Research Article Reliable Lifespan Evaluation of a Remote ...

Research ArticleReliable Lifespan Evaluation of a Remote EnvironmentMonitoring System Based on Wireless Sensor Networks andGlobal System for Mobile Communications

Diego Antoliacuten Nicolaacutes Medrano and Beleacuten Calvo

Group of Electronic Design (GDE-I3A) University of Zaragoza 50009 Zaragoza Spain

Correspondence should be addressed to Diego Antolın dantolinunizares

Received 7 September 2015 Revised 19 January 2016 Accepted 21 January 2016

Academic Editor Fanli Meng

Copyright copy 2016 Diego Antolın et al This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

The use of wireless sensor networks (WSN) for monitoring physical and chemical variables in large areas allows density andfrequency measurements which have been unavailable to date in classical measurement systems To fully take advantage of thistechnology in a particular application besides an accurate design and selection of all the components involved in its operationit is essential to dispose of reliable lifetime estimation prior to deployment This paper presents an experimental approach todetermine the actual lifetime of such battery-operated systemsmaking use of a customWSNarchitecture and for different batteriestechnologies To render a reliable evaluation the energy consumption of the sensor nodes under their different operation modesin correlation with the battery characteristics and the voltage regulation system is jointly considered The result is a complete andpractical lifetime model whose appropriate performance has been validated in a real deployment scenario

1 Introduction

The fast development of wireless communications systemsfor the past two decades has favored the emergence of manypreviously unthinkable applications An emerging exampleis that of wireless sensor networks (WSN) composed of alarge number of sensor nodes capable of monitoring differentphysical and chemical magnitudes from the environment inwhich they are distributed and where the transmission ofdata is done through a suitable RF module These systemsare mostly based on the Low Rate Wireless Personal AreaNetwork (LR-WPAN) IEEE 802154 communication stan-dard This standard is designed to be compliant with therequirement of low-power consumption thus allowing beingfed by small batteries with a lifetime of months or even years

The limited range of the IEEE 802154 RF modules(usually a few hundredmeters) requires the use of techniquesof multihop data transmission to cover larger monitoringareas Inmultihop transmission some sensor nodes act as cellstations for data provided by other nodes Thus it is possibleto achieve greater transmission distances The informationcollected by a WSN should be finally transmitted to a central

node (usually connected to a PC) which will process andrepresent the received data in order to get straightforwardlyinterpretable results

This work presents the development and characterizationof a sensor node platform validated through real WSNdeployment and test Since nowadays WSN energy efficiencyremains a primary challenge for the success of these net-works one key part of thework is the study and analysis of thepower consumption with the objective to obtain a simple butreliable experimental lifetime estimationmodel that preventsa battery network failure

The paper is structured as follows Section 2 shortlypresents the state of the art about environmental monitoringbased on wireless sensor networks Section 3 provides abrief description of the sensor-router nodes including hard-ware software communication characteristics and powermanagement techniques Section 4 addresses the proposedmethodology for modeling the battery lifetime of a basicsensor node within the custom WSN platform Section 5extends this analysis to the coordinator nodes To completethe network description Section 6 is focused on the centralnode where the data collected by the coordinator nodes are

Hindawi Publishing CorporationJournal of SensorsVolume 2016 Article ID 4248230 12 pageshttpdxdoiorg10115520164248230

2 Journal of Sensors

received and properly represented and processed Section 7shows the network test performed to evaluate both thecomplete systemoperation and the battery lifetime predictionmodel Finally Section 8 draws the conclusions

2 State of the Art

Wireless sensor networks (WSN) have become a key environ-mental technology with applications in intelligent agriculture[1 2] diffuse greenhouse gas emission detection smartcompostingmonitoring and control [3] prevention and earlydetection of forest fires or protection of critical infrastructure[4] among others Nevertheless currently available commer-cialWSN generic platforms need considerable improvementsto meet the stringent requirements desirable for real opennature deployments mainly in terms of complexity costlifetime and maintenance As a result the developmentof custom nodes to replace commercial ones to enhanceperformance is gaining strength In this attempt most resultsremain at laboratory test level being rather hazardous to findimplementations validated in an actual deployment scenarioIn fact focusing on the implementation and test of completefull-custom WSN dedicated to monitoring environmentalparameters only a few solutions can be found in the openliterature [5ndash9] However the use of sophisticated and power-consuming sensor devices communication technologies andprotocols results in cost per node energy requirementstransmit data rate and required infrastructure which ren-der these solutions unsuitable for battery-operated nodesdeployed in natural areas [5ndash7] In [5] IP video camerasare used for smoke and fire detection which require highcomputational power increasing the cost of both softwareand hardware in [6] a Digital Enhanced Cordless Telecom-munications (DECT) subnet is connected to a Global SystemMobile (GSM) coordinator which communicates with a PCcontrol center in [7] a combination is proposed betweenZigBee Wi-Fi and satellite communications Solutions in[8 9] focus the power optimization on efficient data commu-nications in [8] a wireless sensor network for microenviron-mental application achieving efficient node data transmissionis presented showing a web-based tool for monitoring thedeployed network controlling data traffic data processingand presentation and remote node control and monitoringin [9] the authors propose a network with a duty cyclemanagement able to address stability in network connectionsand coverage at the same time By synchronizing disjointsensor subsets the system can detect events which could beomitted by classical communication architectures achievinga tradeoff between power consumption and event detectionThis is useful in applications where event lost are not allowedas defense tasks

In this scenario with power node size and cost per nodebeing the restrictive conditions specific novel designs aredesirable to come across all the main challenges encounteredin environmental projects Accordingly a custom low-powerlow-cost reliable WSN platform has been developed byauthors for use in remote areas which can be easily adapted toother monitoring systems Based on a first prototype [10 11]a number of modifications have been included in order to

reach an enhanced WSN implementation mainly concern-ing the following (i) the reduction of power consumptionat both software and hardware levels (ii) communicationdevelopments (different communication protocols such asGSMGPRS and IEEE 802154 can be used adding moreversatility to the proposal) and (iii) time synchronizationstability (a real-time clock fed by a supercapacitor is addedfor data integrity purposes)

Making use of this WSN architecture this paper isfocused on finding a reliable estimation of the lifetime thisis an essential information before deployment especially insystems targeting remote environmental monitoring whichare designed for long-term unattended operation Despiteits importance this key metric is usually parameterizedthrough predictions that often use high level descriptionsandor ideal or basic battery models simplistic assump-tionswhich overestimate lifetime Recent works progressivelyinclude more complete analysis but still exhibit limitationssince they are mainly based on emulation methods usuallydeveloped for particular platforms which ultimately fail toconsider real effects The simulator mTOSSIM [12] evaluateslifetime inWSN but is restricted to platforms embedding theTinyOS operative system power consumption is estimated atmicrocontroller instruction level varying different networkparameters (duty cycle transmission frequency and idle orsleep transceiver modes) and using battery models basedon technical specifications Similarly [13] is an emulated-based method using MSPSim which incorporates batterymodels with nonideal effects and a low-level description ofthe node hardware In [14] authors consider the averagecurrent consumption over the different operation processes(transmission reception and idle or sleep states) and the cor-responding consumption of the different platform elementsin these states but the battery model does not consider theinfluence of self-discharge temperature and recovery effectsover the battery capacity In addition sensors are not includedand the communication is only made between an end deviceand the coordinator which are not real operation conditionsFollowing an experimental approach in [15] authors useLR06 batteries of different brands and capacity in order toevaluate their influence on a node lifetime however thestudy is based on a node in listen state considering neithertransmission nor sensormeasurement processes and thus notdescribing a real network behavior

From the above discussion it is clear that an accuratelifespan prediction model must consider the energy con-sumption of the sensor nodes under their different operationmodes in correlation with the real battery characteristics andthe voltage regulation system that is including all hardwareand software constraints To do so we consider that anexperimental methodology is the best suitable choice Thisis the goal of this paper to attain a complete and practicallifetimemodel based on the remaining energy battery level asstandardized metric through an experimental methodologyusing real nodes under real operating conditions from themeasured power consumption profile and the measured bat-tery discharge pattern the battery discharge rate is inferredPreliminary results were introduced in [16] for a LiPo batteryThis work deepens and completes the study considering two

