Autonomic Self Tunable Proactive Routing inMobile Ad Hoc Networks

Abdelfettah BELGHITHHANA Research Group

National School of Computer SciencesUniversity of Manouba, Tunisia

Email: abdelfettah.belghith@hana-uma.org

Mohamed Amine ABIDHANA Research Group

National School of Computer SciencesUniversity of Manouba, Tunisia

Email: mohamed-amine.abid@hana-uma.org

Abstract—Proactive routing in MANETs induces high signalingoverhead. Increasing the routing period size, while it reduces suchan overhead, prevents to correctly track frequent changes in thetopology and impacts the validity of routing as time goes fartherfrom the start of the routing period. Routes’ validity plays acentral leveraging mission to enhance network performances asforwarding through incorrect routes not only results in trafficwondering inside the network without ever being able to bedelivered to their ultimate destinations but also over consumesvaluable network resources.

In this paper, we propose an autonomic self tuning approachto dynamically gauge the size of the routing period in a way toproperly calibrate between the amount of signaling overheadand the routing validity to yield better performances. First,we propose a distributed algorithm to collect the networkcartography. We then study the validity of this cartography asa function of time and mobility. The validity of the cartographyis then used to dynamically and locally self regulate the routingperiod size in a way to calibrate the signaling overhead and therouting pertinence. Simulation results show that our proposedscheme not only is capable of correctly tracking changes innetwork dynamics but also outperforms conventional proactivealgorithms by doubling the network throughput at moderate tohigh workloads.

Index Terms—: Dynamic self regulation, Proactive routing,MANETs, Network cartography, Route validity.

I. INTRODUCTION

Mobile ad hoc networks (i.e.; MANETs) are characterizedby rapid and frequent topology changes and resource con-straints. Routing in Mobile Ad hoc networks continues tobe a hot and an interesting research topic as it constitutesan essential component for the proper functioning of suchdynamically changing networks. A host of routing proposalswere made, though, only very few of them are standardized.The standardized routing protocols are classified into reactiveand proactive protocols. Reactive protocols such as DSR [6]and AODV [7] calculate routes only when needed, and as suchthey are supposed to generate low control traffic and routingoverhead. Proactive protocols like OLSR [5] and DSDV [3] es-tablish paths for all known source-destination pairs in advanceby periodically exchanging topological information, and as aresult they are stipulated to generate more control traffic thanreactive protocols. Routing overhead, nevertheless, depends onmany factors such as topology, number of nodes, number ofhops, degree and type of mobility, number of flows and the

rate at which traffic streams are established within the network.Numerous simulation studies were conducted on differentscenarios to evaluate the performance of both proactive andreactive routing protocols [8], [9], [4]. Nevertheless, due toto the large number of relevant and complex events thatcan happen in mobile wireless ad hoc networks and theireffects on the performance of the protocols, the results donot necessarily agree as to which family of protocols yieldsbetter performances and lower control traffic and overhead.

In this paper, we focus solely on proactive protocols such as[5] and [3]. The stipulated downside of this type of protocolsis the excessive routing signaling overhead generated by thedissemination of the periodic Hello and Topology Controlmessages. Routing signaling overhead has a direct impact onthe performance of the network. A large signaling overheadincreases channel contention, lowers network performancesand may yield to congestions given the resource constrainednature of wireless networks. On the other hand, as routesare refreshed periodically, the validity or the correctness ofa route provided by a proactive routing protocol decreases astime goes farther from its refresh instant. This validity is ofa major concern and plays a central leveraging mission toprovide better performances. Indeed, if the provided routesare not valid, traffic will wonder for a long time inside thenetwork consuming valuable resources which will amountto a tangible increase in the network perceived workloadwhich in turn will lead to congestions and poor performances.The routing period size which governs both of the amountof signaling overhead and the validity of established routesand consequently the perceived performances of the networkshould be gauged adequately and dynamically as a function ofthe actual network dynamics. The question naturally arises asto how to autonomically calibrate this trade off between routevalidity and routing overhead for better network performances.

