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An Improved Geocast forMobile Ad Hoc Networks

Robert J. Hall, Member, IEEE Computer Society

AbstractGeographic addressing of packets within mobile ad hoc networks enables novel applications, including hard real-time

engagement simulation in military training systems, geographic command and control functions in training and emergency

communications, and commercial messaging applications as well. The most scalable implementation of geoaddressing is via a

geocast protocol, where nodes selectively retransmit packets based on local decision rules. Well-designed retransmission heuristics

yield scalable geographic flooding that outperforms alternative geoaddressing approaches. However, previous geocast

implementations, while effective, fall into two categories. Approaches based on flooding are unscalable due to the high load they

generate. Scalable approaches, on the other hand, have trouble in complex environments, lacking sufficient intelligence about the

necessary directionality of packet flow. The present paper defines a novel geocast heuristic, the Center Distance with Priority (CD-P)

Heuristic, which both significantly improves on reliability of existing scalable geocasts and yet also remains scalable as scenario

complexity increases. This paper describes the new technique as well as an evaluation study comparing it to previous approaches.

Index TermsGeocast, MANET, scalability, wireless.

1 INTRODUCTION

GEOGRAPHIC addressing within mobile ad hoc networks(MANETs [1]) enables interesting new applications.These include hard real-time engagement simulation inmilitary training and testing systems, geographic commandand control in areas lacking network infrastructure, emer-gency communications for disaster response, and commer-cial geographic messaging applications, such as gaming,advertising, and traffic services [2]. In engagement simula-

tion by geometric pairing, as is done by the US ArmysOne Tactical Engagement Simulation System (OneTESS),www.peostri.army.mil/PRODUCTS/ONETESS, an instru-mentation system mounts sensors and a wearable, location-aware computational device on each human trainee andweapon. When a trigger is pulled, instead of firing liverounds, the device sends electronic bullet messages fromthe shooter to all nodes in the geographic region definedby the weapons effect area. The recipients respond withtheir positions, and an adjudicator decides who is actuallyhit by the simulated round [3], [4]. This geometric pairinghas potential to significantly improve upon laser-based

systems in the variety of weapons that can be simulated aswell as the real-world fidelity of the simulation. Key to thishard real-time process is the geoaddressing of the electro-nic bullet message. In geographic command and controlapplications, a mobile node wishes to discover and initiatecommunications with all nodes in a defined geographicarea at the current time, even when the sender has noknowledge of which nodes currently occupy the area. Thiscapability also depends on geoaddressing of packets both

to become aware of which nodes are present and toestablish routes. For example, one could establish commu-nications with mobile devices in a burning building at adisaster site in this way.

MANETs for military training must operate at anunprecedented network scale (up to 10,000 mobile nodes)without installed infrastructure. Disaster situations can leadto the need for scalable communications in densely

populated areas with absent or malfunctioning networkinfrastructure. Commercial applications must operate incrowded situations, such as shopping malls, sportingevents, school campuses, and city streets. These types ofsituations result in geo-addressing applications stressingthe scale limits of traditional MANET technologies.

The most scalable, responsive, and reliable implementa-tion of geo-addressing in MANETs is via a geocast protocol,where location aware nodes broadcast and selectivelyrebroadcast packets based on local decision rules. Ap-proaches based upon simple flooding [5], [6], where everynode within a forwarding zone retransmits every packet,

are well known not to scale beyond a few tens of MANETnodes [4]. By contrast, geocast based on well-designedretransmission heuristics yields a limited and scalablegeographic flooding [4]. Geocast also outperforms thebetter-known alternative geoaddressing approach basedon caching location information at georouters [7]. InMANETs, georouters become hotspots [3] and requirestrictly better connectivity than geocast (i.e., all nodes mustbe connected to the georouter in addition to each other).