Journal of Sensors 3

GSMGPRS

RTC

XBEERF

transceiver

Analog sensors

SEPICDCDC

converter

Digital sensors

Battery

Solar harvesting energy system

DCDC converter

Sensor node Coordinator extra elements

120583C

Figure 1 Sensor node block diagram (RTC real-time clock DCDirect Current RF Radio Frequency GSM Global System forMobile GPRS General Packet Radio Service)

different battery technologies and validates results through areal outdoor deployment

3 WSN Description

31 Sensor Node Architecture The implemented basic nodeconsists of the following core elements (Figure 1)

(i) An 8-bit low-power CMOS ATmega1281 microcon-troller fromAtmel thatmanages and synchronizes thenode operation has been used

(ii) An XBeePro transceiver operating in the 24GHzIndustrial-Scientific-Medical (ISM) free RF band[17] has been included By properly configuring thefirmware parameters that control the transceiveroperation it is possible to drive all the network nodesto a low-power sleep state thus providing a multihopenergy-optimized communications protocol

(iii) A real-time clock (RTC) model PCF2123 from NXPhas been includedThis component has been includedto (i) enable arrangement of the collected data and(ii) detect errors in data transmission from thesequential information that it provides This clockis programmed and activated before the networkis deployed It is firstly powered by a preloadedsupercapacitor which stores the energy required for 5days of RTC operation prior to its deployment thussaving battery resources In addition powering theRTC by a supercapacitor allows replacement of nodebatteries without losing date information

(iv) A power supply system is used consisting of a batteryconnected to a SEPIC DC-DC TPS61131 converterfrom Texas Instruments which supplies the nodecomponents with the required energy and regulatedvoltage level (33 V) Also a 100mF supercapacitorfeeds the RTC for a further 7 days evenwhen the nodebattery is fully discharged thus avoiding the need toreprogram the clock

(v) A 24GHz 710158401015840 and 5 dBi dipole vertical polarizationantenna has been used which allows a maximumoutdoor range of about 1600m

(vi) An IP65 standard protection box is used

32 Sensor Types and Connectivity To attain a versatileconfiguration a custom low-voltage plug-and-play pro-grammable electronic interface suitable for connecting bothactive and passive analog sensors has been developed Pro-grammability allows achieving an optimum reading perfor-mance for every connected sensor

33 Network Communications Depending on theWSN loca-tion communication between the sensor nodes and thecentral node can be carried out in different ways if the WSNis deployed in a region with mains infrastructure (such asindoor applications) nodes could be powered directly fromthe mains In this case energy-saving restrictions can berelaxed However in most cases sensor networks are notinstalled in regions with these infrastructures The nodestherefore send the information to the coordinators usingradiofrequency (RF) low-power communication protocolsmost based on the IEEE 802154 standard Among thesesome protocols [18] can drive the sensor nodes to a low-energy mode but keep the router devices awake These algo-rithms have several disadvantages first sensor distributionmust be carefully selected to cover the transmission areaof all the sensor nodes Also network scalability and self-organization are limited because of the need of performingthe configuration of the nodes as end devices or routers beforebeing installed In addition the operating life of the sensornetwork is clearly limited by the batteries of these routernodes

These limitations are overcome by using the XBeeProtransceiver including the DM-24 firmware as will be shownnext Furthermore this transceiver is fully pinout compatiblewith the WiFly 80211 bg transceiver from Roving Networks[19] that allows the use of the same hardware node with highnetwork versatility

34 Power Management The difficulty of battery replace-ment for sensors deployed in natural environments joinedto the unfavorable cost and size of most of battery rechargesystems makes lifespan a critical issue Thus in order toincrease the network lifetime the nodes use aDynamic PowerManagement (DPM) techniqueThe microcontroller sets thedigital components of the node (transceiver smart sensorsand the microcontroller itself) into their corresponding low-power modes when they are not active Also the polarizationof analog components (like analog sensors amplifiers andfilters) is controlled through an ADG701 digital switch fromAnalog Devices Thus when analog components are not inuse the microcontroller switches off the corresponding biaspath The node transceiver has five controllable power levels(Table 1) that allow optimizing the power consumption of thedevice Finally the IEEE 802154 firmware programmed intothe transceivers allows all the nodes to operate as Full Func-tion Devices (FFD) In this way the network router nodes areenabled to be in sleepmodewithout losing the network archi-tecture unlike the ZigBee specification [20 21] Note thata router which is permanently in the awake mode means acontinuous current consumption of 67mA (duemainly to theRF transceiver) So if fed by a 2Ah battery like the one finally


Table 1 XBeePro power levels

Value of power level AT command Power level (dBm)0 101 122 143 164 18

used in this work the operating life will be limited to only 30hours

Other power management widespread techniques aredynamic voltage scaling and dynamic frequency scalingDynamic voltage scaling (DVS) [22 23] allows reducing thepower consumption in low activity node modes Howeverits implementation requires the use of additional electronicsto provide the suitable different voltage supply values for thenode components increasing the final cost In our case due tothe narrow voltage operation range of the XBee transceiverswhose bias voltage is limited between 28V and 34V thenodes include a SEPIC DC-DC converter that holds thevoltage level between these limits Reducing the bias voltagebelow 28V resets the transceiver thereby hardly limiting theDVS efficiency In addition in applications with a low dutycycle such as environmental monitoring the required linearvoltage converter needed for this method increases the powerconsumption in the low-power operation mode degradingthe node lifetime On the other hand the use of dynamicfrequency scaling (DFS) [24 25] allows reducing power con-sumption by dropping the system frequency according to thenode operation requirements though this technique is onlysuitable for microcontrollers that include specific integratedcomponents In some 120583C without these specifications suchas the one used in this work it is possible to implementDFS using an additional RC circuit oscillator which generatesan output frequency value controlled by an iterative algo-rithm but at a high cost in computational time and energywhich drastically reduces its performance and thus has beendisregarded

35 Commercial WSN Platforms Comparison This basicsensor node component has been designed for flexibilityallowing for easy configuration as a sensor sensor-routeror coordinator node Table 2 shows a comparison of theproposed node with similar commercial devices Crossbownodes present lower energy requirements but with 5 to 10times lower transmission range Therefore for applicationswhere sensors may be located several hundreds of metersapart the need would arise to deploy additional routernodes thereby increasing the network cost and complicatingmaintenanceOn the other hand theWaspmote shows similaroutdoor and indoor transmission ranges However improvedenergy management is achieved in the proposed nodesresulting in better overall performance In addition the use of

a SEPIC enables biasing the nodes with a wider input voltagerange

4 Lifetime Model

Depending on what is under consideration several differentbattery modeling methods can be found in the literature [2627] physical abstract empirical andmixedmodels based ona combination of the previous modeling techniques Physicalmodeling is the most accurate method but at the cost of ahigh complexity that makes its practical application difficultAbstract modeling describes battery operation based onelectronic models and it becomes highly suitable when thefull system is to be simulated as a circuit Finally empiricalmodeling provides the simplestmodels based on a numericalapproach to describe the battery behavior In this work anempirical batterymodel has been developed Following a sys-tematic methodology the proposed modeling considers theeffect of the battery voltage reduction during the dischargeprocess in real operation and thereby permits simple butrealistic lifetime estimation Besides as several characteristicsdiffer between rechargeable and nonrechargeable batterieslifetime analysis has been performed using both battery typeexamples a rechargeable 37 V-2000mAh Lithium Polymer(LiPo) battery and 2 times 15V alkaline LR06 batteries with2200mAh of charge

The energy consumption of the basic sensor node is firstanalyzed to obtain the behavior in the different states over aworking cycle To perform this analysis the node was config-ured as follows (i) both analogue and smart digital sensorswere connected ambient parameters within the housing boxare measured using two low-cost resistive analog sensorsNTC for temperature and Sencera H25K5A for relativehumidity while external parameters were measured usingtwo digital smart sensors Sensirion SHT11 to measure therelative humidity and Intersema MSB5540B for barometricpressure and temperature (ii) a power transmission levelof 18 dBm (worst case) is selected and (iii) the work cycleis set to a measuring time of 1746 s every 900 s (15min)that is a duty cycle of 018 a suitable choice for ourtarget application With this node configuration samplingthe power every 2ms using a 2602A System SourceMeter(SMU) fromKeithley Instruments connected to a PC througha USB-GPIB adapter the power consumption profile ofthe full node presents three different levels the first one((1198751) 898 s) corresponds to the system in sleep mode the

second one ((1198752) 448ms) corresponds to the parametersrsquo

measurement time with microcontroller transceiver andsensors switched on lastly in the third one ((119875

