A Probabilistic Emergent Routing Algorithm for Mobile Ad Hoc Networks

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1 Author mnuscript, published in "WiOpt'03: Modeling nd Optimiztion in Mobile, Ad oc nd Wireless Networks (2003) 10 pges" A Probbilistic Emergent Routing Algorithm for Mobile Ad oc Networks John S. Brs nd rsh Meht Deprtment of Electricl nd Computer Engineering nd the Institute for Systems Reserch University of Mrylnd, College Prk, MD 20742, USA inri , version 1-24 Mr 2010 Abstrct Mobile d hoc networks re infrstructure-less networks consisting of wireless, possibly mobile nodes which re orgnized in peer-to-peer nd utonomous fshion. The highly dynmic topology, limited bndwidth vilbility nd energy constrints mke the routing problem chllenging one. In this pper we tke novel pproch to the routing problem in MANETs by using swrm inteligenceinspired lgorithms. The proposed lgorithm uses Ant-like gents to discover nd mintin pths in MANET with dynmic topology. We present simultion results tht mesure the performnce of our lgorithm with respect to the chrcteristics of MANET, the vrying prmeters of the lgorithm itself s well s performnce comprison with other well-known routing protocols. 1 Introduction A substntil reserch effort hs gone into the development of routing lgorithms for MANETs. A number of routing lgorithms hve been proposed. Some of these re DSDV, OLSR, CGSR, AODV, DSR, TORA, ZRP, LAR nd severl others [11, 13, 14, 15. These protocols cn generlly be ctegorized s either proctive or rective protocols. Proctive protocols build routes in the network constntly, even though there might not be pckets to be trnsmitted between certin set of nodes. Rective (on-demnd) protocols, on the other hnd, ttempt to estblish multihop between pirs of nodes only when there re pckets to be exchnged between these pirs of nodes. Recently there hs been gret interest in so clled Swrm Intelligence [1, [2; set of methods to solve hrd sttic nd dynmic optimiztion problems using coopertive gents (usully clled nts, since the method ws inspired from collbortive efforts in insects). Ant-inspired routing lgorithms were developed nd tested by British Telecomm nd NTT for both fixed nd cellulr networks with superior results [3, 4, 5, 6, 7, 8, 9, 10. AntNet, prticulr such lgorithm, ws tested in routing for dt communiction networks [3. The lgorithm performed better thn OSPF, synchronous distributed Bellmn-Ford with dynmic metrics, shortest pth with dynmic cost metric, Q-R lgorithm nd predictive Q-R lgorithm [1, 3, 4, 5, 6, 7, 8. MANETs operte in distributed nd synchronous mnner. Inspired by the success of nt-gent lgorithms in routing nd lod blncing for fixed communiction networks we first proposed pplictions of the swrm-intelligence ides for dynmic dptive routing in in MANETs in the proposl [16. We initited reserch on these ides since October 2000, nd first presenttion of our results ws given in the seminr [18. Interest in pplictions of nt-bsed routing in MANETs hs risen nd severl ppers hve ppered recently on the subject [17, 19, 20. For instnce, Gunes et l. hve proposed n Ant-bsed pproch to routing in MANETs in [19. Their pproch uses nts only for building routes initilly nd hence is completely rective lgorithm. They hve lso shown some performnce comprisons with other MANET routing protocols bsed on the puse time of mobile nodes. Mrwh et l. [20 hve explored hybrid pproch using both AODV nd Ant-bsed explortion. In our reserch we discovered erly tht there re two centrl chllenges in mking the promising swrmintelligence ides work successfully in the difficult domin of MANET routing. The first is to get the computtions in such form nd implementtion so s to to be fst nd fst converging. This is necessry given the mobile nture of MANETs nd the resulting chnging topology. The second nd most serious is to reduce the overhed (O) creted by these proctive lgorithms. Strightforwrd ppliction of nt-bsed routing, like AnNet or other lgorithms tht were successful in fixed topology networks, does not work well in MANETs due to lrge O. We ddressed both chllenges in our reserch to dte on this problem. This pper describes primrily new methods nd ssocited evlutions for combting the second nd most serious chllenge. We first describe n lgorithm bsed on swrm-intelligence bsed on unicst communictions for control nd signling pckets (nts). We compre this lgorithm (fter improvements) to AODV ( populr routing lgorithm for MANETs [14, 15) nd show tht the overhed requirement of the swrm-intelligence lgorithm significntly hrms its competitiveness. Then we describe new lgorithm which utilizes the inherent brodcst nture of wireless networks to multicst control nd signlling pckets (nts). This second lgorithm competes well with AODV nd we show here severl comprisons by simlutions in stndrd benchmrk for MANETs [12, 15, 22. We describe severl dditionl innovtions we hve introduced in both lgorithms nd in prticulr the dvntge o discovering, storing nd using multiple (rnked) pths between source-destintion pirs. For more detils on our new lgorithms nd their

2 inri , version 1-24 Mr 2010 performnce evlution we refer to [21. Our pproch nd methodology hs strong distributed optimiztion foundtion, which leds towrds promising nlyticl tretment; work on this is under wy nd will be reported elsewhere. In ddition we hve shown, in different prt of our recent work, tht these new lgorithms hve superior routing security properties; significnt discovery given the wek stte of ffirs regrding security in ll existing MANET routing protocols nd routing protocols t lrge. Our work in security nd trust for MANETs hs introduced n importnt innovtion vi novel optimiztion frmework for security nd trust. For further detils bout our results on security nd trust in MANETs we refer to [23, A swrm intelligence bsed unicst lgorithm for MANETs In this section, we describe routing lgorithm for Mobile Ad oc Networks bsed on the swrm intelligence prdigm nd similr to the swrm intelligence lgorithms described in [3, 9. The lgorithm uses three kinds of gents - regulr forwrd nts, uniform forwrd nts nd bckwrd nts. Uniform nd regulr forwrd nts re gents (routing pckets) tht re of unicst type. These gents proctively explore nd reinforce vilble pths in the network. They crete probbility distribution t ech node for its neighbors. The probbility or goodness vlue t node for its neighbor reflects the likelihood of dt pcket reching its destintion by tking the neighbor s next hop. Bckwrd nts re utilized to propgte the informtion collected by forwrd nts through the network nd to djust the routing tble entries ccording to the perceived network sttus. Nodes proctively nd periodiclly send out forwrd regulr nd uniform nts to rndomly chosen destintions. Thus, regrdless of whether pcket needs to be sent from node to nother node in the network, ech node cretes nd periodiclly updtes the routing tbles to ll the other nodes in the network. The lgorithm ssumes bidirectionl links in the network nd tht ll the nodes in the network fully cooperte in the opertion of the lgorithm. 