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Chapter 2

2012 Pinto et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Power Optimization for Wireless Sensor Networks

A.R. Pinto, L. Bolzani Poehls, C. Montez and F. Vargas

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/50603

1.IntroductionWireless Sensor Network (WSN) is a denomination that covers a lot of variations incompositionsanddeployment.Atypicalsensornetworkconsistsofalargenumberoflowcost, low power distributed devices, called nodes, deployed in the environment beingsensed and controlled (Stankovic et al., 2003). In other words, this kind of network iscomposedofahugenumberoftinynodesabletocommunicatewitheachotherthatcanbeused tomonitorhazardousand inaccessibleareas.Thus,eachnode consistsofprocessor,memory,wirelessantenna,batteryand thesensor itself.Nodescansensescalars from theenvironment such as temperature, acoustic and light, butmay alsoprocess and transmitthem by radio. The network can be classified as homogeneous or heterogeneous,whichwouldmean thatsomespecificnodespresentspecialhardwareorsoftwareconfiguration,but even in homogeneous networks, to collect, store and process data from the WSNsnodes,aspecialnode,calledBaseStation(BS),isnecessary.Most of the currently adopted technologies forWSNs are based on lowcost processors,resultinginlimitedenergybudgetandrestrictedmemoryspace.Inmanyapplications,itisexpected that the sensor node last for a long time because in most of the cases thesenetworksareused inremoteareasandrechargingand/orreplacingpowersupplyunits isconsidereddifficultorprohibitiveduetohazardousandinaccessibleplaceswheretheyaresupposed to operate. Further, due to the availability of cheap hardware and variouspossibilitiesfortheradiocommunicationfrequency,numeroustopologiesforWSNcanbeadopted(Akyildiz,2002;Ilyas&Mahgoub,2005;Oliver&Fohler,2010).As previously mentioned, the nodes in these networks are usually inexpensive andthereforeWSNsmaybecomposedofahugenumberofsensornodes,whichthemselvesaredeployed inside and/or around the phenomenon that one desires to monitor. Notnecessarily the sensor nodes geographic position is previously known in all adoptions,

Wireless Sensor Networks Technology and Applications 24

because, when the nodes need to work in hazardous or inaccessible areas, it might beimpossibletoavoidtheirrandomdeployment.Thus, ifthistypeofapplication isadopted,the use protocols and approaches that can selforganize and selfoptimize energyconsumptionofa largenumberofnodes thatcooperate inorder toachieveaglobalgoal,becomes necessary (Akyildiz, 2002). Summarizing, WSN are desired to present thefollowingcharacteristics(Ilyas&Mahgoub,2005): Selforganization; Shortrangewirelesscommunicationandmultihoprouting; LargenumberofnodesandcooperativeeffortsbetweenWSNnodes; DifferentWSNtopologies,whichfrequentlychangeduetobatterydepletionandnode

faults; Constrainedresourcessuchasenergybudget,processingandmemory.Thecharacteristicsaboveand thecapacityof interactionwith theenvironmentdistinguishWSNs from other adhoc wireless networks. WSNs, due to the scarce software andhardware resources, are application oriented; thus, WSN applications are developedfocusing on a specific problems solution. The collaborative efforts between nodes arenecessarytothecorrectlyusetheWSNsresources,thiseffortcanalso increasetheWSNslifetime(Stankovicetal.,2003;Hadim&Mohamed,2006).Itisimportanttohighlightthatthestrategyofdeployingalargenumberofunreliablenodespresentsadvantageswhencomparedwithdeployingfewexpensivebutveryreliablenodes(Ilyas&Mahgoub,2005).Theseadvantagesarelistedbelow: Largerspatialresolution; Higherfaulttolerantdegreeachievedthroughoutdistributedtechniques; Moreuniformcoverage; Easeofdeployment; Reducedenergyconsumption; Increasednetworklifetime.WSNs present a high degree of environmental interaction, depending on where sensornodesaredeployed, implicitandexplicit temporal restrictionsapply. In thiscontext,datafreshnessisanimportantconceptthatdictateshowlongasensedscalarcanbeconsideredusefulandwhenitcanbediscarded.Inthefollowingexample,theinformationgatheredbya securityapplicationbasedonWSN technology, identifiesanypersonwhoenters intoacertain area of the building in a certain time, alldata that exceeds this time limit, isnotuseful.Despite having time constraints, due to the high node density, nondeterminism,noiseandconstrainedWSNresources,itbecomesextremelydifficulttoguaranteerealtimeproperties(Stankovicetal.,2003).Therefore,thesespecialconstraints imposethatnoharddeadlinesareconsideredforWSNapplication.Consequently,criticalrealtimesystemsareoutofscopeforthiskindofnetwork(Koubaaetal.,2009;Oliver&Fohler,2010).WSNcanbeconsideredan innovativeparadigm,whichpermits theemergenceofseveralnewmonitoringapplications,butintroduceschallengesintrinsictothistechnology.Someof

Power Optimization for Wireless Sensor Networks 25

thesechallengesare(Akyildiz,2002;Stankovicetal.,2003;Molla&Ahamed,2006;Yicketal.,2008;Ilyas&Mahgoub,2005): Paradigm change: WSN are basically deployed in order to collect scalars from the

environment and support control applications.TheWSN applicationmust sense theenvironmentand,sometimes,actinonewayortheotherontheenvironment.Thus,itisconsidered critical to obtain a cooperative behaviour of thousands of sensor nodeswherethedatafromjustonenodemaynotbeimportant.Thesensornodesdonothaveapermanentidentificationaddress,duetothefactthatgenerallymessagesarenotsenttoaspecificnodebuttoaspaceorarea(basedonthemessagescontent).Userscanbeinterested in the information about a specificmonitoring area, thus the sensed datafromaspecificnodemaynotbe important,representing thedatacentricapproach inWSNs.Theneedofphysicalenvironmentinteractionalsoimpliesimportantdifferencesbetween WSNs and ordinary adhoc networks, thus classical distributed systemtechniques are not applicable to WSNs. Realtime requirements, noise, high faultoccurrence and nondeterminism also impose a new group of approaches thatmustdealwiththeseconstraints(Molla&Ahamed,2006).

ResourceConstraints:Asnotedabove,WSNsfacesevereresourceconstraints.Themainresource constraints are: limited energy budget, restricted CPU clock, restrictedmemoryaswellasnetworkbandwidth.Thesecharacteristicsimposetheapplicationofnewsolutions.ThefactthatWSNtopologiesarecomposedofahugenumberofnodesrepresentsanew issue thathadnotbeen considered in simpleadhocnetworks.Forinstance, tradeoff approaches that aim at guaranteeing energetic economy and realtimecharacteristicsbecamenecessary(Yicketal.,2008).

Unpredictability:TherearemanyuncertaintiesthatmayaffectaWSN.Firstly,WSNaredeployed in environments with multiple uncontrollable events. Secondly, wirelesscommunication is sensitive to noise; data lost due to radio interference and severalphysical errors is common to networks employed in harsh environments. Thirdly,nodesarenot individuallydependable.Further, it isnotalwayspossible toproperlycalibrate the nodes before employment; routing structures such as paths and theconnectivity can be dynamically added or excluded during the WSN time offunctioning.Theadditionor removalofnodesmightbenecessarydue topermanentfaultsorbatterydepletion.Additionally,theenergylevelinsomenodecansignificantlyvaryevenduringtheinitialdeployment.Last,thenodesmightbephysicallyremovedduetoenvironmentcausesorintentionalcontrolling,thusanetworkrestructuringmustwouldbenecessary.