3) 1316 s)

both sensors and microcontroller are switched off whereasthe transceiver remains on to complete the data transmissionIn measurement (2) and transmission (3) stages the powerconsumption is constant (119875

2= 220mW and 119875

3= 235mW

resp) whereas in sleepmode (1) the current remains almostconstant over all the battery operation range with an averagevalue below 119894

1= 12 120583A

Next the battery behavior is characterized The twoselected alternatives a rechargeable 37 V 2000mAh LiPoand a 2 times 15V LR06 2200mAh alkaline battery were


Table 2 Sensor nodes comparison

CPU parameters IRIS Crossbow Micaz Crossbow TelosB Crossbow Waspmote Libelium Proposed node(this work)

Processor performanceMicrocontroller ATMega1281 ATMega128L MSP430 ATMega1281 ATMega1281Number of bits 8 bits 8 bits 16 bits 8 bits 8 bitsFrequency NA NA NA 8 MHz 4 MHzProgram flash memory 128 kB 128 kB 48 kB 128 kB 128 kBSRAM 8kB 4 kB 10 kB 8 kB 8 kBEEPROM 4kB 4 kB 16 kB 4 kB 4 kBCommunications interfaces UART I2C SPI UART I2C SPI UART I2C SPI USB UART I2C USB UART I2C SPI

Analog to digital converter 10-bit ADC 10-bit ADC 12-bit ADC 10-bit ADC 10-bit ADCVFC

Digital to analog converter NO NO 12-bit DAC NO NOCurrent active mode 8mA 8mA 18mA 9mA 24mAlowast

Current sleep mode 8 120583A lt15120583A 51 120583A 62 120583A lt12 120583Alowast

RF transceiverFrequency band ISM 24GHz ISM 24GHz ISM 24GHz ISM 24GHz ISM 24GHzOutdoor range gt300m 75m to 100m 75m to 100m 750m to 1500m 750m to 1500mIndoor range gt50m 20m to 30m 20m to 30m 60m to 90m 60m to 90mSensitivity minus101 dBm minus94 dBm minus94 dBm minus100 dBm minus100 dBmMax Tx Power 3 dBm 0dBm 0dBm 18 dBm 18 dBm

Current drawReceive mode 16mA 197mA 23mA 5708mA 636mAlowast

Maximum transmission current 17mA 174mA NA 18758mA 154mAlowast

Sleep mode NA 1 120583A 1 120583A 120 120583A lt12 120583Alowast

Power supplyBattery 2 times AA batteries 2 times AA batteries 2 times AA batteries NA LiPo batteryExternal power 27 V to 33 V 27V to 33 V NA 33V to 42 V 18 V to 5V

lowastMeasures include sensors and RTC

LiPo expLiPo pol

Alkaline expAlkaline pol

LiPo

Alkaline

1

2

3

4

5

Volta

ge (V

)

05 1 15 2 250Time (min) times10

4

Figure 2 Experimental battery voltage (V) versus time (min) for aconstant 220mW discharge process

discharged through the SEPIC converter at a constant powerof 225mW to match our working conditions Figure 2 showsthe resulting battery voltage drop versus timemeasured usingan Agilent 34410A Multimeter connected to a PC througha USB-GPIB interface sampling the battery voltage every

60 s In the case of LiPo for battery voltage ranges from42 to 36V the behavior can be modeled according to theapproximated expression in (1) and once the battery voltagefalls below 36V its value quickly drops to 26V For thealkaline batteries operation fails when the voltage acrossboth batteries drops to 18 V that is the minimum operationvoltage of the SEPIC converter In this case the relatedbehavior model is given by (2)

V (119905) = 95591199052 times 10minus10 minus 4275119905 times 10minus5 + 4159

[V 119905 in minutes] (1)

V (119905) = minus21199055 times 10minus15 + 76351199054 times 10minus12 minus 10661199053

times 10minus8+ 713119905

2times 10minus6minus 271119905 times 10

minus3+ 31

[V 119905 in minutes]

(2)

Based on the results on Figure 2 Figure 3 shows themeasured and modeled remaining energy versus batteryvoltage V(119905) for a maximum initial battery charge of2 Ah (120000mAmin) for the LiPo battery and 22 Ah(132000mAmin) for the alkaline one In this paper capacity


LiPo Alkaline

0

5

10

15

Char

ge (m

Am

in)

35 3 25 24Voltage (V)

LiPo expLiPo pol


times104

Figure 3 Experimental and polynomial fit for battery discharge(mAmin) versus battery voltage (V) Initial battery energy 2 Ah(LiPo) 22 Ah (alkaline)

(usually expressed in mAh) and energy (expressed in Joules)are used interchangeably

Then it is possible to determine the battery lifetime byevaluating the remaining charge as a function of the energyconsumed by the full-node electronics By assuming a 15-minute work cycle and taking into account the three differentpower consumption profiles present over cycle (119875

1 1198752 and

1198753) the battery discharge for a work cycle can be estimated

according to

119862 = 1198941int

1199051

1199050

119889119905 + int

1199052

1199051

1198942(119905) 119889119905 + int

1199053

1199052

1198943(119905) 119889119905 (3)

where 1199050-1199051are the limits of the sleep mode timespan where

1198941is set to 15 120583A to ensure a conservative estimation of

the lifetime and partially compensate possible effects notconsidered in the model that could worsen the batteryoperation 119905

1and 1199052define the limits of the measurement

timespan where 1198942= 1198752V(119905) and 119905

2and 119905

3define the

transmission timespan where 1198943= 1198753V(119905) Using this one-

cycle model the evolution in the battery charge is iterativelycalculated over time Figure 4 shows the discharge processfor the two considered battery types connected to a wirelessnode in full operation under our work conditions in viewof different effects ideal discharge (from (3)) consideringmaximum power transmission spikes and considering self-discharge effects In both cases the discharge curve is highlylinear We next analyze power transmission spikes and self-discharge dependences

Discharge spikes have been measured using an AgilentMixed Signal Oscilloscope 9409A connected to an AgilentN2783A current probe Despite these discharge spikesmdashdue to transmission operationsmdashthe operation lifetime isnot significantly affected For this reason the blue and redlines are overlapped in both graphics in Figure 4 In factbattery lifetime can be just slightly increased by selecting thesuitable power transmission level (Table 1) in the maximumtransmission level 4 the power transmission spikes are 1WhighThis value is reduced by 165mWper transmission spikefor each power level reduction Thus assuming an ideal data

LiPo

IdealTransmission peaksSelf-discharge

2000 4000 6000 8000 10000 12000 14000 16000 180000Time (hours)

0

5

10

15

Char

ge (m

Am

in)

times104

(a)

Alkaline LR06 times104


0

5

10

15

Char

ge (m

Am

in)

2000 4000 6000 8000 10000 12000 14000 16000 180000Time (hours)

(b)

Figure 4 (a) LiPo charge evolution (mAmin) versus time (hours)(b) Alkaline battery charge evolution (mAmin) versus time (hours)In both cases ideal (red) and transmission (blue) graphs areoverlapped

transmission process for all data frames (without retries) thelifetime difference between the highest (4) and lowest (0)power level modes is less than 30 hours using the proposedduty cycle

However the repercussion of the self-discharge effectsignificantly influences lifetime especially with rechargeablebatteries In the case of LiPo used in this work it is 20 [27]Thus depending on the application requirements it is veryimportant to make a careful choice of battery Consideringall the main effects for LiPo and the alkaline batteries theequations that represent lifetime are given by

119862 (119905) = minus16447119905 + 120210 [mAmin] (4)

119862 (119905) = minus117585119905 + 134970 [mAmin] (5)

respectively The results obtained using (3) render a lifetimeof around 670 days for a LiPo battery in ideal conditionsHowever by considering the self-discharge effect this valueaccording to (4) falls to 300 days Therefore the use of thisbattery type requires a recharge system if longer operation


time is expected The same estimation is made with thealkaline batteries In this case the ideal lifetime is 500 daysless than the LiPo battery In contrast the lifetime adding theeffect of self-discharge is reduced by only 20 days giving anoperating time of 480 days

5 Coordinator Node Architecture

51 Preliminary Considerations When using a GSMGPRScoordinator there are several options for transmitting datafrom aWSN to a central node First communication technol-ogy must be selected (the Internet mobile phone call shortmessage service etc) Some authors propose the use of Inter-net mobile technology [28 29] However this choice requireshigh infrastructure and communication bandwidth whichmay be impractical in many cases Alternatively the shortmessage service (SMS) requires much less infrastructureand bandwidth making it suitable for such applications Inaddition all providers of mobile phone services provide SMSas a basic service without special subscription Furthermorethis solution can be easily adopted using a portable satellitephone service and can therefore be used worldwide

