Fuzzy Active Queue Management for Congestion Control in Wireless Ad-Hoc

Transcription

1 50 The Iteratioal Arab Joural of Iformatio Techology, Vol. 4, No. 1, Jauary 2007 Fuzzy Active Queue Maagemet for Cogestio Cotrol i Wireless Ad-Hoc Essam Natsheh, Adza B. Jata, Sabira Khatu, ad Shamala Subramaiam Deartmet of Comuter ad Commuicatio Systems, Uiversity Putra Malaysia, Malaysia Abstract: Mobile ad-hoc etwork is a etwork without ifrastructure where every ode has its ow rotocols ad services for owerful cooeratio i the etwork. Every ode also has the ability to hadle the cogestio i its queues durig traffic overflow. Traditioally, this was doe through Dro-Tail olicy where the ode dros the icomig ackets to its queues durig overflow coditio. May studies showed that early droig of icomig acket is a effective techique to avoid cogestio ad to miimize the acket latecy. Such aroach is kow as Active Queue Maagemet (AQM). I this aer, a ehaced algorithm, called Fuzzy-AQM, is suggested usig fuzzy logic system to achieve the beefits of AQM. Ucertaity associated with queue cogestio estimatio ad lack of mathematical model for estimatig the time to start droig icomig ackets makes the Fuzzy-AQM algorithm the best choice. Extesive erformace aalysis via simulatio showed the effectiveess of the roosed method for cogestio detectio ad avoidace imrovig overall etwork erformace. Keywords: Active queue maagemet, ad-hoc etworks, fuzzy systems, itelliget etworks, etwork cogestio. Received Setember 24, 2005; acceted March 28, Itroductio Mobile ad-hoc etwork is a etwork without ifrastructure where every ode ca work as a router. Every ode has rotocols ad services to request ad rovide services to other odes with the cogestio hadlig caability. Traditioally, the cogestio hadlig is doe through Trasmissio Cotrol Protocol (TCP). This rotocol seds cogestio sigal (dro icomig ackets) whe the ode's queue is full (queue legth is maximum). Some studies [4, 11] showed that early droig of icomig acket before reachig the maximum queue legth is a effective techique to avoid cogestio ad to miimize the acket latecy, e. g., Active Queue Maagemet (AQM) dros icomig ackets before the queue is full i cotrast to traditioal queue maagemet which starts droig oly whe the queue i overflowed. Mobile ad-hoc etworks suffer high etwork cogestio due to high Bit Error Rate (BER) i the wireless chael, icreased collisios due to the resece of hidde termials, iterferece, locatio deedet coectio, ui-directioal liks, frequet ath breaks due to mobility of odes ad the iheret fadig roerties of the wireless chael [20]. This substatiates the eed for high adative AQM algorithms with adatig caabilities to high variability ad ucertaity for these tyes of etworks. The roosed fuzzy logic based AQM, called Fuzzy-AQM, is such tyes of algorithms to overcome the above shortcomig i ad-hoc etworks. The alicatio of fuzzy logic to the roblem of cogestio cotrol allows us to secify the relatioshi betwee queue arameters ad ackets droig robability usig if...the... tye of liguistic rules. The fuzzy logic algorithm would be able to traslate or iterolate these rules ito a oliear maig. I this study, the focus is to ivestigate the imact of the traditioal ad Fuzzy-AQM algorithms o the ad-hoc etwork. The cosidered strategy is as follows: Whe the cogestio is detected, the ode uses oe of the AQM olicies to dro the icomig data ackets. Meawhile, it allows the cotrol ackets to ass to the queue usig Dro-Tail olicy. Therefore, the data ackets are droed first whe the ackets dro robability exceeds a certai threshold while the cotrol ackets are still accetable util the queue is full. Cotrol messages are referred to ass to the queue durig cogestio time for the followig reasos: 1. Cotrol messages are used to udate the chages of the etwork toology. Therefore, they revet data acket to be trasmitted through broke aths. 2. Data ackets are coectio orieted, that is, guarateed delivery to their destiatios by TCP. I cotrast, cotrol messages are coectioless ; that is, the droed message will ot be retrasmitted agai. 3. Cotrol message size is very small comared to data acket. Normally i ad-hoc routig rotocols, cotrol message size is 64 bytes while data acket is 512 bytes, i. e., the cotrol message takes small sace i the queue ad fast rocessig time i the ode.