Self*: One of the biggest challenges is to create the WSNs vision in the networkapplicationlayer.DuetothefactthatWSNsaredeployedtooperatewithfewornonehuman intervention, self* characteristics like selforganization, selfoptimization andselfhealing become necessary (Huebscher & McCann, 2008; Oliver & Fohler, 2010).These characteristicsareeasily listedas challenge,howeverareextremelydifficult toachieve.


Highscale/density:ThereareseveralWSNapproaches thatconsidera largenumberofnodes in order to overcome hardware or software faults, thus there is a minimumnumberofnodesthatisnecessarytoguaranteetheWSNsservice.Themainchallengesinclude: theprocessingof this largenumberofgenerateddata, theassurance that theparticular WSN requires the minimum desirable density, and the development ofsolutionsthatrequirethelowestdensityandenergyconsumptioninordertomaximizetheWSNs lifetime.AWSNbasedon a largenumberofnodes that aredeployed inlargeareas isconsidereda largescalesystem.Due to itscharacteristics, thesesystemsaresubjecttofaults,noise,whichsometimescanbecausedbytheWSNitself,andotheruncertainties.Moreover, when a WSN is deployed, itmight be selfoperational andpresent selfmaintenance,due to the fact thathuman intervention is sometimes veryexpensive or even impossible. Therefore, all these characteristics impose severalconflictinggoals.Thesechallenges canbe increasedby the technology scaling,wheretheindustrysminimizationtendency(Akyildizetal.,2008).

Realtime: WSNs operate in the real world, thus realtime features are necessary toguaranteethecorrectfunctionality.Thesesystemspresentimplicitrealtimeconstraints.Theresponsetimeofitstasksisalsoimportant,thusthesystemtasksmustbefinishedasfastaspossible.SeveralWSNspresentexplicitrealtimeconstraints.Forexample,astructuralmonitoringapplicationimposesexplicitdeadlinesforthedatasensing(Kimetal.,2007).However,duetothelargenumberofnodes,nondeterminismandnoise,itmightbeextremelyhardtoguaranteerealtimeproperties.

Security: WSNs can be used in safety critical applications, thus their security is anessential issue to be considered.Denial of Service techniques can be easily executedoveraWSN.Moreover,coordinationandrealtimecommunicationapproachesdonotconsider security issues. Thus, some intruder can easily exploit theseWSN securityfaults. The great dilemma is how to implement security techniques that need largecomputationalresourcesinatechnologythatdealswithseverehardwareconstraints.

Inthisscenario,wherenodesarelikelytooperateonlimitedresources,powerconservationisconsideredoneofthemostimportantconcernsofthesenetworksanddifferentstrategiesandprotocolsneedareadoptedinordertodealwithit(Gholamzadeh&Nabovati,2008).Inmoredetail,networklifetimecanbeenhancedifthesystemssoftware,includingdifferentlayers and protocols, is designed in a way that lowers the consumption of energy(Gholamzadeh&Nabovati,2008).Several techniqueshavebeenproposed in literature inordertodecreasethepowerconsumptionofWSNs.Thesetechniquesarerelatedtodifferentaspects of sensor networks, from hardware platform to Medium Access Control (MAC)protocol,routingandtopologycontrol.ThisChapter is structuredas follows:Section2 summarizes themainapplicationswhereWSNsaredeployedand theirhardwarecharacteristics. InSection3, themainMAC layerapproachesproposed in literature aredescribed. Section 4presents the routing strategiesproposed in order to provide power optimization and consequently increasing WSNslifetime, while Section 5 introduces Transmission Power Control approaches. Section 6introduces Autonomic approaches and finally, in Section 6 the final remarks on theoptimizationofWSNsarepresented.


2.WSNapplicationsandhardwarecharacteristicsWSNs are considered an application oriented technology, thus approaches that aredevelopedforsomespecificapplicationusuallycannotbeusedfordifferentuses.Importantpointsrelated to thehardwarecharacteristicsof thenodesmustbeconsidered inorder toguaranteethesuitablenodeforaspecificapplication.Inmoredetail,aspectsrelatedtothetypeofprocessingunitaswellascommunication,powersupplyandsensingdevicesmustbeconsidered,whenthenodesforaspecificapplicationaredefined.Consideringaspectsrelatedtotheprocessingunit,usuallyamicrocontrollerormicroprocessorisadopted.Inordertochoosetheidealmicrocontrollerforthesystemandduetothefactthatmicrocontrollerswithhighperformanceimplyhigherpowerconsumption,thedesignermustconsiderthedesiredperformancelevel.Anotherimportantpointisassociatedtothefactthatmicrocontrollersusually supportdifferentoperationalmodes, suchasactive, idleand sleepmode,whichdirectlyaffect thepower consumptionof thenode.There isalsoanattractivedesignoptionthatsuggestssplittingtheworkloadbetweentwolowpowermicrocontrollersinsuchwaythatoneofthemicrocontrollersisresponsibleforthesensingcontrol,whiletheotherperforms the networking tasks related to controlling the RF interface and running thealgorithms(Chou&Park,2005).Finally,techniquesliketheonecalledDynamicVoltaScaling(DVS) canbe adopted (Karl&Willig, 2005).DVSdynamically adapts themicrocontrollerspower supply voltage and operating frequency to meet the processing requirement, thustradingoffperformanceandpowersupplyforenergysavings.Different communication devices using mediums like radio frequency or opticalcommunications, for example, can be adopted to exchange data between nodes. Forcommunication, both a transceiver and a receiver are required for the sensor nodes. Theessential taskof thesedevices is toconvertabitstreamcoming fromamicrocontroller intoradiowavesandviceversa. Inmoredetail, the transceiver isnormally regarded the largestpower consumer and optimizing its power can result in significant improvement for thesystem as a whole (Chou & Park, 2005). There are several factors that affect the powerconsumption characteristicsof a transceiver, including its typeofmodulation scheme,datarate, transmissionpower and theoperationalduty cycle (Gholamzadeh&Nabovati, 2008).Manytransceiversallowtheusertosetthepowerlevel.Ingeneral,transceiverscanoperateinthe following distinct modes of operation: Transmit, Receive, Idle and Sleep; allowingswitchingbetweenthemandconsequentlyrealizingenergysavings.Notethattheswitchingbetweentheoperatingmodeshastobemanaged,takingintoaccountthefactthatwakingupatransceiverfromtheSleepmodeandmaking itgotoTransmitmoderequiressomestartuptimeand startup energy.Thus, switchinganode intoSleepmode isonlybeneficial, if theenergynecessary for thenode to comeback into anactivemode is smaller than theenergysavedduringtheSleepmode,whichimpliesthatthetimetothenexteventissufficientlylarge.Regarding thepowersupplydevice,abattery isused inmostof thecases,playingavitalroleindeterminingthesensornodeslifetime.Thus,oneofthemostimportantfactorsthatadesignermustconsider is theratecapacityeffect,which isrelated to thedischargerateor


