research article solar energy harvesting and management in...

Research ArticleSolar Energy Harvesting and Management inWireless Sensor Networks

Muhammad Mazhar Abbas,1 Mohamed A. Tawhid,2,3 Khalid Saleem,4 Zia Muhammad,1

Nazar Abbas Saqib,5 Hafiz Malik,6 and Hasan Mahmood1

1 Department of Electronics, Quaid-i-Azam University, 45320 Islamabad, Pakistan2Department of Mathematics and Statistics, Faculty of Science, Thompson Rivers University, 900 McGill Road,Kamloops, BC, Canada V2C 0C8

3Department of Mathematics and Computer Science, Faculty of Science, Alexandria University, Moharram Bey,Alexandria 21511, Egypt

4Department of Computer Sciences, Quaid-i-Azam University, 45320 Islamabad, Pakistan5 College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan6 Electrical and Computer Engineering Department, University of Michigan-Dearborn, Dearborn, MI 48128, USA

Correspondence should be addressed to Hasan Mahmood; [email protected]

Received 19 October 2013; Accepted 24 February 2014; Published 20 July 2014

Academic Editor: Chih-Yung Chang

Copyright © 2014 Muhammad Mazhar Abbas et al. This is an open access article distributed under the Creative CommonsAttribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work isproperly cited.

Wireless networks comprise of small devices that are typically deployed in environments where paucity of energy seriously restrictsessential operations. The energy source of these devices decreases very quickly during continuous operation and it is pivotal toreplace or recharge frequently the power sources. Sometimes, it is very difficult to perform these functions through conventionalmethods. One attractive solution to this problem is the use of the energy, scattered around us in the environment. The availabilityof energy from the environment is random and uncertain. In this paper, we present a model, schematically and analytically, forsolar energy harvesting with appropriate energy management. We provide analysis and simulations for a solar cell for standard anddifferent irradiance levels. The power of the storage device is also simulated for different times of the day. The proposed model notonly scavenges the energy but also assures the connectivity of the network by optimizing the energy consumption.

1. Introduction

Although wireless networking is not a new field, in the recentera, it has gained a considerable amount of attention. Cellularnetworks and ad hoc networks are the key constituentsof the modern wireless networks. Devices in the cellularnetworks are controlled by the fixed base stations, whereas,in ad hoc networks, the nodes are individually responsiblefor establishing communication links. Each node in ad hocnetwork functions not only as a host but also as a router.The devices in both types of networks are small and usuallybattery powered [1]. The battery has a limited capacity and,therefore, must be replenished periodically or has to bereplaced frequently. During operational time or in the time ofany emergency, it seems to be difficult to perform these tasks.

Sometimes, the network is in a difficult to reach area and itis not possible to replace or recharge the battery. One of thepossible solutions to this problem is to use the energy presentin the surrounding environment. Energy harvesting is theprocess of accumulating and utilizing the energy such as solar[2], mechanical [3], and/or thermal energy [4] present in thesurroundings of the device. All the nodes of the networkare well equipped with energy harvesting devices that canextract or scavenge energy from the environmental energysources. The harvested energy can be used as a supplementto the primary power source of the device or even sometimesdirectly as a primary source. A basic framework for energyharvesting is presented in [5], which emphasizes on learningthe environment. Although the energy harvesting is notso common in cellular and ad hoc networks, it is widely

Hindawi Publishing CorporationInternational Journal of Distributed Sensor NetworksVolume 2014, Article ID 436107, 8 pageshttp://dx.doi.org/10.1155/2014/436107

2 International Journal of Distributed Sensor Networks

Table 1: Energy sources with corresponding harvesting devices.

Energy source Nature of source Transducer

Thermal Fully controllable Thermoelectricelement

Mechanical Uncontrollable andunpredictable

Piezoelectrictransducer

Solar energy Uncontrolled butpredictable Photovoltaic cell

RF energy Partially controllable Antenna

used in sensor networks in recent years. Energy source,harvesting device, storage device, and consumer of energyare the essential parts of any energy harvesting model [6].The two basic concepts about energy harvesting theory arethe total dependence of any system on ambient energy orusing it as a supplementary source. The first idea is veryancient and sometimes not applicable due to the randomand stochastic nature of the environmental energy. The mostfundamental component of any energy harvesting system isthe energy source. The environmental energy sources, dueto their stochastic nature, are categorized as uncontrollablebut predictable, uncontrollable and unpredictable, fully con-trollable, and partially controllable [7]. There is a variety ofenergy sources in the environment, but the most famous andwidely used sources are thermal energy sources, mechanicalenergy sources, solar radiations, and radiofrequency energysources. The second most important component of anyenergy harvesting model is the energy harvesting device,also called transducer. The transducers generate electricenergy from their surrounding energy sources using specificmethods [8]. Different energy harvesting devices, regardingvarious energy sources, are described in Table 1.