2.1 The Opertion of the lgorithm Bootstrpping of the routing tbles Initiliztion nd neighbor discovery is done by single-hop, brodcst messges tht re trnsmitted periodiclly t n intervl of seconds. These messges re used t nodes to build the neighbor list, which is then used for the initiliztion of the routing tble. The initil bootstrpping of the routing tbles is done t node when the first forwrd nt is being sent out to certin destintion. At this time, there re no routing tble entries (i.e. no probbilities for next hops) for tht prticulr sourcedestintion pir. The cretion of the first forwrd nt t node for the source-destintion pir cuses the routing tble entries to be initilized with probbilities for ech neighbor s the next hop for the respective destintion, where is the number of neighbors of the node where the routing tble is being estblished. The uniform probbilities ssigned to ll the neighbors indicte tht nothing is known bout the stte of the network. These probbilities re then djusted by bckwrd nts, when bckwrd nts from the destintion re received t the source node The routing tble The routing tble t ech node is orgnized on perdestintion bsis nd is of the form "! #$&%'! ( )*,+-%'.-(0/213%4&#54" &6789;:. It contins the goodness vlues for prticulr neighbor to be selected s the next hop for prticulr destintion. Further, ech node lso mintins tble of sttistics for ech destintion < to which forwrd nt hs been previously sent; the men nd the vrince (=?>A@B(DCFE >G@ ) for the routes between source node nd destintion node <. The routing tbles then contin the following dt structures: The probbility (goodness vlue) of tking s next hop node I t node!, /0J to eventully rech certin destintion <. The men nd the vrince, L= t node! rech destintion < Forwrd nts Ech node periodiclly sends forwrd nts to rndomly chosen destintion nodes throughout the network. At the time of cretion of the gent, if routing tble entry is not present t the node for tht prticulr destintion, routing tble entry is creted. This is lso true of the forwrding of nts t intermedite nodes. Ech forwrd nt pcket contins the following fields: Source node IP ddress Destintion node IP ddress Next hop IP ddress Stck op count ence, the next hop of the forwrd nt is determined t the sending node nd the forwrd nt is sent in unicst fshion. Tht is, though the forwrd nt is received t ll the neighboring nodes, it is ccepted (t the MAC lyer) only by the node to which it hs been ddressed. The stck of the forwrd nt is dynmiclly growing dt structure tht contins the IP ddresses of the nodes tht the forwrd nt hs trversed s well s the time t which the forwrd nt reched these nodes. Forwrd nts re routed on norml priority queues, tht is, they use the sme queues s norml dt pckets. As such, forwrd nts fce the sme network conditions (queuing nd processing delys, network congestion) s dt pckets. Forwrd nts therefore contin informtion regrding the route tht they hve trversed. 2.2 Routing the Forwrd nts The forwrd nt is routed t ech node ccording to the perdestintion probbilities for the next hop in the routing tble t the current node. Thus, the forwrding of the forwrd nt to

3 / ( [ inri , version 1-24 Mr 2010 is probbilistic nd llows explortion of pths vilble in the network. These gents re henceforth referred to s Regulr Ants, similr to [9 to distinguish them from Forwrd Uniform Ants. When forwrd nt is received t node, it checks to see if it hs previously trversed the node. If it hs not previously trversed the node, the IP ddress of the node nd the current time re pushed into the stck of the nt. In cse the node IP ddress is found in its existing stck, the forwrd nt hs gone into loop nd is destroyed Uniform nts Since forwrd regulr nts re routed uniformly, nd the resulting bckwrd nts reinforce the routes, this cn led to sturtion of the probbilities, tht is the probbilities of one (observed to be the best) route go to 1 nd the probbilities of the other routes go to 0. As result, new routes never get discovered. In dynmic sitution with the possibility of brekge of links nd mobility of nodes, this mens tht the lgorithm is unble to dpt to chnges in the network. To mke the lgorithm fully dptive to mobility nd topology chnges, we introduce nother set of nts, clled uniform nts. These re similr to the gents proposed in [9. N % of the time, insted of creting regulr nts, ech node sends out uniform nts. These re creted in the sme mnner s regulr nts, however they re routed differently. Insted of using the routing tbles t ech node, they choose the next hop with uniform probbility. If the current node hs neighbors, then the probbility of tking neighbor s the next hop is O. This is in contrst to regulr nts which prefer tking next hops with higher probbilities more often. Uniform nts explore nd quickly reinforce newly discovered pths in the network. Further, they ensure tht previously discovered pths do not get sturted Bckwrd nts When regulr or uniform nt reches its destintion, it genertes Bckwrd Ant. The bckwrd nt inherits the stck contined in the forwrd nt. The forwrd nt is dellocted. The bckwrd nt is sent out on the high priority queues. This ensures tht bckwrd nts re propgted in the network quickly, so tht they cn updte the informtion regrding the stte of the network without dely. The purpose of the bckwrd nt is to propgte informtion regrding the stte of the network gthered by the forwrd nts. The bckwrd nt retrces the pth of the forwrd nt by popping the stck, mking modifictions in the routing tbles nd sttistic tbles t ech intermedite node ccording to one of the following lerning rules: 1. / PQ/ (1) / PX/ (2) ere 1 is the reinforcement prmeter. 