International Journal of Foundations of Computer S ien e World S ienti� Publishing CompanyNEIGHBOR AWARE ADAPTIVE POWER FLOODING (NAAP)IN MOBILE AD HOC NETWORKSJUSTIN LIPMANTele ommuni ations and Information Te hnology Resear h Institute, University of WollongongNorth�elds Avenue, Wollongong, NSW 2500, AustraliaandPAUL BOUSTEADTele ommuni ations and Information Te hnology Resear h Institute, University of WollongongNorth�elds Avenue, Wollongong, NSW 2500, AustraliaandJOHN JUDGESydney Networks Resear h Lab, Motorola Australian Resear h CentreBotany, NSW 2000, AustraliaRe eived (re eived date)Revised (revised date)Communi ated by Editor's nameABSTRACTThis paper introdu es Neighbor Aware Adaptive Power ooding, an optimized oodingme hanism used in mobile ad ho networks (MANETS) that employs several me hanisms(neighbor overage, power ontrol, neighbor awareness and lo al optimization) to limitthe broad ast storm problem, redu e dupli ate pa ket re eption and lower power on-sumption in both transmission and re eption. Upon re eiving an optimized broad ast,a relay determines a new set of possible relays (to ontinue the ood) based upon lo alneighbor information and the previous optimized broad ast. Additionally, neighboringrelays only onsider the shared neighbors they are losest to. A relay may perform lo aloptimization (to redu e power onsumption and isolate broad asts) by substituting onehigh power broad ast with two or more low power broad asts, thereby introdu ing addi-tional hops. We show that ompared to blind ooding and multipoint relaying, NAAP ina stati environment greatly redu es the problems asso iated with the broad ast stormproblem, dupli ate pa ket re eption and power onsumption.Keywords: Flooding, Power Control, Broad asting, MANET.1. Introdu tionA MANET is a olle tion of wireless mobile nodes forming a temporary net-work la king the entralized administration or standard support servi es regularlyavailable on onventional networks. Nodes in a MANET a t as routers, forwarding

pa kets. MANETs may undergo frequent hanges in their physi al topology. Mo-bile nodes may move hanging their network lo ation and link status. New nodesmay unexpe tedly join the network or existing nodes may unexpe tedly leave, moveout of range or swit h o�. Portions of the network may experien e partitioning ormerging, whi h is non-deterministi . A MANET may operate in isolation or on-ne ted to a �xed network (Internet) via a base station (gateway). A MANET anbe hara terized by low bandwidth, high error rates, intermittent onne tivity (par-titioning), limited transmission range and devi e power onstraints.In a MANET, the goal of optimized ooding is to maximize the dissemination ofinformation either lo ally or globally throughout a MANET, while in uring minimal ommuni ations and devi e overheads. Given the nature of a MANET, this posesa hallenging problem. Flooding me hanisms must balan e both the requirementsof the appli ation and the onstraints of both the devi e and the MANET. Con-straints that need to be onsidered in lude: power, omputation and ommuni a-tions. Ex essive omputation due to ompli ated ooding me hanisms, une essaryfull power broad asts, re eption and pro essing of une essary pa kets and medium ontention all ontribute to the drain on a mobile devi e's limited power sour e andthe redu tion of hannel utilization.Blind ooding is the simplest me hanism for disseminating information withina MANET. Flooding is used by re-a tive routing proto ols su h as AODV [1℄ andDSR [2℄ to obtain route information. The pro-a tive routing proto ol OLSR [3℄ reliesupon an optimized ooding me hanism, Multipoint Relay (MPR) [4℄, to disseminatelink state information eÆ iently. Flooding may also be used in network managementto distribute state information or in zero start auto- on�guration. In blind ooding,a node broad asts a pa ket, whi h is re eived by its surrounding neighbors. Ea hre eiving neighbor then veri�es that it has not broad ast the pa ket before. If not,then the pa ket is rebroad ast. Blind ooding terminates when all nodes have re- eived and rebroad ast the pa ket. Blind ooding always hooses the shortest path,be ause it hooses every possible path in parallel. Therefore no other algorithm anprodu e a shorter delay, if we disregard ontention whi h may impede its overallperforman e. Additionally it in reases pa ket ollisions at re eiving nodes therebyresulting in pa ket orruption. A signi� ant problem with blind ooding is the highoverhead involved. One me hanism for minimizing this overhead is to redu e redun-dant broad asts. In [5℄ ooding me hanisms whi h attempt to redu e redundantbroad asts are ategorized as probabilisti based ooding, area based ooding andneighbor knowledge based ooding. Probabilisti based ooding requires an un-derstanding of network topology to assign rebroad ast probabilities to nodes. Areabased ooding assumes nodes have a ommon transmission range, therefore nodesonly rebroad ast if they provide suÆ ient additional overage. Neighbor knowledgebased ooding requires that nodes make rebroad ast de isions based upon lo alneighborhood knowledge obtained via bea on messages.