For an efficient network sensor management the opti-mum maximum network size must be estimated Althoughthe number of motes connected to a single coordinator nodein a WSN is theoretically unlimited energy constraints andcost make it unfeasible For most applications networks con-sisting of 100ndash200nodes connected to a single coordinator areadequate for monitoring large areas If more nodes are to beconnected further coordinator nodes can be added to formsubnetworks to send the information from the new items tothe central node

The data transmission protocol to the central nodeposes an important restriction hardware limitations in GSMmodules will limit the data size to be sent through anSMS The module used in this work is a GM862 from Telit[30 31] whose bias voltage ranges from 34 to 42 V withmaximum current peaks of 2 A Assuming that each sensornode collects the information from 16 sensors and a sensormeasurement is represented by 2 bytes the sensor datatransmitted by a node to its coordinator are stored in 32bytes Also a sensor node sends 7 bytes corresponding tothe time and date information provided by the RTC moduleTherefore including other additional parameters such asnode address the total information forwarded from a sensornode to its coordinator is sent in 50 bytes In additionthe GSM module only transmits printable characters thusthe data bytes received represented as hexadecimal valuesare converted to ASCII characters Then the coordinatornode concatenates the information before the transmissionthrough the GSM According to the data size informationis split into the required number of SMS frames and sentconsecutively Figure 5 shows an SMS complete data frameBecause the data received by the network coordinator couldrequire several concatenated SMSs the first bytes of each SMSidentify the position of the SMS in the full SMSs thread Thenext bytes indicate the length of the total information sentby the network coordinator while the following informationcorresponds to the length of the information sent by one

Position SMS

Total length

Partial length

Frame data 1

Partial length

Frame data 2 EOT

Figure 5 Data collection format sent by the GSM coordinatorthrough Telit GM862 module

node The rest of the SMS frame contains the informationcollected by the corresponding sensor nodes including itsidentification and the sensor readings The last byte is an endof transmission (EOT) code

Two different ways exist to store the data received fromthe sensor nodes in the coordinator before forwarding it tothe central node by using either the memory of the nodemicrocontroller or that of the GSM module Although thedata size is similar in both cases the program required tosend data via SMS from the microcontroller memory issimpler Therefore to simplify the coordinator setup andensure correct operation of the system the data are storeddirectly in the microcontroller memory thereby limiting thenumber of sensor nodes to 100 (lt90 SMS per data transfer)

52 Hardware Design The core architecture of a coordinatornode is the same as that of a sensor node (see Figure 1)However the connection of the sensor device to a GSMmodule requires certain changes in the power managementsystem and the software programmed into the microcon-troller As the value of the GSM bias voltage is higher thanthat of the rest of the node components the 37 V LithiumPolymer battery is selected as the energy source It feeds theGSM module directly whereas the rest of the electronics arepowered through the SEPIC DC-DC converter that regulatesthe voltage to the suitable 33 V

The nodemicrocontroller is responsible for arranging thedata provided by the nodes in the network to be sent in theSMSs starting the GSM module and sending the messagesCommunication between the node microcontroller and theGSMmodule is performed through a serial protocol using aUniversal Asynchronous Receiver-Transmitter (UART) portBecause of the difference in the bias voltage of the nodemicrocontroller (33 V) and the GSM module (from 34 to42 V) signal accommodation is required the microcon-troller receiving port is able to accurately read the data sentby the communications module however the receiving portof the GSM module needs voltage attenuation in the logicsignals for a suitable interface

53 Microcontroller Software Control of the GSM moduleoperation is performed by the coordinator node microcon-troller by means of a Finite State Machine (FSM) ThisFSM controls the system evolution through the differentdefined states from the start pulse of 2 s which turns onthe device until the same pulse is sent for shutdown TheFSM also allows monitoring the correct transmission of theGSM AT commands thus avoiding undesired states of thesystem that could drive the communications module to amalfunction Accordingly the FSM evolves to the next stateonly when the GSM module confirms that the commandhas been successfully processedThe FSM flowchart is shown


Device off

Send SMS

ACK ok

ACK ok

Are data to be No

No

No

No

Yes

Yes

Yes

Yes

sent

Introduce last

Device on

Introduce firstparameter configuration

parameter configuration

ACK ok

Figure 6 Coordinator node 120583C FSM flowchart

in Figure 6 In a measurement operation once the wake-upsignal is given by the transceiver to the node components thenode microcontroller first checks if there are data to be sentto the network controller via SMS In this case the 120583C sendsa wake-up pulse to the GSM device Data transmission fromthe 120583C to the GSM module starts when the measurementsof the corresponding node sensors are fully collected bythe node coordinator Parameters of the GSM connectionto the service provider (eg pin number) are sequentiallysent to the module from the node microcontroller verifyingthe proper reception by the corresponding acknowledgment(ACK) signalsWhen themodule configuration is completedthe microcontroller sends the data to be transmitted to theGSM which then starts the corresponding transmission Ifthe process is successfully executed the GSM module isswitched off until a new data transmission is to be started

Parallel to this supervision method a timer controls theconfirmation timeout of the communications module If thistime is exceeded the system restarts the FSM state andrepeats the process of sending the command This time mustbe long enough to avoid the loss of the acknowledgementresponses sent by the GSM module when a command isproperly received and processed We have found that atimeout between 1 and 2 s is adequate A module reset hasalso been implemented which the microcontroller activates

when the module stops answering however this GSM resetmust be avoided whenever possible as it does not send adisconnection notice to the GSM network

54 Power Management TheGSMmodule power consump-tion constitutes the main reduction factor on the coordinatornode battery It includes a standby mode in which it ispossible to receive SMS However if the time between twoconsecutive data transmissions is set over the minute thepower consumption in this standby mode will be higher thanthe consumption for the process of switching on the moduleconnecting to the GSM network transmitting the data andswitching off Typically in environmental applications thetime communication between two consecutive data transmis-sions is usually of several minutes and thus we chose thissecond option of operation for the GSM module Estimatingthe consumption in an SMS transmission cycle by averagingthe measured consumptions over the different states fromthe module startup to switch off with the same sensors asthe previously reported sensor node the coordinator nodereaches 311mA consumption per cycle due to the presenceof the GSMmodule If the node is powered our LiPo selectedbattery (2000mAh) lifetime is below one month for a6000mAhbattery similar in size to those in the sensor nodesthe lifetime will be below 3 months Therefore an additionalenergy harvesting system (Figure 1) is required to extendit

6 Central Node Architecture

61 GSM Data Receiver Module For the central node wheredata are received consumption is not a main issue In thiscase the module is connected to a PC which subsequentlyprocesses the information Therefore an energy source mustbe available for the PC and hence for the GSMmodule thatcan be switched on permanently

The GSM receiver hardware has been implementedfollowing the manufacturerrsquos specifications and using onlythe relevant features for this application thus minimiz-ing the design requirements The power supply complieswith the requirements of the module an output voltagebetween 34 and 42 V providing a maximum peak current of2 A

62 PC Control A software application has been specificallydeveloped for SMS reception data unthreading and infor-mation processing and displayThis software has been imple-mented using the Graphical User Interface DevelopmentEnvironment (GUIDE) from MATLAB The application caninterpret the information received by the coordinator nodeindependently of the communication technology namely itcan process the data irrespective of whether it is received byIEEE 802154 SMS or WLAN It allows selecting the typeof transmission protocol making it possible to both receiveor send data and change the network settings Moreover theapplication can arrange the data collected by various sensortypes and locations (nodes) using the accurate time stampprovided by the RTC chip found in the hardware of the sensornodes


(a) (b)

(c)

Figure 7 (a) Test sensor network deployment Yellow thumbtacks correspond with outdoor nodes and green thumbtacks with indoor nodesThe red thumbtack is the network coordinator (scale 1 2500 copy 2013 Google) (b) node deployment detail and (c) sensor node architecture

Making the desired selection this interface allows datafrom different nodes of the sensor network to be shown It isalso possible to choose the parameters to be monitored (rel-ative humidity inside or outside the node housing box tem-perature etc) In addition a collection of historic data froma specific node or a group of them can be displayed Finallythe interface allows changing different network parameters(measurement frequency maximum network hops meshretries node reset power level node disconnection etc)thus adapting the network architecture to the evolution of theenvironmental conditions

