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Predictive Routing in MANETs Using Colored Pheromones

Balamurali A, Janani R, Pavithra TUG StudentsS.K.Nivetha

Lecturer,

Kongu Engineering College,Perundurai, Erode

Abstract: The major challenging issues inproviding QoS in MANET includes energysaving, node/link interference, traffic loadbalancing and dynamic topology change dueto the inherent mobility present in wirelessadhoc networks. Inspired by SwarmIntelligence (SI), more especially antspheromone update process, found in naturalinsects like ants colony, in this paper, a hybridapproach called Traffic Based QoS

provisioning in MANET with link reliability hasbeen proposed. Different classes of trafficpose different QoS requirements. Also, dueto the mobility of nodes, link failuresignificantly affects the QoS of the underlyingnetwork. Our algorithm meets these two needsby combining both the proactive and reactiveprotocols along with the use of coloredpheromones for different traffic classes.

1.IntroductionA number of definitions for Quality of Service(QoS) can be found in literature. But, the

QoS is described as "the collective effect ofservice performance which determines thedegree of satisfaction of a user of the service"as per ITU-T recommendations. A number ofparameters affect the QoS and it dependsupon the type of communication. QoSconcepts in the Internet are focused on apacket-based end-to-end, edge-to-edge orend-to-edge Communication. QoS can beconsidered at multiple layers of packettransportation and are: availability, bandwidth,delay and jitter. The integration of these QoSparameters increases the complexity of theused algorithms for communication. In theemerging hybrid networks which mix theseveral wireless, broadcast, mobile fixed,there will be QoS relevant technologicalchallenges.

Furthermore, bandwidth, jitter anddelay can change dramatically over time, e.g.through rate adaptation and depend verymuch on the channel quality of the link.Therefore, it becomes even more important to

support different types of traffic along withsome reliability to support link failure.

This paper extends the novel QoS routingalgorithm called Swarm-based DistanceVector Routing (SDVR) based on ant colonyoptimization (ACO). Multiconstrained QoSaims to optimize multiple QoS metrics whileprovisioning network resources and is anadmittedly complex problem. The QoSparameters used to analyze the routing

protocol consists of an additive QoS class:End-to-end delay and jitter, between sourceand destination mobile node. The QoSparameter considered during the routediscovery process is a concave QoS class,the individual nodes residual energy is takenfor routing decisions.We assume that the WMN is used as anaccess network to the internet. Analogous to[1] we group traffic into four classes:

Conversational, such as VoIP or videoconference traffic.

Streaming, where a play-out buffer canmitigate the effects of jitter and where nointeraction takes place; e.g. watching a videostream or listening to a pod-cast.Interactive, with lower bandwidthrequirements such as Web surfing and Webapplications.Background, such as Email and large Filetransfers (e.g. ftp or P2P-Filesharing).Tosupport the requirements of the various trafficclasses, we introduce a notion of color to theconcept of pheromones.2.Proposed Work2.1 Nature inspired approachThe proposed algorithm is hybrid algorithmbased on chemical substances pheromonediffusion and evaporation. The communicationusing pheromone is a process of indirectcommunication through modifications inducedin environment between agents. In theproposed algorithm the route exploration isonly carried out when required so thatunnecessary control traffic is minimized as in

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case of reactive protocols. However, oncedata path is established, other paths to thesame destinations are explored so that in caseof link failure, data could be forwarded inacceptable interruption. This is greatly helpfulin case of QoS applications. This part ofapproach imitates the idea from proactivealgorithms. The algorithm assumesbidirectional symmetric links all the time.As discussed the basic principle of ants, in thisapproach, we have used two artificial ants i.e.,forward ants and backward ants. The forwardants have two responsibilities. First they areused to find out a path to a destination fulfillingthe QoS requirements, whenever there isrequest for data transfer pending for that. Forthis purpose, source produces a forward ant. Itis usually broadcast in this regard but may beunicast in its way if an entry for the requireddestination is found in the routing table of any

intermediate node that matches to the QoSmetrics involving minimum required bandwidthand maximum permissible delay. Along withthese metrics, forward ants also carry thesource and destination addresses, sequencenumber and a stack on which they keep theinformation about all the intermediate nodes.When forward ant reaches to the destination, itdies out to give birth to a backward ant, whichis unicast and directed back to the source onthe path, discovered by the forward ant. Whenbackward ant returns to source from whereforward ant originated, it modifies the routing