Due to the random nature of the environmental energyand unavailability of solar energy during night, the harvestedenergy needs to be stored in an energy buffer. There are twobasic methods to store the harvested energy. It can be storedby using an electrochemical process (rechargeable battery)or by performing a physical separation between electricalcharges in a dielectric medium (super capacitors) [9]. Bothof the choices have some deficiencies. Batteries have higherenergy density than super capacitors but limited number ofcharge discharge cycles. On the other hand, super capacitorshave millions of recharge cycles and have relatively higherpower densities than batteries [10]. The most importantcomponent of the harvesting model, which utilizes harvestedand stored energy, is the load. It can be a small sensornode or any electronic device consisting of a processingunit, transceiver, and regulators. The characteristics of theload, heavily affect the modeling of an energy harvestingarchitecture. The transceiver of any device (load) is usuallythe most energy consuming component. At any moment intime, the available energy should be greater or equal to therequired energy for supporting the load [11]. This fact isdefined as

𝐸harvested (𝑡) + 𝐸stored (𝑡) ≥ 𝐸load (𝑡) + 𝐸loss (𝑡) , (1)

where 𝐸harvested is the energy harvested at any time 𝑡, 𝐸storedis the energy stored in a storage device, 𝐸load is the energyrequired for load, and 𝐸loss is the energy consumed by theauxiliary devices during operation.

The rest of the paper is organized as follows. Section 2briefly describes the different approaches and the relevantenergy harvestingmodels, available in the literature. Section 3presents the proposed energy harvesting and managementmodel, along with block diagram and the operational cir-cuitry. The model is analytically explained in Section 4.Numerical results are presented in Section 5, and Section 6concludes the paper.

2. Related Work and Harvesting Models

There are three types of energy harvesting approaches avail-able in the literature. The native approach is the basic one,which follows the energy neutral operation theory; that is,the energy used for all purposes should be less than or equalto the harvested energy [12]. The second approach is appliedwith an ideal energy buffer; that is, the energy stored afterharvesting is consumed without any internal loss. Also, thebuffer has infinite capacity [13]. The third and the practicalapproach is that the energy consumed is always less thanthat of the energy harvested and stored in a buffer with finitecapacity [14]. Keeping in view the above approaches, differentpatterns/models are described. Most of them use the solar orthermal energy present in the environment for harvesting.The most important and the basic energy harvesting modelsfor sensor networks are described in the following sections.

2.1. Ambient Energy Harvesting Model. In the basic model,the ambient energy is accumulated by the energy harvestingdevice. It is converted into electrical energy and stored in astorage device. It is then sensed by a low power sensor andused for further operation of the node.The node’s transceiveroperates when energy level of the storage device reaches acertain threshold value and stops working (switches to sleepmode), when energy level decreases. Meanwhile, the harvest-ing device accumulates energy and charging process starts[15]. This model has the drawback that during the chargingprocess, node’s operation remains suspended. There may belarge time delay during data transfer, if the ambient energy isnot available. This model comprises of an energy harvestingdevice (EHD), energy storage device (ESD), energy sensor,controller, and load, as shown in Figure 1 [16].

2.2. Two-Storage-Device-Based Model. In this type of energyharvesting model, two-storage-device battery and supercapacitor are used. The super capacitor is used as a primarybuffer/storage device and battery is used as a secondarybuffer/storage device [17]. A switch controls the operationof the load, first through super capacitor and then viabattery, when super capacitor is recharging.This model dealssuccessfully with the life time and the connectivity issues inthe network. The operation of the node continues even ifambient energy is not available for sometime or a storagedevice is ready to be charged. This model fully parasites on

International Journal of Distributed Sensor Networks 3

Ambient energy

Ener

gy se

nsor

Con

trolle

r

Load

Ener

gy h

arve

sting

de

vice

Ener

gy st

orag

e de

vice

Figure 1: Ambient energy harvesting model.

the storage devices and does not consider the variations inthe available environmental energy.