2 Fuzzy Active Queue Maagemet for Cogestio Cotrol i Wireless Ad-Hoc 51 The rest of this aer is orgaized as follows. Sectio 2 summarizes related work o the commo AQM olices issues ad focuses o revious imlemetatios of fuzzy AQM olicies. Followed by cogestio i ad-hoc etworks, the fuzzy droig algorithm as a ew AQM olicy (Fuzzy-AQM), erformace aalyzes of the roosed algorithm, ad fially the coclusios. 2. Related Work The most famous AQM algorithm is Radom Early Detectio (RED) [11]. The RED algorithm maages the queue i a active maer by radomly droig ackets with icreasig robability as the average queue size icreases. It maitais two thresholds that determie the rate of acket dros: A lower threshold (deoted by mi th ) ad a uer threshold (deoted by max th ). For each acket k arrives to the queue, the dro robability for that acket d (k) is give by: d k 0 avg mith maxthmi 1 th max if q mi c if mi th th if avg max avg max th th (1) Where q c is curret queue size, avg is curret average queue size ad max is maximum dro robability. Some revious studies showed the difficulties of choosig the RED arameters [13, 18, 19]. Other studies showed that there is o sigificat beefit to RED over Dro-Tail for the web traffic [5, 6, 13]. Those drawbacks are the mai reasos to default disable of the RED fuctio (or some vedor-secific variat of RED, e. g., Cisco s Weighted RED (WRED) [9]) i most of the available routers today. To overcome these drawbacks, extesios of the RED algorithm had bee roosed to make it more robust ad/or adative, for examle, Stabilized RED (SRED) [22], Flow RED (FRED) [3], Dyamic RED (DRED) [16] etc. The most famous dyamic cofigured RED is the Adative RED (ARED) algorithm roosed by Floyed et al. [12]. I ARED, the max is cofigured dyamically to kee the average queue size avg withi a target rage. May studies used the fuzzy logic system to dyamically calculate the dro robability behavior of AQM olicy. Wag et al. [30] roosed Adative Fuzzy-based RED (AFRED) algorithm to calculate the dro robability usig the curret queue size as the oly iut for the fuzzy system. Some other studies calculate the dro robability based o Fuzzy Exlicit Rate Markig (FERM) algorithm usig two queue state iuts: The curret queue size 'q c ' ad its rate of chage ' q c '. The FERM was imlemeted i [25] for ATM etworks, while i [8, 28] it was imlemeted for differetiated services (Diff-Serv) etworks. I [1, 2, 7, 10, 17, 27, 32], the authors calculate the dro robability usig Fuzzy Proortioal Derivative Cotroller (FPDC) with two iuts: The error 'e' (which is the differece betwee the curret queue size ad the desired queue legth) ad the chage of the error ' e' (which is the differece betwee the curret error ad the revious error). A covetioal fuzzy cotroller use (e, e) as iuts to observe the cotrolled system resose ad its arameters. These arameters are overshoot, rise-time ad settle-time. This set of arameters is ot oly used to evaluate the stability, but the erformace of a system as well, ad ofte is give i secificatio. Usig the same iuts (e, e) to calculate the dro robability of AQM is meaigless ad the fuzzy if...the... rules will ot accurately rereset the queue system behavior. Li et al. [15] have used the curret average queue size 'avg' ad its variace ' avg' as the iut for the Fuzzy Logic Adative RED (FLARED) algorithm to adatively modifyig the chages of ste-size of the arameter max. This scheme tue oly oe arameter of ARED algorithm ad its drawback is the lack to tue other ARED arameters. I this study, we have used fuzzy logic system to calculate the dro robability i ad-hoc etworks usig: The curret queue size ad the umber of eighborig odes. This scheme ca be geeralized to be used i ay etwork where the umber of eighbors' odes reresets the umber of commuicatio liks, or recisely umber of TCP sessios. Table 1 comares various schemes to desig fuzzy AQM algorithms. Fuzzy AQM Scheme AFRED [30] FERM [8, 25, 28] FPDC [1, 2, 7, 10, 17, 27, 32] FLARED [15] Our scheme: Fuzzy-AQM Table 1. The fuzzy AQM schemes. Cogestio Metric Curret queue size 'q c' q c ad its chage ' q c' Error 'e' ad its chage ' e' Average queue size 'avg' ad its chage ' avg' q c ad ode eighbors desity 3. Cogestio i Ad-Hoc Networks Otimized Variable Dro Probability Dro Probability Dro Probability max Dro Probability I ad-hoc etworks, cogestio cotrol is hadled through trasort layer rotocols. The coectioorieted trasort layer rotocol used i ad-hoc etworks is Trasmissio Cotrol Protocol (TCP) [14]. The objectives of this rotocol iclude the settig u of a ed-to-ed coectio, ed-to-ed delivery of data ackets, flow cotrol ad cogestio cotrol. TCP uses widow-based flow cotrol mechaism. The seder maitais a variable size widow whose size limits the

