Research Article SolarCastalia: Solar Energy Harvesting ...downloads.hindawi.com/journals/ijdsn/2015/415174.pdf · Research Article SolarCastalia: Solar Energy Harvesting Wireless

Research ArticleSolarCastalia: Solar Energy Harvesting Wireless SensorNetwork Simulator

Jun Min Yi,1 Min Jae Kang,2 and Dong Kun Noh2

1Department of Software Convergence, Soongsil University, Sangdo-Dong, Dongjak-Gu, Seoul 156-743, Republic of Korea2School of Electronic Engineering, Soongsil University, Sangdo-Dong, Dongjak-Gu, Seoul 156-743, Republic of Korea

Correspondence should be addressed to Dong Kun Noh; [email protected]

Received 12 December 2014; Accepted 1 March 2015

Academic Editor: Jang-Won Lee

Copyright © 2015 Jun Min Yi et al. This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Most existing simulators for WSNs (wireless sensor networks) model battery-powered sensors and provide MAC and routingprotocols designed for battery-powered WSNs. Recently, however, increasingly extensive studies of energy harvesting sensorsystems require the development of appropriate simulators, but there are few related studies on such simulators. Unlike existingsimulators, simulators for energy harvesting WSNs require a new energy model that is integrated with the energy harvesting,rechargeable battery, and energy consuming models. Additionally, the new model must enable applications of the well-knownMAC and routing protocols designed for energy harvesting WSNs and have a convenient user-friendly interface. In this work, wedesign and implement a user-friendly simulator for solar energy harvesting WSNs.

1. Introduction

A sensor network consists of tens to thousands of smartsensor nodes working with very limited resources in terms ofcalculation, storage, communication, and energy. Particu-larly, the core obstacle of existing battery-powered WSNs isa limited lifetime during which the sensor nodes or networksoperate functionally. Manual replacement or recharge ofthe batteries on each node is economically impractical andinordinately expensive. Further, a WSN can be located in anarea where such a replacement or recharge is impossible (e.g.,dangerous area such as war zones or outdoors).

Hence, for a battery-powered sensor network to operateautonomously for a long period a technology that minimizesthe use of limited energy is required. With this aim (i.e., toovercome the short lifetime of a battery-powered wirelessembedded system), previous studies have been conductedprimarily on the energy-adaptive operation in a system ornetwork level (e.g., a routing or MAC layer), which canoperate sensors andmaintain a networkwithminimal energy.However, even with this technique applied, stability anddurability are insufficiently guaranteed, because a battery-based energy source is essentially limited.

The need to overcome the limitations and to satisfy theautonomy and continuance of a sensor node or a network in

energy terms has recently generated interest in systems thatharvest energy sources in the environment autonomously.However, researches on simulators for energy harvestingWSNs have not been conducted thoroughly, and thus suchstudies remain problematic.Moreover, using existing battery-powered WSN simulators for an energy harvesting WSNstudy creates the following difficulties. First, they do notprovide an energy harvesting model for nodes. Because anenergy harvesting sensor node collects energy intermittentlyor regularly, its schedule must be considered continuouslywhen the remaining energy amount is calculated. Second,in contrast to a battery-powered WSN, an energy harvestingWSN uses a rechargeable battery for energy storage. Becausethere are a variety of rechargeable batteries, including SLA,NiCd, NiMH, Li-ion, and Li-polymer, each with its own char-acteristics, a simulator must accommodate the differencesamong the types and characteristics of rechargeable batteries.Third, existing simulators have extendibility issues as aresult of their limitations regarding commercialization ofprograms, private source codes, and programming languages.Hence, designing a new routing, MAC, topology, and mobil-ity protocol is difficult or impossible. Lastly, most simula-tors are command-line implementations which have a user-unfriendly feature [1].