This paper introdu es, NAAP, a new distributed neighbor knowledge based op-timized ooding me hanism for MANETs. NAAP makes use of lo al neighbor infor-mation and employs several me hanisms (neighbor overage, power ontrol, neighborawareness and lo al optimization) to limit the broad ast storm problem, redu e du-pli ate pa kets re eived by a node and lower power onsumption in both the trans-mission and re eption of pa kets during an optimized ood. NAAP attempts todetermine optimal overage sets with minimal power requirements. This minimizesthe power demands on wireless devi es with limited battery life. Redu ing broad- ast power, limits broad ast overlaps thus minimizing medium ontention betweenneighboring nodes and redu ing pa ket ollisions thereby allowing for an in reasein hannel utilization. Through simulation we show that NAAP ompared to blind ooding and multipoint relaying signi� antly redu es problems asso iated with thebroad ast storm problem, dupli ate pa ket re eption and power onsumption.This paper is organised as follows. Se tion 2 des ribes published me hanisms foroptimized ooding. Se tion 3 des ribes neighbor aware adaptive power ooding andprovides an example. Se tion 4 provides a performan e omparison and analysis.Se tion 5 on ludes the paper.2. Optimized Flooding in MANETsIn [6℄[7℄ the problems asso iated with ooding in a MANET are identi�ed andrefered to as the broad ast storm problem. They identify through simulation andanalysis that ooding is extremely ostly and may result in redundant broad asts,medium ontention and pa ket ollisions. They propose several s hemes (proba-bilisti , ounter-based, distan e-based, lo ation-based, luster-based and neighbor- overage) to minimize the e�e ts of broad ast storms by redu ing redundant broad- asts. In the ounter-based method [6℄, ea h node rebroad asts the message if itre eived the message from less than a spe if threshold of neighbors. An expe tedadditional overage fun tion is de�ned, whi h shows that the more times a host hasheard the same broad ast pa ket, the less additional overage the host provides ifit rebroad asts the pa ket. In the lo ation-based method [6℄ ea h node is equipedwith a GPS devi e. Ea h re iever is then able to determine the additional ov-erage from the original sour e of the message that it an o�er. Lo ation-based isshown to outperform ounter-based in rea hability and laten y due to the in reasein a ura y of position information. The neighbor- overage method [7℄ does notrequire GPS, instead it makes use of neighbor information. Neighbor information ismore a urate as it shows whi h nodes have urrent onne tions to ea h other andmay be used indoors, where GPS does not work. Two sets are maintained at ea hnode, a set Nj denoting the neighbors of j and a set Ni denoting the neighbors ofthe node i that node j re eived the broad ast pa ket from. Neighbor identi� ationis a hieved through the use of bea on messages. A node's neighbor informationmay be atta hed to bea on messages. When node j re ieves a broad ast pa ket forthe �rst time from a node i, it determines its overage set through the set over al ulation Cj = Nj � Ni � fig. The overage set Cj is the set of neighbors of

node j that are not also neighbors of node i. This keeps tra k of pending hostsin j's neighborhood that have not re ieved a dire t broad ast from node i as theyare outside node i's broad ast range. Node j is inhibited from rebroad asting thepa ket if Cj is an emptyset. An emptyset implies that all neighbors of node j arealso neighbors of node i. This al ulation is performed by ea h node that re eivesa broad ast pa ket prior to rebroad asting.Two ooding me hanisms are proposed in [8℄, "self pruning" and "dominantpruning". Both methods make use of lo alized topology information to minimizeredundant broad asts. Self pruning makes use of the same set over al ulation usedin neighbor- overage. Dominant pruning, however, makes use of extended neighborinformation within two hops. This extended neighbor information allows for betterde isions to be made. Two hop neighbor information is obtained by neighborsex hanging adja ent neighbor lists. In dominant pruning, the sender spe i�es a setof nodes in a