3 52 The Iteratioal Arab Joural of Iformatio Techology, Vol. 4, No. 1, Jauary 2007 umber of ackets the seder ca sed. The destiatio seds ACKowledgmet (ACK) for ackets that are received. Whe the widow size is exhausted, the seder must wait for a ACK before sedig a ew acket based o a slidig widow ricile. This waitig time is kow as Retrasmissio TimeOut (RTO) eriod. If the ACK does ot arrive withi the RTO eriod, the the seder will assume the acket is lost. The loss of acket is due to the cogestio i the etwork which will yield TCP to start the cogestio cotrol mechaism. Mobile ad-hoc etworks exeriece dyamic chages i the etwork toology due to urestricted mobility of odes. The toology chages lead to frequet chages i the coectivity of wireless liks ad hece routes reestablishmet may be reeated very ofte. This route reestablishmet rocess takes a sigificat amout of time. The route reestablishmet time is a fuctio of trasmissio rage of the odes, distace betwee the source ad destiatio, umber of itermediate odes betwee the source ad destiatio ad ode's velocity. If the route reestablishmet time is greater tha RTO eriod of the source ode, the it will ot receive the ACK ad assumes cogestio i the etwork, followed by retrasmissio of the lost ackets ad iitiatio of the cogestio cotrol mechaism [20]. A schematic illustratio of cogested ad-hoc etwork is show i Figure 1. The source seds its data ackets through ode A, which asses those ackets to ode B the to the destiatio. As soo as the lik betwee the source ad ode A is broke, it starts route reestablishmet rocess ad creates a direct lik with ode B. If this rocessig time is less tha RTO, the source will receive the ACK ad sed other data ackets, or it will resed the revious lost ackets. 4. Fuzzy-AQM Algorithm I this sectio, cocets ad rules of the roosed Fuzzy-AQM algorithm for ad-hoc etworks are itroduced. I the followig two subsectios, we studied the effect of some ode arameters o ackets dro robability. These arameters are used i subsectio C to create the rules of the fuzzy system. Method to desig their membershi fuctios is reseted i the later subsectio. Overall system desig ad its imlemetatio comlexity are reseted i subsectio E ad F. Comatibility of the roosed algorithm with other covetioal algorithms discussed i the last subsectio Effect of q c o Dro Probability Curret queue size q c is the most used idicator i AQM olicy for estimatig the robability of droig the icomig ackets. The dro robability d ca be calculated as [26]: d 2N 2 CT q 2 Where N is a load factor, C is a trasmissio caacity (i ackets/secods) ad T is a roagatio delay (i secods). Assumig a 10 Mbs (2500 ackets/sec) trasmissio caacity with a 100 msec roagatio delay, Figure 2 shows the relatio betwee the dro robability ad the load for various queue sizes. It is evidet that the robability of a acket droig icreases as the load icreases. More ackets i the queue wait for rocessig as load icreases. Thus, it ca be stated that whe the used sace of the queue is high, the dro robability of icomig ackets is also high ad vice versa. Cosequetly, the followig rules are roosed: R1: If q c is low the d ought to be low. R2: If q c is medium the d ought to be high. R3: If q c is high the d ought to be high. Figure 1. Cogestio i ad-hoc etworks Effect of Node Neighborhood Desity o Dro Probability I ad-hoc etworks, the traffic is categorized as: Data ackets ad cotrol messages. The cotrol messages are used to cotiuously udate the odes about the toology chages (ew created or lost liks). For examle, if a ode has two eighbors that meas it will receive two hello messages every secod from them. Besides, receivig a route request messages, a route breaks messages, or data ackets. If that ode has te eighbors, this meas it will receive, i every secod, te hello messages beside bulk amout of cotrol messages ad data ackets. Hece, it is clear that the traffic ass through the odes with few eighborhoods is less tha the others with may eighbors. I equatio 2, the load N ca be writte as: N i0 Where λ i deote flow's rate from the eighbor ode i ad is the umber of eighbors. The cogestio will hae at: d i0 i 1 if C ad q i c c q m (2) (3) (4)