2. / P P A/ VRS1T: / VRS1T: In both the bove cses, the reinforcement prmeter, 1 cn be defined s function of some metric or combintion of metrics, e.g. dely or the number of hops. 1\[ I^_$: `b ere I^A_$: is monotone function of the metric nd is constnt. The bckwrd nt lso updtes the existing estimtes of the forwrd trip time t the source node s well s intermedite nodes. The trip time of this bckwrd nt is used to updte the sttistics. (3) (4) (5) The men nd the vrince, (DCFE : re updted using the following updte rules: = Pe= = (6) where is the men of the nt trip times t the current node,, to the destintion node, <. f is constnt, is the trip time of the nt from the current node to the destintion node <. nd C d PQC d RYf^% = E UWC d : (7) where CFE is the vrince of the nt trip times t the current node,, to the destintion node, <. f, % nd = re the sme s bove Chnges in routing tbles due to node mobility When node or nodes enter into the trnsmission rnge of node, this cretes the possibility of there being new vilble routes to destintion tht ws either rechble by longer route or previously unrechble. The detection of newly moved node is through the beconing mechnism. The ello messges brodcst by ech node give informtion regrding the vilbility of node s next hop. Suppose node moves into the neighborhood of node i, then the IP ddress of is dded to the list of neighbors of i nd vice vers. Node i then djusts its routing tble to include with smll probbility. So, if node i hs existing routes to nodes <5jD(D< E ($klklkl(d<m, it dds s next hop with smll probbility for ech of <Fj&($< E (DkLklkL(D<m. Thus, the probbility of forwrd nt tking is,!.3nw[po&(doqr (8) nd the probbility of the other nodes,,!ts becomes, where.fuv[w.fuu o (9) ws the number of i s neighbors before entered its trnsmission rnge. The sum of the probbilities remins 1. i.e. x u. u [t. This llows the explortion of new routes through node from node i by regulr nts. If new nd efficient routes re

4 J inri , version 1-24 Mr 2010 found with node s n intermedite node, they re quickly reinforced nd cn be utilized for routing dt pckets. When node leves the trnsmission rnge of node i, is removed from i s routing tble. The probbility of tking s the next hop is mde 0 for ll destintions from node i. The probbility distribution is then normlized for ll the other nodes, so tht the sum of the probbilities is 1, i.e. u. u [y Routing dt pckets Dt pckets re routed deterministiclly bsed on the mximum probbility t ech intermedite node from the source node to the destintion node. As such, locl informtion (next hop probbility t n intermedite node) is used in such wy tht globl informtion ( complete route between the source nd the destintion) emerges from it. 2.3 Algorithm prmeters nd other issues The unicst routing lgorithm proposed here hs three key prmeters: The rte t which forwrd regulr nd uniform nts re sent. This determines how quickly the lgorithm discovers new pths, reinforces existing pths, destroys unvilble pths nd dpts to new network topologies. The percentge of forwrd nd uniform nts. This determines the blnce between reinforcing lredy discovered routes nd discovering new routes. The updte function nd reinforcement, used by the bckwrd nts to reinforce the routing probbilities t ech node Brodcst vs unicst in the MANET environment Wireless dt trnsmission offers severl chllenges nd opportunities for routing. One of the dvntges it offers is the inherent brodcst cpbility, tht is, ll the neighbors of node receive ech dt pcket trnsmitted by the node. owever, the lgorithm proposed bove does not tke dvntge of this inherent cpbility of the wireless environment, nd though it is well suited for the wired environment, we note tht this lgorithm leds to high overhed nd inefficient route discovery in the wireless environment. As the number of nodes increses, the number of nts required to find pth to the destintion lso increses rpidly, leding to very high overhed, high delys s well s high pcket losses. Figure 1 shows comprison of the goodput required for AODV nd the unicst lgorithm described bove. Even for low mobility speed of 1 m/s, the overhed required is very high for the unicst lgorithm when compred with AODV. 3 The Probbilistic Emergent Routing Algorithm (PERA) for Mobile Ad oc Networks In this section, we propose n lgorithm bsed on the Swrm Intelligence prdigm tht exploits the inherent brodcst cpbility vilble in the wireless environment. In this pproch, the process of route discovery is crried out by using flooding pproch to obtin nd mintin pths between source-destintion pirs in the network. Route discovery in the lgorithm is done by two kinds of gents or nts - forwrd nd bckwrd. Uniform nts re no longer required or fesible s the forwrd nts re now brodcst rther thn unicst. These gents crete nd djust probbility distribution t ech node for the node s neighbors. The gent pckets, or Ants re of reltively smll (vrible) size. The probbility ssocited with neighbor reflects the reltive likelihood of tht neighbor forwrding nd eventully delivering the pcket. Further, multiple routes between the source nd the destintion re creted. 3.1 Bootstrpping the routing tbles As in the previous lgorithm, neighbor discovery is done using ELLO brodcst messges. owever, the routing tble entry for destintion is initilized t node only fter receiving bckwrd nt from the destiniton. The routing tble is of the sme form s for the unicst lgorithm. The initiliztion of the routing tble is done by incorporting ll the neighbors of node! in the routing tble. Ech node is ssigned n initil probbility, where is the number of neighbors of node!. The routing tbles re then modified to give higher probbility to the node tht the bckwrd nt just cme from, s discussed in section 2.2.2, estblishing pth towrd the destintion. When the metric under considertion is dely, on the receipt of the first bckwrd nt, the vlue of the time tken by the nt to trvel to the destintion from the current node, is ssigned to the men, = nd the vrince, CFE ($CFE : re is ssigned vlue of zero. Modifictions to L= mde upon the rrivl of lter bckwrd nts bsed on the lerning rule s discussed in section On the other hnd, if the metric under considertion is the hop count for instnce, the bckwrd nts s well s the forwrd nts trvel on high priority queues, leding to fster dissemintion of informtion regrding the network sttus. The routing tble nd the tble of locl sttistics t ech node cn be visulized s in Figure 2. { z z z ~ z } z z { z z š œ z { z z z z } z z ~ z z z z z ƒ ˆ Š Œ ˆ Ž Figure 1: Goodput comprison of AODV nd the unicst swrm bsed lgorithm for 20 nodes in n re of 500m X 500m for speed of 1 m/s.