Two previous MANET geocast approaches are limited inmeeting the needs of reliable high-scale applications. Theapproaches of Ko and Vaidya [5] (discussed below) do not

scale adequately. On the other hand, Tiered Geocast [4]does scale, but has trouble in complex environments,lacking sufficient intelligence about the necessary direction-ality of packet flow. For example, the maze-like character of

254 IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 10, NO. 2, FEBRUARY 2011

. The author is with AT&T Laboratories Research, 180 Park Ave, Bldg 103,Florham Park, NJ 07932. E-mail: [email protected].

Manuscript received 27 Apr. 2009; revised 31 Aug. 2009; accepted 28 Feb.2010; published online 25 Aug. 2010.For information on obtaining reprints of this article, please send e-mail to:[email protected], and reference IEEECS Log Number TMC-2009-04-0145.Digital Object Identifier no. 10.1109/TMC.2010.56.

1536-1233/11/$26.00 2011 IEEE Published by the IEEE CS, CASS, ComSoc, IES, & SPS

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urban Manhattan-style geometries presents difficulties dueto the need for precise selection of relay points. The presentpaper makes three primary novel contributions:

. a novel Geocast, based on a new heuristic, the CenterDistance with Priority (CD-P) Heuristic,

. a flexible framework for integrating geocast heur-istics, including the previously studied M and

T heuristics, with CD-P (and others), and. an evaluation study comparing CD-P to existing

geocast heuristics and showing empirically that itoutperforms them, while still scaling well. Thisstudy compares many different combined parametersettings, showing how the integration of heuristicscan lead to better performance on many scenarios.

2 THE GEOCAST FRAMEWORK

Throughout this paper, we assume that each mobile node islocation aware, meaning it knows its location at all times, suchas via an onboard GPS unit. Geocast is a network protocol forsending a packet to all nodes within a defined geographicregion termed as the geocast region. Hall and Auzins [4]describe a multitiered framework for geocast in MANETs.The framework comprises a heuristic-based limited floodingtechnique, termed a flat geocast, that operates within eachsingle tier, a tier being a distinct wireless channel. Typically,distinct tiers will operate at different transmission ranges; forexample, in a military scenario, it may be that vehicles havetwo-tier radios capable both of operating on a short-rangechannel shared with equipment carried by dismountedsoldiers and also of operating on a longer range channel tocommunicate at distance to other vehicles or buildings. In

this way, long-distance geocasts can travel most of the wayvia long-range hops on the long-range tier, but still reachdestinations containing short-range-only capable nodes. Anode receiving a packet on a given tier submits it to flatgeocast independently in each tier for which it has aninterface, so the packet may be bridged between tiers atmulti-interface nodes. All flat geocast parameters are definedindependently for each tier. The purpose of having multipletiers is to enable long-distance traffic without excessive hopcounts as well as to increase spatial reuse of spectrum.Spectrum reuse is increased by sending local traffic only overthe short-range tier, avoiding transmitting such messages

unnecessarily over long ranges. By improving a flat geocast,one, therefore, improves tiered geocast as well. The presentwork improves flat geocast by defining a new heuristic. Tounderstand this, we must first recall the particular flatgeocast approach used, which we term as the Classic Geocastframework (cf. [4]).

The packet p contains a geocast header containinginformation needed for propagating the packet, includingan application type, a geocastID (gIDp)), location of sender,and the center of the targeted geocast region, CGRp). Theapplication type is used as an index to determine the valuesof geocast parameters to use with the packet, including

forwarding zone definitions and parameter values govern-ing the heuristics. The geocast ID is a unique identifierassigned by the originator of the geocast and is carried ineach transmission associated with that particular geocast.

The Classic Geocast framework operating at each node isdiagrammed in Fig. 1. When the node receives a packet fromthe medium, it checks to see if it has heard one previouslyhaving the same geocast ID. If not, it creates a new record forthe ID and enqueues the packet for (possible) laterretransmission; however, in either case, it updates itsstatistics on the packet in the record for the geocast ID.Any packet received from the local application software viathe network API is allocated a new geocast ID and treatedaccordingly by the framework. When a packet reaches thehead of the queue and is ready to be transmitted, readymeaning any backoff interval has expired and the medium is

free, a heuristics check is performed. If passed, the packet istransmitted, otherwise discarded. This late cancelation (i.e.,checking heuristics after backoff and medium free) allowsthe node to suppress extraneous copies of a packet byreacting to information in the most recent transmissions.This is critical to geocast scalability.

