveness. The trade-off be-

AN OPEN-LOOP ENERGY NEUTRAL POWER MANAGER FOR SOLAR HARVESTING WSN

355

tween overhead and reactivity can be tuned by adjust-

ing the n parameter.

Energy Harvesting Sensor and Battery

Recharge Model. An energy harvesting sensor is

used to estimate the recharging rate of the battery.

This value will then be used to compute the recharge

rate using the battery recharge model, namely β. For

a solar panel the harvesting sensor could be a current

meter that measures the output current of the solar

panel. If it is not possible to measure the output

current of the solar cell, the energy harvesting sensor

can be replaced by a light meter.

System Parameters Database (AppDB). The T

settings as well as the task level platform loads (α

)

are stored in a database.

Energy Neutral Power Manager. This block im-

plements the power management strategy. By balanc-

ing the α and β parameters, the system works with

only the energy coming from the environment. In this

case the SoC of the battery is constant over the time.

Its value depends on the past history, i.e. how much

time the system has worked in both negative and pos-

itive energy conditions. Using Equation 2 the condi-

tion for energy neutrality can be expressed as follows:

SoC(t) = SoC(t + nT

) (6)

This condition leads to the following energy neutral

wake-up period:

β− K

leak

(7)

As T

is an integer the result of Eq. 7 must be approx-

imated (the decimal part must be removed). The ceil

function is used to round toward the nearest energy

neutral.

5 SIMULATION RESULTS

We compared the OL-PM with the power manager

presented in (Kansal et al., 2007) that we have

adapted to the model discussed in Section 3. We sim-

ulated the two power managers on a ﬁve days solar

energy data set. This Section presents the simulation

setup as well as the simulation results.

5.1 Simulation Setup and Energy Data

Set Proﬁle

The values for the α parameter were measured for the

TI EZ430 platform (some are reported in Tab. 1). In

order to get realistic values for the β parameter we

measured the light intensity in an ofﬁce. We con-

nected a luxmeter to a PC and repeatedly measured

the light intensity at an interval of ﬁve seconds during

ﬁve days. The values for the β parameter were then

computed using the function of Fig. 2. The results are

plotted in Fig. 4. The system parameters are shown in

100

0 20 40 60 80 100 120

β [µ A]

Time [hours]

Figure 4: The values of β during ﬁve days in an ofﬁce.

Table 2. The overhead of a power management exe-

cution (Q

) has been considered equal to the charge

consumed for a RF transmission.

The battery parameters are reported in Tab. 3.

Table 2: System parameters.

[min, max] Q

[sec] [µAs] [µAs]

OL-PM [1, 120] 128.52 128.52 10

Table 3: Battery parameters.

SoC

min

SoC

max

[µAh] [µAh] [µAh]

37 100 63

5.2 Evaluation Metrics

In order to evaluate both power managers we have de-

ﬁned different metrics:

• Average Data-rate (hRdi). It is a metric of the

performance of the power manager. It is deﬁned

as the ratio between the size of the packet that is

sent (we use a payload of 33 bytes) and the wake-

up period, T

. The average is computed over the

ﬁve days, including the periods of time where the

battery is fully discharged (in this case the data-

rate is zero).

• Maximum and Minimum Data-rate (Rdmax,

Rdmin). Those metrics give an indication of the

peak and minimal performance achievable by a

node using a given power manager.

PECCS 2012 - International Conference on Pervasive and Embedded Computing and Communication Systems

356

• Average SoC (hSoCi). To assess if the power

management algorithm drift. Actually, the aver-

age SoC can be used to determine how far the de-

cisions of the power manager are from the energy

neutral condition.

• Battery Failures (B

). This metric gives a mea-

sure of the correctness and reliability of the power

manager’s choices. A value of 0 means that the

battery is never fully discharged and the node is

always operational.

5.3 Experimental Results

Simulation results for the power manager are reported

in Tab. 4.

Table 4: Performance comparison of Kansal algorithms

(Kansal et al., 2007) and the OL-PM.

hRdi Rd

max

min

hSoCi B

[bits/s] [bits/s] [bits/s] [µAh]

Kansal 29.55 132 0 65.81 9

OL-PM 44.61 132 2.2 88.8 0

From Tab. 4 it can be observed that for the data set

used the OL-PM improves the data-rate by more than

30% compared to the algorithm proposed by (Kansal

et al., 2007). This can be mainly explained by the

fact that the data set has a high energy variability

as it can be observed from Fig. 4. The OL-PM is

able to exploit this variability thanks to of its reactiv-

ity (that can be tuned through the n parameter). The

activation period of this power manager is in the or-

der of tens of seconds. The algorithm proposed by

(Kansal et al., 2007) takes a different approach. It

samples the energy source each 30 minutes and it also

uses an EWMA ﬁlter to predict the energy availabil-

ity. The combination of those two effects excessively

smoothes the data from the energy harvester (β), and

the power manager cannot exploit the peak of energy

availability to increase the data-rate (Rd). The dif-

ferences in reactivity also explains that the battery is

never fully discharged for the OL-PM, while it hap-

pens nine times in ﬁve days for Kansal. As a con-

sequence the node stops sending data, for a variable

amount of time, on average two times a day.

max

is the same for the two algorithms, as it de-

pends on the peak energy harvested by the solar panel,

which is the same in both cases. It is interesting to

note that both power managers’ goal is to operate the

system in energy neutrality. In perfect conditions the

average SoC of the battery should be equal to the ini-

tial SoC (in our case SoC

max

). Inaccuracies in β esti-

mation and lack of reactivity cause a SoC drift. The

drift is only 10µAh, that is 16% of the discharge ca-

pacity (C

) for the OL-PM. It can accurately track the

variation of β and it can operate the system near the

energy neutrality. At the opposite the Kansal algo-

rithm produces a drift of 34µAh, that is 54% of the

discharge capacity C

. This is due to the assumption

made by the authors that the energy harvested is con-

stant over a period of 30 minutes. Actually this as-

sumption is violated many times in our data set. We

therefore conclude that considering the harvested en-

ergy constant over a long period (i.e. 30 minutes)

could lead to performance degradation.