Hindawi Publishing CorporationInternational Journal of Distributed Sensor NetworksVolume 2015, Article ID 415174, 10 pageshttp://dx.doi.org/10.1155/2015/415174

2 International Journal of Distributed Sensor Networks

Table 1: Characteristics of energy harvesting sources [2].

Energy source Characteristics Amount of energyavailable

Harvestingtechnology

Conversionefficiency

Amount ofenergy

harvested

Solar Ambient, uncontrollable,predictable 100mW/cm2 Solar cells 15% 15mW/cm2

Wind Ambient, uncontrollable,predictable — Anemometer — 1200mWh/day

Finger motion Human power,controllable 19mW Piezoelectric 11% 2.1mW

Footfalls Human power,controllable 67W Piezoelectric 7.5% 5W

Vibrations inindoorenvironments

Ambient, uncontrollable,unpredictable — Electromagnetic

induction — 0.2mW/cm2

BreathingHuman power,uncontrollable,unpredictable

0.83W Ratchet-flywheel 50% 0.42W

By just increasing the residual energy to some amount,the energy harvesting model may be mimicked very roughly.However, it is not enough to simulate the actual energy har-vesting WSN, due to the various components of energy har-vesting node and the various environments of deployment.The main reason why we use the simulator prior to the realinstallation is to test the operation, to identify the problems,and to verify if the performance can meet the application’srequirement. The energy harvesting nodes (e.g., the solar-powered nodes) have very different amounts of availableenergy according to their deployment season/location, paneltype/size, and battery type/capacity. Therefore, in order toconfigure the cost-effective energy harvesting node whichcan meet the application’s requirements, a new simulator forthe energy harvesting WSN is positively necessary.

To resolve these problems, this study proposes an efficientsimulator that permits easy various-level analysis of energyharvestingWSNs.Theproposed simulator has been designed,in particular, for solar energy among many other energysources. The characteristics of this simulator with solarenergy harvesting feature are as follows: firstly, because it hasbeen implementedwith consideration of insolation and of thesizes and characteristics of solar panel types, simulation canbe conducted under a range of conditions; secondly, becausethis simulator has been designed by considering the charac-teristics of each type of energy storage, a variety of analysescan be simulated for a storage type; thirdly, owing to theobject-oriented implementation of the simulator, it is easy toadd or revise a new model; fourthly, the simulator provides auser with a user-friendly GUI (graphical user interface) foreasy operation; fifthly, by providing various solar energy-based routing and MAC protocols in the base, a new pro-posed protocol can be compared and analyzed easily; andlastly, with simulation, a user can perform network- andnode-level analyses simultaneously, allowing for an easydetailed performance analysis.

This study consists of the following sections. Section 2reviews related studieswith explanations of energy harvesting

sensor systems and the types and characteristics of existingbattery-powered WSN simulators. Section 3 describes theproposed simulator’s energy model in detail, and Section 4explains some important modules and user interface.Section 5 analyzes the simulation results of a solar energy-based routing protocol by using the proposed simulator andverifies its operation. Section 6 draws conclusions accom-panied by a final comment.

2. Background and Related Work

2.1. Energy Harvesting Sensor System. Recent studies on har-vesting sensor nodes have focused on finding fundamentalsolutions for energy limitation [2]. Energy harvesting sensornodes harvest energy using various nearby environment-friendly energy sources and store and consume the harvestedenergy using rechargeable batteries. Environment-friendlyenergies include solar, wind, blood pressure, breathing, foot-step, and interior environment-generating vibration. Theircharacteristics are listed in Table 1. Further, as shown inTable 2, various types of rechargeable battery can store theseforms of energy, which include SLA, NiCd, NiMH, Li-ion,Li-polymer, and Super-Capacitor, each of which has its owncharacteristics such as energy conversion efficiency, completecharge/discharge count, self-discharge, and memory effects[3]. Because energy harvesting sensor nodes using theseenvironmental energy sources with rechargeable batteriescan be used permanently through energy harvesting, energyharvestingWSNs are less expensive to bemaintained in com-parison with the battery-powered WSN. In addition, due tothe possibility of high quality of service (QoS) and the variousexteriors of sensors, they can be used more widely thanexisting battery-powered sensor nodes. Figure 1 shows exam-ples of existing energy harvesting sensor nodes developed bypreceding studies.They are the solar energy-basedHeliomotenode [4], wind-energy-based AmbiMax [5], functional pro-totype of piezoelectric-powered RFID shoes with mountedelectronics [6], and self-powered push button transmitter [7],