forward list that should rebroad ast the pa ket so that it rea hes allnodes within two hops. This pro ess is repeated every se ond hop until ooding is ompleted. Finding the minimum forwarding list is a set over problem whi h isNP- omplete and has in reasing omputational overhead ( omplexity in reases within reasing mobility) than either self pruning or neighbor- overage. Self pruning isseen as ideal in small networks with high mobility, whereas dominant pruning isideal in large networks with moderate mobility [8℄.MPR ooding [4℄ is a distributed ooding me hanism that makes use of two hopneighbor topology and is employed in the OLSR routing proto ol for the dissem-ination of link state information. MPR aims to redu e the number of redundantretransmissions during ooding. The number of retransmitters is restri ted to asmall set of neighbor nodes unlike blind ooding. This set of nodes is minimizedby eÆ iently sele ting neighbors whi h provide one hop over of the network areaprovided by the omplete set of neighbors. These neighbors are the multipoint re-lays for a given node. The me hanism is distributed as ea h node must determineits own MPR set independent of other nodes. Finding the minimal MPR set isNP- omplete, however the following heuristi is proposed in [4℄ for a node to hooseits MPRs:1. Find all 2-hop neighbors rea hable from only one 1-hop neighbor. Assign the 1-hopneighbors as MPRs.2. Determine the resultant over set - the set of 2-hop neighbors that will re eive thepa ket from the urrent MPR set.3. From the remaining 1-hop neighbors not in the MPR set, �nd the ones that overthe most 2-hop neighbors not in the over set.4. Repeat from step 2 until all 2-hop neighbors are overed.The above me hanisms attempt to minimize the broad ast storm problem by re-moving redundant broad asts or grouping nodes into sets whi h may be rea hed byrelay nodes, thereby greatly redu ing the number of rebroad asting nodes. However,it is possible to also limit the broad ast storm problem by redu ing transmissionpower, whi h minimizes the e�e ts of a broad ast and allows for a redu tion in power onsumption due to transmission. The RNG Broad ast Oriented Proto ol (RBOP)

[9℄ and the Broad ast In remental Power (BIP) algorithm [10℄ are two me hanismsthat perform optimized ooding and make use of transmission power ontrol tolimit the e�e ts of a broad ast. BPOP is a distributed me hanism that requireslo alized topology information to determine a relative neighbor graph. BPOP uti-lizes transmission power ontrol to limit a RNG node's transmission to the furthestRNG-neighbor node during an optimized ood. BIP, however, is a entralized me h-anism that requires global topology information prior building a minimum energy ost broad ast tree from the sour e node of a broad ast.In the next se tion, we introdu e NAAP. NAAP is a distributed ooding me h-anism that makes use of lo al topology information to limit the broad ast stormproblem by not only removing redundant broad asts, but by isolating broad aststhrough transmission power ontrol ombined with lo al optimization. In additionNAAP is neighbor aware, as relays are aware of ea h other and only broad astto neighboring nodes that they are losest to. These me hanisms help to redu edupli ate pa ket re eption thereby further redu ing problems asso iated with thebroad ast storm problem.3. Neighbor Aware Adaptive Power FloodingNAAP is a distributed neighbor knowledge based optimized ooding me ha-nism for MANETs that attempts to minimize the broad ast storm problem. NAAPmakes use of 2-hop neighbor information, an optimized overage set (atta hed toa broad ast pa ket by the previous relay) and employs several me hanisms (neigh-bor overage, power ontrol, neighbor awareness and lo al optimization) to limit thebroad ast storm problem and redu e power onsumption in both the transmissionand re eption of pa kets during an optimized ood.Neighbor overage is used to determine if a relay provides any additional overageto the previous broad asting relay, thereby limiting redudant broad asts. The use ofpower ontrol allows for isolation of broad asts (redu tion of transmission range) andis bene� ial for a few reasons. Firstly, the required