In this paper, we propose a built in and almost cost freealgorithm to collect the cartography of the entire network. Thiscartography will then be used to properly and dynamicallytune the size of the routing period in a way to self regulatethe validity of provided routes and hence attaining betterperformances. Besides, the cartography of the network mayserve several other purposes such as monitoring the mobilityof the nodes, tracking some given nodes, security and last but

2009 IEEE International Conference on Wireless and Mobile Computing, Networking and Communications

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DOI 10.1109/WiMob.2009.54

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not least the enhancement of proactive routing decisions.The rest of the paper is organized as follows. In section

II, we review some relevant related research proposals. Insection III, we first present the required additions neededto collect the network cartography. Then, we introduce anddefine the validity of the network cartography. Finally, wepresent simulation results that evaluate the cartography validityas a function of the elapsed time since the start of thecurrent routing period for different mobility levels. SectionIV proposes an approach based on the collected cartographythat dynamically and autonomously induces the dynamics ofthe network and accordingly adjusts the size of the currentrouting period so as to minimize the induced routing overheadand maximize the routing pertinence. The application of ourproposed autonomic self tuning scheme to two different net-work scenarios is presented in section V. Section IV providesconcluding remarks and some future directions.

II. RELATED WORK

So far, research efforts and studies have not paid enoughattention to the validity of proactive routing, that is the cor-rectness of the established routes within the routing tables, andits impact on network performance. Routing validity decreasesas we get farther from the start of the routing update instant,and as such more frequent updating is necessary if one wantsto keep the routing validity at an acceptable level. However,increasing the routing update frequency amounts to a highersignaling traffic and consequently less resources are left tocarry the data traffic. Our aim is then three fold: keep routingvalidity at an acceptable level, lower the signaling traffic andprovide acceptable performances.

In [14] and [15], the authors focused on routing overheadreduction. In [14], the author integrated the principle of theFisheye State Routing (FSR) [11] into OLSR [5] in a way toreduce the routing overhead. The authors in [15] proposedseveral updating strategies to maximize the routing periodwhile still satisfying certain performance requirements. In[18], [23] and [24], the authors investigated different impactsof refresh interval timers on OLSR performances under varioussimulation scenarios. They distinguished temporal updates(i.e., OLSR Hello messages) from topological updates (i.e.,OLSR TC messages). They showed that temporal updates havea significant impact on throughput but topological updatesdo not. Furthermore, they stipulated that frequent topologi-cal updates in relatively high density networks may on thecontrary degrade the network performances due to the largesignaling overhead. We may question such results as they onlyconsidered very small network scenarios where the lengths ofroutes are kept to only few hops, and as such frequent Hellomessages are surely very sufficient to keep the routing valid.Recall that while it is necessary to lower the signaling trafficwithin the network, it is also important to keep the validityof the routes established within the routing tables as high aspossible. These are two conflicting measures which requireto be adequately and dynamically gauged as a function ofthe network dynamics if we intend to appropriately drive the

network to provide its best possible performances. So far littleattention has been given to the dynamic and automatic cali-bration of the routing period. In [13], the authors investigatedthe impact of certain topology updating strategies on proactiverouting performance. In [19], [20], [21] and [22], the authorsproposed two adaptive proactive routing algorithms the DTMIAD and the DT ODPU which tune the refresh intervalsof temporal updates dynamically and automatically. Oncemore, they considered only temporal updates as they supposedthat more frequent topological updates lead to throughputdegradation in OLSR routing.

Compared to the above proposed approaches, we proposein this paper a novel cartography based approach to autonomi-cally self tune the routing period of proactive protocols such asDSDV and OLSR. The proposed approach concerns both thetemporal and the topological updates. For the special case ofOLSR, it can also be nicely combined to the temporal updatetuning proposed in the above cited references. Extensivesimulations are conducted to evaluate the efficiency of ourautonomous self regulating policy and ascertain the bettermentachieved in network performance.

III. NETWORK CARTOGRAPHY

Recall that link state based proactive routing protocolsalready collect the topology of the entire network, but notits cartography. The cartography is the geographic localizationand connectivity of the different nodes throughout the networkwhile the topology is restricted to the mere connectivity amongthe nodes. On the other hand, distance vector based proactiverouting like DSDV calculates the different routes without anyknowledge or need of the entire topology. In this section, wedevelop a distributed algorithm to build the entire networkcartography based on a distance vector proactive routingprotocol. Specifically, we adopt the DSDV protocol and extendits Hello messages to acquire the best possible valid networkcartography.