International Journal of Distributed Sensor Networks 3

Table 2: Characteristics of rechargeable batteries [3].

Batterytype

Capacity(mAh)

Energy(Wh)

Energy density(Wh/Kg) Efficiency (%) Self-discharge

(%/month)Memoryeffect

Chargemethod

Rechargecycles

SLA 1300 7.8 26 70∼92 20 No Trickle 500∼800NiCd 1100 1.32 42 70∼90 10 Yes Trickle 1500NiMH 2500 3.0 100 66 20 Yes Trickle 1000Li-ion 740 2.8 165 99.9 <10 No Pulse 1200Li-poly. 930 3.4 156 99.8 <10 No Pulse 500∼1000

(a)

Wind generator

Solar panelEcosensor node

Li-polymerbattery

(b)

(c) (d)

Figure 1: Energy harvesting sensor nodes [4–7].

in the order placed. As shown in Figure 1, since energyharvesting sensor nodes use a variety of energy sourcesand storage devices, each sensor can have a different shapeand size. Many energy harvesting sensor nodes in addition tothose in Figure 1 are under development for various applica-tions.

2.2. Battery-Based WSN Simulators. The development ofenergy harvesting sensors has given rise to many studies onnew techniques in the areas of systems, networks, and data,resulting in the modification and subsequent use of simu-lators that were implemented for existing battery-poweredWSNs. Among those simulators, the most commonly usedsimulators are NS-2 [8], TOSSIM [9], AVRORA [10], SENS[11], and OMNeT++ [12], which have the following charac-teristics.

(i) NS-2 is a popular general-purpose network simulatorthat is used for TCP (transmission control protocol),

routing, and multicast protocols. NS-2 provides var-ious network management algorithms and protocolswhich focus on ad hoc network. However, its WSNprotocols are limited to 802.11 and single-hop TDMAprotocols, resulting in an insufficiency of dedicatedWSN protocols. The use of Tcl (tool command lan-guage) is a further obstacle to the use of this simulator.

(ii) TOSSIM which is developed by the TinyOS projectteam at the University of California at Berkeleysupports the MicaZ sensor platform and enablesprecise analysis, resulting from its ability to emulateeven an OS command to the CPU. However, its codeinterruptibility in the compile phase and its inabilityto handle fine-grained timing result in less accu-racy than that of a simulator such as ATEMU [13].In addition, it is compatible only with TinyOS’s appli-cation programs.

(iii) AVRORA which is a Java simulator developed atthe University of California at Los Angeles is widely


Sensor systemSolar energy harvesting system

(i) Sensor device

(ii) Communicationdevice

(iii) MCU

(i) Current sensor(ii) Voltage sensor

Measuring component

Solar panel Rechargeable battery

Loadcontroller

Chargecontroller

Info./controlPower

Figure 2: Structure of an energy harvesting sensor node.

used as an analysis tool for programs written forAVR microcontrollers. Even though this simulatorcan be used for detailed analyses, including individualsensor and program analysis, tests, and profiling, itneither provides a GUI nor supports a network-communication level simulation. Hence, no network-level simulation is available.

(iv) SENS is a cross level simulator that reflects networkcommunication, applications, and physical environ-mental factors. Each module uses real-time informa-tion obtained from the environment and supportspower usage analysis. However, it cannot simulatea MAC protocol accurately and supports only thesound aspects of physical phenomena that areobtained from the sound sensors and actuator.