4 Fuzzy Active Queue Maagemet for Cogestio Cotrol i Wireless Ad-Hoc 53 Where q m is the maximum queue size. Hece, if the eighbors' desity of a ode's is high, the ode's queue will be full quickly ad icreases the robability of cogestio ad vice versa. Cosequetly, the followig rules are roosed: R4: If eighbors' desity is low the d ought to be low. R5: If eighbors' desity is medium the d ought to be high. R6: If eighbors' desity is high the d ought to be high. defie oe arameter; midoit. These MFs maily cotai the triagular shaed MF [23]. The remaiig MFs are as follows: Z-shaed membershi to rereset the whole set of low values ad S-shaed membershi to rereset the whole set of high values. (a) MFs used for the iut variables. Figure 2. Dro robability for the comig load The Rule-Base for Fuzzy Dro Probability To fulfill the fuzzy sets theory, the revious six rules (R1 to R6) ca be combied withi a 2-dimesioal rule-base to cotrol the dro robability adatively as reseted i Table 2. For examle, accordig to Table 2 the first rule is: If q c is Low ad eighbors' desity is Low the d is Low Table 2. Fuzzy-AQM rules for dro robability. Neighbors' Desity Low Medium High Low Low Low Low q c Medium Low High High High High High High 4.4. Membershi Fuctios For Fuzzy Variables After defiig the fuzzy liguistic if-the rules, the Membershi Fuctio (MF) corresodig to each elemet i the liguistic set should be defied. For examle, if the queue size is 5 k bytes ad q c equal to 2 k bytes, usig covetioal cocet, it imlies q c is either low or medium but ot both. I fuzzy logic, however, the cocet of MFs allows us to say the q c is low with 80% membershi degree ad medium with 20% membershi degree. The MFs we roose to use for the fuzzy iuts (q c, eighbors' desity) ad the fuzzy outut ( d ) are illustrated i Figure 3. These MFs are used due to their ecoomic value of the arametric ad fuctioal descritios. I these MFs, the desiger eeds oly to (b) MFs used for the outut variable. Figure 3. Membershi fuctios used for the fuzzy variables. Maxoit is the maximum queue size i q c MF (Table 2), ad it is the umber of the etwork's odes i the eighbors' desity MF. Midoit of q c MF is a threshold that idicates whether the queue is goig to be full soo. The threshold is simly set to 60% of the queue size. The otimal value for this variable deeds i art o the maximum average delay that ca be allowed by the odes. Tseg et al. [29] argue about the cost-effectiveess to have large ad-hoc etworks. They roved by simulatio that ractical sizes of ad-hoc etworks would rage withi about five odes. Therefore, for eighbors' desity MF, midoit should be equivalet to five odes Fuzzificatio, Iferece ad Defuzzificatio The fudametal diagram of the fuzzy system is reseted i Figure 4. Fuzzificatio is a rocess where cris iut values are trasformed ito membershi values of the fuzzy sets (as described i the revious sectio). After the rocess of fuzzificatio, the iferece egie calculates the fuzzy outut usig the fuzzy rules described i Table 2. Defuzzificatio is a mathematical rocess used to covert the fuzzy outut to a cris value; that is, d value i this case. There are various choices i the fuzzy iferece egie ad the defuzzificatio method. Based o these choices, several fuzzy systems ca be costructed. I this study, the most commoly used fuzzy system,

5 54 The Iteratioal Arab Joural of Iformatio Techology, Vol. 4, No. 1, Jauary 2007 Mamdai method, is selected; for further details o this system see [31]. Figure 4. Block diagram for the basic elemets of the fuzzy-aqm Imlemetatio Comlexity Usig fuzzy logic system with AQM, we may achieve comarable or better ru-time comutatio tha urely covetioal methods. This ca be achieved usig looku table. The iut-outut relatioshi of the fuzzy reasoig egie for Fuzzy-AQM is illustrated i Figure 5. This relatioshi ca be stored as a looku table which will result i a very fast executio. 5. Performace Aalysis of the Proosed Fuzzy-AQM 5.1. Simulatio Eviromet Simulatio of the roosed AQM desig was doe usig OMNeT++ versio 2.3 with Ad-Hoc simulator 1.0 [21]. The OMNeT++ is a owerful object-orieted modular with discrete evet simulator tool. Each mobile host is a comoud module which ecasulates the followig simle modules: A alicatio layer, a routig layer, a MAC layer, a hysical layer, ad a mobility layer. Alicatio Layer: This module roduces the data traffic that triggers all the routig oeratios. I all scearios, 15 odes are eabled to trasmit. The traffic is modeled by geeratig a acket burst of 64 ackets set to a radomly chose destiatio that stays the same for all the burst legth. The rate of each burst sedig ackets is 3 ackets/sec. The time elased betwee two alicatio bursts is ormally distributed i [0.1, 3] sec. The acket size is 512 bytes. Routig Layer: The routig model is the heart of the simulator. This model deicts the Ad-hoc Odemad Distace Vector (AODV) routig rotocol, all of its fuctios, arameters ad their imlemetatio [24]. MAC Layer: The simle imlemetatio for this layer has bee used. The outgoig messages (from routig layer) are let ass through to the hysical layer. The icomig oe (from hysical layer) istead is delivered to the routig layer with a MM1 queue olicy with queue size 5k bytes. Whe a icomig message arrives, the module checks a flag that idicate if the routig layer is busy or ot. If so, the message will be saved i the queue usig Dro-Tail, Adative RED, or Fuzzy-AQM algorithm. Note that Dro-Tail is a secial case of AQM with the followig coditio: d 1 0 if q c q m otherwise The arameters of Adative RED (see otatio i [12]) are set at mi th = 1.5k bytes, max th = 3k bytes, max = 0.01, w q = 0.002, = max /4, ad = 0.9. Whe the routig layer is ot busy, the MAC module icks the first message from the queue ad seds it uward. Physical Layer: It deals with the o-fly creatio of liks that allow the exchage of messages amog the odes. Every time a ode moves from its ositio, a iterdistace check o each ode is erformed. If a ode gets close eough (deedig o the trasmissio ower of the movig odes) to a ew eighbor, a lik is created betwee the two odes with the followig roerties: Chael badwidth is 11 Mb/s (IEEE a) ad delay is 10 s. Each ode has a defied trasmissio rage chose from a uiformly distributed umber betwee [90, 120] m. Mobility Layer: The radom wayoit model was adoted for the mobility layer. It is oe of the most used mobility atter i the ad-hoc etwork simulatios. This is because of its simlicity ad its quite realistic mobility atter. I this mobility model, a ode radomly selects a destiatio. O reachig the destiatio, aother radom destiatio is targeted after 3 secods ause time. The seed of movemet of idividual odes rage betwee [11, 16] m/sec. The directio ad magitude of movemet was chose from a uiformly distributed radom umber. Three differet etwork sizes are modeled: 700m 700m ma size with 25 ad 35 odes ad 800m 800m ma size with 45 odes. Each simulatio ru takes 300 simulated secods. Multile rus were coducted for each sceario ad collected data was averaged over those rus Performace Metrics (11) The followig metrics were used for measurig erformace: Dro Ratio: The ercetages of ackets that are droed from the queue due to overflow (cogestio) to the total arrival i the queue. Ivalid Route Ratio: Calculated as follows:

6 Fuzzy Active Queue Maagemet for Cogestio Cotrol i Wireless Ad-Hoc 55 Ivalid Route Ratio i1 Number of ivalid routes i1 Number of valid routes Each time a route is used to forward a data acket, it is cosidered as a valid route. If that route is ukow or exired, it's cosidered as ivalid route. Average Ed-to-Ed Delay: Average acket delivery time from a source to a destiatio. First, for each source-destiatio air, average delay for acket delivery is calculated. The the whole average delay is calculated from average delay of each air. Ed-to-ed delay icludes the delay i the sed buffer, the delay i the iterface queue, the badwidth cotetio delay at the MAC layer, ad the roagatio delay. Routig Overhead: Calculated as follows: Overhead i i1 1 Number of SetCtrlPkt by source Number of received data by destiatio Where is umber of odes i the etwork ad SetCtrlPkt is cotrol ackets used by AODV ad described i Table 3. This metric ca be emloyed to estimate how may trasmitted cotrol ackets are used for oe successful data acket delivery. We use it to study the effect of AQM algorithms o the efficiecy ad scalability of the routig rotocol. Figure 5. The iut-outut relatioshi of the fuzzy-aqm. Message RREQ RREP RERR RREP_ACK Table 3. Cotrol ackets used by AODV. Descritio A Route Request message A Route Rely message A Route Error cotaiig a list of the ivalid destiatios A RREP ackowledgmet message 6. Simulatio Results ad Evaluatios 6.1. Dro Ratio Details The average cotrol messages dro ratio for the roosed Fuzzy-AQM algorithm is less tha other covetioal algorithms as show i Figure 6-a. The ercetage of Fuzzy-AQM imrovemet comared to Dro-Tail ad Adative RED algorithms is: 93.9% ad 74.5% for 25 odes, 65.8% ad 33.5% for 35 odes, ad 75.1% ad 49.7% for 45 odes, resectively. This imrovemet of the fuzzy algorithm is a result of choosig the eighbors' desity arameter to estimate the size of icomig traffic ad hece start the early droig olicy as eeded. Desite the data ackets dro ratio of Fuzzy-AQM is little bit higher tha adative RED, as show i Figure 6-b, this is eough to roduce a higher ehacemet i the cotrol messages dro ratio. This ehacemet is a result of the wide differece betwee the size of data ackets (512 bytes) ad cotrol messages (64 bytes). Cosequetly, at cogestio time, droig oe data acket allows the queue to accet eight cotrol messages. Dro-Tail algorithm does't have ay mechaism to distiguish betwee data ad cotrol ackets like other AQM algorithms. Moreover, the umber of cotrol messages i