5 Û Û Û Û inri , version 1-24 Mr Forwrd nts To crry out the process of Route Discovery, forwrd nts or gents re used. The forwrd nts re somewht similr to the Route Request pckets used by AODV [14 nd DSR [11 routing protocols, but hve some subtle differences. Ech forwrd nt contins the IP ddress of its source node, the IP ddress of the destintion node, sequence number, hop count field nd dynmiclly growing stck. The stck contins informtion bout the nodes tht the forwrd nt trverses nd the times t which these nodes hve been trversed, ie. 0ž v, ( :. When node does not hve record of route to destintion to which it hs to send pcket, it cretes forwrd nt nd brodcsts it to ll its neighbors. Before brodcsting the forwrd nt, the node pushes its own IP ddress on to the stck of the forwrd nt s well s the time t which the nt is creted. enceforth, the node keeps sending forwrd nts periodiclly to the destintion for s long s route is required. When node receives forwrd nt, it checks in the destintion IP ddress field if the ddress corresponds to its own IP ddress. If the forwrd nt is not directed to the current node, the node pushes its own IP ddress nd the time t which the nt ws received t the node. Also, the hop count field of the forwrd nt is decremented by 1. Ech forwrd nt is uniquely identified by the vlues of its source node IP ddress nd the sequence number, i.e. the record %' T13_& /X#5< <&13 ( '! _& - w4&1t:. The sequence number for ech nt is ssigned t the source node nd is unique for tht source nd forwrd nt. Thus, ech node stores the pir %' -13_& / #5<M<D13 ( #$) '! _&! - w4dg1t:, where the #$) '! _&^! - w4&1 is the highest vlue of the sequence number of n nt received from tht source node. The node drops forwrd nts with sequence number less thn or equl to the #$) '! _&ž! T 4&1 tht it receives from the sme previous hop. This voids the dupliction of forwrd nts in the network s well s the estblishment of only the best route through node. If the #$) '! _&2! - 4&1 vlue is greter thn tht previously recorded by the node, the node updtes this vlue. An nt which reches node tht it hs lredy trversed is destroyed in similr fshion. It hs tken circuitous Ä ½ µå ³ À Æ Ä ³ Ç ½ ² ³ µ ¹ º» ¼½ ¾ ³ º ¼ ¹ À º Á Á  µ º µ à µ à î ïð ïñ ö ó î ïð ïñõ ó ô ô ô ª «± ì íí é êë è è è â ã ä à áá ß ß ß Üå ÝÞ æç ÌÊË ÍÎÏÐ Ñ Ø Ù Ú Õ Ö ÈÉ Ô Ô Ô Ò Ó î ïð ïñò ó Figure 2: The routing tble nd locl sttistics mintined t ech node. route nd is therefore not llowed to contribute to the store of informtion regrding the sttus of the network. It is importnt to note tht the forwrd nts trvel on the sme queues s dt pckets. In our experiments, these queues re modeled s FIFO queues. ence, the forwrd nts experience the sme dely nd congestion s the dt pckets when the metric being used is dely. This llows us to reinforce certin routes more thn other routes depending on the current network sttus s perceived by the forwrd nts. When forwrd nt reches the node tht is its intended destintion, the node extrcts ll the relevnt informtion from the forwrd nt. Tht is, the source ddress, the hop count nd the stck. The forwrd nt is then killed, i.e. its memory is dellocted. The informtion obtined by the forwrd nt is then used by the node to crete bckwrd nt. It is importnt to note tht since the the forwrd nt is brodcst t the source nd intermedite nodes, ech forwrd nt will cuse the brodcst of multiple forwrd nts, severl of which my find different pths to the destintion, generting multiple bckwrd nts with the sme source sequence number. Since forwrd nts re re-brodcst t every intermedite node, creting multiple forwrd nts, it cn be seen tht forwrd nt brodcst from the source node my find more thn one route to the destintion, if more thn one routes exist. In the cse when the network is closely connected nd the network dimeter (defined s the minimum number of hops between ny two nodes) is smll, single brodcst forwrd nt successfully finds severl fesible pths to the destintion node from the source node. Further, the forwrd nt lso collects informtion bout ech of these pths, tht is, the number of hops on the pth nd the dely on the intermedite subroutes s well s on the entire route. It should be noted here tht the Route Discovery phse is similr to tht of existing MANET lgorithms like AODV nd DSR, in the sense tht flooding-bsed pproch is used which uses the inherently brodcst medium of the wireless environment to its dvntge. owever, n importnt difference is tht our lgorithm discovers set of routes. Further, we obtin informtion bout these pths nd use this informtion s feedbck to the lgorithm. 