(v) OMNeT++ is an extensible, modular, component-based C++ simulation library and framework, pri-marily for building network simulators. It providescomponent architectures for models which are pro-grammed in C++ and then assembled into largercomponents and models using a high-level language(NED). Due to the GUI environment, it is widelyused in network-related research, including queuingsystems, hardware emulation, or WSNs simulation.

All of these simulators support battery-powered energymodels which contain only energy-consumptionmodel with-out energy harvestingmodel. In addition, because they donotconsider the characteristics of various storage devices, thesesimulators are inappropriate for analyzing the performanceof an energy harvesting system which uses various kinds ofrechargeable batteries. In an attempt to resolve such issues,we designed and implemented a simulator dedicated to solarenergy-powered WSNs.

3. Energy Model of the SolarCastalia

An energy harvesting sensor node can use various types ofenergies in the environment as its energy sources. Amongthe environment-friendly energy sources, we selected solar

energy, which is uncontrollable, but periodically collectiblewith sufficient energy density and high conversion efficiency[14]. The energy model of a solar-powered node is com-posed of three submodels; energy harvesting model, energyconsuming model, and the remaining energy model on therechargeable battery.

As shown in Figure 2, the energy harvesting system of asensor node gathers solar energy using a solar cell or solarpanel, stores the harvested energy in the rechargeable battery,and operates the sensor system using the stored energy.To simulate more accurate energy harvesting system, wedesigned an energy harvesting model that considers insola-tion by weather and climate, the panel size and type charac-teristics, and characteristics of the types of storage that storethe energy harvested. Then, we designed the residual energymodel which is combined with an energy harvesting modeland an energy consuming model.

3.1. Energy Harvesting Model. The core feature of the pro-posed simulator is solar energy collection. We incorporatedthe characteristics of solar panels and rechargeable batteriesused in real solar energy harvesting sensor nodes in orderto simulate the amount of harvested energy as accuratelyas possible compared to an actual node. The kinds of solarpanel and rechargeable battery are so various, as shown inTables 2 and 3, and thus the amount harvested varies withthe selections. In addition, as shown in Figure 3, to reflect theamount of solar energy by weather and season, we actuallymeasured and collected in a database the hourly harvestedsolar energy for a year by usingTI’s EZ430-RF2500-SEHmoteof which panel is 5.9 × 5.7 cm2 thin film. Since insolationchanges every decade [15], themeasured data are valid for thenext ten years.

Based on these characteristics, the amount of energyharvested between time 𝑡1 and 𝑡2 can be calculated using thefollowing:

𝐻

𝑡2

𝑡1 = ∫

𝑡2

𝑡1

𝑃

ratiosize × 𝑃

ratioefficiency × 𝐵

ratioefficiency × 𝐷𝑡𝑑𝑡, (1)


Table 3: Characteristics of each solar cell (panel) type.

Type Characteristics

Crystalline

Singlecrystalline

(i) Conversion efficiency 16∼18%(ii) Regular atomic arrangement(iii) Comparatively good generation in mornings, evenings, and clouds(iv) High conversion efficiency and high durability, but high production cost

Polycrystalline

(i) Conversion efficiency 11∼16%(ii) Simpler production process with less expensive production costs, compared with singlecrystalline(iii) Decreased conversion efficiency due to the irregular atomic arrangement

Amorphous

Amorphous(thin film)

(i) Conversion efficiency 6∼8%(ii) Being inexpensive owing to a substantially small amount of using the crystal(iii) High usage owing to high flexibility(iv) Substantially low conversion efficiency, compared to crystalline

Amorphouscompounds

(i) CIGS (copper, indium, gallium, and selenium) and brass(ii) Generation volume per weight five times greater than single crystalline(iii) Wider application areas and high development possibility due to lightweight and high efficiency(iv) Need for an alternate material because of limited deposits of indium

0 4 8 12 16 20 24

Har

veste

d en

ergy

(mA

h)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

Mar.Jul.