tables of all the intermediate nodes with thenew experience, gathered by the forward ant.For this artificial ants are provided withmemory elements. In this process linkbetween source and destination is establishedand communication starts. During this time,the proactive part of the algorithm activates.This is done by sending the forward antsrandomly on relatively idle paths to explore forpossibly better alternate routes to the currentdestination. Thus reactive and proactivealgorithms are incorporated in the hybridalgorithm. This is helpful in two respects: first

in case of topology change (this often happensin case of MANETs). In such cases it will havemultiple options for the applications because itsearches for other paths also. This will providecommunication without any link failure;secondly, in case of better QoSrequirements of the application, which it isshared for a single path to fulfill, loadbalancing could be applied to facilitate theoperation.

2.2 Colored Pheromone approachAlso, due to the colored pheromone approach,the ants mark the paths through the networkby depositing pheromones with different colorsdepending on the suitability of the path for thecorresponding traffic classes. For this, thealgorithm uses information gained from MAC-layer measurements. E.g. A path with highbandwidth, low jitter, and low delay is suitablefor traffic in the conversational class andwould therefore be marked with pheromonesof color A. We further define that traffic canuse paths marked with a color other than itsown if no path of appropriate color can befound. e.g. if all paths are of such high qualitythat they are all marked with color A, thentraffic of classes 2,3, and 4 can also use theseA-paths. In other words traffic can use a paththat is better than necessary if no othersuitable path is found. Note that traffic should

preferably be routed along its major color sothat traffic with lower requirements does notblock the path of traffic with higherrequirements.3. Link SelectionWhen an ant is to be sent as described abovethe next hop is chosen with probability

rPv, according to the transition rule

rNj

rjc

rvc

c

X

XrPv

,

,~,

(1)

Withc . . . the current nodev . . . the next noder . . . the destination node

rjc

X , . . . the pheromone value

for a certain color for the link from node i tonode n with intended destination d N

cr

. . . the set of neighbors of node i whichknow a path to destination d.

The pheromone color X of X is randomlychosen as one of {A;B;C;D} when the first linkis taken; after the first link the color of the

current ant is determined and stays fixed forthe lifetime of the ant. While the ant stillcontinues to collect information about theother color values it will only search for a pathof the color it has been assigned.

Since the transition rule in Equation 1 definesa probability distribution there is a certainprobability that an ant will not choose the best

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path. In this case the ant has become a so-called exploring ant. While exploiting antsreinforce existing paths exploring ants try tofind new alternative paths.

Measuring Goodness and Path GradingNodes keep running averages of MAC-layermeasurements of the link's bandwidth, jitter,and delay. Let

XDJBW avgavgavggood a ,,: (2)

be the goodness function" with BWavg theaverage bandwidth, Javg the average jitter, andDavg the average delay on the link calculatedusing a sliding window average and

=

d

c

b

a

X(3)

the color vector for the current link taken. Thecolor values a, b, c, and d are in the interval[0; 1] and calculated using thresholds whichmap link attribute values (e.g. a certainbandwidth) to percentage values of the color.This mapping can be implemented using asimple lookup table in the nodes. The antkeeps an ordered list L ant = { ~X1; ~X2; : : : ; ~Xn} of the color vectors ~X encountered alongthe way.

When the ant reaches the destination nodethe path grade is calculated for all colors as

=j

antjjp LXXG (4)

by element-wise multiplication of the colorvectors. In other words a( ~ GP) = a( ~X1) *a( ~X2) * : : : * a( ~ Xn) and b( ~ GP) = b( ~X1)* b( ~X2) * : : : * b(X~n), etc. In this way, theworst hop in the path is accurately rejected asbeing the path bottleneck.

3.1 Pheromone Table Updating

The path grade is then stored in the backwardant (BA) which is sent back to the sourcenode. Along the way it updates the pheromonevalues of all the nodes it passes. If a link is onthe path the pheromone value for choosingthis link (choosing node nas the next hop) isupdated as

)(: ,, prvc

rvc GxgfXX evap += (5)

where GxP denotes that element of the pathgrade vector which matches the color of the

pheromone being updated ( X and GxP withidentical values for x). For all other links,

where node m is not on the path, the amountof pheromone is decreased:

evaprmc

rmc fXX = ,, : (6)

where fevap denotes the evaporation functionand g(GxP ) the enforcement function. Asshown in [7] ,these functions can be chosen

as fevap =1-GxP and g(GxP ) = pGxk

which

results in 0 fevap 1 and 0 g(GxP ) 1for 0 GxP1.

