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Inter-flow and intra-flow interference mitigation routing
in wireless mesh networks
Chiraz Houaidia, Hanen Idoudi, Adrien van den Bossche, Leila Saidane,
Thierry Val
To cite this version:
Chiraz Houaidia, Hanen Idoudi, Adrien van den Bossche, Leila Saidane, Thierry Val. Inter-flow and
intra-flow interference mitigation routing in wireless mesh networks. Computer Networks, Elsevier,
2017, vol. 120, pp. 141-156. �10.1016/j.comnet.2017.03.021�. �hal-01809375�
To link to this article : DOI :
10.1016/j.comnet.2017.03.021
URL :
https://doi.org/10.1016/j.comnet.2017.03.021
To cite this version :
Houaidia, Chiraz and Idoudi, Hanen and
Van den Bossche, Adrien and Saidane, Leila and Val, Thierry
Inter-flow and intra-flow interference mitigation routing in
wireless mesh networks. (2017) Computer Networks, vol. 120.
pp. 141-156. ISSN 1389-1286
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Inter-flow
and
intra-flow
interference
mitigation
routing
in
wireless
mesh
networks
Chiraz
Houaidia
a,∗,
Hanen
Idoudi
a,
Adrien
Van
Den
Bossche
b,
Leila
Azouz
Saidane
a,
Thierry
Val
ba ENSI, University of Manouba, Tunisia b IRIT, University of Toulouse, Toulouse, France
Keywords: QoS Routing Available bandwidth Interference Conflict graph
a
b
s
t
r
a
c
t
Inthispaper, weaddresstheproblemofQoSsupportinanheterogeneoussingle-radiosingle-channel multi-ratewirelessmeshnetwork.Weproposeanewroutingmetricthatprovidesinformationaboutlink quality,basedonPHYandMACcharacteristics,includingthelinkavailability,thelossrateandthe avail-ablebandwidth.Thismetricallowstoapprehendinter-flowinterferencesandavoidbottleneckformation bybalancing trafficload onthe links. Basedonthe conflictgraphmodel and calculation ofmaximal cliques,wedefineamethodtoestimatetheavailablebandwidthofapathwhichconsiders,inaddition, intra-flowinterferences. Finally,weproposearoutingprotocolthatsupportsthismetricandwestudy bysimulationitsperformancescomparedtodifferentexistingroutingmetricsandprotocols.Theresults revealedtheability ofourprotocol(LARM)to supportthe networkscalability aswell asitsability to chooserouteswithhighthroughputandlimiteddelay.
1. Introduction
WirelessMeshNetworks(WMNs)areaflexible,quickly deploy-able wireless networking solution that takes advantage from the absenceofarigidinfrastructure.Thesenetworksareusedto pro-vide ruralareas,where broadband infrastructure is not available, withareliableInternetaccessbasedonmulti-hopconnections[1]. Tosupportnext-generationapplicationswithreal-time require-ments, WMNs must provide improved Quality of Service (QoS) guarantees[2,3].
Theproblemofinterconnectingthemulti-hopwirelessnetwork tothebackbonerequiresQoSguaranteenotonlyoverasinglehop, butalsooveranentirewirelessmulti-hoppath.Subsequently,the QoSparametersneedtobepropagatedwithinthewholenetwork, inordertoextendreliabilityandhighperformanceconditions.
Moreover, routing isconsidered asa key issueofQoSsupport overwirelessmulti-hopnetworks,sinceitdetermineswhetherthe comingdatatrafficcanbeservedonahighqualitypathornot.
Ontheother hand,inaWMN,a nodetransmits itsdatavia a shared wireless channel, which has inherent broadcast andlossy
∗ Corresponding author:
E-mail addresses: [email protected] (C. Houaidia), Hanen.idoudi@ ensi.rnu.tn (H. Idoudi), [email protected] (A. Van Den Bossche), leila.saidane@ ensi-uma.tn (L.A. Saidane), [email protected] (T. Val).
characteristics. Data transmissions over this wireless channel are constrainedbyinterferenceandlimitedbandwidthresources[4].
Besidestheinterferencecomingfromthephysicalenvironment, a coming data traffic mayinterferewith two typesof traffics, 1) neighboring traffic including the traffic cross the samenode and adjacent nodes, commonly called inter-flow interference, 2) this datatraffic itself(wecallitself-traffic)alongthepath,commonly calledintra-flowinterference.Toensureaccuratepathquality esti-mation,boththesetwotypesofinterferenceshouldbeconsidered [4].
However,estimatinginterferenceinawirelessnetworkand cir-cumventing its effects is not a trivial task since the amount of interference depends on many factors including the radio prop-agation environment, spatial node distributionand MAC protocol dynamics.Therefore,addinginterference-awarenesstotherouting protocoldecisionsiscrucial.
Identifying paths withmaximum available bandwidth is, also, one of the main issues concerning QoS in WMNs. The available path bandwidthiscommonlydefinedasthemaximumadditional rate aflow can pushbeforesaturating its path[5].As communi-cationsinWMNsaremulti-hop,thebandwidthconsumedbydata flowsandtheavailableresourcestoanodearenotlocalconcepts, but arerelated tothe neighboringnodesin carrier-sensingrange [6]. Hence,the challenge introduced hereis how toestimate the available bandwidthforanincomingdataflowontheentirepath without violatingthe bandwidthguaranteesof existingflows and
withtheonlyknowledgeoftheresidualchannelresourcesofeach link. Then,estimatingthewidestpathwhenaccountingfor intra-flow andinter-flowinterferencesremainamajorchallengefor in-terferenceawarerouting.
This paper focuses on the problem of identifying the maxi-mum available bandwidth path from a source to a destination, which is alsocalled theMaximum Bandwidth Problem (MBP)[7]. Weproposeanewroutingmetricthatcapturestheconceptoflink availability based on the PHY and MAC parameters, such as the loss rate, theavailable bandwidth, etc.We use theconflictgraph modelandcalculationofmaximalcliquestoestimatetheavailable bandwidth ofapath whenaccountingforintra-flow interference. Finally, we propose a routing protocol that supports this metric and we study its performance by simulation compared to differ-entexistingroutingmetricsandprotocolsintheliterature.Results showedthat ourproposalsupports thenetworkscalabilityandis wellabletochooserouteswithhighthroughputandlimiteddelay, thus,betterdeliveryofdatatraffic.
The restofthispaperisorganized asfollows:Afterpresenting an overviewoftherelatedworksinSection2,weexplainthe de-signofourresiduallinkcapacitybasedroutingmetricwith inter-ference considerationinSection3.Section4describesourrouting protocolindetails,andSection5presentsourextensivesimulation results.WefinallyconcludeourpaperinSection6.
2. Relatedworks
In thecontextofmesh networks,QoSguarantees aretypically provided forbandwidth and/or end-to-enddelay. Theproblemof QoSisimportantforthesenetworkssincethey aretypicallyused forprovidingbroadbandwirelessInternetaccesstoalargenumber ofdistributedusersandnetworks.QoSsupportinmultihop wire-lessnetworkshasbeenstudiedextensivelyinliteratureandithas beenaddressedfromanumberofaspects.AlargenumberofQoS solutions provideQoSby integratingwireless linkqualitymetrics. New metrics, such as ETX [8], ETT [9], WCETT [9], etc. [10], are proposedtowardsaquality-awarerouting,inordertomorereflect thevariations oflinkqualitycausedbytransmissioncapacity,loss probability,interferences,etc.ETXrepresentsthenumberoftimes a nodeexpectstotransmitandretransmita packetfora success-fuldelivery.ETTisanimprovedversionofETXwheretheETTofa linkisdefinedastheexpectedMAClayerdurationforasuccessful transmissionofapacket.Byaccountingforboth thelinkcapacity andqualityofalink,thismetricoffersabetterestimationand en-suresbothreliabilityandefficiency.WeightedCumulativeExpected Transmission Time (WCETT) is the first multi-channel metric for meshnetworks.Itisdeterminedbytheamountoftimeusedbya frame toattendadestinationandthemaximumtimeperiod con-sumedonlinkssharingthesamechannel.Themainmotivationfor WCETT wasto specificallyreduceintra-flow interferenceby mini-mizing thenumberofnodeson thesamechannel inthe end-to-endpath.