ad-hoc etwork is much higher tha data acket; to rovide cotiuous udate of toology chages. Those two reasos affect a high cotrol messages dro ratio for the Dro-Tail algorithm as show i Figure 6-a Ivalid Route Ratio Details The Fuzzy-AQM algorithm has less average ivalid route ratio comared to other covetioal AQM as show i Figure 7. This decremet of the roosed algorithm is about: 20.3% ad 23.1% for 25 odes, 31.1% ad 14.6% for 35 odes, ad 22.4% ad 12.9% for 45 odes, resectively. Iformatio about route breaks is broadcasted as a RERR message. The Fuzzy-AQM algorithm allows more cotrol messages to ass the queue to the uer routig layer as show i Figure 6. This icreased umber of received cotrol messages hels the odes with Fuzzy-AQM to be more accurate to toology chages ad have recise udated routig tables, hece, have less ivalid routes Average Ed-to-Ed Delay Details Figure 8 idicates that the roosed Fuzzy-AQM algorithm has lower average ed-to-ed delay comared to other covetioal algorithms. This decremet is aroximately: 17.2% ad 6.3% for 25 odes, 24.1% ad 11.6% for 35 odes, ad 33.6% ad 21.6% for 45 odes, resectively. The odes that have covetioal AQM algorithms have higher ivalid route ratio as show i Figure 7, therefore they suffer loger routig delay to recover from broke aths ad discover ew oes. To recover a broke ath, a RERR message must first be lauched from the itermediate odes to tell the source ode about the broke lik. The source ode deletes the corresodig etry from its routig table. The RREQ

7 56 The Iteratioal Arab Joural of Iformatio Techology, Vol. 4, No. 1, Jauary 2007 must the be broadcasted from the source to the destiatio, ad a RREP cosequetly has to be trasmitted back to the source. Data ackets are buffered at the source ode durig this rocess ad the duratio of their bufferig adds more time delay to the ed-to-ed delay. The odes with Fuzzy-AQM algorithm, o the other had, have reliable routig tables that miimize the eed to this recovery rocess. (a) Cotrol messages dro ratio comariso. (b) Data ackets dro ratio comariso. Figure 6. Dro ratio comariso. Figure 7. Ivalid route ratio comariso Routig Overhead Details As exected, the AQM algorithms do't have major effect o the routig rotocol efficiecy or scalability as show i Figure 9. These algorithms maximize the umber of 'received' cotrol messages, meawhile they have o effect o 'set' cotrol messages (see equatio 10). This is because the cotrol messages used i AODV are broadcast messages; that is, they will ot be reset if they are droed or lost. The Dro-tail algorithms has worst routig overhead ratio as the umber of ode icrease as a result of icreasig data ackets dro ratio which is clear i Figure 6-b. Meawhile, the data ackets droig ratio is early the same for adative AQM algorithms (ARED ad Fuzzy-AQM) that results i o major differece i routig overhead ratio Dro Probability Values I Dro-Tail algorithm, d always take a static value of 1 to start acket droig at overflow. I Adative RED algorithm, d icreases liearly betwee the two thresholds mi th ad max th i deedet o the average queue size 'avg'. Some studies [26] showed that usig liear d fuctio ca result i forced dros whe q c exceeds max th or lik uder-utilizatio whe q c decreases to zero. This is a evidet that the origial liear dro fuctio does ot erform well withi a wide rage of loads. The d values used by the roosed Fuzzy-AQM for radomly chose ode i the 25 odes simulated etwork are show i Figure 10. It is evidet that the dro fuctio is o-liear ad a high load requires a disroortioately higher d tha a low load to kee the queue size i the same rage. No-liearity of d fuctio is also clear i the iut-outut relatio as show i Figure 5. The comariso betwee the average d values used by every ode i the 25 odes ad the 35 odes etworks is show i Figure 11. Due to higher eighbors' desity, 35 odes etwork have higher d values tha 25 odes etwork. This is a result of icreasig eighbors' desity which will also icrease the umber of cotrol messages. 7. Coclusios I this study, a ovel AQM algorithm (Fuzzy-AQM) based o fuzzy logic system was suggested. This algorithm for early ackets droig is imlemeted i wireless ad-hoc etworks i order to rovide effective cogestio cotrol by achievig high queue utilizatio, low acket losses ad delays. The roosed scheme is cotrasted with a umber of well-kow AQM schemes through a wide rage of scearios. From the simulatio results, the efficiecy of the roosed fuzzy AQM olicy i terms of routig overhead, average ed-to-ed delay ad average acket losses are