A. Cartography augmented DSDV

First of all, we intend to get a correct and valid cartographyand therefore we will not tolerate delayed routing information.As such, we propose to distinguish between hello messagesand data packets. Hello messages are to be transmitted assoon as possible before any other data packets. As such,received or locally generated Hellos are put at the head ofthe IP sending queue in front of any awaiting data packets.Secondly, we assume that each node is capable of known itsown geographical location. Recent availability of small andinexpensive low power GPS receivers and approaches for in-ducing relative coordinates based on signal strength provides ajustification for such an assumption [16]. For instance, the APSprotocol [17] is a distributed hop by hop positioning algorithmthat approximates the absolute positions of all nodes giventhat only a small fraction of nodes possess a self positioningcapability. The APS algorithm works as an extension to adistance vector proactive routing protocol, and as such it canbe assumed for this current work if only a small fraction of

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nodes are location or position aware. Consequently, beforeforwarding a Hello or sending its locally generated Hellomessage, a node includes also its own perceived position.Note, in particular, that a forwarded hello contains both itsoriginating node’s position and its forwarding node’s position.

We propose a cartography capable augmented DSDV proto-col that works exactly as the original DSDV where each nodebroadcasts a Hello message periodically (i.e.; every Helloms-gperiod DSDV period) to announce itself and forwards usefulreceived Hellos. Recall that a useful received Hello is a Hellomessage that brings a new routing information and as suchupdates the local routing table. Recall also that an entry inthe routing table gets automatically flushed after a maximumduration period (Max duration period). The addition to theoriginal DSDV to built the network cartography is that weinclude in a new Hello the position of its originator node andwe also include the position of the forwarding node in eachforwarded Hello. It is then through the exact same numberHello messages of the original DSDV that a node can maintainits routing table up to date and built the network cartography.

B. Cartography validity definition

At any time, the correctness of the collected cartography ismeasured against the real actual cartography of the network.Note that the actual instantaneous cartography of the networkcan be extracted from the simulator but it cannot be knownin practice. Since nodes are mobile they are continuouslychanging their positions. The correctness, hereafter named thevalidity, of the collected cartography falls in time until a newwave of routing updates is launched. It is then interesting toquantify the validity of the collected cartography as we getfarther from the start of the routing period. More interestingly,it is of utmost importance to tune and regulate dynamicallythe size of the routing period in a way to keep the validityat a certain acceptable level. Indeed, the validity of thecartography represents the validity of the routes calculated bythe underlying proactive routing protocol. As such, keepingthe validity of the cartography at a high level will leveragenecessarily the network overall performances. This will be thesubject of the next section. For now let us precise the meaningof the validity of our collected cartography.

Consider a target node N. When N advertised itself (i.e.,sent its own generated Hello), it was at position (x0, y0).In the routing table of a node A that had already heardN’s Hello, a new entry for N was created, showing (x0, y0)as N’s coordinates. Since node N is mobile, its positionvaries as a function of time, and consequently it will be atposition (xt, yt) at time t during the same current routingperiod. We say that N’s position, as indicated by A’s currentcartography, is valid as long as the distance between therecorded position (x0, y0) and the actual current position(xt, yt) is less than a tolerated predefined value denoted by

d. That is:√

(xt − x0)2 + (yt − y0)

2 ≤ d. The validity ofthe cartography, as perceived by any given node, say nodeA, represents the percentage of nodes having valid positionsamong all nodes. d is a tuning parameter whose value is

relative to the transmission range used, and is in general asmall fraction of this range.