3.3 Bckwrd nts When forwrd nt reches the destintion node tht it is intended for, the destintion node cretes new gent, bckwrd nt. The purpose of the bckwrd nt is to retrce the pth of the corresponding forwrd nt tht triggered its cretion. It uses the informtion contined in the forwrd nt on the reverse pth to chnge the probbility distribution t ech node nd updte the routing tbles to reflect the current sttus of the network more ccurtely. When node receives forwrd nt tht is intended for it, the node cretes new gent, bckwrd nt. The IP ddress of the source node of this gent is the destintion ddress of the bckwrd nt nd the current node is the source of the bckwrd nt. The bckwrd nt is similr to the forwrd nt, it contins the following fields:

6 U e inri , version 1-24 Mr 2010 Destintion IP ddress : The IP ddress of the source of the forwrd nt, Source IP ddress : The IP ddress of the current node, i.e. the node creting the bckwrd nt, op count, The stck of the forwrd nt, The sequence number of the forwrd nt - this is not unique nymore for the set of bckwrd nts. The bckwrd nt trvels in unicst fshion bck to the source node. It is forwrded on high priority queues. The stck of the forwrd nt is used to route it. Using the ddress t the top of the stck, the node forwrds the bckwrd nt to the correct next hop. Suppose tht forwrd nt from source node is received t node <. Node < genertes bckwrd nt. When the bckwrd nt is received t the next hop (lso the penultimte hop of the corresponding forwrd nt), node I, the stck of the bckwrd nt is popped once. The resulting informtion is the following: The IP ddress of the current node I, The,, the time t which the corresponding forwrd nt ws received t node I. The time t which the bckwrd nt ws creted t 0\, ø its source node <,. Then, the time tken to rech the destintion of the forwrd nt from 0, the current node is the difference,, The number of hops from the current node I to the destintion < re clculted by subtrcting the vlue in the hop count field from the network dimeter. These vlues re used to updte the routing nd locl sttistics tbles t the intermedite nodes I. If routing tble entries for destintion < do not exist t node I, new ones re creted with the neighbor list of the node I. All the neighboring nodes re given probbility, where is the number of neighbors of the node of O I. The routing tbles re then redjusted ccording to the probbility rules discussed in section If routing tble entries for < lredy exist t node I, they re updted so s to increse the probbility (goodness, preference) of tking s the next hop, the node from which the bckwrd nt hs just been received, node I to rech the destintion <. The updte rules used re the sme s those used for the previously discussed unicst lgorithm nd hve been described in section To further illustrte the functioning of the lgorithm for individul nts s well s individul nodes, Figure 3 depicts the lgorithm flow for ech nt, while Figure 4 depicts the lgorithm flow t ech node. The chnges required to the routing tbles due to mobility of nodes re the sme s for the unicst lgorithm nd hve been discussed in section Routing dt pckets The dt pckets cn now be routed vi number of possible schemes:! " # $ %! & ' ( # > * 1 A / * B / 7. / * * : ;. * / 3 i Z S j k l m n o p q r o s m o l t r u t v w t o p q x l y u z v { s u x s ut } ~ k ~ k u l ~ l u p o w t o w v u x l ù ú û ü ú ý þ ÿ ÿ ÿ þ þ ÿ ÿ þ þ þ þ ÿ ÿ þ þ þ þ ÿ ÿ þ þ ÿ ÿ þ þ þ ÿ ) * +, * -. / * * / 5 0 / / : ;. * / 8 6 : 6 * 1 7 < * 6 = > *? 7 3 ) * +, * - - * / 5 0 / B 8 C C 7 1 D / B 5 0 / * * / 8 : * / * - - * / 5 0 / ; ; 8 : ; + / 8 * / 8 4, A * E 4 6 * / * F 4 ; B : * 4 * 6 7 E 4 6 * G ; 8 * / * G : 4 0. C * 1 7 Figure 3: Algorithm for ech nt. b O U V O W c O N X P N Q P Y T N Z R Z [ d Z Q f Z S T [ Y P T [ O Y g I J K L J M N O P Q R P S T R O U V O W P W O N X P N Q P Y T T O P Q Z S T [ Y P T [ O Y [ W P U P R \ Z T W O N T Z Q Z S T [ Y P T [ O Y [ S ^ _ W W Z N Z Q ƒ ˆ Š ˆ Œ Š Ž ˆ Œ ` P [ T [ Y W O N P Y P Y T Y [ R P S T ^ P R \ X P N Q P Y T N Z R Z [ d Z Q h N [ [ Y P T O N g š œ ž š œ ž ž ž œ œ ž ª ž ž Figure 4: Algorithm t ech node. š œ ž œ ž ž œ œ ª ž ž «œ ž ž œ ž š š š œ ª