Among thesemetrics,some improvementsofETX,such asETT metric, onlyconsider thetotal capacityofa wireless linkanddo not account forpossible degradationofthebandwidthdueto in-terferencesorparalleldatatransmissions[11].Someother propos-als onlytreat theintraflow orthe interflowinterferences butnot both atthe same time. We have made, in a previous work [12], an experimentalperformancestudyofsomeoftheproposed met-ricsandbasedontheresultsfoundwepropose,inthiswork,new routingmetricsspecificallydesignedforwirelessmeshnetworks.
Fromanotherperspective,asignificantnumberofQoSsolutions for wireless multi-hopnetworksprovide QoSguarantees by inte-grating routingwithresourcereservation.Authors in[13]propose aContention-awareAdmissionControlProtocol(CACP)whichisan admissioncontrolalgorithmforsingle-channelmulti-hopnetworks
basedontheknowledgeoflocalresourcesavailableatanodeand the effect of admitting a new flow on the neighborhood nodes. Based on the carrier sensing mechanism at the MAC layer, they estimatethelocalavailablebandwidthatanode.Theperformance evaluationofCACPwas madeassociatedtoDSR, butitisgeneric enoughtoworkwithalmostanyexistingon-demandrouting pro-tocol forad hocnetworks. Similar solutions havebeen proposed [14–16]inwhichtheavailable bandwidthatanodeisconsidered as the minimum bandwidth within a two-hop neighborhood of that node. Here alsothe route discovery process iscoupled with admissioncontrol.
Inanothersolution[17],authorsproposetwonewmechanisms foravailablebandwidthestimationatanode.Thefirstmechanism increases the carrier-sensing range so that flows which are near theboundaryofthelattercanalsobetakenintoaccount.However increasing the carriersensing range is not supported by current hardware as vendors do not allow low level access to the wire-lessfirmware. In the second mechanism, they used the classical conceptofpacketprobesandestimatedavailablebandwidthusing probedispersionobservedbasedonmathematicalmodels.The au-thorsof [17] integrate bandwidth estimation andadmission con-trolwiththe on-demandLUNAR routing protocol. MARIA : Mesh Admission control and QoS Routing with Interference Awareness [18]isanothersolution.WithMARIA,alocalconflictgraphis com-putedateachnodeandanadmissioncontrolisperformed.Nodes, then, exchange their flow information periodically and compute their available residual bandwidth based on the local maximal cliqueconstraints.Admissiondecisionismadebasedonthe resid-ualbandwidthateach node.Authors alsoproposean on-demand routing protocolwhichincorporatesthe admissioncontrol during theRouteDiscoveryphase.
Some of the discussed existing QoS routing protocols oper-ate withthe knowledge of the available bandwidth of each link [19,20].Intheseworks,theavailablebandwidthofapathis com-puted,generally,basedontheavailablebandwidthofeachlinkon thispath. LiuandLiao [21]gave a newlink metricwhich is the availablebandwidthofthelinkdividedbythenumberofits inter-feringlinks. Thepathbandwidthisthusdefinedastheminimum valueoftheweights ofall itscomposinglinks. Inthemechanism described in[22],the available bandwidthof a pathis the mini-mumbandwidthamongthelinksonthepathdividedbythe num-berofhopsonthepath.Suchformulaisnotabletoreflectthe ex-actpathbandwidth.Authorsin[23]proposeaprotocolthatchecks thelocalavailablebandwidth ofeachnode todeterminewhether itcansatisfythebandwidthrequirementofanewdataflow.Some works[24–26]considertheTDMA-basedMACmodelandfocuson howtoassigntheavailabletimeslotsoneachlinkforanewflow inordertosatisfythebandwidthrequirementofthenewflow.
Thealgorithm suggestedin[31,33] isusedto generateconflict graphs independently of the underlying interference model. Au-thors used the notion of radio co-location interference, which is caused and experienced by spatially co-located radios in multi-radio multichannel (MRMC) WMNs.They experimentally validate theconcept,andproposeanewall-encompassingalgorithmto cre-ate a radio co-location aware conflict graph. This novel conflict graphgenerationalgorithmisdemonstratedtobesignificantly su-periorandmoreefficientthantheconventionalapproach,through theoreticalinterferenceestimatesandcomprehensiveexperiments. The results of an extensive set of simulations run on the IEEE 802.11gplatformstronglyindicatethattheradioco-locationaware conflictgraphsareamarkedimprovementovertheirconventional counterparts.
Authors in[34]propose an interference-awarechannel assign-ment algorithm andprotocol for multi-radio wireless mesh net-worksthataddressoverlappingchannelassignments.Theproposed solutionutilizes a novelinterferenceestimation technique
imple-mented ateach mesh routerto minimize interferencewithin the mesh network and between the mesh network and co-located wireless networks. An extension to the conflictgraph model,the multi-radio conflict graph, is used to model the interference be-tween the routers. They demonstrate their solutions practicality through the evaluation of a prototype implementation in a IEEE 802.11 testbed and they carried out an extensive evaluation via simulations.
These proposals of available bandwidth calculation cannot be solved inpolynomial-time. Eventhoughwecan findtheavailable bandwidth of a given path, it is not easy to identify a schedule thatachievesthatbandwidthsincetheschedulingproblemisalso NP-complete[26].
Furthermore, most of these works consider the total offered linkcapacityanddon’taccountforpossibleinter-flowinterferences which is the main cause of link bandwidth degradation. In ad-dition, in multi-hop WMNs, the consumed and the available re-sourcestoadataflowarenotlocalconcepts,butarerelatedtothe neighboringnodesintheendtoendpath.Then,weneeda com-plete routing solution that gives accurate estimation of available bandwidthalongagivenpath.
Ourmaingoalistodevelopapracticalroutingprotocolthat al-lowspacketstogothroughtheestimatedwidestpath.Thiswidest pathisidentifiedwhenconsideringbothintra-flowandinter-flow interferences.
3. Residuallinkcapacitybasedroutingmetricwith interferenceconsideration
The objective of any QoS solution is to provide applications withguaranteesintermsofbandwidth,delayandjitter.Providing theseguaranteesinadistributedandwirelessenvironment gener-allyrequiresaclearestimationofavailable resourcesvis-a-vis in-dividualrequirementsofeach node,datafloworapplication.This knowledgeisthereforecrucialforanycommunicationonashared channelinthewirelessnetwork.
Sinceitisdifficulttocompletelyseparateintimeandfrequency simultaneous transmissions inwireless networks, some transmis-sionswillbeproducedatthesametimeandinthesamefrequency band. These simultaneous transmissions may, depending on the networktopology,interfereanddegradenetworkperformances.
Mostof theexisting routing metrics inthe literature consider onlythetotalcapacityofawireless linkanddonot takeinto ac-count the possible degradation of bandwidth dueto interference orsimultaneous datatransmissions.Moreover, thereare few pro-posalswhichaddresstheproblemofbothintra-flowandinter-flow interferenceatonce.Weconducted,inapreviouswork[11,12],an experimental performance study of some of the existing metrics and based on its results we propose, in this work, a new rout-ing metric specificallydesignedfor wireless meshnetworks. This metricaimstoaccurately measuretheavailablecapacityofalink whentakingintoaccountboththecurrentuseofthelinkand pos-sibleinterferenceswithneighboringlinks.Infact,inter-flow inter-ference occurs whendifferent flows are beingtransmitted atthe sametime andthensharingthesameavailable resource.Inother words, the interflow interference affects the amount of residual channel resources on each link that will be allocated for a new flow[27].