power for a transmission distan eof d between two nodes is proportional to d�. Typi ally � takes a value between 2and 6, depending on the hara teristi s of the ommuni ations medium [10℄. In thispaper we make the assumption that the power onsumed by a transmitter's ampli�er ir uitry for pa ket transmision is proportional to the square for the distan e (� = 2)and the pa ket size [11℄.Se ondly, isolating a broad ast in reases the probability of only ne essary nodeshearing a broad ast. This helps to both redu e dupli ate pa ket re eption and thepower onsumed with pa ket re eption at the re eiver. Thirdly limiting the nodesthat will hear a broad ast redu es medium ontention, in reases medium utilizationand redu es probability of ollisions. Neighbor awareness implies that neighboringrelays are aware of ea h other and of their shared 1-hop neighbors. As seen in �gure1 neighboring relays (nodes 4 and 5) will only onsider shared nodes (nodes 9 and18) that they are losest to when determining an optimized broad ast. This helps

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6Figure 1: Neighbor Awareness/Lo al Optimizationto limit the range of nodes to whi h a relay will rebroad ast to. Lo al optimizationsubstitutes one high power broad ast with two low power broad asts as seen in�gure 1 where node 6 be omes a relay for node 5 thereby ontinuing the broad astto nodes 14 and 15. This introdu es an additional hop and allows for the bene�tsof power ontrol to be applied.In NAAP we make the following assumptions:� Power aware MAC - the MAC layer allows for power ontrol prior to transmissionby NAAP.� Bea on messages - nodes emit "HELLO" style bea on messages (ea h ontain-ing a list of one hop neighbors) to inform neighbors of their existan e and otherneighbouring nodes.� Neighbor information - nodes maintain a list of neighboring nodes up to twohops obtained from bea on messages. Let Nj and N(j) denote the neighbors of anode j.� Optimized relay set - relays atta h both the urrent relay set and any prior relaysthat are neighbors of the urrent relay to the pa ket being ooded. This is referedto as Oi.� GPS or neighboring signal strength - neighbor information ontains eitherpositional oordinates obtained through GPS or a re eived signal signal strengthmeasurement. This allows nodes to determine the respe tive minimal transmissionpower required to rea h sele ted neighboring nodes, whi h form the forwarding pathof the optimized ood.In �gure 1 the dashed arrows show broad asts from relay nodes 4 and 5 withoutneighbor awareness. The dashed broad ast ir les are full power broad asts andthe shaded ir les are power ontrolled broad asts. Using �gure 1 we provide anintuitive explanation of NAAP, where: u = 1, i = f4; 5g and j = f6; 8; 9; 14; 15; 18g1. Upon re eiving a broad ast message(s) from a broad asting node u, ea h nodein i (sele ted by u as a relay) determines whi h of its one-hop neighbors alsore eived the same message.2. Ea h relay in i determines its remaining neighbors, whi h did not re eive amessage (based upon its knowledge). Ea h relay then determines its losest

set of nodes from j ompared with other neighboring relays in i and allo atesthese nodes to its optimized relay set that it is responsible for rebroad astingto.3. If nodes in the resulting optimized relay set are not of an equivalent distan efrom the relay, it may perform a lo al optimization on the set to sele t aminimal subset of relays that will ensure delivery to remaining nodes in theoriginal optimized set. Otherwise the relay determines a transmission rangeequal to that of the farthest neighbor it is responsible for.In order to implement NAAP, we have made use of neighbor set al ulations.The three algorithms naap(), lo alOptimize(Oj) and PM(m;A;B) implement theNAAP me hanisms. A sour e node initiating a ood in NAAP must �rst determinean initial optimized overage set of nodes using the algorithm lo alOptimize(Oj).The optimized overage set is then atta hed to the broad ast pa ket. The broad astpower is adjusted to the maximum required transmission power in the optimized overage set prior to transmission. Relays on re eiving a broad ast pa ket from anode i determine an optimized