Sep.Dec.

Panel size: 5.9 × 5.7 cm2

Panel type: thin filmWeather: sunny

Time (h)

Figure 3: Amount of energy harvested by time in March, July,September, and December, which is stored in database.

where 𝐻𝑡2

𝑡1 is energy that is harvested between times 𝑡1 and

𝑡

2, and it is a value that is calculated by multiplying theactual measurement value (𝐷

𝑡) by the weighting value of

the characteristics. The terms on the right hand side of theequation are the size ratio of the solar cell (𝑃ratiosize ), the charac-teristic based on the solar cell type (𝑃ratioefficiency), and the char-acteristics based on battery type (𝐵ratioefficiency), respectively, andthey are calculated using the following:

𝑃

ratiosize =𝑃

inputsize

𝑃

basesize, (2)

𝑃

ratioefficiency =

𝑃

inputefficiency

𝑃

baseefficiency⊆ {𝑃single, 𝑃poly, 𝑃thin, 𝑃compounds} , (3)

𝐵

ratioefficiency

=

𝐵

inputefficiency

𝐵

baseefficiency⊆ {𝐵SLA, 𝐵NiCd, 𝐵NiMH, 𝐵Liion, 𝐵Lipolymer} ,

(4)

where 𝑃basesize is the size of the solar cell which is used when wemake the reference values in a database,𝑃inputsize is the size of thesolar cell to be simulated, 𝑃baseefficiency is the energy conversionefficiency of the solar cell material used in the reference valuemeasurement, 𝑃inputefficiency is the energy conversion efficiency ofthe solar cell material to be simulated, 𝐵baseefficiency is the energyconversion efficiency of the rechargeable battery used in thereference valuemeasurement, and𝐵inputefficiency is the energy con-version efficiency of the rechargeable battery to be simulated.Note that (3) and (4) were precalculated using the panel typeand battery type, and they exist as constant values such as𝑃single and 𝐵SLA.

3.2. Energy Consumption Model. Generally, the power con-sumption of the sensor node can be calculated by adding thepower consumptions for data-transmitting, data-receiving,and others such as data-sensing or data-processing [16]. Sinceour simulator considers the types of rechargeable battery,we added the self-discharged energy of the battery, whichreflects the characteristics of the battery type, to the energyconsumption model. The following shows the energy con-sumption model between time 𝑡1 and time 𝑡2:

𝐶

𝑡2

𝑡1 = (𝐶RX + 𝐶TX + 𝐶system + 𝐶

𝑡1

battery) × (𝑡1− 𝑡

2) , (5)


where𝐶𝑡2

𝑡1 represents the energy consumed at one sensor node

between 𝑡1 and 𝑡2, and its respective terms are calculated asfollows:

𝐶RX = 𝑘1 × 𝛽,

𝐶TX = 𝑘2 × (𝛼1 + 𝛼2𝑑𝑝) ,

𝐶system = PW × DC,

𝐶

𝑡

battery = 𝐸𝑡

residual × 𝑆discharge.

(6)

In other words, the energy consumed when receiving thetransmission (𝐶RX) is estimated using the number of bits persecond (𝑘1) and the energy consumed per bit (𝛽). Further-more, the energy consumed when transmitting (𝐶TX) is cal-culated using the number of bits transmitted per second (𝑘2),the energy consumed per bit (𝛼

1), and the amplifier’s energy

𝛼

2𝑑

𝑝 (𝛼2is the energy consumed per bit in the amplifier, 𝑑

is the transmission distance, and 𝑝 is a value of 2∼4, whichdepends on the environment). The energy consumed by thesystem excluding the TX/RX data (𝐶system) is calculated bymultiplying the consumed electricity (PW) by the duty cycle(DC). Finally, the decreasing value of energy that is due tothe self-discharging of the battery (𝐶𝑡battery) is estimated usingthe remaining energy of the battery at a certain time (𝐸𝑡residual)and the self-discharging ratio (𝑆discharge).