7 Network Test

To validate the WSN operation a network prototype consist-ing of 11 sensor nodes was deployed for a 6-month periodat the Faculty of Science at the University of Zaragoza Thenode distribution is shown in Figure 7 Green thumbtacksrepresent nodes located inside the building whereas yellowthumbtacks are nodes located on the roof The networkcoordinator is represented by a red thumbtack Table 3 showsthe complete deployment network characteristics and infor-mation on the nodesrsquo spatial distribution (location heightline or non-line of sight power transmission and role) Allmatch the previously performed analysis that is each sensornode includes an NTC thermistor and an analog SenceraH25K5A sensor for monitoring temperature and relativehumidity in the sensor housing box external parameters

are monitored through a Sensirion SHT11 smart relativehumidity and temperature sensor and an IntersemaMS5540Bsmart pressure sensorThe duty cycle is of 900 s (15min) with1746 s awake time (includingmeasurement and transmissionoperations) and power supply is provided by a 37 V LiPobattery through a SEPIC DC-DC TPS61131 with fixed 33 Voutput

Before deploying the network sensor nodes are config-ured to assure a fast network connection maximum trans-mission power level (18 dBm) sleep support mode (whichallows setting all the nodes to sleep mode) and low sleepperiod After deploying the nodes the central node starts asearching process Once a node is associated to the networkits parameters are set to their final values (sleep and wakeperiods maximum network hops etc)

The tested network revealed that the success frametransmission ratio is highly dependent on the suitable con-figuration of the transceiver firmware The inclusion in thetransmitted data of the timing information provided by theRTC included in the node electronics enabled both timearrangement of the received frames and the determination ofthe missing messages As the date of every node transmissionis made known through its RTC the actual date providedby the PC allows organizing the node transmissions intime Also as the central node knows the number of nodesin the network it is possible to discover any unsuccessfultransmissions by comparing the number of transmissionsreceived from other nodes following the time corresponding


Table 3 Deployment characteristics

Sensor node Location High Line of sight Power level RoleCoordinator Indoor Second floor mdash 4 (18 dBm) CoordinatorMote 1 Outdoor Second floor LOSlowast 4 (18 dBm) RouterMote 2 Indoor Fifth floor LOS 4 (18 dBm) End deviceMote 3 Indoor Third floor LOS 4 (18 dBm) RouterMote 4 Indoor Fourth floor NLOSlowastlowast 4 (18 dBm) End deviceMote 5 Indoor Fourth floor NLOS 4 (18 dBm) End deviceMote 6 Outdoor Second floor NLOS 4 (18 dBm) End deviceMote 7 Outdoor Second floor NLOS 4 (18 dBm) RouterMote 8 Outdoor Second floor NLOS 4 (18 dBm) RouterMote 9 Outdoor Second floor NLOS 4 (18 dBm) RouterMote 10 Outdoor Second floor NLOS 4 (18 dBm) End deviceMote 11 Indoor Third floor NLOS 4 (18 dBm) RouterlowastLOS line of sight lowastlowastNLOS non-line of sight

Table 4 Package Error Ratio

Indoor nodes Rate () Outdoor nodes Rate ()LOS (end device) 097 LOS (end device) 098LOS (router) 1 LOS (router) 1NLOS (end device) 236 NLOS (end device) 13NLOS (router) 26 NLOS (router) 14

to the maximum number of transmission retries per node(mesh retries) Table 4 shows the average Package Error Ratio(PER) for the presented configuration with mesh retrieslimited to three and the maximum network hops to six

The environmental monitoring results obtained in node1 over two days are shown in Figure 8 Figure 8(a) cor-responds to the temperature measured in the environment(blue) and inside the container (red) Figure 8(b) is therelative humidity in the environment (blue) and inside thecontainer Finally Figure 8(c) shows the barometric pressureAs shown in the figures the inside relative humidity is almostconstant This indicates that the prototype is adequatelyisolated from the environment which is the desired effectusing an IP65 box container In the sameway the temperatureevolution presents similar values inside and outside thedifference given by the slower inertia presented by the inboxsensor at temperature variations

The suitability of the battery discharge model developedin this work was tested by monitoring the battery energyevolution in nodes 1 6 7 8 and 11 (Figure 9) for onemonth Compared to measurements the developed model(including transmission spikes and battery self-discharge)shows an average error of below 5 for outdoor nodesand 10 for indoor nodes Differences between data anddischarge model are due to not only the model errors but alsothe data sending retries not included in the model In factnote that indoor node batteries show less adequate modelfitting This difference is mainly due to the number of dataframes lost In this case the node retries required to send lostframeswhich are set by themesh retries transceiver parameter

consume more battery energy thereby reducing its operatinglife

Different tests have been carried out to check the effectsof inclusion of new nodes in the network already deployedworking and achieving successful resultsThe network recon-figuration due to the changes in the topology uses a workcycle that is during the first cycle after deployment of the newnodes the system registers their inclusion then data beginsto be sent to the coordinator in the next cycle Additionallythe effects on battery life for a node acting as a routerfor up to 10 sensor nodes have been analyzed Using thepreviously specified network parameters the measurement-transmission energy profile presented by a router is similarto that of a sensor node but presents additional power peakswhose influence on the average power consumption is below15 per routed node Therefore more powerful batteriesmust be considered with mesh topologies where nodes closeto the coordinator are acting as routers for several sensordevices

8 Conclusions

Thispaper presents a general approach to accomplish a simplebut reliable lifetime prediction of a battery-powered wirelesssensor node For this an experimental based methodologyincluding all hardware and software constraints has beendeveloped using a custom low-power low-cost reliable WSNplatform Thanks to it prior to deployment in the field it ispossible to estimate the applicationrsquos lifetime while adoptingstrategies (adjustment of parameters such as power transmis-sion level and duty cycle) to increase the network lifetimeThe analysis of different kind of batteries allows comparingthe best selection for each network deployment dependingon the application Finally through a real deployment wehave been able to test the complete system validating thelifetime predicting model while the WSN has proved to berobust for environmental monitoring One shortcoming thathas emerged is that due to the high energy consumption ofthe GSM modules included in the coordinator units theiroperating life is rather restricted Thus present studies are


InsideOutside

10 20 30 40 50 600Time (hours)

10

20

30

40

50Te

mpe

ratu

re (∘

C)

(a)

0 10 20 30 40 50Time (hours)

20

30

40

50

60

70

Relat

ive h

umid

ity (

)

InsideOutside

(b)

10 20 30 40 500Time (hours)

988

990

992

994

996

998

Pres

sure

(mba

r)

(c)

Figure 8 Data acquisition for node 1 inside (red) and outside (blue) the box (a) temperature (b) relative humidity and (c) barometricpressure

LiPo

Self-discharge modelReal measure indoor nodesReal measure outdoor nodes

0

5

10

15

Char

ge (m

Am

in)

1000 2000 3000 4000 5000 6000 7000 80000Time (hours)

times104

Figure 9 Lifetime with ideal self-discharge model comparing withlifetime measures for indoor and outdoor nodes

oriented towards the development of an efficient energyharvesting system for the coordinator node in order toincrease network autonomy

Conflict of Interests

The authors declare that the work presented in this paper hasno conflict of interests

Acknowledgment

This work was supported in part by MICINN-FEDER(TEC2012-30802 TEC2014-52840-R and TEC2015-65750-R)

References

[1] P Marino F P Fontan M A Domınguez and S OteroldquoAn experimental ad-hoc WSN for the instrumentation ofbiological modelsrdquo IEEE Transactions on Instrumentation andMeasurement vol 59 no 11 pp 2936ndash2948 2010

[2] J Gutierrez J F Villa-Medina A Nieto-Garibay and M APorta-Gandara ldquoAutomated irrigation system using a wirelesssensor network and GPRS modulerdquo IEEE Transactions onInstrumentation and Measurement vol 63 no 1 pp 166ndash1762014

[3] O Casas M Lopez M Quılez et al ldquoWireless sensor networkfor smart composting monitoring and controlrdquo Measurementvol 47 no 1 pp 483ndash495 2014

[4] L Buttyan D Gessner A Hessler and P LangendoerferldquoApplication of wireless sensor networks in critical infrastruc-ture protection challenges and design optionsrdquo IEEE WirelessCommunications vol 17 no 5 pp 44ndash49 2010

[5] J Lloret M Garcia D Bri and S Sendra ldquoA wireless sensornetwork deployment for rural and forest fire detection andverificationrdquo Sensors vol 9 no 11 pp 8722ndash8747 2009