C. Simulation set up

To ascertain the validity of the cartography as a function ofthe mobility, the traffic load and the elapsed time since the startof the current routing period, we conducted an extensive setof simulations. We have considered a simulation area of 300mby 300m , with 50 mobile nodes using the Random Waypointmobility model [10]. We used a transmission range of 100 m,a tolerance d of 10 meters that is a tolerance equals to onetenth of the used transmission range, a network capacity of11 Mbps and a maximum retransmission count equals to 7.We used a priority IP module at the network layer to enforcethat Hellos are treated before any awaiting data packet. Thepriority queue maximum size is 100 packets. Furthermore, thisqueue is handled such as an arriving new IP data packet isonly accepted if less than 70 packets (data and Hellos) arepresent in the queue otherwise it is rejected at the IP level.Hello messages are only rejected (dropped) if the queue iscompletely full. This enforces a further layer for the priorityhandling of the Hello messages as they are the responsible forthe cartography dissemination. Finally, the routing updatingperiod is set to 20 seconds. and the Max duration period,representing the life time of an entry in the routing table, isset to 30 seconds. All required modifications are ported on theOMNET+ network simulator.

D. Simulation results

Fig. 1 portrays the validity of the cartography as a functionof the elapsed time since the start of the routing period and fordifferent node speeds. Recall that we are using a priority IPhandling and therefore the network load has a very little impacton the validity of the cartography. For a null node speed (nomobility), we get a validity of one hundred percent. For speedshigher or equal to 1 m/sec, the validity of the cartography getsat its maximum around instant 1 sec which is the time requiredto get the maximum of Hellos throughout the network. Moreinterestingly, the validity of the cartography once it reaches itsultimate maximum value, stays there for a while and then startsdecreasing as the elapsed time since the start of the routingperiod gets farther. The duration of this stay, however, dependson the node mobility. For a node speed of 1 m/sec the stay isof 10 seconds at the maximum validity of one hundred percentwhile for a node speed of 10 m/sec we observe a rather quickstay at a maximum validity of ninety six percent. 1 It wouldthen be interesting if one can dynamically sense the level ofthe network dynamics to deduce the point at which the validitystarts decreasing below a certain predefined threshold. Such apoint provides the size of the routing period to use if we wantto drive the network with valid routes or at least with the bestpossible percentage of valid routes above the fixed threshold.

IV. ROUTING PERIOD AUTONOMIC SELF REGULATION

The validity of the network cartography as time progressessince the start of the current routing period is a function of

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0 2 4 6 8 10 12 14 16 18 200

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V = 0

V = 1

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Fig. 1: Cartography validity as a function of the elapsed time

nodes’ speeds. As portrayed in the previous figure, this validityreaches its maximum at or before an elapsed time equals to 1seconds. The value of this maximum depends on the speedused. Proactive routing validity is intimately related to thevalidity of the network cartography. It is important to maintainthe cartography validity, or equivalently the routing validity,beyond a certain level since invalid routes not only will notdeliver their traffic but more drastically over consume valuablenetwork resources as each packet is retransmitted up to theretransmission count limit defined by the underlined MACprotocol. To this end, we propose to dynamically sense thenetwork dynamics to determine the point at which the validitystarts decreasing below a predefined threshold.

The question naturally arises as to how to track the validityof the cartography in practice since we ignore the real actualcartography of the network. In the aforementioned discussionabout the cartography validity, we assumed that an oracle isthere to provide us with the real actual nodes’ positions at anyinstant (the real actual network cartography). However, not allis lost. Indeed, at the start of each routing period, that is aninstant 0 of each period just before the start of the new waveof Hello messages, a node saves its perceived cartography,denoted by C0, which is really the maximum cartographycollected at the beginning of the previous period. At instant,say 2 seconds, this node finishes collecting its newer perceivedcartography, denoted by C2. This later cartography, at thisvery instant of 2 seconds, may adequately represent the realunknowing network cartography, and consequently the nodeby comparing C0 to C2 gets the validity of C0 and canappropriately and accordingly increase, decrease or keep thecurrent size of its current routing period.

Let us consider the ith routing period and a node Ahaving a cartography C0 at instant t=0 (at the start of thecurrent period), and a new cartography C2 at instant t=2within this same period. Let N be an advertised node havingposition (x0, y0) in C0 and position (x2, y2) in C2. NodeA considers that the position of node N as indicated in itsC0 as valid if the distance between (x0, y0) and (x2, y2) isless or equal than the assumed tolerance value d; that is if√

(x2 − x0)2 + (y2 − y0)2 ≤ d.