overage set Oj based upon the previous optimized overage set by using the algorithm naap().The algorithm PM(m;A;B) ompares the distan e or required transmissionpower of nodes in a set A from both a node m and nodes in a set B. If a node inA is loser to node m than the nodes in set B, then that node from set A is addedto the resultant set. This algorithm is used at line 6 to remove nodes from Oj thatmay overlap with nodes in Oi, where the nodes in Oi may have a lower requiredtransmission power to a spe i� overlapping node than node j. This allows nodesin Oi to keep the neighbors to whi h they have the lowest required transmissionpower, but lose the neighbors that they may share with another node in Oi, whi hhas a lower required transmission power. Algorithm PM(m;A;B) is also used atline 7 in lo alOptimize(Oj) to determine an optimal set of relay nodes where nodesthat are loser to a hosen relay are removed from the optimized set Tj , e�e tivelyallo ating those nodes to the relay. This pro ess is repeated until all nodes areallo ated to a relay.At line 4 in naap() the set al ulation Oj = Nj � Oi � heardi � fig is usedto determine a set of neighboring nodes that are unique to node j and not thesour e, node i, of the broad ast. Ea h node upon re eiving a broad ast also deter-mines whi h neighbor nodes would have heard the broad ast. This may not in ludeany previous optimized nodes. At line 7, we determine if all nodes in Oj have anequivalent required transmission power within a small toleran e. If not, this im-plies that some nodes are loser to node j and others further away. The fun tion,lo alOptimize(Oj) attempts to determine a minimal set of loser neighbors of nodej that provide omplete overage of j's further neighbors through the introdu tionof an additional broad ast. This de omposes a large transmission from node j to allits neighbors into smaller transmissions from the minimal set of loser neighbors.Transmission power onsumption is redu ed and the area of e�e t is minimizedthereby in reasing hannel utilization. It is possible to make use of other lo al opti-

mization algorithms whi h may rely on more intelligent me hanisms to allo ate theminimal set of next hop neighbors.Given the �rst order radio model [11℄ used in the simulation, there is a ostasso iated with re eiving and transmitting pa kets. This ost does not only on ernthe propogation of the signal but also the ost of powering re eiver and transmitterele troni s as well as pro essing requirements. Therefore it is ne essary to limit thede omposition of a broad ast as introdu ing ex essive hops may have a negativee�e t. In this paper, we have only limited the de omposition to those nodes thatare beyond a ertain distan e from the broad asting node. We de�ne this as theRangeLimit whi h is 33 per ent of the maximum transmission distan e. Giventhat the ost of two hops may (depending on the re eiver and transmitter power onsumption) ex eed that of one hop. It will be ne essary in future work to explorethe ost asso iated with introdu ing additional hops and limiting the distan e ofnodes to be used in lo al optimization.Algorithm naap()1. i previous broad asting relay2. j urrent broad asting relay3. heardi nodes that may have heard previous broad ast4. Oj Nj �Oi � heardi � fig5. if Oj 6= ;6. then Oj PM(j; Oj; N(Oi \Nj))7. if nodes in Oj do not have equivalent required transmission power8. then Oj lo alOptimize(Oj)9. MaxTransmissionPower maximumPower(Oj)10. Oj Oj + (Oi \Nj)Algorithm lo alOptimize(Oj)1. RangeLimit MaxTransmissionDistan e � 0:332. Tj Oj3. Oj ;4. while Tj 6= ;5. do n minimumPowerneighbor(Tj)6. if Distan e(n) > RangeLimit7. Tj PM(j; Tj ; N(n))8. Tj Tj � n9. Oj Oj + n10. return OjAlgorithm PM(m;A;B)1. Result ;2. for all x 2 A3. for all i 2 B4. if (x 2 A and x 2 N(i)) and (distan e(m;x) < distan e(i; x))5. then Result Result+ x6. return Result3.1. NAAP Worked ExampleFrom Figure 2 the neighbors of ea h node are as follows:

N(1) = f2; 3; 4; 5g; N(2) = f1; 16g; N(3) = f1; 7g; N(4) = f1; 5; 8; 9; 18g; N(5) =f1; 4; 6; 9; 14; 15; 18g; N(6) = f5; 14; 15; 18g; N(7) = f3; 17g; N(8) = f4; 9; 10; 18g; N(9) =f4; 5; 8; 18g; N(10) = f8; 11g; N(11) = f10; 12; 13g; N(12) = f11g; N(13) = f11g;N(14) = f5; 6; 15g; N(15) = f5; 6; 14g; N(16) = f2g; N(17) = f7g; N(18) = f4; 5; 6; 8; 9gNode 1 initiates a broad ast and determines O1 = f2; 3; 4; 5g. O1, whi h is thenatta hed to pa ket P and broad ast at the maximum required transmission powergiven the nodes in O1. P is re eived by nodes 2, 3, 4 and 5. We ignore nodes 2 and3 and on entrate on ooding at nodes 4 and 5 as this is the more omplex area ofthe network. Nodes 4 and 5 both al ulate optimized overage sets using naap().Nodes 4 and 5 determine heard1 = f2; 3; 4; 5g.At Node 4: O4 = N4 �O1 � heard1 � f1g = f8; 9; 18gO4 = PM(4; O4; N(O1 \N4)) = f8; 9gThe neighbor nodes in O4 have equivalent required transmission power. There-fore node 4 will adjust its transmission power to the maximum required tranmissionpower found in O4 and not perform the lo al optimization.At Node 5: O5 = N5 �O1 � heard1 � f1g = f6; 9; 14; 15; 18gO5 = PM(5; O5; N(O1 \N5)) = f6; 14; 15; 18gThe neighbor nodes in O5 do not have an equivalent required transmissionpower. Therefore node 5 will perform a lo al optimization, by alling the fun tionlo alOptimize(Oj). The nodes with minimal required transmission power in O5 arefound and their neighbors subtra ted from O5 to determine a minimal power re-quirement, whi h may introdu e an extra hop ensuring all nodes are rea hed thusminimizing power onsumption.j = minimumPowerNode(T5) = 18T5 = PM(5; T5; Nj) = f6; 14; 15; 18gT5 = T5 � f18g = f6; 14; 15gO5 = O5 + f18g = f18gj = minimumPowerNode(C5) = 6T5 = PM(5; T5; Nj) = f6gT5 = T5 � f6g = ;O5 = O5 + f6g = f6; 18gOptimization terminates when T5 is an empty set. O5 now ontains the lo allypower optimized minimum set of nodes. The node now adjusts its transmissionpower to the maximum power in O5.O5 = O5 + (N5 \ O1) = f4; 6; 18g

The neighboring nodes of node 5 that are also in the previous optimized setare added to the �nal optimized set and atta hed to the pa ket. This allows nodesre eiving a broad ast from node 5 to take a ount of nodes in node 1's optimizedset and the set of neighbors to whi h they would be broad asting, whi h may belo ated lose. Nodes 6 and 18 determine the set heard5 = f6; 18g.At Node 6: O6 = N6 �O5 � heard5 � f5g = f14; 15gO6 = PM(6; O6; N(O5 \N6)) = f14; 15gThe neighbor nodes in O6 have an equivalent required transmission power.Therefore no lo al optimization is performed.At Node 18: O18 = N18 �O5 � heard5 � f5g = f8; 9gO18 = PM(18; O18; N(O5 \N18)) = ;Node 18's optimized set is an emptyset, therefore broad ast is inhibited. Simi-larly nodes 14 and 15 will also have emptysets.3.2. Worked Example ResultsFigures 2, 3 and 4 show blind ooding, MPR and NAAP in a MANET from asour e node 1 to all nodes respe tively. The arrows in the �gures show the desti-nations of a broad ast from a relay during a ood. Bla k olored nodes are relays,grey olored nodes are not. In the blind ooding example it is possible to see thepotential for medium ontention and pa ket ollisions. Additionally, the problemof dupli ate pa ket re eption an also be seen. In MPR this is limited somewhatdue to the redu ed number of broad asts. However, previous MPRs will alwayshear the next MPR's broad ast and in areas where MPRs are neighbors (nodes 4and 5) there may well be medium ontention, pa ket ollisions and dupli ate pa ketre eption. The use of neighbor awareness and lo al optimization ombined withpower ontrol in NAAP helps to alleviate these problems as seen in �gures 1 and 4.Simple analysis of the �gures yields the following results:Me hanism Equivalent Full Power Broad asts Transmitted / Re eived Pa ketsBlind 18.0 18 / 52MPR 9.0 9 / 31NAAP 4.9 10 / 214. ResultsFigures 5, 6, 7 and 8 show the results obtained through simulation for blind ooding, multipoint relay ooding and NAAP ooding. The simulation generates afully onne ted stati topology within a 1000 meter by 1000 meter area. Given theproblem with �nding a fully onne ted random topology at low on entrations of