[6] A Flammini D Marioli E Sisinni and A Taroni ldquoEnviron-mental telemonitoring a flexible GSM-DECT-based solutionrdquo


IEEE Transactions on Instrumentation and Measurement vol56 no 5 pp 1688ndash1693 2007

[7] K Liolis S Pantazis V Gennatos S Costicoglou and IAndrikopoulos ldquoAn automated fire detection and alertingapplication based on satellite and wireless communicationsrdquo inProceedings of the 5th Advanced Satellite Multimedia SystemsConference and the 11th Signal Processing for Space Communi-cations Workshop (SPSC rsquo10) pp 270ndash277 IEEE Cagliari ItalySeptember 2010

[8] X Cao J Chen Y Zhang and Y Sun ldquoDevelopment ofan integrated wireless sensor network micro-environmentalmonitoring systemrdquo ISA Transactions vol 47 no 3 pp 247ndash255 2008

[9] S He J Chen and Y Sun ldquoCoverage and connectivity in duty-cycled wireless sensor networks for event monitoringrdquo IEEETransactions on Parallel and Distributed Systems vol 23 no 3pp 475ndash482 2012

[10] D Antolın A Bayo N Medrano B Calvo and S CelmaldquoData management of wireless sensor network implementedin rural environments with SMS communication protocolrdquo inProceedings of the IEEE Sensors Conference pp 2002ndash2005IEEE Limerick Ireland October 2011

[11] D Antolın A Bayo N Medrano B Calvo and S CelmaldquoWubiNet a flexible WSN for applications in environmentalmonitoringrdquo in Proceedings of the IEEE International Instru-mentation and Measurement Technology Conference (I2MTCrsquo12) pp 2608ndash2611 IEEE Graz Austria May 2012

[12] J MMora-Merchan D F Larios J Barbancho F J Molina J LSevillano and C Leon ldquomTOSSIM a simulator that estimatesbattery lifetime in wireless sensor networksrdquo Simulation Mod-elling Practice and Theory vol 31 pp 39ndash51 2013

[13] W Dron S Duquennoy T Voigt K Hachicha and P GardaldquoAn emulation-basedmethod for lifetime estimation of wirelesssensor networksrdquo in Proceedings of the IEEE InternationalConference onDistributedComputing in Sensor Systems (DCOSSrsquo14) pp 241ndash248 IEEE Marina Del Rey Calif USA May 2014

[14] F Kerasiotis A Prayati C Antonopoulos C Koulamas and GPapadopoulos ldquoBattery lifetime prediction model for a WSNplatformrdquo in Proceedings of the 4th International Conference onSensor Technologies and Applications (SENSORCOMM rsquo10) pp525ndash530 IEEE Venice Italy July 2010

[15] H A Nguyen A Forster D Puccinelli and S GiordanoldquoSensor node lifetime an experimental studyrdquo in Proceedings ofthe 9th IEEE International Conference on Pervasive Computingand Communications Workshops (PERCOMWorkshops rsquo11) pp202ndash207 Seattle Wash USA March 2011

[16] DAntolinNMedrano andBCalvo ldquoAnalysis of the operatinglife for battery-operated wireless sensor nodesrdquo in Proceedingsof the 39th Annual Conference of the IEEE Industrial ElectronicsSociety (IECON rsquo13) pp 3883ndash3886 IEEE Vienna AustriaNovember 2013

[17] XBEEXBEE-PRO DigiMesh 24 OEM RF Modules DatasheetDigi International 2008

[18] S FarahaniZigbeeWireless Networks and Transceivers Newnes2006

[19] RN-XV Data Sheet RN-XV-DS v03 2011 httpwwwroving-networkscom

[20] ZigBee Alliance ldquoZigBee specificationrdquo Document 053474r172008

[21] ZigBee Alliance ldquoZigBee-2007 layer PICS and stack profilesZigBeerdquo Document 08006r03 2008

[22] R Cheour K Lahmar andM Abid ldquoEvolution of wireless sen-sor networks and necessity of power management techniquerdquoinProceedings of the Faible Tension Faible Consommation (FTFCrsquo11) pp 75ndash78 IEEE Marrakech Morocco June 2011

[23] U Kulau F Busching and L Wolf ldquoA nodersquos life increasingWSN lifetime by dynamic voltage scalingrdquo in Proceedings ofthe 9th IEEE International Conference onDistributed Computingin Sensor Systems (DCoSS rsquo13) pp 241ndash248 IEEE CambridgeMass USA May 2013

[24] V-THoangN Julien andP Berruet ldquoIncreasing the autonomyof wireless sensor node by effective use of both DPM andDVFSmethodsrdquo in Proceedings of the IEEE Faible Tension FaibleConsommation (FTFC rsquo13) pp 1ndash4 IEEE Paris France June2013

[25] S Park A Savvides and M B Srivastava ldquoBattery capacitymeasurement and analysis using lithium coin cell batteryrdquo inProceedings of the International Symposium on Low Electronicsand Design (ISLPED rsquo01) pp 382ndash387 San Francisco CalifUSA August 2001

[26] R Rao S Vrudhula and D Rakhmatov ldquoBattery modeling forenergy-aware systemdesignrdquo IEEEComputer vol 36 no 12 pp77ndash87 2003

[27] Data Sheet ldquoLi-polymer battery packsrdquo Specification Type585460 2000mAh 2006

[28] A R Al-Ali I Zualkernan and F Aloul ldquoA mobile GPRS-sensors array for air pollution monitoringrdquo IEEE Sensors Jour-nal vol 10 no 10 pp 1666ndash1671 2010

[29] C K Harnett ldquoOpen wireless sensor network telemetry plat-form for mobile phonesrdquo IEEE Sensors Journal vol 10 no 6pp 1083ndash1084 2010

[30] TelitTelitModules SoftwareUserGuide 1vv0300784Rev3 TelitRome Italy 2010

[31] GM862 FamilyHardwareUserGuide 2009 httpwwwround-solutionscomtechdocsGM862GM862-QUAD PY HardwareUser Guide r1pdf

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Active and Passive Electronic Components

Control Scienceand Engineering

Journal of



RotatingMachinery


Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Submit your manuscripts athttpwwwhindawicom

VLSI Design



Shock and Vibration


Civil EngineeringAdvances in

Acoustics and VibrationAdvances in



Electrical and Computer Engineering

Journal of

Advances inOptoElectronics


Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of


Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014


Chemical EngineeringInternational Journal of Antennas and

Propagation




Navigation and Observation



DistributedSensor Networks



received and properly represented and processed Section 7shows the network test performed to evaluate both thecomplete systemoperation and the battery lifetime predictionmodel Finally Section 8 draws the conclusions

2 State of the Art

Wireless sensor networks (WSN) have become a key environ-mental technology with applications in intelligent agriculture[1 2] diffuse greenhouse gas emission detection smartcompostingmonitoring and control [3] prevention and earlydetection of forest fires or protection of critical infrastructure[4] among others Nevertheless currently available commer-cialWSN generic platforms need considerable improvementsto meet the stringent requirements desirable for real opennature deployments mainly in terms of complexity costlifetime and maintenance As a result the developmentof custom nodes to replace commercial ones to enhanceperformance is gaining strength In this attempt most resultsremain at laboratory test level being rather hazardous to findimplementations validated in an actual deployment scenarioIn fact focusing on the implementation and test of completefull-custom WSN dedicated to monitoring environmentalparameters only a few solutions can be found in the openliterature [5ndash9] However the use of sophisticated and power-consuming sensor devices communication technologies andprotocols results in cost per node energy requirementstransmit data rate and required infrastructure which ren-der these solutions unsuitable for battery-operated nodesdeployed in natural areas [5ndash7] In [5] IP video camerasare used for smoke and fire detection which require highcomputational power increasing the cost of both softwareand hardware in [6] a Digital Enhanced Cordless Telecom-munications (DECT) subnet is connected to a Global SystemMobile (GSM) coordinator which communicates with a PCcontrol center in [7] a combination is proposed betweenZigBee Wi-Fi and satellite communications Solutions in[8 9] focus the power optimization on efficient data commu-nications in [8] a wireless sensor network for microenviron-mental application achieving efficient node data transmissionis presented showing a web-based tool for monitoring thedeployed network controlling data traffic data processingand presentation and remote node control and monitoringin [9] the authors propose a network with a duty cyclemanagement able to address stability in network connectionsand coverage at the same time By synchronizing disjointsensor subsets the system can detect events which could beomitted by classical communication architectures achievinga tradeoff between power consumption and event detectionThis is useful in applications where event lost are not allowedas defense tasks