To allow combining heuristics fhig, the frameworkemploys the following structure for the heuristics check,where p is the packet that is ready to be transmitted:

Passp InFwdZone?p ^ h1p _ h2p _ . . .:

First, the node must be located physically in the forwarding

zone defined by ps application type and other headerinformation. There are many ways of defining forwardingzones (e.g., see [5]) appropriate to different assumptionsabout terrain, scenario distances, etc. These may differbased on what application the particular message type isdesigned to support, such as long-range artillery simulationversus short-range command and control. Next, it mustpass at least one of the fhig heuristic predicates. Of course,we assume that the stats recording routine keeps informa-tion necessary to support each of the fhig. We also assumethat there is an h0 which is true whenever the packet isoriginated by the node, ensuring that each packet is

transmitted by its originator. We suppress this detail inwhat follows.

This framework can be instantiated to yield manyexisting geocast types. For example, if all hi are FALSE,

HALL: AN IMPROVED GEOCAST FOR MOBILE AD HOC NETWORKS 255

Fig. 1. Classic Geocast framework.

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then we get a simple broadcast for each geocast, with no

retransmissions. This is scalable, but unreliable. If, on theother hand, h1p TRUE, we get simple flooding re-stricted to forwarding zones. This is exactly the basic Ko-Vaidya geocast [5]. It is reliable, but unscalable, resulting infar too many retransmissions per geocast.

Between these two extremes is the flat geocast defined byHall and Auzins [4]. They show it to be more reliable thansimple broadcast and more scalable than simple flooding. Ituses two primary heuristics: MinTrans (M) and Threshold(T). The M Heuristic, hM, counts the number of transmis-sions heard for each geocast ID. hM is TRUE if and only ifthis count is less than the M parameter. Thus, a node willretransmit the packet if it has not already heard M copies.M is valuable in adding pure redundancy to the propaga-tion to help combat problems, like collisions, as well as tohelp the propagation get out of local minima it might,otherwise, be stuck in by hill climbing directly toward thedestination (as with the CD heuristic below).

The T Heuristic, hT, is based on the location of eachtransmission heard. hTp is TRUE iff the closest among alltransmitters of packets x with gIDx gIDp are at least adistance T away from this node. T is valuable for spreadingthe geocast propagation outward to cover distant areas thatmay not have been covered, the idea being that if a node isrelatively far from all previous transmitters, it is more likely

to cover nodes around corners or out of transmission rangeof prior transmissions. It pushes outward and can helpgeocast propagation get out of local minima.

Fig. 2 shows the transmission sequence for ClassicGeocast (M 2, T 40% of radio range) in a simplesituation with one radio obstacle. The forwarding zone iselliptical and the geocast region is circular. The geocastoriginator is node B. The first transmission reaches nodes Aand C. Node A is out of the forwarding zone, so it does notretransmit. Because of the M Heuristic, node C does, withnodes D and E hearing it. These are the nodes in the geocastregion, so they process the packet. However, due to the M

Heuristic, D also retransmits it. E does not. (Ds transmis-sion was too close for hT, and E had already counted twotransmissions, so hM was not satisfied either.) Forwardingzone flooding would have led to nodes B, C, D, and E

retransmitting, while simple broadcast would have hadonly B transmit, with D and E failing to receive it at all. Bycontrast, (M 2; T 40%) avoids exhaustive retransmis-sions while still traversing the obstacle.

3 THE CD-P HEURISTIC

As discussed in [4], Classic Geocast is effective in a wide

variety of situations, more scalable than simple flooding,and more reliable than simple broadcast. However, recentexperience has exposed a weakness in urban terrain. Thelatter is characterized by restricted, maze-like radio lines ofsight (due to buildings, etc.) and multipath effects.