7 ² ¾ ½ inri , version 1-24 Mr The dt pckets cn be routed on the bsis of the highest probbility for the next hop t node for the dt pcket s eventul destintion. This cretes complete globl route by using locl informtion. 2. The dt pckets cn lso be routed probbilisticlly. Previous results [3 for swrm intelligence lgorithms show excellent results for this method in the cse of sttic networks with reltively smll topologies. owever, this might not be suitble method for MANETs with rpid topology chnges. 4 Simultion Results Network Simultor 2 [22 discrete event simultor ws used to simulte our lgorithm. At the physicl lyer, rdio propgtion distnce for ech node ws set to ±³² nd the chnnel cpcity ws ± 4".-. Our model does not support rdio cpture [15 so, in the cse of pcket collisions ll pckets re dropped. The IEEE Distributed Coordintion Function (DCF) [12 s implemented in NS2 ws used s the Medium Access Control (MAC) protocol. The communiction medium is brodcst nd nodes hve bidirectionl connectivity. Ech simultion ws run for 900 seconds. Multiple runs with different seed vlues were conducted for ech scenrio nd the collected dt were verged over those runs. The lgorithm ws developed s seprte NS2 routing lyer protocol. The mobility model used ws the Rndom Wypoint model. We use the throughput, the goodput nd the verge endto-end pcket trnsmission dely for comprisons. All the simultions were crried out with the sme seed for the given simultion scenrio nd hence the results cn be directly compred for the routing lgorithms. %%<D.B -0[ +B1F%' º¹+O.M T [ M #$D#2.-#5_ D^13_DG µ <Z#$ 13%' -D1F, %'D#56.ø# _ D^13 _& µø <Z#$ 13%' TD13 (10) M #$D#.-#5_ D;13 _ µø<ž#$ < G "! #$&%'!», #$D#2.-#5_ D2 G!2I 1F%' %' -1F_D (11) The end-to-end dely is the intervl between the instnt source genertes pcket nd the time t which the destintion receives the pcket. The end-to-end dely is ggregted for ech pcket for ech source-destintion pir. The verge per pcket end-to-end dely through time intervls of 100 seconds is then clculted s the number of source-destintion pirs nd the number of pckets received is known. 4.1 op count bsed optimiztion In these experiments, we used the hop count s the metric for opertion of the lgorithm (insted of dely). The network consisted of ± nodes, rndomly plced in n re M M x ². ¼ source nd destintion pirs were rndomly chosen from these ± nodes. Ech source trnsmitted?.ø# _ DB _. Nodes in the simultion were mobile. 4.2 Mobility speed In these experiments,the mobility speed ws vried between to ± B, i.e.,. (0,5,10,20,15,20) wm. Ó ÐÑÒ ÎÏ Ï Á ½ À ¾ À ½ ¾ ½ ½ ¾ ½ ¾ À ½ À ¾ Â Ã Ä Å Æ Å Ç È É Ê Ë Ì Í Figure 5: Vrition in goodput with mobility. Figure 5 shows the goodput s function of the node mobility speed. It is seen tht the goodput decreses with increse in mobility. This is to be expected since with n increse in mobility, lrger number of forwrd nts re required to be sent to discover new routes nd modify nd updte existing routes which re no longer vilble for pcket trnsmission. ð ïï ìíî êë çèé Ù Ö Ù Õ Ù Ô Ø Ö Õ Ô Ô Ú Ù Ô Ù Ú Õ Ô Õ Ú Û Ü Ý Þ ß Þ à á â ã ä å æ Figure 6: Percentge pcket loss for vrying mobility. Figure 6 shows the percentge pcket loss s function of the mobility. With nd low mobility ( B ), the pcket loss is. With speeds of ² B, the pcket loss is under ±ºñ. owever, with incresing mobility, the pcket loss increses linerly. Thus, even the incresed rte of sending nts (s evidenced by the decresed goodput) does not serve to mintin low percentge of pcket loss. To keep the pcket losses low, the rte of sending nts hs to be incresed non-linerly. 4.3 Rte of sending forwrd nts In these experiments the rte of sending forwrd nts ws vried for different mobility speeds nd the behvior of the lgorithm ws studied. Tble 1 shows the vrition in goodput nd percentge pcket loss s function of the ø\ 0 (the time period between the trnsmission M of M two forwrd nts) for ± nodes in n re of ² Nò² with speeds of

8 " & % $ #! inri , version 1-24 Mr 2010 ANT INTERVAL Goodput % Pcket Loss Tble 1: Goodput nd % Pcket Loss with ANT INTERVAL with mobility of 10 m/s. ANT INTERVAL Goodput % Pcket Loss Tble 2: Goodput nd % Pcket Loss s functions of ANT INTERVAL for mobility of 5 m/s. wm nd puse time of ² _&. For high vlue of 0 Z 0, the pcket loss is high. This is explined by the fct tht informtion regrding the current stte of the network is not updted rpidly. The lgorithm fils to dpt in mny cses resulting in high pcket loss. owever, s the period between the sending of two consecutive forwrd nts is decresed, the pcket loss reduces significntly. This shows tht the lgorithm dpts to the chnges in the network quickly s the number of forwrd nts being sent increses. With vlue of 15 seconds for the ø\ 0, the pcket loss is ¼*k ó³ôõñ. Tble 2 shows similr results with speeds of ² B. For low vlue of 0\ ø 0, the pcket loss is lower thn for higher vlue. Further, it is importnt to note tht the pcket loss for vlues of 0\ ø ž0 100 nd 150 re the sme. This is becuse the increse in the number of forwrd nts tht re sent is not sufficient to cuse n increse in performnce in terms of goodput nd pcket loss. The goodput therefore goes down since the pcket loss remins constnt. Tble 3 shows similr results with speeds of h wm. Since the mobility is very low, the dptivity required of the lgorithm is reltively low. Even by sending nts t higher rte, there is no chnge in the pcket loss, since single forwrd nt sent t the strt of the simultion obtins enough dt for ll dt pckets to be successfully routed. of the vlue of the reinforcement. The speeds of the nodes re ² wm nd B. The goodput is higher for speed ²Z wm, due to the fewer number of nt pckets required to discover vilble routes. Figure 8 shows the vrition of the percentge pcket loss s function of the vlue of the reinforcement for speeds of ² B nd B. As expected, the pcket loss is lower for lower speed of movement. owever, it is importnt to note tht with n incresing vlue of the reinforcement pplied, the pcket loss first increses nd then decreses. This is becuse wek (smll) pplied reinforcement implies tht routes do not get positively reinforced to sufficiently high degree. In the sitution where mobility exists in the network, this reduces the dptivity of the lgortihm, leding to stle routes being used for the trnsmission of dt pckets. If the reinforcement pplied is incresed beyond certin vlue, it cuses the routes to be reinforced too fst. This leds to routes tht my not ctully be the best routes being used for the trnsmission of dt pckets. 5 Comprison with AODV We compred the lgorithm proposed in section 3 with AODV [14, 15 in terms of throughput, dely nd goodput. 5.1 Goodput comprison Figure 9 shows comprison of the goodput for AODV nd PERA for scenrio with 20 nodes in n re of û ø û û ö ú ù ø ö ö ö ü ö ü ø ö ü ù ö ü ú ý þ ÿ þ þ 4.4 Reinforcement The lerning rule used in our experiment is rule 2 in section 2.2.2, which llows using cost function, I^_$: s described in section In this experiment the vlue of the reinforcement used to updte the routing tbles t the nodes is vried between 0.1 nd 0.5 (0.1, 0.15, 0.20, 0.30, 0.40, 0.50). Figure 7 shows the vrition of the goodput s function Figure 7: Vrition in goodput vs reinforcement prmeter. ; ;: < = > A B C D E F F G I J K L ANT INTERVAL Goodput % Pcket Loss ' ' " ' $ ' & ( ) * +, -. / ) 0 ) + 1 Tble 3: Goodput nd % Pcket Loss s functions of mobility with 1 m/s. Figure 8: Percentge pcket loss with vrying reinforcement prmeter.

9 M Ò Ö ÕÔ ¼ ± M B N ² B B nd puse time of ² with the nodes moving with speeds of _&. Since the mobility is low, the overll goodput for both lgorithms is high. g f d e b c c R N M R M M Q M P M O M h i j k l m n o ³ ² œ œ š µ ¹ º» š œ ž ž ž ª «N M M N M M O M M P M M Q M M R M M M S T U V W X Y T Z [ Y T U \ ^ \ _ Z [ ` ^ Figure 11: Throughput comp. AODV/PERA, 1 m/s. inri , version 1-24 Mr 2010 Figure 9: Goodput comp. of PERA nd AODV t 1 m/s. Figure 10 shows comprison of PERA nd AODV for the sme scenrio s bove, but with mobility speed of B. The goodput is observed to be lower thn tht of AODV. This is becuse forwrd nts re sent more frequently to llow quick dpttion to the network conditions. Œ Š Š y p x p w p v p u p t p s p r p q p p Ž p r p p t p p v p p x p p q p p p z { } ~ { { ƒ ƒ ˆ Figure 10: Goodput comp. of AODV nd PERA t 10 m/s. 5.2 Throughput Figures 11 nd 12 show the throughput comprisons for AODV nd PERABfor mobility speeds of ø B nd B nd puse time _&. At the lower speed, the throughput is the sme for both AODV nd PERA, however, t the higher speed, the throughput is slightly less for PERA in some cses. This is becuse with mobility, PERA djusts grdully to the chnges in topology. 5.3 Dely Figures 13 nd 14 show the comprison of dely for AODV nd PERA. Both lgorithms show lrge initil dely, which is required for routes to be set up. Subsequently, AODV shows lrge delys gin in situtions with high mobility. PERA on the other hnd, shows low delys in ll cses, s insted of buffering dt pckets until new route id found, PERA delivers the dt pcket through n lternte route. Ø Õ ÑÒÓ Á ½ ¼ Á ¼ ¼ À ¼ ¼ ¾ ¼ ½ ¼ ¼ ½ ¼ ¼ ¾ ¼ ¼ ¼ ¼ À ¼ ¼ Á ¼ ¼ ¼ Â Ã Ä Å Æ Ç È Ã É Ê È Ã Ä Ë Ì Í Ë Î É Ê Ï Í Ð Ù Ú Û Ü Ý Þ ß à Figure 12: Throughput comp. AODV/PERA, 10 m/s. í î ò ï í î ò í î ñ ï í î ñ í î ð ï í î ð í î í ï í á â ã ä å æ ç â è é ê ë ç ì í ñ í í ó í í ô í í õ í í ð í í í ö ø ù ú û ü ý þ ü ø ÿ ÿ ý þ Figure 13: Dely comp. of AODV nd PERA t 1 m/s. 0 / +.- * +,& ' () % &! " #! $ Figure 14: Dely comp. of AODV nd PERA t 10 m/s.