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Figure 4: NAAP oodingnodes, nodes are randomly pla ed within broad ast range of other already randomlypla ed nodes. This ensures a fully onne ted random topology with an average nodedegree varying from 7.5 to 34.2. Nodes have a maximum transmission range of 100meters. Time is divided into epo hs. The MAC layer is assumed to be ideal.There is no medium ontention nor hidden-node s enario within the simulationas it is assumed that during an epo h all nodes an omplete their transmission.The transmission medium is error free. A bidire tional link between two nodesis assumed upon re eption of a bea on message. All nodes re eive broad astedmessages. Therefore the "rea hability" is always 100 per ent.A �rst order radio model as dis ussed in [11℄ is used. In this model a radiodissipates Eele = 50nJ=bit to run the ir uitry of a transmitter or re eiver and afurther �amp = 100pJ=bit=m2 for the transmitter ampli�er. The model is furtherextended to in lude the power asso iated with pro essing a pa ket for transmis-sion and re eption by E omp = 600uJ=pa ket. The radios have power ontrol and onsume the minimal required energy to rea h the intended re ipients. A pa ketlength k of 4000 bits is used. The equations used to al ulate transmission ostsand re eiving osts for a k-bit message and distan e d are:Transmission:ETx(k; d) = Eele � k + �amp � k � d2 +E ompRe eption:Eele (k) = Eele � k +E ompA random node in the topology is sele ted as the initial node of a ood. Ea hrandom topology is used to determine the performan e. The simulation is run 100times with a di�erent seed for ea h number of nodes. The results are averaged and

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Figure 6: Dupli ate Pa kets Re eived95% on�den e intervals are generated. The �gures show the performan e of ea h ooding me hanism as the on entration of nodes is in reased.Figure 5 shows the power onsumption. It an be seen that NAAP providesredu ed power onsumption over blind ooding and multipoint relay. At 100, 220and 340 nodes: NAAP requires on average 85% less power than that onsumedby blind ooding. Similarly NAAP requires 17%, 34% and 40% less power thanmultipoint relaying. This is be ause NAAP performs a lo al optimization to ad-just a node's transmission power to only in lude the nearest ne essary neighboringnodes required to ontinue a ood thus not only minimizing required transmissionpower, but limiting power onsumption due to the ost of re eiving and pro essingdupli ate pa kets.Figure 6 shows the number of dupli ate pa kets re eived by all nodes during a ood. It an be observed that NAAP shows a signi� antly lower rate of in rease indupli ate pa kets re eived ompared to blind ooding and multipoint relaying. At100, 220 and 340 nodes: NAAP nodes re eive on average 90% less dupli ate pa k-ets than blind ooding. Similarly, nodes re eive 26%, 45% and 52% less dupli ate

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Figure 8: Overheadpa kets than multipoint relaying. This shows the e�e tiveness of NAAP at redu -ing medium ontention as there is a orrelation between the number of dupli atepa kets re eived and medium ontention.Figure 7 shows the number of transmissions that o ur. All me hanisms show anin rease in the number of transmission whi h is linear with respe t to the numberof nodes. NAAP and multipoint relaying show a lower rate of in rease than blind ooding with NAAP requiring on average 52% less broad asts than blind ooding.NAAP requires more transmissions than multipoint relaying as it introdu es addi-tional redu ed power broad asts in its attempt to limit both power onsumption,dupli ate pa ket re eption and medium ontention.Figure 8 shows the average overhead per pa ket in bytes in urred by NAAPas the optimized set is atta hed to ea h broad ast pa ket. This allows a node to al ulate the most e�e tive optimized set at the time of broad ast and need onlybe sent when ooding is in progress. In MPR the multipoint relay set is normallydistributed through bea on messages and therefore in urs overhead in bea on mes-sages. To ompare with NAAP we append the multipoint relay set to ea h pa ket