theamountofcurrentdrawfromthebattery.Drawinghighercurrentthantheratedvalueleadstoasignificantreductioninbatterylife,duetothefactthatthediffusionofelectrolytefalls behind the rate at which they are consumed at the electrodes. It is important tohighlight thatmostof theapplicationsofWSNs involvedeploying sensornodes inharshandremoteenvironmentandthereforeitisdifficulttouseordinaryrechargingschemesforbatteries. In particular cases, an alternative is to adopt external energy resources likesunlightorwind.Finally, sensors in WSNs translate physical phenomena to electrical signals and can beclassified as analog or digital devices depending on the type of output they produce.Basically,thereareseveralsourcesofpowerconsumptioninasensor:signalsamplingandconversionofphysicalsignals toelectricalones,signalconditioning,andanalog todigitalconversion(Gholamzadeh&Nabovati,2008).Passivesensors,suchastemperaturesensors,consumelesspowerthanactivesensors,likesonar,whichneedenergytosendoutasignaltoprobetheobservedobject.Indeed,thesamplingrate is importantandhigherfrequencysampling requires more energy. In this context, sensors should acquire a measurementsample only if needed,when needed,where needed andwith the right level of fidelity(Raghunathanetal.,2006).Thisstrategyreducestheenergyconsumedinthesubsystemandsometimes reduces the processing and communication load as well. Thus, the use ofmechanisms able to change thebit resolutionofmeasurement samplesand the samplingrate as well as using adaptive spatiotemporal sampling, exploiting redundancy andcorrelations models to predict a measurement instead of actually making it and finally,hierarchicalsensing,canprovidepowerconsumptionoptimization.Inthenextparagraphs,severalWSNapplicationswillbebrieflypresented.Accordingtothetemporal requirementsof theapplications, theymaypresent significantdifferences in theapplied algorithmic solutions. For instance, an environment monitoring application canrequire deadlines of minutes, while in a military application temporal validity is muchsmaller. Some kinds of applications need periodic sensing and sending, while otherapplicationsneedaneventdrivenapproach.Possible applications of WSNs include environment monitoring, military, domotic andindustrial monitoring and control. For instance, an application of habitat monitoring ispresentedin(Mainwaringetal.,2002).ThepresentedWSNhasbeendeployedontheGreatDuck Island. Its main goal is to correlate the measurements of some microclimate data(temperature,lightandhumidity)withthebirdnestactivityontheisland.Thisapplicationpresents relaxed realtime requirements, thus the main goal of the Great Duck Islandapplication is themaximizationof thenetworks lifetime,as it isexpected that theWSNsinfrastructure stays active during months or even years without human intervention.Therefore, the intervalsbetween sendingmessagesandbetweenone sensingandanothermaybereducedsignificantly.AnotherexampleforaWSNsapplicationisstructuralmonitoring,inthiscasealinearWSNtopology was used to monitor the Golden Gate Bridges structure, and thus a routingtechniquehad tobe applied to assure themessagesdelivery to theBS atone endof the


construction.Thisapplicationisbasedonaccelerometersensorsthatdetectmodificationsinthephysicalstructureofthebridge(Kim,S.etal.,2007).Finally,otherkindsofWSNsutilizationarestatedbelow: Automotive industry: Cars are equipped with sensor and actor networks, which can

interact with highway or street WSN infrastructures in order to increase trafficefficiencyorautomatetollpayment;

Monitoring and automation of factory systems: Industrial robots can be equipped withthousandsofwired sensors.These sensorsmustbe connected toa central computer.The high economic cost and themobility restrictions ofwired sensor are favour theutilizationofWSNsinthiskindofrobots.

Intelligenthousing:WSNspermitthathousescanbeequippedwithmovement,lightandtemperature sensors,microphones used for voice activation and pressure sensors inchairs are also examples of WSN utilization in building automation. Thus, airtemperature, natural and artificial lighting and other components can be tunedaccordingtospecificuserneeds;

Merchandise tracking:Logisticand transportationcompaniesmayuseWSN technologyin order to track ships, transporters, containers or single goods that are beingtransported;

Precision agriculture: Irrigation control and precise pesticide application are possiblewiththehelpofWSNutilizationonfarmlands;

Harsh area monitoring: Exploration and monitoring of harsh areas may be possiblethroughouttheuseofWSNs;

Freshwaterqualitymonitoring:WSNsmaybeusedforfreshwatermonitoringduetotheirnonintrusivenessandsmallsize;

Military application: Position and movement control of troops and vehicles, targetdetection,nonhumancombatareamonitoringaswellaslandmineremovalorbuildingexploration are just some examples of possible utilizations of WSNs for militaryapplications.

Toconclude,WSNscanbeappliedindifferenttypesofapplicationsandtheselectionofthesuitablehardwaredependson the systems requirements, theavailable resourcesand theenvironmentwherethenetworkshouldoperate.

3.MAClayerapproachesAspreviouslymentioned,thelifetimemaximizationofWSNsisoneofthemostimportantconcernswhendealingwiththeuseofWSNs.Thisismainlyrelatedtothefactthatsensornodesare consideredunavailablewhen thebattery level isdepleted. In this context, it isimportanttonotethatcommunicationamongnodesisthemajorenergyconsumerprocessinWSNs.Asignificantportionofthenodesenergyisspentonradiotransmissionsandonlisteningtothemediumforanticipatedpacketreception(Gholamzadeh&Nabovati,2008).Inotherwords,sendingorreceivingmessagesrequiressignificantlymoreenergythandata


processingortheacquisitionbythesensors.Moreover,asinglemediumforcommunicationis sharedby thenodesandnetworkperformance largelydependsonhowefficientlyandfairly thesenodesshare themedium.MACprotocolcontrols thecommunicationnodes inWSNs and regulates access to the shared wireless medium such that the performancerequirements of the underlying applications are satisfied (Sohraby et al., 2007). Thus, acarefuldefinitionofprotocolsandalgorithmsforefficientcommunicationhasbecomeoneofthemostimportantissuesinWSNsinordertoimprovetheirlifetime.Basically,theMACprotocolmust be energy efficient andmust try to reduce the following issues related toenergyconsumptionphenomena(Demirkol,2006): Packetcollision:Whenonenodereceivesmorethanonepacketatthesameinstant,itis

considered that apacket collisionoccurred.Therefore, allpacketsmustbediscardedandtransmittedagain.

Overhearing:When some node receives packets that are addressed to another sensornodeoverhearingoccurred;

Controlpacketoverhead:TheuseofcontrolpacketsinordertocoordinatetheWSNmustbeminimized;

Idlelistening:Idlelisteningoccurswhensomenodeisinthelisteningmodeofachannelthatisnotbeingused.

Overemitting: Overemitting is caused when the message delivery fails due to thedestinationnodesinactivity.

It is important tohighlight that the collisionsofmessages is considered themost criticalaspect,which causes the discarding of all involvedmessages and forces the network toretransmitincreasingitsenergyconsumption.Thus,anenergyefficientMACprotocolmustavoid collisions and reduces the energy dissipation related to idle channel sensing,overhearingandoverheadtoaminimum(Ilyas&Mahgoub,2005).Regarding the typesof communicationpatterns, fourdifferent types canbe identified forWSNs(Demirkol,2006): Broadcast:Generally BSs use the broadcast communication pattern (sink) to transmit

certain information to all nodes under its controls. The broadcast information mustcontainconsults,softwareupgradesorsomecontrolpacket.Thebroadcastpatterncanonly be used, when all destination nodes are inside the radio coverage of thetransmitternode;

Localgossip:Localgossipistobeconsideredwhennodessensesomeeventandsenditto other nodes that are located inside the same location (same cluster).This kind ofcommunicationoccurswhenonenodesendsitsmessagestoitsneighbours,insidethesamecoveragearea;

Convergecast: This type of pattern is used when a group of sensors sends theirpackets to a specific node. The destination node can be a clusterhead, a fusioncentreoraBS;

Multicast: Some scenarios imply thatmessages need to be sent to a group of sensornodes,thusjustthesensorsthatbelongtothisgroupreceivethemessage.