3.2 Traffic SendingTraffic is always sent along the best suitablycolored path found and FAs are piggy-backedon the data packets. In this way, when thepath becomes overloaded, its pheromonevalue will fall over time because its goodnessdecreases. Once it becomes worse than thesecond-best path traffic will automaticallyswitch to the second-best path.When no suitable path is found traffic may

choose paths which are better than necessaryaccording to the mapping. When TCP traffic issent, the backward ant is piggy backed ontothe TCP acknowledge packet (ACK).Therefore, depending on the current windowsize one BA will travel the reverse path ofseveral FAs just like one TCP ACK mayacknowledge several TCP segments.

3.3 Algorithm InitializationWhen the algorithm starts the

pheromone tables are initialized with equal

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values for all rvc

X , resulting in an equalprobability for each link to be chosen. Thenthe route discovery part of the algorithmproactively starts to send out forward ants.

3.4 Implementation for a four node Network

between source and destinationConsider a network having four nodes J1, J2,J3 and J4 with the probabilities of eachnode calculated as 0.1, 0.2, 0.3 and 0.4respectively. A Source S needs tocommunicate to Destination D with a minimumprobability of Quality of Service 0.3. A forwardant is sent through node J4 because Sourcenode will find the desired QoS in routingtable. Source will start communicating toDestination through node J4. In due course oftime proactive forward ants come into action tofind the alternative paths, which has desired

probabilities of QoS. Thus, in case of failure /degradation Quality of Service, source willswitch over through Node J3, as J3 has alsoprobabilities of Quality of Service 0.3. Whilecommunicating through nodes the source willcontinue sending forward ants to other pathsfor finding the probabilities needed for desiredquality of service. In case better probabilitiesare found, the communication is switched overor balanced through those nodes havingdesired probabilities of Quality of Service.

4. ImplementationThe proposed work can be simulated usingQualNet. QualNets analysis capabilities allowthe modeler to analyze the quantitativeperformance of protocols in terms of controloverhead, route acquisition delay, and route

acquisition success rate. Qualitative protocolperformance can be measured in terms ofpacket delivery ratio, latency, and jitter of datapackets. It can also provide statistical data onprotocol performance when subject toincreasing network size, increasing number ofhops between sources and destinations, nodedensity, network load, network load, number ofsource and destination pairs, and increasingmobility.

QualNet simulations can be configured toaccurately model realistic scenarios in the fieldwith good correlation on the end to endstatistics. These simulations can then beextended, or re-run with different parameters,to provide the modeler with the ability toanswer additional questions about networkperformance and optimization, withoutresorting to costly and time-consuming fieldexercises.

5 . Conclusion and Future WorksWe have introduced CPANT (Colored

Pheromone ANT routing), a novel ant routingalgorithm which extends Ant Hoc Net withcolored pheromones to support different QoSclasses of traffic. Traffic poses partlyorthogonal requirements on the underlyingnetwork with regards to bandwidth, delay, andjitter. Our algorithm uses colored pheromones

to grade links for orthogonal values ofgoodness and mark different routes suitablefor these classes of traffic.Future Work will include testing CPANT on awide variety of topologies and load situationsand fine-tuning of algorithm parameters.

6.References[1]3GPP, Technical Specification Group (TSG)

Services and System Aspects. Universalmobile telecommunications system (UMTS);QoS concept and architecture. TechnicalSpecification 3G TR 23.107, ETSI, 2000.

[2]C. B. et al. Distribution of Agent basedSimulation with Colored Ant Algorithm. In A.Verbraeck and W. Krug, editors, Proc. 14thEuropean Simulation Symposium, 2002.

[3]A Swarm-based Distance Vector Routing toSupport Multiple Quality of Service(QoS)Metrics in Mobile Adhoc Networks. Journalof Computer Science 3(9):700-707, 2007Science publication.

[4]Paul Barom .J,2006. A pheromone aidedmultipath QoS routing protocol and itsapplications in MANETs, PhD Thesis,Pennsylvania State University

[5] M. Umlauft and E. Michlmayr. Antalgorithms for routing in wireless multi-hopnetworks. In Y. Xiao and F. Hu, editors, Bio-inspired Computing and CommunicationNetworks. Taylor and Francis, 2008.

[6] QualNet Network Simulator Homepage,Oct. 2009 http://www.scalable-networks.com/products/qualnet/