In this scenario with power node size and cost per nodebeing the restrictive conditions specific novel designs aredesirable to come across all the main challenges encounteredin environmental projects Accordingly a custom low-powerlow-cost reliable WSN platform has been developed byauthors for use in remote areas which can be easily adapted toother monitoring systems Based on a first prototype [10 11]a number of modifications have been included in order to

reach an enhanced WSN implementation mainly concern-ing the following (i) the reduction of power consumptionat both software and hardware levels (ii) communicationdevelopments (different communication protocols such asGSMGPRS and IEEE 802154 can be used adding moreversatility to the proposal) and (iii) time synchronizationstability (a real-time clock fed by a supercapacitor is addedfor data integrity purposes)

Making use of this WSN architecture this paper isfocused on finding a reliable estimation of the lifetime thisis an essential information before deployment especially insystems targeting remote environmental monitoring whichare designed for long-term unattended operation Despiteits importance this key metric is usually parameterizedthrough predictions that often use high level descriptionsandor ideal or basic battery models simplistic assump-tionswhich overestimate lifetime Recent works progressivelyinclude more complete analysis but still exhibit limitationssince they are mainly based on emulation methods usuallydeveloped for particular platforms which ultimately fail toconsider real effects The simulator mTOSSIM [12] evaluateslifetime inWSN but is restricted to platforms embedding theTinyOS operative system power consumption is estimated atmicrocontroller instruction level varying different networkparameters (duty cycle transmission frequency and idle orsleep transceiver modes) and using battery models basedon technical specifications Similarly [13] is an emulated-based method using MSPSim which incorporates batterymodels with nonideal effects and a low-level description ofthe node hardware In [14] authors consider the averagecurrent consumption over the different operation processes(transmission reception and idle or sleep states) and the cor-responding consumption of the different platform elementsin these states but the battery model does not consider theinfluence of self-discharge temperature and recovery effectsover the battery capacity In addition sensors are not includedand the communication is only made between an end deviceand the coordinator which are not real operation conditionsFollowing an experimental approach in [15] authors useLR06 batteries of different brands and capacity in order toevaluate their influence on a node lifetime however thestudy is based on a node in listen state considering neithertransmission nor sensormeasurement processes and thus notdescribing a real network behavior

From the above discussion it is clear that an accuratelifespan prediction model must consider the energy con-sumption of the sensor nodes under their different operationmodes in correlation with the real battery characteristics andthe voltage regulation system that is including all hardwareand software constraints To do so we consider that anexperimental methodology is the best suitable choice Thisis the goal of this paper to attain a complete and practicallifetimemodel based on the remaining energy battery level asstandardized metric through an experimental methodologyusing real nodes under real operating conditions from themeasured power consumption profile and the measured bat-tery discharge pattern the battery discharge rate is inferredPreliminary results were introduced in [16] for a LiPo batteryThis work deepens and completes the study considering two


GSMGPRS

RTC

XBEERF

transceiver

Analog sensors

SEPICDCDC

converter

Digital sensors

Battery


DCDC converter


120583C



3 WSN Description



















4 Lifetime Model





3) 1316 s)


2= 220mW and 119875

3= 235mW


1= 12 120583A















LiPo expLiPo pol


LiPo

Alkaline

1

2

3

4

5

Volta

ge (V

)

05 1 15 2 250Time (min) times10

4









minus3+ 31


(2)



LiPo Alkaline

0

5

10

15

Char

ge (m

Am

in)

35 3 25 24Voltage (V)

LiPo expLiPo pol


times104




1 1198752 and


according to

119862 = 1198941int

1199051

1199050

119889119905 + int

1199052

1199051

1198942(119905) 119889119905 + int

1199053

1199052

1198943(119905) 119889119905 (3)






2and 119905

3define the




LiPo


2000 4000 6000 8000 10000 12000 14000 16000 180000Time (hours)

0

5

10

15

Char

ge (m

Am

in)

times104

(a)



0

5

10

15

Char

ge (m

Am

in)

2000 4000 6000 8000 10000 12000 14000 16000 180000Time (hours)

(b)




119862 (119905) = minus16447119905 + 120210 [mAmin] (4)

119862 (119905) = minus117585119905 + 134970 [mAmin] (5)








Position SMS

Total length

Partial length

Frame data 1

Partial length

Frame data 2 EOT








Device off

Send SMS

ACK ok

ACK ok

Are data to be No

No

No

No

Yes

Yes

Yes

Yes

sent

Introduce last

Device on



ACK ok











(a) (b)

(c)



7 Network Test















8 Conclusions



InsideOutside

10 20 30 40 50 600Time (hours)

10

20

30

40

50Te

mpe

ratu

re (∘

C)

(a)

0 10 20 30 40 50Time (hours)

20

30

40

50

60

70

Relat

ive h

umid

ity (

)

InsideOutside

(b)

10 20 30 40 500Time (hours)

988

990

992

994

996

998

Pres

sure

(mba

r)

(c)


LiPo


0

5

10

15

Char

ge (m

Am

in)

1000 2000 3000 4000 5000 6000 7000 80000Time (hours)

times104





Acknowledgment


References




































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










GSMGPRS

RTC

XBEERF

transceiver

Analog sensors

SEPICDCDC

converter

Digital sensors

Battery


DCDC converter


120583C



3 WSN Description



















4 Lifetime Model





3) 1316 s)


2= 220mW and 119875

3= 235mW


1= 12 120583A















LiPo expLiPo pol


LiPo

Alkaline

1

2

3

4

5

Volta

ge (V

)

05 1 15 2 250Time (min) times10

4









minus3+ 31


(2)



LiPo Alkaline

0

5

10

15

Char

ge (m

Am

in)

35 3 25 24Voltage (V)

LiPo expLiPo pol


times104




1 1198752 and


according to

119862 = 1198941int

1199051

1199050

119889119905 + int

1199052

1199051

1198942(119905) 119889119905 + int

1199053

1199052

1198943(119905) 119889119905 (3)






2and 119905

3define the




LiPo


2000 4000 6000 8000 10000 12000 14000 16000 180000Time (hours)

0

5

10

15

Char

ge (m

Am

in)

times104

(a)



0

5

10

15

Char

ge (m

Am

in)

2000 4000 6000 8000 10000 12000 14000 16000 180000Time (hours)

(b)




119862 (119905) = minus16447119905 + 120210 [mAmin] (4)

119862 (119905) = minus117585119905 + 134970 [mAmin] (5)








Position SMS

Total length

Partial length

Frame data 1

Partial length

Frame data 2 EOT








Device off

Send SMS

ACK ok

ACK ok

Are data to be No

No

No

No

Yes

Yes

Yes

Yes

sent

Introduce last

Device on



ACK ok











(a) (b)

(c)



7 Network Test















8 Conclusions



InsideOutside

10 20 30 40 50 600Time (hours)

10

20

30

40

50Te

mpe

ratu

re (∘

C)

(a)

0 10 20 30 40 50Time (hours)

20

30

40

50

60

70

Relat

ive h

umid

ity (

)

InsideOutside

(b)

10 20 30 40 500Time (hours)

988

990

992

994

996

998

Pres

sure

(mba

r)

(c)


LiPo


0

5

10

15

Char

ge (m

Am

in)

1000 2000 3000 4000 5000 6000 7000 80000Time (hours)

times104





Acknowledgment


References




































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation
















4 Lifetime Model





3) 1316 s)


2= 220mW and 119875

3= 235mW


1= 12 120583A















LiPo expLiPo pol


LiPo

Alkaline

1

2

3

4

5

Volta

ge (V

)

05 1 15 2 250Time (min) times10

4









minus3+ 31


(2)



LiPo Alkaline

0

5

10

15

Char

ge (m

Am

in)

35 3 25 24Voltage (V)

LiPo expLiPo pol


times104




1 1198752 and


according to

119862 = 1198941int

1199051

1199050

119889119905 + int

1199052

1199051

1198942(119905) 119889119905 + int

1199053

1199052

1198943(119905) 119889119905 (3)






2and 119905

3define the




LiPo


2000 4000 6000 8000 10000 12000 14000 16000 180000Time (hours)

0

5

10

15

Char

ge (m

Am

in)

times104

(a)



0

5

10

15

Char

ge (m

Am

in)

2000 4000 6000 8000 10000 12000 14000 16000 180000Time (hours)