2.3. Ambient Energy and Two-Storage-Device-Based Model.This type of energy harvesting model uses a combinationof ambient energy and the two storage devices, battery,and a super capacitor. A DC-DC converter is assigned toeach device for regulation. The accumulator directly suppliesenergy to the node for operation as well as charging thesuper capacitor and battery placed in parallel. All devices areconnected to a power manager, which controls the operationof node [18]. There are different states in this model such assoft start, battery help, battery charge, overpower, and turnoff.Although this model ensures connectivity, it is complicatedand has to manage two storage devices on limited energyresources. This model, known as autonomous hybrid energystorage model, is fully dependent on a power manager.

3. New Energy Harvesting Model withEnergy Management

Energy scavenging from the environment is one of theattractive solutions to the power depletion problem in nodesparticipating in a wireless network. Its performance can beenhanced, if the harvested energy is efficiently managed [19].An energy harvesting model in conjunction with properenergy management system is presented in this paper. Eachnode in the network is equipped with this energy system.The proposed model is simple and depends on the ambientenergy as well as the storage device, shown in Figure 2. Thismodel comprises of two units, energy harvesting (EH) unitand energy management (EM) unit. EH unit includes photo-voltaic (PV) cell,maximumpower point tracker (MPPT), andDC-DC converter. EM unit consists of energy storage device(ESD) which is a rechargeable battery in the proposedmodel,controller (electronic relay), and load.

3.1. Components of the Model. The different componentsof the block diagram of the proposed model are brieflydescribed as follows.

3.1.1. Photovoltaic Cell. Solar energy harvesting is one of themost common ways of employing ambient energy sources,supporting or replacing battery power supplies. Solar cellsare used to convert the sunlight into direct electrical cur-rent, using the photovoltaic effect. The output current ofa photovoltaic cell is mainly dependent on its terminalvoltage and the light intensity, irradiating the cell [20]. The

−

+

MPPTcontroller

Electronic relay

PVpanel

DC-

DC

conv

erte

r

Load

Energy storagedevice

Figure 2: Energy harvesting and management model.

I

V

−

+

IPV

ISH

RSH

RS

ID

Figure 3: Equivalent electrical circuit of a photovoltaic cell.

current-voltage (IV) and power-voltage (PV) characteristicequations of the photovoltaic cell can be described from theequivalent circuit shown in Figure 3 [21]. Consider

𝐼 = 𝐼PV − 𝐼0 [exp(𝑞 (𝑉 + 𝐼𝑅

𝑆)

𝐾𝑇) − 1] −

𝑉 + 𝐼𝑅𝑆

𝑅SH, (2)

where the PV cell output current 𝐼PV is approximately equal tothe short circuit current 𝐼SC, 𝐼0 is the reverse leakage current,𝑅𝑆is the series resistance, 𝑅SH is the parallel resistance and is

usually denoted by 𝑅𝑃,𝐾 is the Boltzmann’s constant,𝑇 is the


6

4

2

0

−20 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Voltage (V)

3

2

1

0

−1

Curr

ent (

A)

Pow

er (W

)

IVPV

Figure 4: IV and PV curves of a photovoltaic cell.

standard temperature, and 𝑞 is the charge of an electron. Theabove equation can be modified as

𝐼 = 𝐼SC − 𝐼0 [exp(𝑞 (𝑉 + 𝐼𝑅

𝑆)

𝐾𝑇) − 1] −

𝑉 + 𝐼𝑅𝑆

𝑅𝑃

. (3)

Equation (3) is very complex and difficult to solve. Its solutioncan be simplified by taking some assumptions. It is assumedthat, for the best performance of a solar cell,𝑅

𝑃approaches to

infinity, 𝑅𝑆approaches to 0, and [exp(𝑞(𝑉 + 𝐼𝑅

𝑆)/𝐾𝑇)] ≫ 1.

The above equation can be solved as

𝐼 = 𝐼SC − 𝐼0 [exp(𝑞𝑉

𝐾𝑇)] . (4)

For an open circuit with 𝑉 = 𝑉OC, we have 𝐼 = 0. From (4),we have

𝐼0= 𝐼SC [exp(

−𝑞𝑉OC𝐾𝑇)] . (5)

Putting value of 𝐼0from (5) in (4), we have a more practical

equation for describing the characteristics of a solar cell.Consider

𝐼 = 𝐼SC [1 − exp(𝑞 (𝑉 − 𝑉OC)

𝐾𝑇)] . (6)

Now, as 𝑃 = VI, the value of power can be calculated using

𝑃 = 𝑉[𝐼SC − 𝐼SC exp(𝑞 (𝑉 − 𝑉OC)

𝐾𝑇)] , (7)

where 𝑃 is the output power and 𝐼 is the value of achievedcurrent. Using the above equations, the current-voltage (IV)and power-voltage (PV) characteristics curves are shown inFigure 4.