8 Fuzzy Active Queue Maagemet for Cogestio Cotrol i Wireless Ad-Hoc 57 roouced tha other AQM olices, with caabilities of adatig to high variability ad ucertaity i the mobile ad-hoc etworks. Figure 9. Routig overhead comariso. (a) 25 odes. (b) 35 odes. Figure 10. Dro robability values used by a ode. (c) 45 odes. Figure 8. Average ed-to-ed delay comariso. Figure 11. Average d values used by 25 ad 35 odes etworks. Refereces [1] Al-Frihat J., Advaced Queue Maagemet Algorithms for Comuter Networks, Studies i Iformatics ad Cotrol Joural, vol. 14, o. 2, , Jue [2] Aoul Y. H., Nafaa A., Negru D., ad Mehaoua A., FAFC: Fast Adative Fuzzy AQM Cotroller for TCP/IP Networks, i Proceedigs

9 58 The Iteratioal Arab Joural of Iformatio Techology, Vol. 4, No. 1, Jauary 2007 of IEEE Global Telecommuicatios Coferece, vol. 3, , November [3] Aweya J., Ouellette M., ad Motuo D. Y., A Cotrol Theoretic Aroach to Active Queue Maagemet, Comuter Networks, vol. 36, o. 2-3, , July [4] Brade B., Clark D., Crowcroft J., Davie B., Deerig S., Estri D., Floyd S., Jacobso V., Mishall G., Partridge C., Peterso L., Ramakrisha K., Sheker S., Wroclawski J., ad Zhag L., Recommedatios o Queue Maagemet ad Cogestio Avoidace i the Iteret, Request for Commets (RFC) 2309, Aril [5] Bradauer C., Iaaccoe G., Diot C., Ziegler T., Fdida S., ad May M., Comariso of Tail Dro ad Active Queue Maagemet Performace for Bulk-Data ad Web-like Iteret Traffic, i Proceedigs of the 6 th IEEE Symosium o Comuters ad Commuicatios, Hammamet, July [6] Christiase M., Jeffay K., Ott D., ad Smith F. D., Tuig RED for Web Traffic, IEEE/ACM Trasactios o Networkig, vol. 9, o. 3, , Jue [7] Chrysostomou C., Pitsillides A., Hadjiollas G., Sekercioglu Y. A., ad Polycarou M., Fuzzy Logic Cogestio Cotrol i TCP/IP Best Effort Networks, i Proceedigs of the Australia Telecommuicatios, Networks ad Alicatios Coferece (ATNAC'03), Melboure, Australia, December [8] Chrysostomou C., Pitsillides A., Rossides L., Sekercioglu Y. A., ad Polycarou M., Cogestio Cotrol i Differetiated Services Networks Usig Fuzzy-RED, IFAC Joural Cotrol Egieerig Practice, vol. 11, o. 10, , October [9] Cisco Systems, Weighted Radom Early Detectio o the Cisco Series Router, March [10] Di Fatta G., Hoffma F., Lo Re G., ad Urso A., A Geetic Algorithm for the Desig of a Fuzzy Cotroller for Active Queue Maagemet, IEEE Trasactios o Systems, Ma, ad Cyberetics, Part C, vol. 33, o. 3, , August [11] Floyd S. ad Jacobso V., Radom Early Detectio Gateways for Cogestio Avoidace, IEEE/ACM Trasactios Networkig, vol. 1, o. 4, , August [12] Floyd S., Gummadi R., ad Sheker S., Adative RED: A Algorithm for Icreasig the Robustess of RED s Active Queue Maagemet, available at htt:// org/floyd/red.html, [13] Iaaccoe G., May M., ad Diot C., Aggregate Traffic Performace with Active Queue Maagemet ad Dro from Tail, ACM SIGCOMM Comuter Commuicatio Review, vol. 31, o. 3, July [14] Jacobso V., Cogestio Avoidace ad Cotrol, i Proceedigs of ACM SIGCOMM 88, vol. 18, o. 4, , August [15] Li Z., Zhag Z., Addie R., ad Clerot F., Imrovig the Adatability of AQM Algorithms to Traffic Load Usig Fuzzy Logic, i Proceedigs of the Australia Telecommuicatios, Networks ad Alicatios Coferece (ATNAC'03), Melboure, Australia, December [16] Li D. ad Morris R., Dyamics of Radom Early Detectio, i Proceedigs of ACM SIGCOMM'79, Frace, , [17] Li W., Wog A., ad Dillo T., A Novel Fuzzy Logic Cotroller (FLC) for Shorteig the TCP Chael Roudtri Time by Elimiatig User Buffer Overflow Adatively, i Proceedigs of the 28 th Australasia Comuter Sciece Coferece (ACSC2005), vol. 38, , Newcastle, Australia, Jauary [18] May M., Bolot J., Diot C., ad Lyles B., Reasos ot to Deloy RED, i Proceedigs of the 7 th Iteratioal Worksho o Quality of Service (IWQoS 99), , Jue [19] Misra V., Gog W. B., ad Towsley D., Fluid- Based Aalysis of a Network of AQM Routers Suortig TCP Flows with a Alicatio to RED, ACM SIGCOMM Comuter Commuicatio Review, , [20] Murthy C. S. ad Maoj B. S., Ad-Hoc Wireless Networks: Architectures ad Protocols, Pretice Hall, [21] OMNeT++ web site, available at: htt://www. omet.org, [22] Ott T. J., Lakshma T. V., ad Wog L., SRED: Stabilized RED, i Proceedigs of IEEE INFOCOM, vol. 3, , [23] Pedrycz W., Why Triagular Membershi Fuctios?, Fuzzy Sets System, vol. 64, , [24] Perkis C., Royer E. M., ad Das S. R., Ad-Hoc O-Demad Distace Vector (AODV) Routig, available at: htt:// 13.txt, Febraury [25] Pitsillides A., Sekercioglu Y. A., ad Ramamurthy G., Effective Cotrol of Traffic Flow i ATM Networks Usig Fuzzy Logic Based Exlicit Rate Markig (FERM), IEEE Joural o Selected Areas i Commuicatios, vol. 15, o. 2, , February [26] Plasser E., Ziegler T., ad Reichl P., O the No-Liearity of the RED Dro Fuctio, i Proceedigs of the 15 th Iteratioal Coferece o Comuter Commuicatio, Idia, vol. 1, , August 2002.