(b)




119862 (119905) = minus16447119905 + 120210 [mAmin] (4)

119862 (119905) = minus117585119905 + 134970 [mAmin] (5)








Position SMS

Total length

Partial length

Frame data 1

Partial length

Frame data 2 EOT








Device off

Send SMS

ACK ok

ACK ok

Are data to be No

No

No

No

Yes

Yes

Yes

Yes

sent

Introduce last

Device on



ACK ok











(a) (b)

(c)



7 Network Test















8 Conclusions



InsideOutside

10 20 30 40 50 600Time (hours)

10

20

30

40

50Te

mpe

ratu

re (∘

C)

(a)

0 10 20 30 40 50Time (hours)

20

30

40

50

60

70

Relat

ive h

umid

ity (

)

InsideOutside

(b)

10 20 30 40 500Time (hours)

988

990

992

994

996

998

Pres

sure

(mba

r)

(c)


LiPo


0

5

10

15

Char

ge (m

Am

in)

1000 2000 3000 4000 5000 6000 7000 80000Time (hours)

times104





Acknowledgment


References




































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation






















LiPo expLiPo pol


LiPo

Alkaline

1

2

3

4

5

Volta

ge (V

)

05 1 15 2 250Time (min) times10

4









minus3+ 31


(2)



LiPo Alkaline

0

5

10

15

Char

ge (m

Am

in)

35 3 25 24Voltage (V)

LiPo expLiPo pol


times104




1 1198752 and


according to

119862 = 1198941int

1199051

1199050

119889119905 + int

1199052

1199051

1198942(119905) 119889119905 + int

1199053

1199052

1198943(119905) 119889119905 (3)






2and 119905

3define the




LiPo


2000 4000 6000 8000 10000 12000 14000 16000 180000Time (hours)

0

5

10

15

Char

ge (m

Am

in)

times104

(a)



0

5

10

15

Char

ge (m

Am

in)

2000 4000 6000 8000 10000 12000 14000 16000 180000Time (hours)

(b)




119862 (119905) = minus16447119905 + 120210 [mAmin] (4)

119862 (119905) = minus117585119905 + 134970 [mAmin] (5)








Position SMS

Total length

Partial length

Frame data 1

Partial length

Frame data 2 EOT








Device off

Send SMS

ACK ok

ACK ok

Are data to be No

No

No

No

Yes

Yes

Yes

Yes

sent

Introduce last

Device on



ACK ok











(a) (b)

(c)



7 Network Test















8 Conclusions



InsideOutside

10 20 30 40 50 600Time (hours)

10

20

30

40

50Te

mpe

ratu

re (∘

C)

(a)

0 10 20 30 40 50Time (hours)

20

30

40

50

60

70

Relat

ive h

umid

ity (

)

InsideOutside

(b)

10 20 30 40 500Time (hours)

988

990

992

994

996

998

Pres

sure

(mba

r)

(c)


LiPo


0

5

10

15

Char

ge (m

Am

in)

1000 2000 3000 4000 5000 6000 7000 80000Time (hours)

times104





Acknowledgment


References




































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










LiPo Alkaline

0

5

10

15

Char

ge (m

Am

in)

35 3 25 24Voltage (V)

LiPo expLiPo pol


times104




1 1198752 and


according to

119862 = 1198941int

1199051

1199050

119889119905 + int

1199052

1199051

1198942(119905) 119889119905 + int

1199053

1199052

1198943(119905) 119889119905 (3)






2and 119905

3define the




LiPo


2000 4000 6000 8000 10000 12000 14000 16000 180000Time (hours)

0

5

10

15

Char

ge (m

Am

in)

times104

(a)



0

5

10

15

Char

ge (m

Am

in)

2000 4000 6000 8000 10000 12000 14000 16000 180000Time (hours)

(b)




119862 (119905) = minus16447119905 + 120210 [mAmin] (4)

119862 (119905) = minus117585119905 + 134970 [mAmin] (5)








Position SMS

Total length

Partial length

Frame data 1

Partial length

Frame data 2 EOT








Device off

Send SMS

ACK ok

ACK ok

Are data to be No

No

No

No

Yes

Yes

Yes

Yes

sent

Introduce last

Device on



ACK ok











(a) (b)

(c)



7 Network Test















8 Conclusions



InsideOutside

10 20 30 40 50 600Time (hours)

10

20

30

40

50Te

mpe

ratu

re (∘

C)

(a)

0 10 20 30 40 50Time (hours)

20

30

40

50

60

70

Relat

ive h

umid

ity (

)

InsideOutside

(b)

10 20 30 40 500Time (hours)

988

990

992

994

996

998

Pres

sure

(mba

r)

(c)


LiPo


0

5

10

15

Char

ge (m

Am

in)

1000 2000 3000 4000 5000 6000 7000 80000Time (hours)

times104





Acknowledgment


References




































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation















Position SMS

Total length

Partial length

Frame data 1

Partial length

Frame data 2 EOT








Device off

Send SMS

ACK ok

ACK ok

Are data to be No

No

No

No

Yes

Yes

Yes

Yes

sent

Introduce last

Device on



ACK ok











(a) (b)

(c)



7 Network Test















8 Conclusions



InsideOutside

10 20 30 40 50 600Time (hours)

10

20

30

40

50Te

mpe

ratu

re (∘

C)

(a)

0 10 20 30 40 50Time (hours)

20

30

40

50

60

70

Relat

ive h

umid

ity (

)

InsideOutside

(b)

10 20 30 40 500Time (hours)

988

990

992

994

996

998

Pres

sure

(mba

r)

(c)


LiPo


0

5

10

15

Char

ge (m

Am

in)

1000 2000 3000 4000 5000 6000 7000 80000Time (hours)

times104





Acknowledgment


References




































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










Device off

Send SMS

ACK ok

ACK ok

Are data to be No

No

No

No

Yes

Yes

Yes

Yes

sent

Introduce last

Device on



ACK ok











(a) (b)

(c)



7 Network Test















8 Conclusions



InsideOutside

10 20 30 40 50 600Time (hours)

10

20

30

40

50Te

mpe

ratu

re (∘

C)

(a)

0 10 20 30 40 50Time (hours)

20

30

40

50

60

70

Relat

ive h

umid

ity (

)

InsideOutside

(b)

10 20 30 40 500Time (hours)

988

990

992

994

996

998

Pres

sure

(mba

r)

(c)


LiPo


0

5

10

15

Char

ge (m

Am

in)

1000 2000 3000 4000 5000 6000 7000 80000Time (hours)

times104





Acknowledgment


References




































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










(a) (b)

(c)



7 Network Test















8 Conclusions



InsideOutside

10 20 30 40 50 600Time (hours)

10

20

30

40

50Te

mpe

ratu

re (∘

C)

(a)

0 10 20 30 40 50Time (hours)

20

30

40

50

60

70

Relat

ive h

umid

ity (

)

InsideOutside

(b)

10 20 30 40 500Time (hours)

988

990

992

994

996

998

Pres

sure

(mba

r)

(c)


LiPo


0

5

10

15

Char

ge (m

Am

in)

1000 2000 3000 4000 5000 6000 7000 80000Time (hours)

times104





Acknowledgment


References




































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation



















8 Conclusions



InsideOutside

10 20 30 40 50 600Time (hours)

10

20

30

40

50Te

mpe

ratu

re (∘

C)

(a)

0 10 20 30 40 50Time (hours)

20

30

40

50

60

70

Relat

ive h

umid

ity (

)

InsideOutside

(b)

10 20 30 40 500Time (hours)

988

990

992

994

996

998

Pres

sure

(mba

r)

(c)


LiPo


0

5

10

15

Char

ge (m

Am

in)

1000 2000 3000 4000 5000 6000 7000 80000Time (hours)

times104





Acknowledgment


References




































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










InsideOutside

10 20 30 40 50 600Time (hours)

10

20

30

40

50Te

mpe

ratu

re (∘

C)

(a)

0 10 20 30 40 50Time (hours)

20

30

40

50

60

70

Relat

ive h

umid

ity (

)

InsideOutside

(b)

10 20 30 40 500Time (hours)

988

990

992

994

996

998

Pres

sure

(mba

r)

(c)


LiPo


0

5

10

15

Char

ge (m

Am

in)

1000 2000 3000 4000 5000 6000 7000 80000Time (hours)

times104





Acknowledgment


References




































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation






































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation









Research Article Reliable Lifespan Evaluation of a Remote ...

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