3.3. Remaining Energy Model. Finally, the energy manage-ment module traces the remaining quantity of energy basedon (1) and (5), as shown in

𝐸

𝑡

residual = 𝐸init + 𝐻𝑡

0+ 𝐶

𝑡

0, (7)

where 𝐸𝑡residual is the remaining amount of energy at time 𝑡,𝐸init is the initial energy of the battery, 𝐻𝑡

0is the amount of

energy harvested until time 𝑡, and𝐶𝑡0is the amount of energy

consumed until time 𝑡.In addition, the sensor node operates in three different

modes, depending on the remaining amount of energy.Whenthe remaining amount of energy is zero (𝐸𝑡residual = 0),indicating that no energy remains in the node, the nodeenters the sleep mode, and energy consumption stops. Whilein a battery-powered system, a node’s life ends in that caseand maintenance such as replacement is required; an energyharvesting system enters the sleepmode and then reactivates,when additional energy is harvested. In contrast, if the node’sremaining energy amount is equal to the battery capacity(𝐸

𝑡

residual = 𝐵capacity), then any harvested energy has no spaceto be stored, so the energy harvestingmodel ceases operation.In the normal mode (0 < 𝐸𝑡residual < 𝐵capacity), the energyharvesting model and the energy consumption model oper-ate simultaneously. Further, to reflect rechargeable batterytype characteristics, if the complete charge/discharge countexceeds the predetermined charge/discharge count of theselected battery type at time 𝑡, then the 𝐸𝑡residual value is set tozero since the performance of a rechargeable battery cannotbe guaranteed.

Table 4: Routing and MAC protocols for energy harvesting WSNs,which is embedded in SolarCastalia.

Layer Protocol

Routing(i) PISA (priority-based path selection algorithm) [18](ii) DEHAR (distributed energy harvesting awarerouting) [19](iii) AOR (adaptive opportunistic routing) [20]

MAC (i) ODMAC (on-demand MAC) [21](ii) QAEE (QoS aware energy-efficient) MAC [22]

4. Implementation Issues

The proposed simulator was implemented based on anexisting sensor simulator called Castalia [17]. Based onCastalia, we modified the design as well as embedding newcomponents, to support energy harvesting feature.

4.1. Structure Modified for the SolarCastalia. As shown inFigure 4, the SolarCastalia is able to provide the node-levelsimulation as well as the network-level simulation. Eachsimulated node is composed of several software componentswhich are closely cooperating such as sensor manager,resource manager, mobility manager, application, and othercommunication composite modules. In this research, weespecially focus on the resource manager, and thus wedepartmentalize it into devices and energy submanagers, andthen drive all energy-related modules to the energy subman-ager.

The energy submanager consists of the energy harvest-ing, energy consumption, residual energy modules, and thedatabase for a reference. Firstly, as explained in Section 3.1,the energy harvesting module simulates the amount of har-vested energy as accurately as possible, by using the databaseand the user configurations about the simulation environ-ment such as the panel type and size, the storage type andcapacitor, season, and weather. And then, the energy con-sumption module simulates how much energy is consumedby the node as time goes on, by applying (5) in Section 3.2.Finally, the remaining energy module traces the quantity ofavailable energy with the information of energy harvestingmodule and energy consumption module. According to theamount of residual energy, the node determines its operatingmode such as energy-depletion, fully charged, and normalmodes as described in Section 3.3.

In addition, we realize somemajor routing andMACpro-tocols, which are designed for a solar-powered WSN, in thecommunication composite module, so as to make it easy tocompare and verify the performance of a newly proposedscheme in MAC and routing layers. The protocols providedin SolarCastalia are summarized in Table 4.