We propose inthefollowing, thestudyof ascenario to high-lightthisphenomenon.
Inthissimulationweconsidersixnodesconfiguredasshownin Fig.1(a).Theradiotransmissionrangeissetat250mandthe in-terferencezoneis550m.NodeAisout ofthetransmissionrange ofnodeC,butinitsinterferencezone.ThenodeEisinthe trans-missionzoneofthenodeAandisoutoftheinterferencerangeof thenodeC.ThreeCBRtrafficflowsof2 Mbpsareestablished
be-tween thenodesAandB,thenodesEandFandthenodesCand D. Transmissions are spaced by a period of 10 s. The links have differentcapacities,asshowninFig.1(b).
As showninFig.1(b),afterthetransmissionofflows 1and2, node C is still ableto admit flow 3.However, since node Ais in the interference zone of node C, flow 3is capable of consuming the residual capacity of A which is,in fact, insufficient. In other words,evenifthelocalbandwidthpermits,nodeCdoesnot have enoughneighboringbandwidth(whichis,thebandwidthavailable ontheotherlinksinthepath)forflow3.
Toovercometheimpactofinter-flowinterference,weproposea firstmetriccomponentcalledResidualLink Capacitybasedmetric (RLC)givenbytheequationbelow:
RLCl=Bl−
Txl
ω
(1)WhereBl isthelinkbandwidthandTxcorrespondstothe traf-fic occupying the link l in transmission and in reception during thetimewindow
ω
.Theroutingprotocolselectsthelinkwiththe greater RLC. A route’s RLC corresponds then to the minimum of RLCsoflinkscomposingtheroute.RLCroute=min
(
RLCl)
l∈route (2)Using thismetric, each link is initialized to its bandwidth so thattheroutingprotocolcanchoosefromthestarttheroute offer-ing thegreaterbandwidthandthussupportingthegreatertraffic. Sinceitisbasedonrealexchangeofdatainthenetwork,theRLC based metric gives a real estimationand thus allows a more ef-ficient routing. Thisrouting metric isload sensitivei.e. the route decision changeswhen thereare linkswithlargerresidual band-widthwhicharemoreconvenienttosupportlargerdatatraffic.
This metric is improved to consider intra-flow interference. Intra-flow interference occurswhena datapacket isbeing trans-mitted over multiple links along a path. In order to avoid con-flict at the receiving node, some links may remain idle. We de-scribehereaftertheinterferencemodeladoptedinthisworkand the bandwidth estimation of each link. Our newmetric Residual LinkCapacityBasedRoutingMetricwithInterferenceconsideration (RLCI)isdesignedbasedonthisinformation.
3.1. Preliminaries
Inthissection,wegiveanoverviewoftheclique-basedmethod forcomputingtheavailablepathbandwidth.
Inwirednetworks,nodesareabletoknowtheamountof avail-able resourcesinthemediumandhowmuchbandwidth isbeing used.However,inwirelessnetworks,wherethemediumisshared betweenmultiplenodes,communicationfromonenodemayaffect thebandwidthofneighboringnodes.Therefore,neighboringnodes should cooperateina distributedmannertocorrectlyidentifythe available resources which areno longerlocal concepts.Generally, we distinguishtwotypesofinterferences:intra-flowinterferences andinter-flowinterferences.
Tomodeltheinterferencerelationshipbetweenlinks,one com-monmethodistheuseofinterferenceconflictgraph.Thismethod isusedinseveralexistingworks[28,29].Givenawirelessnetwork, eachlinkbecomesanodeintheconflictgraph.Iftwolinksinthe wirelessnetworkinterferewitheachotheri.e.cannotbeactive si-multaneously, we put an edge betweenthe corresponding nodes intheconflictgraph.TheexampledepictedinFig.2illustratesthe interference modeling using conflictgraph. The wireless network based on a six-link chain topology is given in Fig. 2(a) andthe corresponding conflict graph is givenin Fig. 2(b). Assuming that all nodeshavethesametransmissionorcommunicationrangeRc
andthesameinterferenceorsensingrangeRs asrepresented,we
Fig. 1. Inter-flow interference : (a) Simulation topology; (b) Residual link capacity variations.
(a) (b)
Fig. 2. Illustration for interference model. (a) The original graph, (b) The conflict graph.
becausenodebcannottransmitandreceiveatthesametime.Link 1and3arealsoconflictingbecausenode c′stransmissionwill in-troduceenoughinterferenceforthereceptionatnodeb.
Aninterferencecliqueinthewirelessnetworkisasetofvertices that mutuallyconflict with each other.In the conflict graph,the correspondingnodesoftheselinksformacompletesubgraph.
Amaximalinterferencecliqueisacompletesubgraphthatisnot containedinanyothercompletesubgraph.Forexample,1,2,3and 3, 4,5 are maximal cliques while 1,2 and1, 3are not maximal cliques.
Relying on this clique-based formulation, we describe below themethodtocapturebandwidthsharingamonglinkswithinthe path.Givenawirelessnetwork,wedenote{Q1;...;Qk}asthe
max-imalinterferencecliquesetofthenetwork,Cqasthecapacityofa
clique q,B(l) asthetotalbandwidthoflinklandB(p) asthe esti-mation ofthe available bandwidthofpath p.Then,considering a
pathp=<l1,l2,...,lh>,theavailablebandwidthofthepathpis estimatedasfollows[27]: B
(
p)
=min q∈Qp Cq;Cq= 1 P l∈q(
B(1l))
(3)Therationalebehindtheformulais:transmissionsonthelinks in a clique cannot be concurrent but occur in a serial manner. Thus,P
(
B(1l))
representsthetime ittakesfor1Mbitdatato tra-verseallthelinksintheclique q.Cqisthusthebandwidthavail-ableoverthecliqueq.Theavailable bandwidthofthepathisthe bandwidthofthebottleneckclique.
Let’sillustratetheexampleinFig.2(a):Considerthepathp=< a;b; c; d; e;f>.LetB(1),B(2),B(3),B(4)andB(5)ofthenetworkin Fig.2(a)be10,50,25,20and5 Mbps,respectively.
Therearetwomaximalcliquesonthispathwhichare{1,2,3} and{3,4,5}. C{1,2,3}= 1 1 10+ 1 50+ 1 25 =50 8 =6.25Mbps And: C{3,4,5}= 1 1 25+ 1 20+ 1 5 =100 29 =3.44Mbps
Theestimatedavailablebandwidthofpathpis:
min
{
6.25,3.44}
=3.44Mbps.However, thesize ofa maximalclique dependsonhow many linksinterferewitheachother,whichdependsontheinterference modeladoptedinthenetwork.Duetothepopularityofthe802.11 technology,wedevelopourworkbasedonthisMACprotocol.Both
Fig. 3. TRCA interference model with r = 2.
Fig. 4. Conflict graph under TRCA interference model (a) The original graph, (b) The conflict graph.
the two-way handshake DATA/ACK and the four-way handshake RTS/CTS/DATA/ACKof802.11requirethereceiverofadatapacket to sendan ACK back tothe senderofthe datapacket.Therefore, forapackettransmissiontobesuccessful,boththesenderandthe receivershouldnotbeinterferedbyothernodes.aisinterferedby anothernode bifaiswithintheinterferencerangeofb.Inother words, thetransmissions onlinks (u; v) and(s; d) are successful at thesame time ifandonly ifboth s anddare outside the in-terferencerangesofuandv.Thismodelisreferredasthe bidirec-tionaltransmissionmodelorTransmitter-ReceiverConflictAvoidance (TRCA) interference model andis adoptedby manyexisting works [11].