prior to broad ast as done with sour e based MPR me hanisms [12℄[13℄. Blind ooding in ures no additional overhead. Ea h entry in a NAAP optimized set is 4bytes as it ontains the IP address of a node. An optimized set may ontain morethan one entry. It an be seen that as the number of nodes in reases ( on entrationin reases) the required overhead does not grow signi� antly. This overhead does notin lude the overhead asso iated with neighbor dis overy through bea on messages.5. Con lusionsIn this paper, we have introdu ed, NAAP, a new lo alized ooding me hanismfor MANETs. NAAP employs several me hanisms (neighbor overage, power on-trol, neighbor awareness and lo al optimization) to limit the broad ast storm prob-lem and redu e power onsumption in both transmission and re eption. NAAPrequires 2-hop neighbor information obtained through periodi bea on messagesand optimized overage set information atta hed to broad ast pa kets.The aim of NAAP is to minimize problems asso iated with the broad ast stormproblem by not only limiting redundant broad asts, but by redu ing the number ofdupli ate pa kets re eived by nodes during a ood. We have shown that omparedto blind ooding, NAAP in a non-mobile environment requires on average 85% lesspower, 52% less transmissions and 90% less dupli ate pa kets re eived to ompletea ood. At 100, 220 and 340 nodes: NAAP ompared to multipoint relaying to omplete a ood onsumes 17%, 34% and 40% less power respe tively; nodes re- eive 26%, 45% and 52% less dupli ate pa kets respe tively. As a result of theseredu tions, it an be seen that NAAP will introdu e less medium ontention andpa ket ollisions than blind ooding or MPR.In this paper, we have not onsidered the e�e ts of mobility or a realisti MAClayer su h as IEEE 802.11. We intend in future work to explore these problems andalso onsider me hanisms for limiting overhead as well as introdu ing additionaloptimizations to NAAP.Referen es1. Charles E. Perkins and Elizabeth M. Royer. "ad ho on-demand distan e ve tor(aodv) routing". In Pro eedings of the Se ond Annual IEEE Workshop on MobileComputing Systems and Appli ations, pages 90{100, Febuary 1999.2. David B. Johnson, David A. Maltz, and Josh Bro h. "DSR: The Dynami Sour eRouting Proto ol for Multihop Wireless Ad Ho Networks", in Ad Ho Networking, hapter 5, pages 139{172. Addison Wesley, 2001.3. P. Ja quet, P. Muhlethaler, A. Qayyum, A. Laouitim, and L.Viennot. Optimizedlink state routing. draft-ietf-manet-olsr-06.txt, 2000.4. A. Qayyum, L. Viennot, and A. Laouiti. Multipoint relaying: An eÆ ient te h-nique for ooding in mobile wireless networks. In Pro eedings 35th Annual HawaiiInternational Conferen e on System S ien es, 2001.

5. B. Williams and T. Camp. Comparison of broad asting te hniques for mobile adho networks. In Pro eedings of MOBIHOC, June 9-11 2002.6. Sze-Yao Ni, Yu-Chee Tseng, Yuh-Shyan Chen, and Jang-Ping Sheu. The broad aststorm problem in a mobile ad ho network. In Pro eedings of the �fth annualACM/IEEE international onferen e on Mobile omputing and networking, pages151{162. ACM Press, 1999.7. Yu-Chee Tseng, Sze-Yao Ni, and En-Yu Shih. Adaptive approa hes to relievingbroad ast storms in a wireless multihop mobile ad ho network. pages 481{488.International Conferen e on Distributed Systems, 2001.8. Hyojun Lim and Chongkwon Kim. Multi ast tree onstru tion and ooding inwireless ad ho networks. In Pro eedings of the 3rd ACM international workshopon modeling, analysis and simulation of wireless and mobile systems, pages 61{68.ACM Press, 2000.9. J. Cartigny, D. Simplot, and I. Stojmenovi . Lo alized minimum-energy broad ast-ing in ad-ho networks. In Pro eedings of IEEE INFOCOM, 2003.10. J.E. Wisielthier, G.D. Nguyen, and A. Ephremides. On the onstru tion of energy-eÆ ient broad ast and multi ast trees in wireless networks. In Pro eedings IEEEINFOCOM, Tel Aviv, Isreal, 2000.11. Wendi Rabiner Heinzelman, Anantha Chandrakasan, and Hari Balakrishnan.Energy-eÆ ient ommuni ation proto ol for wireless mi rosensor networks. In Pro- eedings of the Hawaii International Conferen e on System S ien es, January 2000.12. W. Peng and X. Lu. Ahbp: An eÆ ient broad ast proto ol for mobile ad ho networks. In Journal of S ien e and Te hnology - Beijing, China, 2002.13. J. Lipman, P. Boustead, and J. Judge. Utility-based multipoint relay oodingin heterogeneous mobile ad ho networks. In Pro eedings of The Workshop onthe Internet, Tele ommuni ations and Signal Pro essing (WITSP'02), Wollongong,Australia, pages 27{33, 2002.