InthenextsubsectiontheIEEE802.15.4StandardandtheZigBeetechnologyaredescribedandthemainMACapproachesabletoreducepowerconsumptionaresummarizedaswell.Another subsection,will present furtherMAC approaches aiming at the optimization ofMSNs.

3.1.IEEE802.15.4standardandtheZigBeetechnologyThemaingoalof theZigBee technology is toenableWSNscomposedof largenumberofnodes to function with reduced energy consumption. Most WSN technologies likeMicaMotesuseZigBeeinordertoachievehigherlifetimelevelsfortheirWSNapplications.TheZigBeenetworkarchitectureisbasedontheOpenSystemsInterconnection(OSI),howeverexclusivelythemore important layerswere implemented.ZigBeeadoptstheIEEE802.15.4standard, which only defines the lower layers: the physical layer and the MAC layer(ZigBee,2010).The physical layer may operate on two frequencies 868/915MHz or 2.4GHz, with 16channels and 250Kbps of maximum transmission rate. The IEEE 802.15.4 MAC layer isbased on the Carrier Sense Multiple Access with Collision Avoidance (CSMACA)mechanism.Note thatZigBee technologydiffers from otherwireless technologiesdue toseveralreasons:lowerdatatransmissionrate,lowerenergyconsumption,lowercost,higherselforganizationandmoreflexiblenetworktopologies(ZigBee,2010).TheIEEE802.15.4standardhasbeenproposed in2003andhasbecomeade factostandardfor lowenergy consumptionand lowdata rate transmissionnetworks.The IEEE802.15.4MACprotocol supports twokindsofoperationalmodes that canbe selectedbya centralnodecalledPersonalAreaNetwork(PAN)coordinator.Thetwomodesare: Beaconless mode: where MAC protocol functions are based on a CSMA/CA without

beaconpacket; Beaconmode:wherebeaconsareperiodically sentby thePANcoordinator inorder to

synchronizenodes thatareassociatedwith itand todelimita superframe.During thesuperframedurationallnodetransmissionmustoccur.Moreover,duringthecontentionperiod of this frame theMACprotocol is ruled by the slottedCSMA/CA.The IEEE802.15.4withbeaconmodecanusethesynchronizationandthecontentionfreeperiodthatisbasedonaguaranteedslottime.

Thus, theZigBeeAlliance is responsible for theZigBee technology standardization. Inmoredetail,theapplicationandnetwork layersaredefinedbytheZigBeeAlliance itself,while thephysicalandMAC layersarebasedontheIEEE802.15.4standard.ZigBeemayalsoconsidertime synchronization according to an optional superframe structure; the ZigBee technologypossesses an address scheme that canhandleup to 65.000nodes.Moreover, three kinds oftopologiesaresupported:star,meshandclustertree.TheStartopologyisconsideredthesimplesttopologyandisbasedonamanytoonecommunicationtopology,whichmeansthatallnodesarecoveredbythePANcoordinatorantenna,andareabletosendthemessagesinjustonehop.


However,themeshandclustertreetopologiesrelyonaroutingprotocolinordertodeliverthemessages to thePANcoordinator.Mesh topologydoesnotallowclusterheadsandnodes tocommunicate with each other. Still different, the cluster tree topology is based on theorganizationofnodes intoclusters.Basically,thestartopology isconsideredsimplerthanthemeshandclustertreeonesduetothefactthatnoroutingprotocolisnecessary(ZigBee,2010).IEEE802.15.4standardisbasedonCSMA/CAMACalgorithm,itsbeaconlessmodedoesnotimposethesendingofaperiodicalbeaconbythePANcoordinator(IEEE802.15.4,2008).Twoparametersareconsideredinthebeaconlessmode:thefirstisdenominatedNBthatisthe Number of times CSMA/CA is required to backoff, the second is called BackoffExponent (BE),standing for thenumberofbackoffperiodsadevicemustwaituntil itcanaccessthecommunicationchannel.The first step of the CSMA/CA algorithm is the initialization of NB and BE. After theinitialization,theMAClayermustwaitarandomperiodof0to(2BE1)andthenrequiretheClearChannelAssessment(CCA)fromthephysicallayer.Whenthechannelisconsideredoccupiedbyotherdevice,theNBandBEisincrementedby1bytheMAClayer,thoughtheMAC algorithmmustguarantee thatBEnevergrows abovemacMaxBE.Moreover,whenNBsvalueisabovemacMaxCSMABackoffs,theCSMA/CAalgorithmmustquitandreturnstheaccesschannelfailurestatus.Finally, the Beaconless CSMA/CA algorithm is sensitive to three parameters: macMaxBe(standard value 5), macMaxCSMABackoffs (standard value 4) and macMinBE(standardvalue3).Thesestandardvaluesfortheparametersmayhelptodecreaseenergyconsumptionduetothefactthatdevicestrytosendjustfivetimesbeforethetransmissionsabortion, however incrementing these values tends to increase the networkscommunicationefficiency.Thus, the IEEE802.15.4protocol isnotable todealwithdensenetworktopologies,thembeingnetworksthatarebasedonalargenumberofnodes.

3.2.OtherMACapproachesSeveral otherMAC approacheshave beenproposed in literature in order toprovide thereductionofpowerconsumption inWSNs.InthenextparagraphsthemainsolutionsthatexploretheoptimizationofMACprotocolsaresummarized.TechniquesusedintheMAClayerofWSNsofteninvolvetheuseofTimeDivisionMultipleAccess(TDMA)andDutyCycles(DC).ThemainideabehindtheTDMAtechniqueistodividethetimespentbydevicesoverchannelaccesses into so called time slots, each one used exclusively by one device. Therefore, byapplyingthistechnique,everydevice,beforesendinganymessages,needstobooksuchaslotof time in advance.A TDMAMAC protocol is proposed in (Shi& Fapojuwo, 2010). ThistechniqueisbasedonacrosslayeroptimizationinvolvingMACandphysicallayers.ThemaingoalofthepresentedtechniqueistoreducetheoverallenergyconsumptionbasedonaTDMAschedulingwiththeshortestframelengthforclusteredWSNs.