We define the transmission range of a node to be one hop, whiletheinterferencerangetoberhopsandtosimplifyour dis-cussion,wesetr=2butourresultscanbeextendedtoanyvalue ofr.Then,accordingtothisassumption,theinterferencemodeling ofchainednodeswouldbeasshownintheFig.3below:
We also consider a single-radio single-channel configuration. We use theprevious networkin Fig. 3to illustrate the TRCA in-terferencemodel.
BasedontheconflictgraphinFig.2bassumingr=1whichis nottheTRCAinterferencemodelweareusinginthispaper,when node a sends data to node b, node c is not allowed to transmit since itis intheinterference rangeofb. Thismeansthat links1 and 3interfere witheach other.Then, each maximal clique con-tainsthreeconsecutivelinks.
UnderTRCAmodel,whennodea sendsdatatonodeb,noded isnotallowedtotransmitsinceitisintheinterferencerangeofb. Thismeansthatlinks1and4interferewitheachother.Then,each maximalcliquecontainsfourconsecutivelinks(cf.Fig.4b).
Based on the TRCA interference model, since all maximal cliquesintheconflictgraphcontainatleastfourinterferinglinks, theformulaforestimatingtheavailablebandwidthofapathp be-comesasfollows: B
(
p)
= min 1≤k≤(h−4)Ck; Ck= 1 1 B(k)+ 1 B(k+1)+ 1 B(k+2)+ 1 B(k+3) (4)Where B(k) represents the available bandwidth of the link
(
lk,lk+1)
.Furtherdetailsaboutthisclique-basedestimationcanbefound in the following works [10]. According to the example in Fig. 1(a) andunder TRCAinterference model,the estimatedpath bandwidthofthepathp=<a,b,c,d,e,f>is:
Where: C1= 1 1 10+ 1 50+ 1 25+ 1 20 =4.76Mbps And C2= 1 1 50+ 1 25+ 1 20+ 1 5 =3.22Mbps Then,B
(
p)
=min{
4.76,3.22}
=3.22Mbps. 3.2. MetricdesignIn thissection,we introduceournovel metricbasedon resid-uallinkcapacityandaccountingforbothintra-flowandinter-flow interferences. Thepurposeofthismetricis tomeasureaccurately the residual capacity of each link when considering the possible conflictwitheventuallyothertransmissionsoccurringatthesame time.Theroutingdecision,then,willbebasedonlinksofferingthe greatest capacity,inother words,onwidestpaths.Toavoid inter-flowinterferences,weusedtheResidualLinkCapacity(RLC)metric defined previously. Thismetric measures accurately the available bandwidth overa linksinceitcaptures theamountofdataofall flows crossingthespecifiedlink.Weapply,then,thecliquebased bandwidth estimationinorder toconsiderpossible intra-flow in-terferences. To model theinterferences, we used the TRCA inter-ference modeldescribed previously.Thisformulationguaranteesa globalandunique viewinsidethenetworki.e. thateverynodein thenetworkwillbeawareofthewidestneighboringlinksableto supportadditionaltraffic.
Eachnodefirstcomputestheresidualcapacityofitslinks.Then, foreachpathfromthisnodetoadestinationnode,itcomputesthe available bandwidthoverthispathusingthecliquebasedformula introduced previously.WedetailinSection4therouting protocol used to getinformationneededforthe available pathbandwidth calculation.
Givenawireless network,we denote{Q1;...;Qk}asthe
maxi-malinterferencecliquesetofthenetwork,Cqasthecapacityofa
clique q, RLCl asthe Residual LinkCapacity oflinkl andAB(p)as theestimationoftheavailablebandwidthofpathp.Foreachlink l,RLCl isestimatedasfollows:
RLCl=TBl−
Txl
ω
(5)Where,TBl correspondstothetotalavailablebandwidthoflink
l andTxl corresponds totheamount ofdataoccupyingthe linkl during thetimewindow
ω
.Then, considering a path p=<l1,l2,...,lh>, the available
bandwidthofthepathpisnolongertheminimumofRLCsoflinks composingthepathbutitisestimatedasfollows[27]:
AB
(
p)
= min 1≤k≤(h−4)Ck; Ck=µ
1 RLCk + 1 RLCk+1 + 1 RLCk+2 + 1 RLCk+3¶−1
(6)Hence, each node knows the residual capacity ofneighboring linksandisabletomeasurethewidestpathtoadestinationnode whileconsideringallpossibleinterferingtransmissions.
The main advantage ofour routing metricRLCI consists ofits considerationforbothintra-flowandinter-flowinterference. How-ever,thismetricisnotisotonicandaddressestheproblemoflocal visionofavailable resources.Wedefinetheisotonicitypropertyas introducedin[27]:
Fig. 5. Problem of local vision in the widest path estimation.
Definition. Left-isotonicity : The quadruplet(S; ;
ω
; ≥ ) is left-isotonic ifω
(a) ≥ω
(b) impliesω
(ca) ≥ω
(cb),foralla; b; c∈S,whereSisasetofpaths,isthepathconcatenation oper-ation,ω
isafunctionwhichmapsapathtoaweight,and≥isthe orderrelation.Toillustratethisproblem,weconsidertheexampleofthe net-work inFig. 5wherenumberson thelinksindicate theavailable link capacitiesinMbits/s,thewidestpathfromnode ato nodey is”a,b,c,d,y”becausethepath”a,e,f,g,y”presentsa bottleneck (g,y) limited to 5 Mbits/s. However, fromnode x to node y, the widestpathisinstead”x,a,e,f,g,y”becauseitcomprisesthelink (f,g)offeringgreatercapacity.
According to our metric RLCI, the calculation of the available bandwidthsoneachpathofthiscasestudyisgiveninthe follow-ingequations: AB
(
a,b,c,d,y)
=³
1 10+ 1 10+ 1 10+ 1 10´
−1 =2.5Mbits/s (7) AB(
a,e,f,g,y)
=³
1 10+ 1 10+ 1 20+ 1 5´
−1 =2.22Mbits/s (8) AB(
x,a,b,c,d,y)
=³
1 5+ 1 10+ 1 10+ 1 10´
−1 =2Mbits/s (9) AB(
x,a,e,f,g,y)
=³
1 5+ 1 10+ 1 10+ 1 20´
−1 =2.22Mbits/s (10)In this case, a suitable routing mechanism isneeded to unify thevisionofallnetworknodes.
The studyofthisperformance metricrequiresintegrationinto a routing protocol adapted to compensate for its non-isotonicity. The protocolspecification andevaluation ofperformance will be detailedinthenextsection.
4. LinkAvailabilitybasedRoutingMechanism(LARM)
4.1. Designconsiderationsandpathselection
Westudiedinapreviouswork[32]throughsimulationsthe im-pact of PHY/MAC protocols on higher layers. This study has not been done toannounce orpromote oneparticular routing proto-col asthe“winner” protocolhavingthebestperformance butitis donetochecktheconditionswhereeachoftheproposedprotocols achievesthehighestefficiency.
Results haverevealedanotable superiorityinthegeneral per-formance of proactive routing protocols when used with IEEE 802.11n based MAC layer, particularly when the network get denser.
Reactive routing protocols are better fittinghighstability net-works. Theyare not very suitable formesh networks because of thedelaysrequiredtoestablishavalidrouteforeachdatasending. Infact,evenifmeshnodesarestatic,linksbetweennodesevolve andtheircharacteristicschangepermanently.