Fig. 3 shows an example urban scenario. In it, nodes arelocated in streets and avenues of a Manhattan-stylegeometry; the dark squares represent buildings that com-pletely block radio signals from penetrating through them.(Thus, connectivity is purely line-of-sight.) The transmis-sion range of each radio is assumed to be three blocks; so,for example, node 1 can hear node 3 and vice versa. We

attempt to send a geocast from the node at third and A tothe dark circle centered at first and B. A typical ClassicGeocast transmission sequence is shown. Transmission 1 isfrom the originator. It is heard by everyone on A Avenue.Due to the M Heuristic, transmission 2 is sent from a nodeon A between first and second, as shown. Finally, since thenode below first on A is beyond the T Heuristic distance, hTcauses transmission 3 as shown. All three transmissions areheard only by nodes on A Avenue. Unfortunately, nodes offof A Avenue did not hear them, so the geocast fails to reachthe geocast region.

One can compensate for this within Classic by either

increasing M or decreasing T; however, while increasingsuccess rate these also increase transmissions. The maincontribution of this paper, the (CD-P) Heuristic, solves thisproblem more cost effectively, resulting in a more reliable


Fig. 2. Classic Geocast transmission sequence.

Fig. 3. A geocast difficult for Classic Geocast to handle.

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and less costly geocast that also scales up. This sectiondescribes CD-P in two steps.

Step 1: The Center-Distance (CD) Heuristic. To supportthe CD Heuristic, we first augment the statistics collectionprocess as follows: each time a node hears a transmission ofa geocast packet p for geocast ID i, it calculates the distancefrom the transmitter of p (whose position is in the geocastheader) to the center of the geocast region CGRp). If thisdistance is the least of all such distances for all transmis-sions it has heard with geocast ID i, then it is recorded.Denote this recorded minimum value CDisti).

Next, we add a new heuristic predicate hCD into theoutgoing heuristics check logic shown in Fig. 1. hCDp istrue if and only if the nodes own distance to CGRp) is lessthan CDist(i). This is added into the Classic Geocastdisjunction, so the other heuristics still operate as well:InFwdZone?p ^ hTp _ hMp _ hCDp.

To illustrate how this helps, Fig. 4 shows a typicaltransmission sequence for the same scenario as before, butwith nodes executing the CD heuristic as well. This time,

the first three transmissions are the same as before.However, the node at first and A realizes that even thoughT and M Heuristics do not fire, it is closer to CGRp) thanthe previous three transmitters, so it transmits. This is heardby the nodes on first Street, fulfilling the geocast.

Scalability Problems. As described thus far, the CDheuristic is similar to Ko and Vaidyas Scheme 2 [5]. Thedifference is that under their scheme, the node compares itsown center distance only to that of the transmitter it heardfirst. In CD, we compare to all transmitters heard of thepresent geocast. This leads to fewer copies of the packetbeing transmitted, and hence, better scalability.

However, both approaches still have significant scal-ability problems. Consider Fig. 5. In this situation, once theoriginator transmits, all other nodes in line of sight willjudge themselves closer to the CGR than the originator.

Under Ko-Vaidya Scheme 2, they will all decide to transmit,so there will be as many transmissions as simple flooding,which is impractical for more than a few nodes as thenumber of transmissions scales up.

Using the CD Heuristic, on the other hand, this floodingwill not occur for a single transmission. Suppose that thereare n nodes, and that the MAC layer implements fair accessto the medium, i.e., each node that is ready to transmit hasan equal probability of being the one to transmit. BecauseCD bases its transmit decision on all copies of the packet ithas heard (not just the first as in Ko/Vaidya Scheme 2), eachtransmission effectively prevents all transmissions fromnodes to its left in the figure. After the originator transmits,fair medium access implies that the expectation value of thenumber of transmissions prevented by the second transmis-

sion is %n=2, so the number of nodes that could possiblytransmit third is %n=2. Similarly, the third transmission willbe expected to prevent %n=4, leaving %n=4 candidates forthe fourth, etc., until N is the only candidate after %lg n 1 steps. Thus, for a single transmission, on average, weexpect Olg n retransmissions.