4.2. Graphical User Interface. Designing an efficient userinterface requires the following characteristics: when usedfor the first time, its usage method must be intuitive andconsistent overmany components for efficient learning. Afterlearning is completed, its use must be effective. Keeping these


Sensor manager

Application

Radio

Resource manager(energy, CPU, memory) Energy submanager

Devices submanager

Energy harvesting

Energy consumption

Remaining energy

DatabasePanel

Rechargeable batteryWeather

CPU manager

Memory managerPhysical process 1

Wireless channel

Routing

MAC

Bypass routingMultipass rings routing

802.15.4802.15.6TMAC

Tunable MAC

Virtual class

New routing and MACRouting

PISADEHAR

AOR

MACODMAC

QAEE

Routing

MAC

To/from physical process

To/from wireless process To wireless channel

Any module(read only)

Com

mun

icat

ions

com

posit

e mod

ule

Simple function callingMessage passing

Resource manager(energy, CPU state,

memory)

Mobility manager

Location

Node 1 Node 2 Node n· · ·

Figure 4: Structure of the proposed SolarCastalia simulator.

Figure 5: Graphic user interface of SolarCastalia.

aspects in mind, we provided the proposed simulator witha GUI. The GUI is intuitive compared with a command-line interface and allows efficient testing through variousfunctions such as memorization of preceding simulationcommands, storage of simulation sets, and autocompletion.

Figure 5 shows the GUI of our simulator which is devel-oped with Tcl/Tk 8.5GUI toolkit in Linux platform. As the

figure shows, various parameters required for simulation,such as node count, routing and MAC protocols, solar paneland storage types, and weather, can be set easily, and thetopology, the transmitting/receiving distance, and the like canbe viewed effectively through the sensor field window. Theconfiguration of the node or topology can be set using thetop menu.


Table 5: Simulation environment.

Parameter Simulation time Field size Node Topology type Packet rateValue 172800 (s) from midnight 25 by 25 (m) 13 Random (Sink-Center) 0.1Parameter TX power Routing MAC Weather SeasonValue −15 dBm (3∼6) GPSR Tunable MAC (CSMA/CA) Sunny FallParameter Solar cell Cell size Rechargeable battery Battery capacity Initial energyValue Thin 32 by 21 (cm) NiMH 2500 (mAh) 113 (mAh)

Table 6: Simulation result of each node.

Info. Node0 1 2 3 4 5 6 7 8 9 10 11 12

Hop Sink 2 1 3 1 1 1 3 2 2 2 2 2Miss (%) Sink 0.16 0 0.97 0 0.23 0 0.12 0.95 1.02 0 0 0.28Consumed (J) 1555 1556 1558 1556 1557 1557 1558 1556 1556 1557 1556 1556 1557Harvesting (J) 1936 1742 1545 1742 1741 1721 1742 1742 1740 1740 1742 1742 1740Remaining (J) 881 686 487 686 686 684 663 686 686 683 686 686 684

0 5 10 15 20 25

0

5

10

15

20

25

3

9

11

4

2

1051

8

712

6

Sensor

Sink(0)

Figure 6: Network topology (25m by 25m).

5. Operation Verification

To verify the operation of the proposed simulator, we com-pared the results of our simulator with that of a battery-powered WSN simulator, with all conditions (except theenergy portion) remaining the same, and we analyzed theproposed simulator in energy metric.

As shown in Figure 6 and Table 5, simulation was con-ducted for a total of 172,800 seconds from 12 a.m. (midnight)with a squared area of 25m by 25m, and its topology containsa sink node positioned at the center and the other twelvenodes positioned randomly. The transmission power of eachnode was −15 dBm which covers 3∼6m. Each node sensesthe data every ten seconds and transmits its sensory data tothe sink node using GPSR routing and the CSMA/CA-basedTunable MAC protocol. The detailed environmental settingis shown in Table 5, and the duty cycle of the nodes wasset for the daily average amount of harvested energy to be

approximately 10% greater than the daily energy consump-tion, thereby preventing an undesired blackout time.