Ontheproactiveside,OLSR,cannotmeetthenon-isotonicityof ourmetricbecauseoftheuseofMPRsetswhichrestrictvisionto twohopsandthusgiveapartialviewofthenetwork.
Hence,tocaptureaccuratelythevariabilityoflinksthroughput, we adopta proactiverouting protocolthat we calledLink Avail-ability based RoutingMechanism - LARM. This protocol is based on DestinationSequenced Distance Vector (DSDV) routing proto-colwithanumberofimprovementsthatwecansummarizeinthe followingpoints:
– Explicitdetectionofneighborswithlinkqualitycomputation, – Flexibilityanddynamic routingdecisionadaptabilitywithRLCI
routingmetric,
– Triggeredandaperiodicupdatesofroutingtableinsteadof pe-riodicupdates,
– selection,accordingtoan estimationoftheofferedbandwidth, asetofcandidatepathstobediffusedasbestpathsfor neigh-boringnodes,
– Updatescontrolledbyathresholdbetweentheoldvalueofthe routingmetricandthenewvalueinordertoensurestabilityof theroutesandminimizeroutingoverhead.
Moreover,sinceourroutingmetricRLCIisessentiallybasedon theresidual capacityofthefirst fourhops toa destinationnode, we propose that each source node includes this portion of the routeinthedatapacket.
Ourprotocol,asdescribed,canleadtosignificantrouting over-headwhichcandegradenetworkperformancesespeciallywhenit getsdenser.Forthatreason,weproposesomesolutionstoreduce andoptimizethisoverheadintermsofnumberandsizeofcontrol messages.
1. Thedisseminationofnewroutinginformationisperformed when needed andnot periodically in order to avoid over-loading the network by control messages containing un-changed orrelatively stable information.In other words,a nodebroadcastsanewpathtoadestinationonlywhenthe residualcapacitiesoflinksformingthepathhaveundergone considerablechangeswhichcould,then,changetherouting decision.Theestimationofthischangeanditsimportanceis setaccordingtoathresholdvaluethatwedescribeindetail later.
2. The threshold value of the RLC of a link is important be-causeit influences, firstly, theknowledge of link state and secondly theoverhead induced by updates. A frequent up-dateof thismetric allows adaptabilityof the routing deci-sionandmakesitabletoreactquicklyandeffectivelyto net-workscalability interms oftopology andload. However, a frequentupdateofthismetriccanleadtonetworkoverload bycontrolmessages.We proposeathresholdvalue propor-tionaltodatatrafficthroughalinkandtotalcapacityofthe givenlink inorder to estimate the linkoccupation andits availabilitywhen neglectingthe RLCvariationsdueto con-trolmessagesexchange.
3. Toavoiddisseminate all possiblepaths to a given destina-tion,weselectonlykpotentialcandidatepathsasbestpaths for neighbor nodes based on the available bandwidth on eachpath.Thiseliminates pathswithverylow throughput, reducesthesizeofcontrolmessageswhichbetteradaptsour solutionforscalability.
4. SincethecalculationofthemetricRLCIisessentially based ontheresidualcapacityofthefirstfourhops only,we sug-gestthateachnodebroadcastsforeachdestinationthefour nexthopsfromitselfandtheirrespectiveresidualcapacities. Thisreducesthegeneratedoverhead.
Table 1
Topology information base format.
Dest. Path Next hop Second next hop Third next hop Fourth next hop AB(p) Timer Best path
D P1 n1 n2 n3 n4 AB ( p 1 ) T ( p 1 ) 0/1
RLC ( s, n 1 ) RLC ( n 1 , n 2 ) RLC ( n 2 , n 3 ) RLC ( n 3 , n 4 )
P2 m1 m2 m3 m4 AB ( p 2 ) T ( p 2 ) 0/1
RLC ( s, m 1 ) RLC ( m 1 , m 2 ) RLC ( m 2 , m 3 ) RLC ( m 3 , m 4 )
4.2. Routinginformationupdate
Below, we detail ofour routing mechanism. We give, first, an overviewoftheroutinginformationmessagewhichshouldbe dis-seminated in the network in order to ensure reliable path se-lection. Afterward, we present the topology informationdatabase used to store routing information aboutall destination nodes in thenetwork.Wethendescribe,ourpacketforwardingmechanism which satisfies the consistency requirement. We apply Eq.(6) to estimatetheavailablebandwidthofapath.Tosimplifyour discus-sion,intherestofourpaper,weuseavailablebandwidthinstead ofestimatedavailablebandwidthwhenthecontextisclear.Onthe other hand,widestpathreferstothepaththathasthemaximum estimatedavailablebandwidth.
First,allthenodesinthenetworkexchangeperiodicallyHELLO messagestoannouncetheirneighborhoodandtodiffuse informa-tionabouttheamountoftransmitteddataduringa timewindow
ω
on each link with symmetry consideration. Upon receipt of a HELLOmessagefromanewneighbor,thenode,usingacross-layer mechanism,calculatestheresidualcapacityofthelinkconnecting toitandinsertanewentryintheroutingtable.Whenthe neigh-bor originatingthe HELLO message is alreadyknown, we simply updatethecorrespondingresiduallinkcapacity.In thetraditional distance-vectormechanism, a node onlyhas toadvertisetheinformationofitsownbestpathtoitsneighbors. Eachneighborcanthenidentifyitsownbestpath.InSection3,we mentionedthatifanodeonlyadvertisesthewidestpathfromits ownperspective,itsneighborsmaynotbeabletofindthewidest path.
In ourrouting protocol, ifa node findsa newpath ordetects changes intheestimated availablepath bandwidth,it will adver-tise thispath informationto its neighborsusing a Routing Infor-mation Vector (RIV). Given a path p from a source node s to a destination node d,node s advertises the tuple(s, d, NH, RLCNH,
SNH,RLCSNH,TNH,RLCTNH,FNH,RLCFNH).NH,SNH,TNHandFNHare
the next hop, the second next hop, the third next hop and the fourth next hop on pfrom s, respectively. RLCNH, RLCSNH, RLCTNH
and RLCFNH are the corresponding residual link capacities. Based
on the informationcontained inthis RoutingInformation Vector, each nodeknowsthelinkquality aboutthe firstfourhopsofthe identified pathandcancalculateits availablepathbandwidth us-ingEq.(6).
4.3. Routingtable
Each node maintainstwo tables: the traditional routing table usedinDSDVandatopologyinformationdatabasewherethenode maintainsall pathsadvertisedby itsneighborsorfound byitself. In addition, for each path p, each node computes andmaintains theavailablepathbandwidthAB(p)andatimerindicatingthe de-layfromthelastupdate.
Finally,thenode indicates witha Booleanvalue,ifthispathp is the widest path or not accordingto its own localvision. This widestpathshouldbe storedintherouting tabletoo.Theformat ofthetopologyinformationdatabaseisasfollows(Table1):
Based on DSDV,each routing entryistaggedwith asequence numberwhichisoriginatedbythe destination,sothatnodescan quickly distinguish stale routes from the new ones. Given two routeentriesfromasourcetoadestination,thesourcealways se-lectstheonewiththelargersequencenumber,whichisnewer,to bekeptintheroutingtable.
After the network accepts a new flow or releases an existing connection,thelocalavailablebandwidthofeachlinkwillchange, and thus the widest path from a source to a destination may evolve. In order to avoidfrequent updates and network flooding withcontrolmessages,wedefineaRLC_Thresholdproportionalto the total capacityofa link. When thechange ofthe residual ca-pacityofalink islargerthanthisthreshold,thenodewill adver-tisethenewinformationtoitsneighbors.After receivingthenew bandwidthinformation,theavailablebandwidthofapathtoa des-tinationmayberecalculated.