The rest of our experiments investigated the im-

pact of varying the reactivity of the power manager,

on the overall performance of the OL-PM. Figure 5

shows the average SoC, the average data rate and the

SoC

min

obtained for different values of n. As previ-

100

5 10 15 20 25 30

<SoC> [µAh], <Rd> [bits/s], SoC

min

[µAh]

<SoC>

SoCmin

<Rd>

<SoC>

SoCmin

<Rd>

Figure 5: The effects of varying the power manager reactiv-

ity (n) on the hSoCi, SoC

min

and hRdi.

ously discussed the more the SoC diverges from the

SoC

max

the more the energy neutrality condition is vi-

olated. Experimental results show that for a n smaller

than 10 the system tends to perform poorly. Actually

the gain induced by the increased reactivity is coun-

terbalanced by the overhead. The impact of the power

management overhead is higher when β is lower (i.e.

during night). During those periods the system can-

not actually work in energy neutrality even with the

smallest value of T

. As it can be seen, the aver-

age SoC as well as the average data-rate tends to be-

come stable for a n comprised between 10 and 20. For

values greater than 20 the lack of reactiveness of the

power manager deteriorates the global performance.

Finally , the Fig. 6 depicts the instantaneous val-

ues of the SoC, the data-rate (R

) and the energy har-

vested (β) during the second and third day for both

approaches. As shown, the OL-PM reactiveness al-

lows the data-rate to accurately follows the energy

AN OPEN-LOOP ENERGY NEUTRAL POWER MANAGER FOR SOLAR HARVESTING WSN

357

100

120

140

35 40 45 50 55 60 65 70

β [uA], Rd [bits/s], SoC [uAh]

time [hours]

beta

SoC

Open-Loop

100

120

140

35 40 45 50 55 60 65 70

β [uA], Rd [bits/s], SoC [uAh]

time [sec]

beta

SoC

Kansal

Figure 6: Execution traces of the OL-PM (n = 10) and Kansal during the second and third day.

harvested proﬁle. In the meantime, for Kansal the

data-rate is less correlated to the β and leads to a larger

SoC drift than with our OL-PM.

6 CONCLUSIONS

In this paper, we have proposed a modeling approach

for an energy harvesting wireless sensor node. Based

on the derived model we have developed a power

manager that can dynamically adapt the duty-cycle

of the node to operate in energy neutral condition.

Experimental results have shown that up to 30% per-

formance improvement can be achieved compared to

a state of the art power management algorithm. We

have also studied the impact of the power manage-

ment reactiveness on the global system performance.

Our analysis has shown that the deviation from the en-

ergy neutrality condition is related to the reactiveness

of the power manager. Therefore, this outcome can

be used to increase the power management efﬁciency

by adding an adaptive control of the reactiveness. Fu-

ture works will also focus on using SoC estimation to

adapt the power manager policies. Finally, we aim to

extend our approach for different energy harvesting

system (piezoelectric, thermoelectric) and batteries.

ACKNOWLEDGEMENTS

This work is supported by the French National Re-

search Agency (ANR) project GRECO bearing refer-

ence ANR-2010-SEGI-004-04.

REFERENCES

Kansal, A., Hsu, J., Zahedi, S., and Srivastava, M. (2007).

Power management in energy harvesting sensor net-

works. ACM Transactions on Embedded Computing

Systems (TECS), 6(4):32–es.

Kansal, A., Potter, D., and Srivastava, M. B. (2004). Per-

formance aware tasking for environmentally powered

sensor networks. SIGMETRICS Perform. Eval. Rev.,

32:223–234.

Nelson, J. (2003). The physics of solar cells. Imperial Col-

lege Press London.

Shin, D., Kim, Y., Seo, J., Chang, N., Wang, Y., and Pe-

dram, M. (2011). Battery-supercapacitor hybrid sys-

tem for high-rate pulsed load applications. In DATE,

pages 875–878. IEEE.

TexasInstruments (2011). ez430-rf2500 solar energy har-

vesting kit. http://focus.ti.com/docs/toolsw/folders/p

rint/ez430-rf2500-seh.html.

Toriyama, T., Yajima, M., and Sugiyama, S. (2001).

Thermoelectric micro power generator utilizing self-

standing polysilicon-metal thermopile. In Micro Elec-

tro Mechanical Systems, 2001. MEMS 2001. The 14th

IEEE International Conference on, pages 562–565.

IEEE.

Vigorito, C., Ganesan, D., and Barto, A. (2007). Adaptive

control of duty cycling in energy-harvesting wireless

sensor networks. In Sensor, Mesh and Ad Hoc Com-

munications and Networks, 2007. SECON’07. 4th An-

nual IEEE Communications Society Conference on,

pages 21–30. IEEE.

Yi, J., Ki, W., and Tsui, C. (2007). Analysis and design

strategy of uhf micro-power cmos rectiﬁers for micro-

sensor and rﬁd applications. Circuits and Systems

I: Regular Papers, IEEE Transactions on, 54(1):153–

166.

PECCS 2012 - International Conference on Pervasive and Embedded Computing and Communication Systems

358