Through the simulation, we confirmed that the dailyaverage energy consumption of each node was approximately352mAh and that the daily average amount of harvestedenergy was approximately 394mAh. Because the initialremaining energy on the battery was 113mAh (500 J), asshown in Figure 7(a), the existing battery-powered systemexhausted its energy, equivalent to the initial energy, insix to seven hours, and the network stopped operating.However, the amount of energy on the energy harvestingsystem fluctuates as times goes by, thus ensuring continuousoperation of the nodes.

Figure 7(b) shows the change in the remaining energy ofa specific node that began operation at 12 a.m. (midnight)and remained in operation for two days. Daytime energyharvesting kept the system operating at night and in theearly morning. Additionally, the remaining energy at thesecond midnight was greater than that of the first midnight,indicating that controlling the duty cycle prevented nodeblackout.

Moreover, Figure 7(c) shows the change in the energyharvested at each node for one day, and an intentionaldeviation is found in the nodes with a consideration of thedifferent amounts of harevested energy among nodes, whichcould occur in a real-life environment as a result of cloud,shadow, dust, or the like.

Lastly, Figure 7(d) shows the energy consumption of eachnode. Node 6, which was positioned close to the sink nodeand was required to relay the packets fromNodes 8, 12, and 7,was transmitting the greatest number of packets, thus con-suming the most energy. Node 2 which was required to relaythe packets from Nodes 3, 9, and 11 shows the similar results.The nodes with the second highest energy consumption wereNodes 12, 9, 4, and 5, each of which had one neighbor node towhich it can relay packets.The energy information and packettransmission rate for each node are found in Table 6.


0 5000 10000 15000 20000 25000 30000 35000

0

100

200

300

400

500

600

Average of all energy harvesting nodesAverage of all battery-powered nodesSample energy harvesting node #1Sample energy harvesting node #2

Rem

aini

ng en

ergy

(J)

Time (s)

(a) Remaining energy of battery-powered nodes and energy harvestingnodes

Rem

aini

ng en

ergy

(J)

Time (s)0 35000 70000 105000 140000 175000

0

350

700

1050

1400

1750

Average of all energy harvesting nodesSample energy harvesting node #1Sample energy harvesting node #2

∗

(b) Two-day trace of remaining energy of energy harvesting nodes

Rem

aini

ng en

ergy

(J)

0 15500 31000 46500 62000 77500

0

42

84

126

168

210

252

294

Average of all energy harvesting nodesSample energy harvesting node #1Sample energy harvesting node #2

Time (s)

(c) The amount of energy harvested by energy harvesting nodes

Rem

aini

ng en

ergy

(J)

86240 86295 86350 86405

1550.4

1551.6

1552.8

1554.0

1555.2

1556.4

1557.6

Energy harvesting node 0Energy harvesting node 2Energy harvesting node 3Energy harvesting node 9

Time (s)

(d) The amount of energy consumed by energy harvesting nodes

Figure 7: Simulation result.

6. Conclusion

We proposed an energy harvesting WSN simulator that canemulate various solar energy harvesting sensor systems byconfiguring the rechargeable battery and solar panel types,the panel size, and the like. In addition, it can simulate theweather and seasonwhen the targetWSN is intended to oper-ate and can also support major routing and MAC protocolsdesigned for energy harvesting WSNs. Finally, it facilitatesmore intuitive simulation through a user-friendly GUI and

has scalability resulting from object-oriented programming.Consequently, we expect the proposed simulator to be usedwidely as a research tool to improve the characteristics andperformance of energy harvesting WSNs.

Conflict of Interests

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


Acknowledgment

This research was supported by Next-Generation Infor-mation Computing Development Program through theNational Research Foundation of Korea (NRF) funded bythe Ministry of Education, Science and Technology (no.2012M3C4A7032182).

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