5. Performanceevaluation
In thissection, we conduct the simulation experimentsunder NS-3 [30] toinvestigate the performance of ourrouting protocol forfindingthemaximumavailablebandwidthpath.Wecarriedon simulationsintwodistinctparts:afirstpartdedicatedtothe per-formanceevaluationofourmetricRLCIcomparedtoexisting met-rics such as Hop-Count (HC), ETX and ALM. The second part is carried out to evaluateour routing mechanismLARM in compar-ison with other existing protocols such asOLSR, DSR, DSDV and HWMP. This choice considers differentrouting strategies, diverse routingmetricsanddistinctPHY/MAClayersassociated.Forall per-formancescenarios,weconsiderasingle-radiosingle-channel con-figuration.
5.1. Routingmetricevaluation
The following simulations show how the estimation of the available path bandwidth calculated by our metric RLCI achieves a gain inthroughput by identifying widest paths,ensuring, thus, better traffic fluidity. We considered a random network topology with40nodessharinganareaof2500×2500m2 (seeFig.6).We
chose a random distribution of thenodes inorder to obtain dif-ferent densitylevels. This allows usto studyseveralinterference scenarioswhichisourfirstobjective.Inotherwords,ourapproach canbevalidatedorverifiedinthebestandworstinterferencecase scenario. Manyworks [35,36],however,have showedthat strate-gic nodeplacement inwirelessmesh networkswouldgive better network performancesinterms ofcoverage,connectivity andfair meshcapacity.
We generated 12 TCP data flows with an average rate of 1 Mbits/s, i.ea traffic load of30% ofthe total numberof nodes. These dataflows are transmittedatdifferenttimesso asto have differentestimationsoftheavailablebandwidthforeachflow.The pairsofnodesincommunicationareselectedaccordingtothe dis-tance betweenthem so as tohave differentroute lengths. Fig.7 represents,foreach metric,theaveragethroughputvariationwith the traffic charge injected in the network. The results show that the throughput decreasesprogressively withthetraffic load. This
Fig. 6. Network Topology: 40 nodes distributed over a surface of 2500 × 2500 m 2 .
Fig. 7. Throughput evolution with the traffic charge.
decrease is dueto data losscaused by possible collisions or sat-urated channel. Our metric RLCI incorporated in LARM protocol performs better than the other metrics with an important traf-fic load. Indeed, routing solutions basedon shortestpath quickly suffer from network congestion anddo not propose adaptability strategytosuchsituation.WithETX,bymeasuringthelossrateon links, itenablestheroutingprotocoltoselectpaths withbest de-livery rates.However, asthelossrate estimationisbasedonlyon smallprobemessagesandasitdoesn’tconsidertheimpactof in-terferenceondatadelivery,ETXmayunderestimatedatalossand, then,donotguaranteeoptimalrouting.
RLCI implements several performance criteria at once, allow-ing it to establish the best performance compromise and thus assess the best link quality. Compared to ETX and ALM, RLCI is based onresidual linkcapacitywhich enablesit toquicklyadapt the routing decisionintolinksoffering moreavailable bandwidth andthen betterappropriate to supportmoretraffic. Furthermore, RLCIconsiderstheimpactofintra-flowandinter-flowinterferences
whichenablesittobetter fitsituationsofcongestionor through-putdegradationcausedbyneighborhoodtraffic.
TofurtherdetailthecontributionofourmetricRLCI,we repre-sentedinFig.8thecumulative throughputperpairofnodesand thisfordifferentpathsobtainedwithRLCI compared,respectively to, HC, ETX and ALM. Each data flow is represented by a point: the y-axis denotes the throughput values obtained for the met-ricRLCI,thexaxisdenotesthethroughputobtainedforthe met-ricHC,ETX andALMrespectively.Thefunctiony=xcorresponds tothecasewherethe twometrics selectthesamepathorpaths achievingthe same throughput.The points above the line y= x representsthedataflow forwhichRLCI givesbetterperformance asHC,ETXorALMrespectively.Theresultsofthefigureshow bet-terperformanceofRLCIcomparedtoHConmostdataflows.This isillustratedbythedenseregionabovetheliney=x.Thisregion showsthatroutingbasedonthemetricRLCIoftenfindspathswith higherratescomparedtoHCbasedroutingwhichisinsensitiveto linkqualityandtrafficload.
Comparedto ETX andALM, the same observations are made. There are, however,some cases whereALM and ETX offerlarger throughputthanthatobtainedwithRLCI.Thisstatement is repre-sentedbythefew pointsbelowthe liney= x.However, the dif-ferencesbetween the throughputs obtainedare minimal in most cases. For example, a flow, providing a throughput equal to 512 Kbits/swithETX,providesathroughputequaltoabout460Kbits/s withRLCI.
Werandomlyselect74randommulti-hopdataflowsand inves-tigatethethroughputofindividualflowproducedbythedifferent routing metrics. Fig. 9 shows the simulation resultsof the flows whicharesorted accordingto thethroughputofourmetricRLCI. Thisfigure also showsthe gapbetween the practical throughput andtheestimatedavailablebandwidth.
Wefirstanalyzethedifferencesbetweentheresultsofthe con-sideredmetrics.WecanobservethatwithHC,throughputsarethe smallest.Indeed,basedonlyondistance,itismorelikelytoselect overloaded and interfering paths with poor link quality for data transmission.ETXandALMperformbettercomparedtoHC. How-ever, we can observe that they do not work well in some cases
100 200 300 400 500 600 700 800 900 1000 1100 100 200 300 400 500 600 700 800 900 1000 1100
Throughput when RLCI (Kbits/sec)
Throughput when HC (Kbits/sec) y=x 100 200 300 400 500 600 700 800 900 1000 1100 100 200 300 400 500 600 700 800 900 1000 1100
Throughput when RLCI (Kbits/sec)
Throughput when ETX (Kbits/sec) y=x 100 200 300 400 500 600 700 800 900 1000 1100 100 200 300 400 500 600 700 800 900 1000 1100
Throughput when RLCI (Kbits/sec)
Throughput when ALM (Kbits/sec) y=x
Fig. 8. Per pair cumulated throughput of RLCI, (a) compared to HC; (b) compared to ETX; (c) compared to ALM.
sinceETXandALMofagivenpatharebothdeducedfromasimple additionofETXandALMoflinksformingthepath.Thesemetrics neglect the performance degradation caused by interference be-tween successivelinksandmayselectalow available bandwidth path. Therefore,ourmetric isrelatively moreefficient forfinding thehigh-throughputpath.Wenowinvestigatewhythereisa dif-ferencebetweenthepracticalthroughputandtheestimated avail-ablebandwidth.Fig.8showsthatthepracticalthroughputmaybe more thanor lessthanthe estimatedone. First,our work devel-opsanunderestimateofthetrueavailable bandwidthbecausethe theoreticalcalculationofourmetricdoes nottakeintoaccountof packetoverheadsandcollisionsintheMAClayer,whichreducethe actual throughputin a realnetwork. Anotherfactorthat leads to thepracticalthroughputislessthanthetheoreticalthroughputis
100 200 300 400 500 600 700 800 900 1000 1100 0 10 20 30 40 50 60 70 80 Throughput (Kbps) Flow ID LARM-RLCI OLSR-ETX HWMP-11s DSDV-HC DSR-HC Estimated Bandwidth
Fig. 9. Throughput of data flows.