Inordertoalsoreduceenergyconsumption,theTDMAMACprotocolisusedin(Wu,Y.etal.,2010).Here,themainfocusistoschedulethesensornodeswithconsecutivetimeslotsatdifferentradiostates,suchas: transmitting,receiving, listening,sleeping,and idle.Due tothe fact that sensor nodes consumedifferent levels of energy at each state the optimumschedulingofthesestatescouldachievethereductionofenergyconsumption.FlexiTP is a TDMAMAC protocol that schedules nodemessages based on the socalledsleepschedulingapproach(Leeetal.,2008).Thesleepingschedulingschemerequires thatsensornodestoexclusivelytransmitandreceivepacketsattheirowntimeslotandturnintosleep state until their slots turn is up again. FlexiTP also provides routing, timesynchronizationtasksandsensornodesmaysenseaswellasroutedata.PEDAMACS is another TDMAMAC protocol designed formultihop WSNs (Ergen andVaraiya,2006). It can improve thenetworks lifetimeby severalyearswhen compared tootherMACprotocolssuchasrandomaccessprotocolsthatmayreachmonthsorjustdaysofnetworklifetime.However,thisTDMAprotocoldoesnotpresentagoodperformancewhenappliedtoWSNswithdynamictopologies,astheyarecommoninharshenvironments.Complementarily, theDC techniquedivides theoperating timeofdevices in twoperiods:activeandinactive,alsodenominatedsleepingtime.Theshortertheperiodofactivityisincomparison with the period of inactivity, the longer the devices remains inactive andconsequentlyachievesgreaterenergysavings.Asdownside,aconsequentreductionofthemaximum transmissionrate in thenetwork is tobeobserved. If,on theonehand,TDMAenablesdevicestobecomemoreorganized inordertoavoidcollisions;ontheotherhand,theDCtechnique isabletoavoidcaseswhereanodebecomessimultaneouslyactivewithothernodesthathadbeeninactivebefore,preventinganodetowaitformessagesthatwillneverarrive,andfinallyavoidingthewasteofenergy.Thesetechniquesgenerallyallowtheprotocolstodealwithcollision,idlelisteningandoveremitting problems, but have overheads associated to sending and processing of controlmessages. Such extracosts can be unnecessary in applications where the density of thenetwork is small and where few devices transmit simultaneously. In this scenario,contentionbasedprotocolssuchasCarrierSenseMultipleAccesswithCollisionAvoidance(CSMACA)seemtobemoresuitable.NotethattheCSMA/CAMACprotocolpresentintheIEEE802.15.4standard(IEEE802.15.4,2008)isinefficientforlargenetworks.ARotationalListeningStrategy(RLS)forWirelessBodyNetwork(WBN)ispresentedin(Tsengetal.,2011).WBNsareaspecialkindofWSNsthataredeployedoverahumanbodyarea inordertosenseandtransmitscalarsasforexamplethebodystemperature.TheRLSapproachisbasedonthedivisionofchannelaccesspartitioningitintominislotsthatareallocatedtonodes.Another type of WBN application is presented in (Omeni et al., 2008), where a MACprotocol implemented in hardware by a 0.13mCMOS process isdescribed. In order toavoid collisionswith nearby transmitters in awireless body network, a standard listenbeforetransmittechniqueisbeingused.Thetimeslotoverlaphandlingwasreducedbasedonawakeupfallbackapproach.


AdifferentWirelessBodyNetworks(WBN)MACprotocolisproposedin(Otaletal.,2009).ThemaingoalofthisMACprotocolisthemaximizationofthebatterylifetimeofeachindividualbodysensorswhilemaintainingthereliabilityandmessagelatencyofdatatransmissionsatthesame level. To do so this MAC protocol is based on a crosslayer fuzzy rule schedulingalgorithmandaenergyawareradioactivationpolicyforrealisticmedicalapplications.AnEnergyAwareHybridDataAggregation(EDHDAM)techniqueispresentedin(Kim,M.etal.,2011).ItaimsatminimizingtheenergyprobleminasynchronousMACbasedWSNs.Thenodesclosertothesinkspendmoreenergythanother,thisisduetothefactthattheyreceiveandsendmoredatatotheWSNssinkthennodesthatarefarawayfromthesink.Thus, the EDHDAM technique is designed to adaptively control the number of datatransmissionsinordertoavoidthebeforementioneddownside.AgametheoreticMACapproachforWSNsispresentedin(Zhaoetal.,2009).TheMACofnodesinthistechniqueisbasedonanincompletelycooperativegamemode.ThisapproachdenominatedGMAC,where time isdivided into superframes, each superframehavingtwoparts:anactivepartanda sleepingpart.During the sleepingpart,allnodes turnofftheir radios to saveenergyandduring theactivepart, if somenodehaspackets to send,thesewillpassonthechannelthatisbasedontheincompletelycooperativegame.MultiplecrosslayerprotocolsthatintegrateMACandWSNsnetworklayersarepresentedin(Rossi&Zorzi).AlltheseMACprotocolsarecostawareregardingresidualenergies,linkconditions,andqueuestate.The routing layerchooses thebest relaycandidatesbasedonthe MAC protocol information. In this manner, the number of inrange devices, thatcompeteforonechannelaswellastheinterferencearereduced.AtechniquenamedSMAC,amediumaccesscontrolbasedoncoordinatedadaptivesleepscheduling, ispresented in(Yeatal,2004).SMACtriestoavoidtheoverhearingproblemby lowdutycycle operations in a multihop WSN. The SMAC approach organizes thesensornodesintovirtualclustersbasedoncommonsleepschedulinginordertoreducethecontroloverheadandenabletrafficadaptivewakeups.Finally,MRMACisaMACprotocolthatreducestheendtoenddelayaswellastheenergyconsumptioninWSNs.Theapproachpresentedin(Hongetal.,2010)reducestheendtoenddelaybasedon twometrics:NextPacketArrivalTime (NPAT)andMediumReservationInformation (MRI). When a sender transmits a packet, the NPAT and MRI metrics areenclosed in the packet in order to make possible that its intended receiver reserves themedium. The simulations presented by show that the MRMAC approach is able tosignificantlyreduce,bothendtoenddelayandenergyconsumption.

4.RoutingapproachesWSNscanbeadoptedforawiderangeofapplications,butinallofthem,themaintaskofthenodes is to sense and collectdata,process it and transmit the information to the sitewhereitispossibletoanalysethemonitoredparameters.Toefficientlyachievethistask,itis


required thedevelopmentofanenergyefficient routingprotocol tosetuppathsbetweenthenodes and thedata sink (Sohraby et al., 2007).Due to the fact that sensornodes areenergy constrained, great part of the WSNs protocols aim at minimizing the energyrequiredforcommunications.Basically,theenvironmentcharacteristicscoupledwithscarceresourcesandenergylimitationmaketheroutingproblemverychallenging.Thus,thepathselection must be such that the lifetime of the network is maximized. In this context,differentstrategiescanbeadoptedinordertofacewiththisproblem.Onesimplestrategyistoavoidbadqualityroutesbecauseunreliabilityofwireless linkshasanadverseeffectontheir performance. Link failures and packet losses lead to many retransmissions andtherefore,resultinhigherpowerconsumption.A clustering protocol called REACA is presented in (Quan et al., 2007). REACAsfunctioning is divided into two cycles: the first cycle is dedicated to the networkconfigurationwhilethesecondcyclehandlesthemessagetransmission.Theclusterheadtobechosenisbasedonthebatterylevelofallnodesthatcomposethecluster.Thus,thenodethat presents the highest battery energy level is chosen to function as clusterhead.Moreover, a routing algorithm is proposed and REACA is validated by mathematicalanalysis.EARQby(Heoetal.,2009)isaroutingprotocolbasedontheWSNsenergylevel.EARQisabletoguaranteedependability,temporalconstraintsandenergyeconomy.ThemaingoalofEARQistousethepathwiththegreatestenergylevelinsideaWSN.ItsauthorsprovebysimulationsthatEARQmaybeimplementedinaWSNforindustrialapplication.However,EARQ was not validated for any WSN prototype. Another clustertree WSN routingprotocolwasproposedby(Alippietal.,2009).TheapproachcalledMMSPEEDisaroutingprotocolthatisabletoguaranteeprobabilisticQuality of Service (QoS) metrics in WSNs. It was proposed by (Felemban et al., 2006),considersdifferentoptionsof speeddelivery in the timedomainandguaranteespackagedelivery.Severaldependabilityrequirementsareprovided,whicharebasedonseveralpathoptions.Theendtoendrequirementsareprovidedinalocatedfashion;thisisdesirableintermsofscalabilityandadaptabilityindynamicanddenseWSNs.However,theutilizationofgeographical routing imposes thatnodesneed toknow theirgeographical localization.Thus, the proposing authors considered that each node would possess GPS devices ordistributedlocalizationalgorithms.Thisresultsinconsiderableproblems,asGPSdevicesdonotworkproperly in indoorenvironmentsanddistributed localizationalgorithms imposeanextraoverheadduetotheextrapackageexchange,sincethenodesneedtoperiodicallybroadcasttheirgeographicallocalization.TheqSwitch,asimplepathroutingalgorithmproposedin(Wu,X.etal.,2008),isaroutingtechnique used to support the nonuniform node distribution strategy that is used tomitigatetheenergyholeprobleminWSNs.ItsauthorsalsoshowthatinacircularmultihopWSNwithnonuniformnodedistributionandconstantdatasendingtheunbalancedenergyconsumptionisunavoidable.