The validity of cartography C0 of routing period i, denotedby Vi(C0) is then the percentage of nodes having validpositions among all nodes. Depending on the value ofVi(C0), node A will adjust its current routing period sizeT(i) relatively to the previous routing period size T(i-1). Thedynamic regulation of the routing period size is then governedby the followings rules:

• if Vi(C0) ≥ P1 then T (i) = T (i − 1) + 1• if Vi(C0) ≤ P2 then T (i) = T (i − 1) − 1• if P2 ≤ Vi(C0) ≤ P1 then T (i) = T (i − 1)

In this work, we deliberately choose (P1 = 95, P2 = 80),Tmin = 3s and Tmax = 10s. Recall that we consider therouting update period number as the sequence number to use.This suppose that upon the entry to the network, a nodeshould know the current period number, its time origin andits duration. These three quantities can be easily provided bythe routing layer of the station emitting the Beacon and fromwhich we got the association at the 802.11 MAC Layer [1] [2].However, this does require an adequate synchronization frame-work at the MAC layer similar to the one proposed in [12].

Note also that the above regulation rules assume that themaximum cartography is collected within 2 seconds from thestart of the current routing period as portrayed on Fig. 1. Thisamounts to say that all nodes starts a new wave of Hellomessages at the same time. The question naturally arises asto how to maintain the same period size for all nodes and toforce these periods to start at the same instant. Otherwise,the time origin of the routing period may differ from onenode to another and consequently Hello messages will bespread over the time axis. This is indeed accomplished bydetermining both the current period size T (i) and the startinstant of the next routing period (the time origin of T (i+1)).We enforce the sizes of the ith routing period at all nodes tobe of at most one second difference. Moreover, only a shift ofat most one second is permitted on the time origins of the ithrouting period at different nodes. This is accomplished underthe tacit hypothesis that the network remains connected andthat (P1−P2) is large enough to prevent one node to incrementits current routing period while another node decrements itsown. The later hypothesis is usually met since Hello messagesare treated in priority and nodes build the same cartographyof the network.

Now, since the size of the ith routing period may differ byone second from one node to another and since its time originmay also differ by one second from one node to another, thecartography validity should be calculated by comparing theold cartography of instant one second before the start of thecurrent routing period denoted by (C−1) against that of instant3 seconds after the start of the current routing period denotedby (C3). This is also the very reason that dictates to set thevalue of Tmin to at least 3 seconds. Note also that the initialvalue of the routing period size is set to Tmax/2.

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To bring out the effectiveness of our proposed self regula-tion approach of the routing period according to the currentnetwork dynamics, we run a simulation for 1600 secondswhere we variate the nodes’s speed each 400 seconds. Weconsidered a speed of 3 m/sec, then 6m/sec, then 2 m/secand finally 0 m/sec. We used the same simulation set up asbefore concerning the area, the number of nodes, the mobilitymodel, the transmission range and a tolerance of 10 meters.Fig. 2 clearly portrays that the proposed approach sensescorrectly the changes in nodes’ speeds and dynamically adjustthe routing period size accordingly. For the first 400 secondssubperiod, the speed is kept at 3 m/sec and consequently arouting period equals to Tmax = 5s is constantly used. Atthe start of the second subperiod, the speed is increased to 6m/sec. Here, we observe that the correct routing period size of3 seconds as indicated by Fig. Fig. 1 is automatically reachedafter 2 routing periods (after less than 10 seconds). At thestart of the third subperiod, we lowered the speed to 2 m/sec.The routing period gets quickly to its ultimate value of 7 to8 seconds. For the fourth subperiod, we further lowered thespeed to 0 m/sec and we note that our self regulating approachthrived appropriately to increase the routing period size to itsultimate value (namely Tmax = 20s), though slowly since weonly add one second at each step.