So far so good, but there is a more subtle problem thatarises when there is more than one geocast originatedconcurrently. For illustration, suppose that all nodesoriginate distinct geocasts to the geocast region shown.All such geocasts will result in node N transmitting, as it isclosest to CGR among all nodes in line of sight of origin. N

will, therefore, have to transmit all geocasts, resulting in along queue wait at N. If the queue is FIFO at N, then eachgeocast will have a long wait, on average. Consider also thenode immediately to Ns left. This node will also transmitall geocasts except those for which N happens to transmitfirst. Since N is slow, this will be most geocasts.Continuing this reasoning leftward in the diagram, we seethat each node will transmit all geocasts not transmittedfirst by a node to its right. This leads to a quadratic totalnumber of transmissions for n geocasts.

Fig. 6 illustrates the order of transmissions for CD. In thisfive-node example, which uses a random (but fixed, for

illustration) medium-access order (DBECA), each nodeinitiates one geocast to the circle during a given time unit.Above each node is the order of processing of packets(lowest first). Circled nodes are actually transmitted, while


Fig. 4. The geocast of Fig. 3 propagated using the center-distanceheuristic.

Fig. 5. Example showing that CD does not scale well in all cases.

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lined out nodes are canceled by the heuristic. By canceled,we mean that the heuristic predicate hCD is false, so theframework does not transmit the packet. For this example,we assume the Classic Geocast heuristics (M and T) are notoperating. The subscript numbers indicate transmissionorder. Thus, at the outset, each node places its geocast in itsqueue; packet a in As queue, b in Bs, etc. Since the random

medium-access order dictates D goes first, D transmitspacket d first. (All of nodes A-E hear each of each otherstransmissions in this example.) Packet d is placed in thequeue at each other node, as shown, behind the one alreadypresent. Next in the medium-access order, node B transmitspacket b, the second overall transmission, with b being putinto the queue at each other node. Next in the order, Etransmits its packet e, then C transmits c, and A transmits a.Each of these packets is enqueued at each of the other nodeswhen it is heard. After these first five transmissions, D getsto transmit again; this time (the sixth overall transmission)its copy of packet b. Since it is closer to CGR(b) than B itself,

it is not canceled. Next, node B accesses its queue but,because it is farther from CGR(d) than D itself, it cancels itscopy of packet d. Similarly, it cancels its copies of e and c aswell. However, it finds it is closer to CGR(a) than A, so ittransmits a as the overall seventh transmission. Next in line,E dequeues and transmits its copy of d, since it is closer toCGR(d) than node D. Proceeding in this way, C transmits a,A transmits nothing since it has canceled all other packets(due to being farthest away), etc.

For the example of Fig. 6, CD produces 14 transmissionsaltogether: E transmitted all five packets, D transmittedfour, C and B two each, and A one. Note the tendency of

packets to pile up in the queues of nodes closer and closerto CGR.

Among all possible medium-access orderings, CD canproduce as few as 9 or as many as 15. While this example

illustrates merely one medium-access order, it is chosenbecause it is representative of the average case. To

investigate the average case, we created a small study ofthe linear scenario of Fig. 5, but generalizing to n nodes (for2 n 71). In this study, instead of a fixed access order,the simulation uses a completely random and fair medium-access scheme. That is, each time the medium becomes free,all nodes having a packet to transmit have equal probabilityto be the next to transmit. The upper curve in Fig. 7 showsthe results for CD. It graphs transmissions (averaged over100 runs for each n) versus number of nodes. The markedpoints are the simulation results, while the dashed curvesare close-fit descriptions. Clearly, CD results in approxi-mately 0:25nn 1 transmissions for an n-node linearscenario, as n gets large, which is n2.