An approach denominated the Energy Efficient Broadcast Problem (EEBP) in ad hocwirelessnetworksispresentedin(Lietal.,2004).TheEEBPsideacanbedescribedbythefollowingphrase:inagivenanadhocwirelessnetwork,findabroadcasttreesuchthattheenergycostof thebroadcast tree isminimized. Itsauthorsconsider thatall thenetworksnodespresent a fixed level of transmissionpower.As solution three routing approachesaimingattheminimizationofthenetworksconsumptionareproposed.AsleepschedulingsolutioncalledGreenWaveSleepScheduling(GWSS),whichhasbeeninspiredbysynchronizedtrafficlights,ispresentedin(Guhaetal.,2011).Themaingoalofthis approach is to support theWSNs routingduty cycling.A greenwave is amovingsequence of consecutive active states (green lights), and some packet may move in asequence of active nodes. Thus, nodes in sleep mode are compared to red lights, andpackagesmaynotbe routed througha sleepingnode. Itsauthors show that, consideringlargeWSNsarrangedinstructuredtopologies,GWSSachievesalmostthesameendtoendlatencyasthatofnonsleepschedulingWSNs.

5.TransmissionpowercontrolapproachesPower conservation is so important because nodes are usually operating on limitedbatteries.Aspreviouslymentioned,MACprotocolsareabletomanageenergyconsumptionduring WSN communication, which is the most energyconsuming event in WSNs.However,oneinterestingsolutioninordertoincreaseWSNslifetimeisbasedonadjustingits nodes transmission power. On the one hand, maintaining the lowest possibletransmission power represents a interesting solution in order to minimize the energyconsumption and consequently increase the networks lifetime. On the other hand, thelowestpossibletransmissionpowercanincreasetheWSNsvulnerabilitytotheinterferencefluctuations caused by bad SignaltoInterferenceplusNoiseRatio (SINR) (Kim&Know,2008).Extensiveempiricalstudiesconfirmthattheradiocommunicationsqualitybetweenlow power sensor devices varies significantly with time and environment. Thisphenomenon indicates that the existing topology control solutions, which use statictransmission power, transmission range and link quality, might not be effective in thephysical world (Lin et al., 2006). In this context, online transmission power controltechniquesthattakeintoaccountenvironmentvariationshavebecomeessentialinordertoaddressthisissue.Several Transmission Power Control (TPC) approaches have been proposed in theliterature.Basically,theTPCalgorithmcanreducetheenergyconsumptionandimprovethechannel capacity. Inmoredetail,TPC solutionsusea single transmissionpower forthewholenetwork,notmakingfulluseoftheconfigurabletransmissionpowerprovidedby radio hardware to reduce energy consumption or assume that each node chooses asingle transmission power for all the neighbours, which is know as neighbourlevelsolutions. Indeed,mostexistingWSNsuseanetworklevel transmissionpower foreachnode.


TherearemanyTPCstudies,whichmainlyfocusonimprovingthechannelcapacity(Monksetal.,2001;Ho&Liew,2007).Recently,experimental studies (Donetal.,2004)(Linetal.,2006)have shown thatTPC reduces energy consumption in lowpowerWSNs. InPowerControl Algorithm with Backlisting (PCBL), each node sends packets at differenttransmissionpowerlevelstodeterminetheoptimaltransmissionpowerbasedonthePacketReception Ration (PRR). In (Lin et al., 2006), an Adaptive Transmission Power Control(ATPC) algorithm is proposed in order to achieve the optimal transmission powerconsumptionforspecifiedlinkqualities.EmployingaATPCalgorithm,theReceivedSignalStrength(RSS)andtheLinkQualityIndicator(LQI)forradiochannelsareusedtoestimatethe optimal transmission power level, and employ a feedbackbasedATPC algorithm todynamically adjust the transmission power over time. Thus, the result of applying thisalgorithmisthateverynodeknowsthepropertransmissionpowerleveltouseforeachofits neighbours and every node maintains good link qualities with its neighbours bydynamicallyadjustingthetransmissionpowerthroughondemandfeedbackpackets.However,theeffectproducedbydifferent inferencesourcesmustbeconsideredwhenthegoalistheimplementationofWSNsinthephysicalworld.ManyWSNdevicesavailableonthemarkedoperateon the2.4GHz ISMbandandarevulnerable to the interferences fromotherwireless networks such as the IEEE 802.11WLANs or the IEEE 802.15.1Bluetooth(Kim&Know,2008).Generally,thetransmissionpoweroftheWSNdevices is lowerthanthatofWLANorBluetoothdevices.Therefore,theTPCalgorithmforWSNshastocarefullyconsider the interferences caused by other 2.4GHz wireless devices, which can causesignificantperformancedegradation. In thiscontext,apracticalTPCalgorithm forWSNs,named Interference Aware Transmission Power Control (ITPC) algorithm has beenproposedin(Kim&Know,2008).TheITPCalgorithmisbasedontheideathateachnodeadjusts theRSS target toprovide theacceptableSINRwhen interferencesaredetected. Inmore detail, the ITPC algorithm consists of two functional procedures: the twotiertransmissionpowercontrolandtheRSStargetadjustment.Initially,theproperRSStarget,whichmaysatisfythedesiredPRRisdetermined.BasedonthisRSStarget,eachnodetriestoadjustitstransmissionpowertokeeptheRSSvaluewithintheupperandthelowerRSStargetvaluesbyusingthetwotiertransmissionpowercontrolprocedure.Theneteffectofthisoperationisthattheproposedalgorithmtriestoreachasatisfyinglinkqualityquicklyeven if therearesmallscale linkqualityvariations.When the interference isdetected, theRSS target and the transmission power are increased immediately by the RSS targetadjustmentproceduretoprovideanappropriateSINR.Twodifferentlocalalgorithmstoindividuallyadjustthenodestransmissionpowerarepresented in (Kubishetal.,2003).Such localapproachesdonot requireanyparticularMAC protocol or dedicated protocol for route discovery. The socalled Local MeanAlgorithm (LMA) implements that each node periodically sends a lifemessage and allreceivingnodesrespondwith lifeacknowledgemessages.Beforesendingnew informationeachnodecountsthereceivedacknowledgemessagesandcomparesthisnumbertothevalue set as thresholds. In the case the node received lessmessages then the inferiorthreshold, the transmissionpower is increasedby factorAinc foreverynodemissing to