Table 2 Simulation parameters. Parameter Value Simulation duration 200 s Topology 250 0 × 250 0 m Number of nodes 20-40-60-80-100 Sensing range 250 m Frequency band 2.4 GHz Transmission power 30 dBm
Propagation model Nakagami-m
Datarate 1Mbits/s
Packet size 1024 Bytes
Buffer size 100 packets
OLSR-Hello interval 2 s
OLSR-TC interval 5 s
RANN interval 3s
Proactive PREQ interval 3 s
the assumptiononinterferencerange.We assume2-hop interfer-encebutsituationslikeFig.1canhappen.Thepracticalthroughput is thussmaller thanthe estimatedpathbandwidth.Onthe other hand,simulation resultsshowthat ourapproachgivesan overes-timation foralmost alloftheflows withlargehop-count distance which can be explainedby thefact that our metricisstill based on traffic history.Thistraffic can changefrominstantly andso,a givenlinkcouldbemoreavailableaftersignificanttraffici.eit of-fersgreaterrealbandwidththantheestimatedone.
5.2. Routingmechanismevaluation
In this section, we evaluate the performance of our routing mechanismLARMwithcomparisontootherexistingprotocols.The studyisdoneunderdifferentstrategiesandPHYandMAC param-etersinordertostudythepossibleinteractionandcoexistenceof such protocols in a heterogeneous multi-rate mesh environment and this,to figure outifthe choice ofcombiningPHY/MAC/NWK couldaffecttheoverallnetworkperformanceandwhy.We consid-ered amultitude ofcombinationsof theprotocolstack asshown inFig.10.
Several proactive, reactive and hybrid routing protocols, im-plementing each a different routing metric, are considered : LARM(RLCI), DSR(HC),OLSR(ETX),DSDV(HC)andHWMP (ALM). We remind that HWMP isimplemented atlayer 2butis consid-eredhereasaroutingprotocolfororganizationalreasons. Simula-tionparametersaregiveninTable2.
Endtoenddelay
Fig.11showstheaverageendtoenddelayforthevarious com-binations whileincreasingthenumberofdeployednodesandthe
Fig. 10. Considered protocol stack.
End
to
End
Delay
(ms)
Number of nodes
End
to
End
Delay
(ms)
Number of nodes
Packet
Loss
rate
(%)
Number of nodes
Packet
Loss
rate
(%)
Number of nodes
Fig. 12. Loss rate evolution with the traffic load : (a) low traffic; (b) important traffic.
traffic load in the network: low traffic if only 30% node pairs of thetotalnumberofdeployednodesarecommunicating,important trafficif70%.
It identifies the average time between sending a packet from a transmitter node and receiving it by the destination node, in milliseconds. This period includes all potential delays caused by queues,delaysattheMAClevel,retransmission,radiopropagation andtransfertime.
The results show that, for all combinations, the end to end delay increases with the size of the network, because in general, increasing the number of nodes in the net-work, neighborhood changes and the number of hops
be-tween the source and the destination are also increasing. Thus, the delays caused by buffering delays and queues at in-termediate nodes contribute significantly to the end to end delay.
This increase is particularly significant with DSR and HWMP protocols.Thiscanbe explained,fromarouting pointofview,on the one hand, by the reactive nature ofthese two protocols and particularlydelaysneededateach“routerequest” toestablishvalid routes.Moreover,sincetheDSRroutingdecisionisbasedsolelyon thenumberofhops,thisgivesadvantagetootherprotocols incor-poratinglinkqualitymetricstoachievebetterperformance partic-ularlyintermsofdeliverydelay.
Traffic Charge (%)
Traffic Charge (%)
Fig. 13. Throughput for a network topology of : (a) 40 nodes; (b) 80 nodes.
Fordensetopologies,networksbasedontheIEEE802.11nMAC layerandMIMOtechnologyprovide,inmostcases,fasterdelivery. This is mainly dueto the link capacityand high throughput of-feredbythephysicallayer.Inallconditionsandtrafficdensity,our protocolLARMperformsthebestthankstotheRLCImetricwhich incorporatesthediversityofratesandbenefitsfromlargelink ca-pacitiesanddataaggregationmechanismofIEEE802.11nMAClayer particularlyincaseofheavytraffic.ComparedtoOLSR,delay varia-tionofourprotocolLARMshowssomesimilarityduetothe proac-tive natureofbothroutingprotocols.However,LARM, functioning with ourmetric RLCI,is moreable toselect widestpaths andto ensurefasterpacketdelivery.Itoffersanaverageendtoenddelay slightlylowerthanOLSRthroughitsabilitytoborrowroutes offer-ingmorethroughput.Notethatforthetopologiesof20nodesand
40nodes,the valuesof thedelays generatedby LARM andOLSR arenotnullandareequalto,respectively,0.921msand1.756ms forLARMand0.927msand2.276msforOLSR.
Lossrate
Fig.12showsthelossratewhichiscalculatedfromthenumber oflostdatapacketsfromalltransmitteddatapackets.The results show that forall scenarios, the loss rate is affected by both the numberofnodesandthetrafficload.
Ingeneral, forlow traffic, thelossrate issignificantly affected bytheroutingdecision.Routingprotocolsbasedonthehopcount arelessreliablebecausetheshortestpathsmaycontainsaturated, interferingor breakable linkscausing, indeed, datacorruption or packetloss.
0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 20 30 40 50 60 70 80 90 100 Routing Overhead (%) Number of nodes LARM-RLCI OLSR-ETX HWMP-11s DSDV-HC DSR-HC 1.5 2 2.5 3 3.5 4 4.5 5 5.5 20 30 40 50 60 70 80 90 100 Routing Overhead (%) Number of nodes LARM-RLCI OLSR-ETX HWMP-11s DSDV-HC DSR-HC
Fig. 14. Routing Overhead evolution with traffic charge. (a) low traffic; (b) important traffic.
Throughput
The average throughput is given in Fig. 13. It is expressed in kbits/s andmeasures thetotalnumberofbitsofreceived packets during thesimulationperiod.Sincethethroughputvariationwith thenetworkdensityisnotsignificant,wehavechosentorepresent theevolutionofthisperformancecriteriabasedonthetrafficload whichismorerepresentative.Weusedtworepresentative topolo-giesnamely,a40nodesnetwork(seeFig.13(a)) andanetworkof 80nodes(seeFig.13(b)).
Results show that,depending onthe traffic load, the through-putdecreasesgradually.Thisdecreasereflectsthelossespreviously identified.
OurprotocolLARMperformsthebestwithanimportanttraffic load. Itmaintainsagoodthroughputlongerbecauseofhischoice ofthemostavailablelonksofferingthegreatestrates.Routing pro-tocols basedonhopcountprefertheshortestpathsandtherefore thenetworkquicklysuffersfromcongestionandthereisno reac-tion to address this situation.OLSR achieved better results com-paredto DSDVandDSR,thanks tothe capacityofitsETX metric to measure thelink lossrate, therefore,to choose linkswiththe greatestdeliveryrates.However,sincetheETXestimationisbased only onsmall probemessages, thismaylead itto underestimate dataloss.
HWMP performance is very similar to DSR witha slight im-provement. Indeed, the metric ALM considers, inaddition to the errorrate,linkcapacities.Butthisvalueistheoreticalonlyand cor-respondstothetotalcapacityofthelink.
Routingoverhead
Fig.14showsthenormalizedrouting overhead.Itiscalculated fromthe numberoftransmittedcontrol packets amongdelivered datapackets.
The similar appearance of most results leads us to conclude that the PHY/MAC layers haveno significant impact on this per-formance metric.From arouting pointofview,wecannote from theseresultsa significantoverheadgeneratedby DSDVcompared to otherprotocolsandthisindependentlyofthelowerlayers and the traffic load. Thisdisparity is dueto the massive exchange of routing information particularly in dense networks. The HWMP protocolalsosuffersfromasignificantoverheadsinceitusesboth typesofcontrolmessages(proactivetreebaseddisseminationand reactiveone).