10 inri , version 1-24 Mr Conclusion In this pper we hve proposed set of routing lgorithms for MANETs bsed on the swrm intelligence prdigm. In our experiments we observe tht end-to-end dely for swrm bsed routing is low compred to AODV. owever, the goodput for these lgorithms is lower thn for AODV in scenrios with high mobility. Acknowledgement This reserch ws supported in prt through collbortive prticiption in the Communictions nd Networks Consortium sponsored by the U.S. Army Reserch Lbortory under the Collbortive Technology Allince Progrm, Coopertive Agreement DAAD References [1 E. Bonbeu, M. Dorigo nd G. Therulz, Swrm Intelligence: From Nturl to Artificil Systems, Oxford University Press, [2 M. Dorigo nd G. DiCro, Ant Colony Optimiztion: New Met-euristic, Proc Congress on Evolutionry Computtion, July 6-9, 1999, pp [3 G. DiCro nd M. Dorigo, AntNet: A Mobile Agents Approch to Adptive Routing, Technicl Report IRIDIA/97-12, Universite Libre de Bruxelles, Belgium, [4 G. DiCro nd M. Dorigo, Ant Colonies for Adptive Routing in Pcket-Switched Communictions Networks, Proc. PPSN V - Fifth Interntinl Conference on Prllel Problem Solving from Nture, pp , Amsterdm, ollnd, September 27-30, [5 G. DiCro nd M. Dorigo, AntNet: Distributed Stigmergetic Control for Communiction Networks, Journl of Artificil Intelligence Reserch, vol. 9, pp , [6 E. Bonbeu, F. enux, S. Guerin, D. Snyers, P. Kuntz nd G. Therulz, Routing in Telecommunictions Networks with Smrt Ant-like Agents, Proc. of 2nd Interntionl Workshop on Intelligent Agents for Telecommunictions Applictions 98, Pris, Frnce, July, [7 M. eusse, D. Snyers, S. Guerin nd P. Kuntz, Adptive Agent-Driven Routing nd Lod Blncing in Communiction Networks, Proc. ANTS 98, First Interntionl Workshop on Ant Colony Optimiztion, Brussels, Belgium, October 15-16, [8 R. Schoonderwoerd, O. E. ollnd nd J. L. Bruten, Ant-Like Agents for Lod Blncing in Telecommunictions Networks, Proc. First ACM Interntionl Conference on Autonomous Agents, pp , Mrin del Rey, Cliforni, [9 D. Subrmnim, P. Druschel nd J. Chen. Ants nd Reinforcement Lerning : A Cse Study in Routing in Dynmic Networks, in Proceedings of IEEE MIL- COM,(Atlntic City, NJ), [10 T. White nd B. Pgurek, Towrds Multi-Swrm Problem Solving in Networks, Proc. Third Interntionl Conference on Multi-Agent Systems (ICMAS 98), July 1998, pp [11 D.B. Johnson nd D.A. Mltz. Dynmic Source Routing in Ad oc Wireless Networks, in Mobile Computing, T. Imielinski nd. Korth, eds., Kluwer Acdemic Publishers, Norwell, Mss., 11996, pp [12 IEEE Computer Society LAN MAN Stndrds Committee, Wireless LAN Medium Access Protocol (MAC) nd Physicl Lyer (PY) Specifiction, IEEE Std , The Insititute of Electricl nd Electronics Engineers, [13 J. Broch, D. Mltz, D. Johnson, Y. u nd J. Jetchev, A Performnce Comprison of Multi- op Wireless Adhoc Network Routing Protocols, Crnegie Mellon MONARC Project, October 1998, [14 C.E.Perkins nd E.M.Royer, Ad-oc On Demnd Distnce Vector Routing, Proceedings of the IEEE Workshop on Mobile Computing Systems nd Applictions (WMCSA), Februry [15 C.E.Perkins (Edt.), Ad oc Networking, Addison Wesley, [16 J. S. Brs, Dynmic Adptive Routing in MANETs, Proposl for Collbortive Technology Allince in Communictions nd Networking, Consortium led by Telcordi, with the University of Mrylnd s prtner, Technicl Are 1, Project 1.2 write-up for Autoconfigurtion nd Dynmic Routing in MANETs, October [17 I. Kssblidis, M. A. El-Shrkwi, R. J. Mrks II, P. Arbshhi nd A. A. Gry, Swrm Intelligence for Routing in Communiction Networks, in Proceedings of IEEE Globecom 2001, Sn Antonio, Texs, [18 J. S. Brs, Dynmic Adptive Routing in MANETs: New Algorithms Using Swrm Intelligence, Distinguished Lecture in the ARL CTA C & N Technicl Tlk Series, July 25, 2002; presenttion vilble t [19 M. Gunes, U. Sorges nd I. Bouzizi, ARA - The Ant Colony Bsed Routing Algorithm for MANETs, in Stephn Olriu, editor, Proceedings of the 2002 ICPP Workshop on Ad oc Networks (IWAN 2002), pp IEEE Computer Society Press, August [20 S. Mrwh, C. K. Thm nd D. Srinivsn, Mobile Agents Bsed Routing Protocol for Mobile Ad oc Networks, in Proceedings of IEEE Globecom, [21. Meht, Dynmic Adptive Routing in Mobile Ad oc Networks, M.S. Thesis, ECE Dept., University of Mrylnd, December [22 NS2 Mnul nd Documenttion, [23 L. Eschenuer, V. Gligor nd J. S. Brs, On Trust Estblishment in Mobile Ad-oc Networks, in Security Protocols, Proc. of 10th Interntionl Workshop, Springer Lecture Notes in Computer Science (LNCS), Cmbride, UK, April, [24 L. Eschenuer, On Trust Estblishment in Mobile Ad- oc Networks, M.S. Thesis, ECE Dept, University of Mrylnd, My 2002.