achieve the threshold. If this number is in the range between the minimum andmaximum threshold no changes to transmission power aremade. Similarly, theLocalMeanNumber ofNeighbours (LMN) algorithmworkswith life and life acknowledgemessages. In addition to the LMA approach the life acknowledgemessage contains thenodes own count of neighbours. Thereby each node receives a number of messagescontaining a value indicating the numbers of neighbours of the sending node, thencalculatesameanvaluefromallthereceivedmessagesandusesthisvalueaswellasthenumber of nodes that responded to its lifemessage to calculate the so calledNodeRespvalue to be comparedwith the thresholds and, if the case, the transmission power isadaptedasdescribedintheLMAtechnique.Thesetwotechniquesarecomparedtofixedand global algorithms and, in the given indoor scenario, are outperforming the fixedapproacheswhile reaching only about half the lifetime of networks employing globalalgorithms such as the Equal Transmission Power (ETP) algorithm. It is noted thatcomparing such approaches toETP the local algorithms are almost competitivewhenlooking at the networks confidence level and on top are scalable and easilyimplementable,whichglobalalgorithmsarenot.Finally, a Transmission Power Self Optimization (TPSO) technique is presented in(Lavratti et al., 2012). It basically consists of an algorithm able to guarantee theconnectivity as well as an equally high Quality of Service (QoS) concentrating on theWSNs Efficiency (Ef), while optimizing the necessary transmission for datacommunicationineachnode.Thetechniqueaimsatadjustingeachnodetousethelowestpossible transmission power while maintaining the connectivity to the WSN and thereliabilityof thenetworkasawhole.This tradeoffbetween theWSNsEfand thedatatransmission energy consumption is evaluated in different EMI environments. Itsdecentralized algorithm runs on the application layer and uses anEf value calculated,whichadoptsthenumberofreceivedmessagesandtheestimateofsentmessages.ThisEfis compared with the targeted Ef in order to decide about adjustments to the nodestransmission power. Experimental results show that the automatic adaption presentsadvantageswhencomparedwithapproachesusingfixedtransmissionpower.ItisshownthatthetechniqueisabletoguaranteethetradeoffbetweenEfandpowerconsumption.The TPSO behaviour is shown in Figure 1. It is possible to notice that the energydissipated by a node with fixed transmission power is much higher than the energyconsumed by a node running the TPSO algorithm. The session values in the xaxisrepresenttheelapsedtime.Figure2showstheWSNsEfandtheWSNsenergyconsumptionwithrespecttotheWSNusing the fivepredefined transmissionpower levelsand to theWSNadopting theTPSOtechnique.WecanobservetheeffectivenessoftheTPSOtechniquewithrespecttotheuseofprefixedtransmission power levels. In detail, we can see this network using the maximumtransmissionpower levelreachesabout80%ofEf,butconsumesabout50mW.s,while theWSNadoptingtheTPSOalgorithmreachesthesameEfconsumingonlyabout25mW.s.


Figure 1. ComparisonoftheenergyconsumptionoftheTPSOtechniquewithrespecttoWSNoperatingwiththetransmissionpowerfixedtolevel4(Lavrattietal.,2012).

Figure 2. EvaluationoftheTPSOtechniquewithrespecttoWSNsEfandenergyconsumptionapplyingAM/FMnoiseof14dBm(Lavrattietal.,2012).

Figure3depictstheEfoftwoWSNs,onewiththetransmissionpowerlevelsetto0andonewith theTPSO technique.TheWSNwith the fixed transmission level is able to reach anaverageEfof46.4%,whiletheothernetworkisachieving86.6%.AstheWSNwiththeTPSOtechnique is switching to higher transmission power levels to cope with the introducednoiseitneeds253%moreenergytoreachthehigherlevelofeffectiveness.

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Figure 3. ComparisonoftheWSNsEfoftheTPSOtechniquewithrespecttoWSNoperatingwiththetransmissionpowerfixedtolevel0(Lavrattietal.,2012).

The results obtained during the experiments demonstrated the convenience of using theselfoptimization algorithm instead of setting the maximum transmission power level.WhenaWSNwithouttheTPSOtechniqueisconsidered,thetransmissionpowerissetatthebeginning of the communication and remains the same during its entire lifetime. ThischaracteristiccanbenegativeconsideringaWSNinarealenvironmentwheretheinherentnoise isnotnecessarilyconstant.Therefore,due to the fact that the inherentenvironmentnoise is completely variable and random, theTPSO techniquewill always guarantee thelowestpossibletransmissionpowerduringthecommunicationandthetarget_Efwhenitispossible(Lavrattietal.,2012).

6.AutonomicapproachesIBM introducedthetermautonomiccomputing in2001todescribecomputersystemsableto selfmanage themselves (KephartandChess,2003).Themainpropertiesofapproachescategorizedasself*are: selfconfiguring selfoptimizing selfhealing selfprotection.

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Each one of them is described in (Huebscher and McCann, 2008). A brief definition ispresentedbelow: Selfconfiguration: the systems ability to configure itself according to high level

goals; Selfoptimization:thesystemcandecidetostartachangeinthesystemasproactive,in

ordertooptimizetheperformanceorqualityofservice; Selfhealing:thesystemdetectsanddiagnosesproblems,whichcanbeeitherfaultybits

inamemorychiporasoftwareerror; Selfprotection: the system is able to protect itself against malicious attacks or

unauthorizedchanges.Even thoughdenseWSNspresentseveraladvantages,selfmanagementcharacteristicsarerequired in order to deal with the management of a large number of nodes. Selfmanagementtechniquesarepartofautonomiccomputingmethodologies,whichcanalsobeused to manage WSNs with conflicting targets (energy efficiency, selforganizing, timeconstraintsandfaulttolerance).Themaingoalofselfmanagementisthedevelopmentofacomputing system that does not need the human intervention to operate. This way,computingsystemsareabletoselforganizeandselfoptimizethemselves,oncetheyfollowglobalobjectivedictatedbyasystemadministrator(PintoeMontez,2010).For instance, in dense WSNs composed of several sensor nodes in a star networktopology, in case the network presents conflicting goals (increase dependability andenergy efficiency, while meeting time constraints), the conventional IEEE 802.15.4protocoldoesnotseem tobeable todealwith thecomplexities.Forexample,when thenumber of nodes in a network is increased, in order to achieve better reliability theWPANmaybecongested,andfewermessagesarriveinthebasestationontime.Inorderto demonstrate the WSNs behaviour in this situation, experiments using TrueTimesimulator1wereperformed.TwometricscalledEf(efficiency)andQoFhavebeenadopted.Whileefficiency isametricthatmeasurestheratiobetweensentandreceivedmessages;QoFrepresentstheaveragenumberofreceivedmessagesbythebasestationoveracertaintimespan.Figure4showsthatwhendensitynetwork is increased,QoF increasesslowly,butcommunicationefficiencyquicklydecreases.(PintoeMontez,2010)proposeaGeneticMachineLearningAlgorithm (GMLA)aimedatapplications thatmake use of tradeoffs betweendifferentmetrics.Themain goal of theGMLA approach is to improve the communication efficiency, in a communicationenvironmentwherethenetworktopologyisunknowntothebasestation.Simulationswereperformedonrandomstartopologiesassumingdifferentlevelsoffaults.ObservingFigure5 itcanbenotice that thecommunicationefficiencymaintains thesamelevelwhen IEEE802.15.4 isused.However,whenGMLA isused, it ispossibletonoticeagainofalmost10%incommunicationefficiency. 1freelyavailableathttp://www.control.lht.se/truetime.


Figure 4. IEEE802.15.4simulatedbehaviour.

Figure 5. ComparisonofGMLAandIEEE802.15.4.

ItispossibletonoticethatIEEE802.15.4presentsastaticbehavior,andthatitdoesnotlearnbettercommunicationpatternswhentopologychangesarefaced(Figure6).

Figure 6. GMLAEfficiencyandQoFvalues.

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AnanalysisofFigure7indicatesthattheQoFismaintainedatalmostthesamelevel,inallsimulations.However, thehigher levelofEfwas achievedwith 1,000 round simulations.This may be explained through GMLAs learning behaviour, which tries differentconfigurationswhenlongersimulationsarerun.