10 Fuzzy Active Queue Maagemet for Cogestio Cotrol i Wireless Ad-Hoc 59 [27] Re F., Re Y., ad Sha X., Desig of a Fuzzy Cotroller for Active Queue Maagemet, Comuter Commuicatios, vol. 25, o. 9, , Jue [28] Rossides L., Chrysostomou C., Pitsillides A., ad Sekercioglu Y. A., Fuzzy Logic Cotrolled RED: Cogestio Cotrol i TCP/IP Differetiated Services Networks, Soft Comutig Joural, vol. 8, o. 2, , December [29] Tseg Y., Li Y., ad Chag Y., O Route Lifetime i Multiho Mobile Ad-Hoc Networks, IEEE Trasactios o Mobile Comutig, vol. 2, o. 4, , October [30] Wag C., Li B., Sohraby K., ad Peg Y., AFRED: A Adative Fuzzy-Based Cotrol Algorithm for Active Queue Maagemet, i Proceedigs of the 28 th Aual IEEE Iteratioal Coferece o Local Comuter Networks (LCN'03), , October [31] Yager R. R. ad Filev D. P., Essetials of Fuzzy Modelig ad Cotrol, Joh Wiley & Sos, , [32] Yafei F., Fegyua R., ad Chuag L., Desig a Active Queue Maagemet Algorithm based o Fuzzy Logic Decisio, i Proceedigs of IEEE Iteratioal Coferece o Commuicatio Techology (ICCT'03), vol. 1, , Aril Essam Natsheh obtaied his MSc i comuter egieerig from the Arab Academy for Sciece ad Techology i Egyt, i He subsequetly worked as a lecturer at Al-Alamiah Istitute for Comuter ad Techology, SA, i He worked as a lecturer also at the Iformatio Systems Deartmet of the Kig Faisal Uiversity, SA, from 2002 to Sice 2003, Natsheh has bee a member of a research grou headed by Dr. Jata A. at the Uiversity Putra Malaysia, which ivestigates issues related to the desig ad aalysis of ad-hoc wireless etworks. Sabira Khatu received her BSc (Hos.), MSc i alied mathematics ad PhD o hydromagetic stability from the Uiversity of Rajshahi, Bagladesh i 1988, 1990, ad 1994, resectively. She received her secod PhD i commuicatios ad etworkig from Uiversity Putra Malaysia i She became a lecturer at the Deartmet of Comuter Sciece ad Egieerig, Uiversity Khula, Bagladesh i 1991, ad romoted to assistat rofessor i She joied the Deartmet of Comuter & Commuicatio Systems Egieerig, Uiversity Putra Malaysia i She is a active researcher of Tema roject ad MyREN Research Commuity. She is a member of IEEE. Her research iterest sas broadbad ad wireless commuicatios, ad etwork maagemet, icludig software defied radio ad IPv6. Shamala Subramaiam comleted her PhD from Uiversity Putra Malaysia i Curretly, she is a lecturer at the Deartmet of Commuicatio Techology ad Networks, Faculty of Comuter Sciece ad Iformatio Techology, Uiversity Putra Malaysia. Her research iterest icludes schedulig algorithms, cogestio cotrol, real-time systems, modelig, ad simulatio. Adza B. Jata obtaied his MSc i digital systems from Crafield Istitute of Techology, UK, i 1982 ad his PhD i seech recogitio systems from the Uiversity College of Swasea, UK, i Sice 2002, he is a associate rofessor at the Deartmet of Comuter ad Commuicatio System at the Uiversity Putra Malaysia, where he has bee coductig research i comuter etworkig, atter recogitio, ad digital systems desig.