3.1.2. Maximum Power Point Tracking. An energy harvestingmodel is highly efficient with the use of MPPT techniques.Any model designed with MPPT tracker extracts the maxi-mum power from the transducer and delivers it to the loadand storage device, such as battery, in our case. Duringmodeling of a transducer, an adaptively controlled voltageregulator tries to keep the load resistance approximatelyequal to the source resistance [22]. The most suitable MPPTtechniques for solar energy harvesting are fuzzy logic control,current sweep, IMPP-VMPP computation, state-basedMPPT[23], and neural networks [24].

3.1.3. DC-DC Converter. A DC-DC converter is an essentialpart of the energy harvesting and management model. It isused to obtain a regulated and maximum DC voltage for theload. Its choice depends upon the source of energy used aswell as on the storage device. The DC-DC converter used inthis model is switch mode based [25].

3.1.4. Load. The energy harvested and stored in the recharge-able battery (RB) is used to operate the load, which is anysmall electronic device or transceiver [26] of a communi-cation node. The load characteristics play an important andunavoidable role. The main users of the harvested energy inany model are processor of the device and/or transceiver,which sends and receive the data. A processor of a deviceoperates in sleep active mode [27] and the transceiver actsas transmitter and/or receiver. The necessary condition foroperation of a transceiver by harvesting source is given as

𝑉𝐿(min) < 𝑉 ≤ 𝑉𝐿(max), (8)

where 𝑉𝐿(min) and 𝑉𝐿(max) are the minimum and the maxi-

mum operating voltages of the load. When 𝑉 ≈ 𝑉𝐿(min), the

electronic relay switches on, and when 𝑉 < 𝑉𝐿(min), the relay

shifts the load to the storage device. The detail is given in thefollowing analytical model.

4. Analytical Model

The energy arrival at a harvesting node in a solar naturalenvironment is best modeled as a stochastic process due tothe random nature of sunlight [28]. Each node operates withambient energy and the battery remains in reserve and canbe used only when ambient energy is not available or too lowto operate the transceiver. The accumulated ambient energy,after conversion into electrical energy, is shifted directly tothe node’s transceiver through a DC-DC converter for itstransmission and reception process and remaining energyis transferred to the storage device (battery) through anelectronic relay for charging.This process continues until theambient energy is below a certain level 𝐸th1. At this stage,the ambient energy supplied to the storage device stops andis fully delivered to the transceiver of the node. Moreover,when the ambient energy falls below the operating voltage ofthe load, the relay shifts the node’s transceiver to the batterystored energy and then back to the ambient energy, whenits amount exceeds the threshold value 𝐸th1. In this way, thenode continues its operation evenwhen ambient energy is not


available.The system enhances the network life time and alsosolves the connectivity problem.

The model is analytically described as follows. Let theoutput power of the harvesting device (solar cell) of themodelattached with any node 𝑈 during time interval (𝑡

1, 𝑡2) be

denoted by 𝑃pv,𝑈 and given by (7). Let 𝐸in,𝑈 be the initialenergy of the storage device and 𝑃

𝑎,𝑈the power consumed

by the auxiliary devices of the node 𝑈. Then, the availableharvested power is given as

𝑃ℎ,𝑈(𝑡1, 𝑡2) = 𝑃pv,𝑈 (𝑡1, 𝑡2) − 𝑃𝑎,𝑈 (𝑡1, 𝑡2) . (9)

The power required for transceiver operation is given by

𝑃transciver,𝑈 = 𝑃𝑡,𝑈 + 𝑃𝑟,𝑈, (10)

where𝑃𝑡,𝑈

and𝑃𝑟,𝑈

are the transmission and reception powersof a node, respectively. The available harvesting energy andenergy used by the transceiver are given by (11) and (12),respectively. Consider

𝐸ℎ,𝑈(𝑡1, 𝑡2) = ∫

𝑡2

𝑡1

𝑃ℎ,𝑈(𝑡) 𝑑𝜏, (11)