Figure 7. Sent(SM)andReceivedMessages(RM).

Consequently,itispossibletoconcludethattheGMLAapproachisabletodothetradeoffbetweenQoFandEfandGMLAuses lower levelsofenergythanIEEE802.15.4.However,thisapproach isonlysuitable forapplicationswithahomogeneoussignal throughout theentiremonitoringarea.Also in (Pinto e Montez, 2010), a Variable Offset Algorithm (VOA), which targets theoptimization of the communication efficiency in dense WSNs with star topology, isproposed.TheVOA canbeeasily implemented into IEEE802.15.4devices,as it isa lightmiddleware that is implemented at the application layer.Themain targetofVOA is thecommunicationefficiencythroughtheuseofrandomoffsetsbeforethetransmissionofdatabytheslavenodes.TheVOAalgorithmwasassessedwith thehelpofanexperimental setupbasedon realsituationsandoneoftheexperimentshasbeenperformedbyvaryingthenumberofslavenodes.The resultsare shown inFigure8.Thegoalwas toevaluate the influenceof thenumber nodes on the Ef and QoF metrics. When compared with VOA, IEEE 802.15.4presents similar results for justone case:anetworkwith4 slaves.When thenumberofslaves increases, the difference between VOA and IEEE 802.15.4 become greater. ThedifferenceofefficiencybetweenVOAandIEEE802.15.4whenconsidering29slavesisofmore than 100%. These results show that VOA has a satisfactory performance andmaintains a minimum QoS level even with a high number of slaves (Pinto e Montez,2010).

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TheDecentralized PowerAwareWireless SensorNetwork (DPAWSN) approach has themaingoalofmaintainingaminimumQoF,whileimprovingtheEfandsavingenergy.Thisapproachcanbeconsideredasdecentralized,duetothefactthatnodeshaveautonomytodecidewhether to sendornot to sendmessages.On theonehand,a certainQoF level isimposedbynetworkadministrator,on theotherhand, theWSNs lifetime is increasedbythepowerawarenessdecisiontakenineachnode.Themain ideabehindDPAWSN isthatthebasestationwillcontroleachnode inordertodecreasesorincreasesthetransmissionratewhentheQoFlevelisaboveorbelowthetargetvalue.Thus,DPAWSNisabletomaintainaQoFlevelandincreasetheWSNslifetimeduetothefactthatnodespresentaselfishbehaviourasthenodestransmissionrateiscalculatedbasedonindividualremainingvoltage.ThetestassessmentwasconductedinanoisyenvironmentwithanunspecifiednumberofcomputerscommunicatingusingIEEE802.11.


Figure 9. Figure.9.DispersionGraphicof30tests(eachpointrepresentssentmessagesoflowerorhigherbatterylevelnodes).

Figure9showsthecorrelationbetweensentmessagesandtheinitialvoltageineachnode.Notethatthe initialvalueonthexaxisis2000mV,whichrepresentstheminimumbatteryconditions foranode towork. It ispossible tonotice that two regionsarepresent in thegraph:thefirstonerepresentssentmessagesofnodeswithalowerlevelofbatteryandthesecondonerepresentssentmessagesofnodeswithahigherlevelofbattery.Inmoredetail,eachpointrepresentstheaveragenumberofsentmessages(yaxis)overtheinitialvoltageaverageofthenode(xaxis).TheeffectivenessofDPAWSNisconfirmedbyFigure9,sincelower battery level are applied to nodes that send lessmessages and nodeswith higherbatteryleveldosendmoremessages.Finally,DPAWSNisabletoautonomicallyadjustthetransmissionratebasedonthevoltagelevel of the nodes. Moreover, it is able to achieve a targeted QoF imposed by systemadministrators.

7.FinalconsiderationsandfuturedirectionsWSNs represent one of the most interesting solutions to monitor and sense data inhazardousor inaccessibleareas.ThisChapterpresentedvariouspossibleapproaches thatdesignersmightadopt inorder toprovideanenergyefficientWSN.Due to theenormousvariety of environments that these networks may be used in, numerous challenges andconstraintshavetobeconsideredwhenchoosingtheoptimizationapproach.Also,differentgoalsandapplicationscallfordifferenttargetsthatmaynotbeachieveduptoasatisfyinglevel at the same time. Therefore, some approaches offer to define a tradeoff betweenopposing aims, such as QoS and energy consumption. Each solution presented in thisChapterisaimingatsolvingatleastoneidentifiedcauseofextensiveenergyconsumption.The approaches are using the WSNs different design levels to increase the networkslifetime,QoS,and/oroptimizeotherpointsthatmayormaynotsuitthedesigner.

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TakingintoaccountthatlifetimemaximizationisoneofthemaingoalsofWSNapproaches,andas theWSNdevices consumemore energyduring the transmissionand receptionofpackets,eveninshortdistances,thanothertaskssuchasthoserelatedtoprocessing,sensingand data storing, the design of efficient protocols and communication algorithms is aresearchdirection.Thecurrenttendencygoestowardssolutionsthatinvolveatradeoffbetweenmorethanoneconstraint or that may adapt or change the behaviour of the WSNs nodes during itsemploymentareshowingthatresearchesareawareofthecomplexityandunpredictabilityoftheenvironmentandtaskofsuchnetworks.However, some existing research areas are becomingmore relevant,mainlydue to therecent technologyevolutionof thesenetworks.Forexample, themostpopularmotes intheyears20001010werebasedon4 to8MHzprocessorsand128Kbytesmemory;butrecentlytherearemoteswith180MHzprocessorandupto4Mbytesmemory,supportingJava Virtual Machine. Therefore, the hardware evolution trend directs researches toimplementmore sophisticated and robust approaches in an autonomic anddistributedwaywithmultiobjectiveoptimizationapproaches,howeverwithpowerconsumptionasanimportantgoal.Thegradualreplacementofveryexpensivecentralizedsensorsystemsbyasetofwirelesssensor nodes, which operate in a collaborative and autonomic way (mainly with selfmanagement and selfhealing properties) is also becoming a trend. Thus, multiagentapproaches and lightweightoptimization techniques are emerging as an alternative.Thisfactismainlyduetothedistributedandoptimizedwaythattheseapproachesperform.Moreover,thegradual increase inthemotes localstoragecapacityhas inducedtheuseofWSNdatamules.Therespectiveresearchfocusesonthedevelopmentofarchitecturesandalgorithmswherenodesmust locallystorage theirsenseddatauntilmobilenodes (mobilebasestations)gatherthisinformation.AsfinalconsiderationitistobestatedthatthevarietyofchallengeshasgeneratedanevengreaternumberofapproachestodealwiththeconcernsthatWSNsface inallthepossibleharshandnoisyenvironmentstheymayfacetoday.Itisnowthedesignerstasktofindthebestmatchorcombinationtooptimizethenetworkaccordingtoitsenvironment,itstasks,anditsmostimportantrequirementsandconstraints.Asthereisnosolutionthatmaycoverallproblemsatthesametime,thecorrectanalysisofproblemstobeexpectedhasbecomeoneofthemostimportantpartsoftheworkoftodaysdesigners.

AuthordetailsA.R.PintoUniversidadeEstadualPaulista,UNESP,BrazilL.BolzaniPoehlsandF.VargasEngineeringSchool,CatholicUniversityofRioGrandedoSul,PUCRS,Brazil


C.MontezPGEAS,UniversidadeFederaldeSantaCatarina,UFSC,Brazil

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