The exchange of routing information between the different nodes ofthe networkwouldbe the main factorinthe overhead. Indeed,inaproactiveroutingstrategy,controlleddisseminationas the use of MPR sets with OLSR, significantly reduces the
num-ber of control messages circulating in the network. LARM man-ages thisoverheadby usingonlytriggered updatesallowing itto generatelessoverheadcomparedtoDSDV,butits performanceis slightly lessercompared to OLSR, particularlyin dense networks. Including the first 4 links of the path in the header ofthe data packet alsogenerates additional overhead but remains negligible vis-a-vis theoverheadinduced bytheexchange ofrouting tables. Sincethecalculationofthisoverheadisbasedsolelyonthe num-ber of messages, it can probably provide more degraded perfor-manceifthecalculationwasmadeaccordingtothesizeofcontrol messages.
Theroutingmetricslightlyaffectsthisoverhead.Indeed,asETX andALM usecontrol messagesforcollecting routing dataforthe calculationofthemetricwhichcanbeasourceofadditional over-head when there is important traffic or in case of a dense net-work. With its cross-layer mechanism, our protocolLARM avoids thissourceofoverhead.
5.3. Synthesis
Performance evaluation conducted on the ns-3 simulator showed, ingeneral,goodperformanceofourmetricRLCI incorpo-rated into the protocolour LARM. In terms of endto end delay, ourmetriccanchooselinksofferinghighthroughputandenabling fasterdelivery ofdatapackets.Coupled withaMAClayeroffering highheterogeneouscapacities,ourrouting mechanismreached,in most scenarioand topologies,relatively briefdelayscompared to other protocolsandmetrics. Byfocusingonthe lessloaded links, our routing solutionpreventsthe formationofbottlenecks which isusuallythemaincauseofdataloss.Thisfeatureexplainsthe re-sultsoflossratewhichremainsfairlylowcomparedtoother solu-tionsevenindensenetworkand/orheavytraffic.Theadvantageof ourmetricintermsofthroughputisdoubleeffect:first,ourmetric calculates thewidestpathtowarda destinationandgivesa guar-antee ontherateofferedforeachdatatransmission.Ontheother hand,themetricRLCIconsidersbothintra-flowandinter-flow in-terferences, whichpermits aproper, accurate andcontinued esti-mationofavailablebandwidthonthenetwork.Byexploitingthese aspects, the routing strategy based on this metric RLCI acquires adaptability to network scalabilityin terms oftopology and load andthusoffersQoSguaranteesforterminalnodes.
However, intermsofoverhead,ourroutingsolutionrequiresa significant exchange of routing information to ensure a coherent andunifiedview ofavailableresourcesonthenetwork.This over-head isinevitable butcan be managedifwe properlychoosethe parametersofourmetricRLCI.Indeed,theoverheadfactorstrongly depends on the update frequency of routing information on the network.Thisparameterrepresentsacompromisebetweenthe ac-curacyofthecalculatedinformationandbroadcastingcostsofthis information.Forthispurpose,wehaveprovided,inafurtherwork, a detailed studyofthis factorto measure its impacton the effi-ciencyofourroutingsolution.
6. Conclusion
In this paper, we investigated the maximum available band-width path problem, which is an important issue to support quality-of-service in wireless mesh networks. We exploited PHY andMACcharacteristicsoftheIEEE802.11nlayertodesignan ef-ficientroutingmetricthatestimatestheavailablepathbandwidth when takinginto accountboth intra-flow andinter-flow interfer-ences.
Ourmetricmeasures,first,theresiduallinkcapacityinorderto address the ability oflinks tosupport additional traffic andthus prevent the creation of bottlenecks. The route selection is based on the evolution of this metric over time. Based on the conflict
graph model andcalculation of maximal cliques, we proposed a methodtoestimate theavailable bandwidthwhenaccountingfor theneighborhoodinterferences.Finally,we definedarouting pro-tocolthatsupportsthismetric.
Our solution also exploits link-diversity to perform load-balancing at each wireless hop by spreading the traffic load of theincomingflow onmultipleconcurrent linkswhileconsidering intra-flowinterference.
The performances of this protocol were evaluated by simula-tionwithcomparisontootherexistingroutingmetrics and proto-cols (DSR-HC, OLSR-ETX, HWMP-ALM, DSDV-HC). Results showed substantial performance improvements compared todifferent ex-istingroutingprotocolsandmetricsspeciallyintermsofdelayand throughput.
The isotonicityproperty of the routing metric would improve theefficiencyofoursolutionparticularlyintermsofoverhead,for that reason we propose in a further work, to studythe feasibil-ityofanisotonic pathweightbasedon ourRLCImetric.It might be interesting, also,to adapt our routing solution to an applica-tiontakingadvantageofamulti-channelcontext.Inthiscase,one shoulddistinguishinterferenceon thesametransmissionchannel tothosethatmayoccuronadjacentchannels.
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Chiraz houaidia is currently an assistant professor at the Higher Institute of Applied Science and Technology of Sousse in Tunisia and a mem- ber researcher in the CRISTAL laboratory, Tunisia. She earned her engineering and master degrees in computer science at the National School of Computer Science (ENSI), Tunisia in 2010 and 2011, respectively. She received her Ph.D degree jointly from ENSI and University of Toulouse JEAN JAURES, France(where she was also member of IRIT, Toulouse), in 2016. Her research focuses on issues related to wireless networking, sensor and mesh networks, routing, quality of service (QoS), cross-layer designs, etc.
Hanen Idoudi is an associate professor in Computer Science at the National School of Computer Science, University of Manouba, Tunisia and a member researcher in the CRISTAL laboratory.
She earned her engineering and master degrees in computer science both from the National School of Computer Science, Tunisia in 2001 and 2002, respectively. She received her Ph.D degree jointly from the same institution and University of Rennes 1, France (where she was also member of IRISA , INRIA , Rennes), in 2008.
Her research focuses on issues related to wireless networking: ad hoc, sensor, mesh and cognitive radio networks, MAC optimization, networks modelling and performances evaluation, routing, quality of service (QoS), energy conservation and scheduling.
Adrien van den Bossche is an assistant professor at the University of Toulouse Jean Jaurès and a member of the IRT team of the Institut de Recherche en Informatique de Toulouse, France. He has obtained his Ph.D in Computer Science in 2007. His research focuses on Medium Access Control and localisation/ranging in both wireless ad-hoc/sensors/mesh networks and the Device Layer of the Internet of Things (DL-IoT). He is involved in the Smart Home of Blagnac, a prototype house dedicated to maintaining the elderly at home. He teaches networking and embedded computing at the Institut Universitaire de Technologie de Blagnac, France.
Leila Azouz Saidane is Professor at the National School of Computer Science (ENSI), at The University of Manouba, in Tunisia and the Chairperson of the PhD Commission at ENSI. She was the Director of this school and the supervisor of the Master’s Degree program in Networks and Multimedia Systems. She is the co-director of RAMSIS group of CRISTAL Research Laboratory (Center of Research in Network and System Architecture, Multimedia and Image Processing) at ENSI. She collaborated on several international projects. She is author and co-author of several papers in refereed journals, magazines and international conferences.
Thierry Val obtained his Ph.D. in computer science at Blaise Pascal University, ClermontFerrand, France, in 1993. In 1994, he became a lecturer at the University of Toulouse, where he currently teaches networks and computing systems. He obtained his HDR in 2002.He is now a professor for the University of Toulouse at the Blagnac Institute of Technology. He was sub-manager of the LATTIS laboratory, where he managed a research activity on wireless local networks and related protocols. He is now a member of IRIT-CNRS laboratory of Toulouse in IRT team. His current research focuses on wireless networks in smart homes, DL-IoT (Device Layer-Internet of Things).