𝐸transciver,𝑈 (𝑡1, 𝑡2) = ∫𝑡2

𝑡1

𝑃transciver,𝑈 (𝑡) 𝑑𝜏. (12)

This ambient energy is supplied directly to the transceiver foroperation and if

𝐸ℎ,𝑈(𝑡1, 𝑡2) > 𝐸transciver,𝑈 (𝑡1, 𝑡2) (13)

the surplus energy charges the battery and is calculated as

𝐸charge,𝑈 (𝑡1, 𝑡2) = 𝐸ℎ,𝑈 (𝑡1, 𝑡2) − 𝐸transciver,𝑈 (𝑡1, 𝑡2) . (14)

Now, the total energy of the storage device becomes

𝐸SD,𝑈 (𝑡1, 𝑡2) = 𝐸in + 𝐸charge,𝑈 (𝑡1, 𝑡2) ,

0 < 𝐸SD,𝑈 ≤ 𝐶,(15)

where 𝐸in and 𝐶 are the initial energy stored and maximumcapacity of the storage device, respectively. The total energyof the node can be calculated as

𝐸Total,𝑈 (𝑡1, 𝑡2) = 𝐸transciver,𝑈 (𝑡1, 𝑡2) + 𝐸SD,𝑈 (𝑡1, 𝑡2) . (16)

If 𝐸ℎ,𝑈= 𝐸th1, the energy charging the storage device is

suspended; that is, 𝐸charge,𝑈 = 0, and from (14), we have

𝐸ℎ,𝑈= 𝐸transciver,𝑈. (17)

That is, the total actual harvested energy is used for thetransceiver’s operation of the node 𝑈. Furthermore, if

𝐸ℎ,𝑈< 𝐸transciver,𝑈 (18)

then the electronic relay switches the node to its residualbattery energy until again the condition in (13) is fulfilled.At this stage, the relay switches it back to the actual ambientharvested energy.

5

4.5

4

3.5

3

2.5

2

1.5

1

0.5

0

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Voltage (V)

Curr

ent (

A)

Figure 5: IV curve of a photovoltaic cell.

2.5

2

1.5

1

0

−0.5

0.5

−1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Voltage (V)

Pow

er (W

)

Figure 6: PV curve of a photovoltaic cell.

5. Numerical Discussions and Simulations

By using (6), the IV characteristics curve of a solar cell atstandard temperature 𝑇 = 25∘C is plotted in Figure 5. Forsimulations, the value of open circuit voltage 𝑉OC is taken as0.625 Volts and short circuit current 𝐼SC as 4.5 Amperes. Itis evident from the IV plot that, for a fixed value of 𝐼SC, thevoltage increases to its maximum value 𝑉max shown at kneepoint in the plot. After this point, as 𝑉max approaches to thevalue of 𝑉OC, the current decreases sharply.

The maximum power is achieved from the plot betweenvoltage and power shown in Figure 6, also known as PV curveof a solar cell. It is clear from the plot that power of solar cellincreases with the increase in voltage, until a point arrives at𝑉max, where we achieve maximum power. After this, there isa drastic decrease in the power as 𝑉 approaches to 𝑉OC.

The IV and PV curves vary for different values ofirradiance (𝐺) as shown in Figures 7 and 8, depicting that 𝐼SCis dependent on 𝐺, given as

𝐼SC (𝐺) = 𝐼SC(standard)𝐺

𝐺standard, (19)


4

3.8

3.6

3.4

3.2

3

2.8

2.6

2.4

2.2

20 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Voltage (V)

Curr

ent (

A)

G = 800W/m2

G = 825W/m2

G = 850W/m2

G = 875W/m2

G = 900W/m2

G = 925W/m2

G = 950W/m2

G = 975W/m2

G = 1000W/m2

Figure 7: IV curve at different values of 𝐺.

3

2.5

2

1.5

1

0.5

0

0

0.1 0.2 0.3 0.4 0.5 0.6 0.7

Voltage (V)

Pow

er (W

)

G = 800W/m2

G = 825W/m2

G = 850W/m2

G = 875W/m2

G = 900W/m2

G = 925W/m2

G = 950W/m2

G = 975W/m2

G = 1000W/m2

Figure 8: PV curve at different values of 𝐺.

where 𝐺standard is the standard irradiance value in the peakhour of a solar day. The value of 𝐼SC in (6) and (7) is replacedwith 𝐼SC(𝐺) and is plotted in Figures 7 and 8, respectively.