0 200 400 600 800 1000 1200 1400 16000

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Fig. 2: Routing period autonomic self tuning

V. NETWORK PERFORMANCE EVALUATION

We now turn our attention to evaluate the impact of ourproposed autonomic self tuning scheme on the performance ofthe network. We consider two different simulation scenarios.Our first simulation scenario is exactly the network set upused above concerning the simulation area of 300mx300m, 5Omobile nodes, a transmission range of 100m, a tolerance of 10m and the RandomWay point mobility model. We consider 10different traffic flows between 10 mobile source nodes and 10mobile destination nodes all chosen randomly among the 50nodes. Simulations are run for 1500 seconds where we useda node speed of 2 m/sec for the first 1000 seconds and thenwe switch to a node speed of 3 m/sec for the remaining 500seconds. The first 500 seconds are considered as a transientregime and consequently only performances emanating fromthe last 1000 seconds are taken into account. Finally, the

period size for the common proactive routing protocol (i.e.,The DSDV protocol) is fixed to 20 seconds. Recall that theperiod size for the adaptive proactive protocol based on ourautonomous self regulating scheme is computed dynamicallyand amounts to 6 to 7 seconds when a node speed of 2 m/secis used and to 5 seconds when a speed of 3 m/sec is used asalready portrayed on Fig. 2.

We shall limit our attention here solely to the networkthroughput which is considered as one of the major perfor-mance metrics. Fig. 3 portrays the network throughput, definedhere as the total number of correctly received packets perflow, as a function of the traffic load per flow and for boththe common proactive protocol and the adaptive protocol. Weclearly observe the superiority of the adaptive protocol andthis for the entire range of traffic loads. At moderate to highworkloads, the common proactive protocol is unable to copewith the loss of routing validity as it uses a routing periodof 20 seconds. Indeed, as it can be observed from Fig. 2, thevalidity of the cartography corresponding to 2 m/sec speedstarts decreasing under the level of 90 percent from aroundinstant 7 and that corresponding to 3 m/sec starts decreasingunder the ninety percent form around instant 5. As a result, thecommon proactive protocol works mostly under a poor routingvalidity. For a high traffic load the adaptive protocol yieldsmore than double the throughput of the common proactiverouting protocol. We may legitimately, however, question on

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3x 10

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ρ (Packets/sec)

Number of received Packets

Adaptative Proactive ProtocolCommon Proactive Protocol (T=20sec)

Fig. 3: Throughput as a function of the network load for the300mx300m network

some of the parameters used in our studied scenario such as thesize of the network its density and its diameter, the mobilitymodel and the period size adopted for the common proactiveprotocol among many others. Further legitimate questions,among others, may concern the packet dropping ratios at boththe IP and the MAC levels, the average end to end delayand the average number of hops undertaken to reach thedestinations. While we do not have enough space to commenton all of these, we limit ourselves to just consider a secondscenario of an area of 400mx400m with 100 nodes where weimmobilized 10 equally spaced source nodes at the left edgeof the area and 10 equally spaced destination nodes at theright edge of the area. Our ten considered traffic flows are

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then using these fixed source-destination pairs. As such, weassured a route length of at least 5 hops. Recall that all otherparameters are as defined in the first scenario. Furthermore, weadopt here two different period sizes for the common proactiveprotocol: a period size of 3 seconds and a period size of 20seconds. Fig. 4 portrays the resulting throughput.

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Fig. 4: Throughput as a function of the network load for the400mx400m network

Here again, we observe the net superiority of our proposedscheme. It is also interesting to observe that for low trafficloads, the common proactive protocol delivers better with thesmallest considered period size of 3 seconds as the validityof the routes is certainly at a very high level (normally at100 percent) and there is still enough room to handle the datatraffic. As the workload gets higher, the largest period sizestarts to be more effective as it lowers the signaling traffic.The common proactive protocol with this large value, however,forwards traffic through invalid routes most of the time andconsequently delivers far behind the adaptive routing protocol.

VI. CONCLUSION

We proposed a distributed algorithm to compute the net-work cartography which we then used to appropriately anddynamically adjust the current routing period size in a way tocalibrate the signaling overhead and the routing pertinence.

Simulations showed that our autonomous scheme is capableof properly tracking the network dynamics and accordinglyadjusting the current routing period size. Further refinementsof our proposed schemes are being investigated to speed upthe dynamic local self adjustment of the period size. Moreoverenhanced cartography based versions of location based routingprotocols could also be easily envisioned. Which protocol typeto use, under what circumstances, is also being investigatedyielding to a polymorphic vision of proactive routing inMANETs.

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