The plot in Figure 7 depicts that the value of short circuitcurrent 𝐼SC increases from 3.2 Amperes to 4Amperes as 𝐺varies from 800Watts/m2 to 1000Watts/m2. The value of𝑉OC also varies with different values of 𝐺. It is evident fromFigure 8 that there are different PV curves for differentirradiance levels. The value of power increases with the

1000

950

900

850

800

750

700

650

600

6 8 10 12 14 16 18 20

Time of day (hours)

Irra

dian

ce (W

/m2)

Figure 9: Irradiance against time of a solar day.

5

4.8

4.6

4.4

4.2

4

3.8

3.6

3.4

3.2

3

840 860 880 900 920 940 960 980 1000

Max

imum

curr

ent (

A)

Irradiance (W/m2)

Figure 10: Maximum current versus different values of 𝐺.

increase in irradiance levels. For each irradiance level, weachieve maximum power, but the maximum power for 𝐺 =1000Watts/m2 is greater than all others. In Figure 9, theirradiance is plotted against time of a solar day.

The irradiance level increases as the Sun continues itsjourney to peak point. It is clear from the plot that theirradiance level is smaller in the morning hours and keepson increasing as the Sun rises up in the sky. This increasein irradiance level continues till about 12:00 p.m., when theirradiance level reaches to its maximum value being equalto a standard value 𝐺 = 1000Watts/m2. After 12:00 p.m.,the irradiance level starts decreasing as the day passes andwhen the Sun sets, it reaches to a very low level. The plotin Figure 10 is between irradiance levels in a day and themaximum current. This can be obtained from the maximumpower, which increases with the increase in irradiance level.The plot shows that we can achieve maximum current whenthe irradiance level is equal to its standard value.


1.7

1.6

1.5

1.4

1.3

1.2

1.1

1

0.9

0.8

0.7800 820 840 860 880 900 920 940 960 980 1000

Irradiance (W/m2)

Batte

ry p

ower

(Am

pere·h

our)

Figure 11: Battery power versus different values of𝐺 in the morninghours.

2.6

2.5

2.4

2.3

2.2

2.1

2

1.9800 820 840 860 880 900 920 940 960 980

Irradiance (W/m2)

Batte

ry p

ower

(Am

pere·h

our)

Figure 12: Battery power versus different values of𝐺 after 12:00 p.m.

As shown in the analytical model, the surplus energy istransferred to the storage device, so the battery power varieswith the irradiance levels, as shown in Figures 11 and 12.

In the morning hours, the value of 𝐺 is small and themaximum power extracted by MPPT from EHD is also lessthan the power required for the load (transceiver’s operation).During this period, the power of storage device is used alongwith the harvesting power for the transceiver’s operation.The battery power decreases initially with the increase inirradiance level, till themaximumpower achieved crosses thepower limit required to run the operation of a transceiver.After this point, the power of the storage device increases.Until again the maximum power achieved falls below thepower required for load. When the Sun sets the load isshifted on the storage device and its power starts consumingcontinuously.

The plot in Figure 13 shows the power values of SD againsthours of the day. In the morning hours from 6:00 a.m. to8:00 a.m., the battery power reduces as it is used for the load’s

3

2.5

2

1.5

1

0.5

6 8 10 12 14 16 18

Time of a day (hours)

Batte

ry p

ower

(Am

pere·h

our)

Figure 13: Battery power versus hours of a day.

operation along with harvested energy. When the Sun rises,the value of harvested power increases and ultimately thebattery power also increases.The plots show that this increasein power continues till about 4:00 p.m. In the evening, thisvalue again reduces, as it is evident from the plot.

6. Conclusion

As the environmental energy is randomly distributed overall the nodes of a wireless network, therefore, an energyharvesting model along with energy management systemis proposed. The analytical behavior of the model is alsodescribed. Somenumerical simulations are presented to showthe characteristics for IV and PV parameters. The proposedmodel is simple and efficiently manageable in order to obtaina better performance of the network in a perpetual fashion.This also assures the connectivity and long life of the network.In the future, the energy management algorithm on networklayer will be proposed.

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper.

Acknowledgments

The research of the second author is supported in partby the Natural Sciences and Engineering Research Councilof Canada (NSERC). The research of the first and sev-enth author is supported in part by HEC Grant no. 1-308/ILPUFU/HEC/2009-609. The authors are grateful tothe anonymous referees for their helpful and constructivecomments.

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