The evaluation study in the next section provides moreempirical evidence that this CD scalability problem is notmerely theoretical, and that it arises commonly in densescenarios. Of course, the case of all geocasts being directedto the same geocast region is special; however, densescenarios often have many instances of these (overlappingin space and time) as subscenarios.

Step 2: Adding Priority Queuing. To ultimately solvethis scalability problem, the CD-P Heuristic imposes theadditional condition that each node prioritizes its transmitqueue (cf., Fig. 1) by center-distance improvement. For eachpacket in a nodes transmit queue, we assign it a priority

Prio as follows: first, any nongeocast packet, such as unicasttraffic, and any geocast packet, which has not beentransmitted at all yet, have Prio 0. For geocast packetsp, let dp be the distance from the node to CGRp). Then,

Priop max0;CDistgIDp dp:

Thus, the greater the improvement (i.e., reduction ofdistance to CGR), the higher the priority, and hence, thesooner the packet will be transmitted.

Intuitively, we wish to transmit sooner those packets thatcan most quickly move toward the centers of their geocastregions. Heuristically, this is because the greater the

progress toward the center, the more other nodes will besuppressed due to being farther from center.

Fig. 8 illustrates the transmissions resulting from thesame five-node example as above using the CD-P queue


Fig. 6. Example CD transmission order: each node A; . . . ;E originatesone geocast to the lower right circle.

Fig. 7. Simulation results for CD and CD-P in n-node instances of thelinear scenario of Fig. 5.

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prioritization (and the same medium-access order). Thebehavior using CD-P is very different. First, even thougheach node enqueues its own packet first, it is not necessarilythe first the node transmits. For example, node E transmitsits copies of packets b, c, d, and a before transmitting itsown (e). This is because the priority of e is zero, while thepriorities of those other packets are greater than zero. The

scenario proceeds as follows: first D transmits its packetbecause it is first in the medium-access order. Next, Btransmits b, canceling its copy of d, since d was alreadytransmitted closer to CGR(d). Next, E transmits its copy ofb, not its own packet, e, and not d either because b is ofhigher priority by virtue of the amount of progress towardCGR (b) being larger than the progress achieved bytransmitting (d). Next, medium-access order is C, whichhas canceled its own copies of b and d, so transmits c. Next,A transmits its own packet, having canceled b, c, and d. Dgets its second turn at the medium and transmits its copy ofa, since that is of higher priority (for it) than c. B has nothingleft to transmit at this point, so E proceeds to transmit c,

which is highest priority among {a, c, d, e}. Note that since awas transmitted by D, a has dropped in priority below c. Atthis point, nodes A, B, C, and D have empty queues, so Eproceeds to transmit the remainder in priority order: d, a,and, finally, e. This scenario illustrates the nodes transmit-ting earlier the packets that can cancel the most otherpackets at other nodes sooner (e.g., E choosing c at step 7instead of the others). Overall, CD-P produces 10 transmis-sions, four fewer than CD produced.

In general, CD-P improves dramatically on CD for theselinear scenarios. Thelower curve in Fig. 7 shows theresults ofsimulating CD-P in the linear scenarios. The data points

converge very closely to the function 0:77n lg n, which growsmuchmoreslowlythanthequadraticgrowthoftheCDcurve.

We have focused here on the linear scenarios to illustrateboth the operation of CD-P and its differences with CD as

well as the subtle scalability problem of CD and the fact thatCD-P solves it. The general case, of course, will have moreand varied communication patterns, so one naturallywonders if, when packets are targeted to many geocastregions, will the mechanism still work and have thesame benefits? Of course, one can always construct worst-case scenarios where the more general case reduces theadvantage of CD-P. However, the more interesting questionis how CD-P behaves compared to CD and other scalableapproaches (like probabilistic geocast) in a representativeset of situations. The simulation study in the next sectionaddresses this.

Implementation Issues. Implementing CD-P and ClassicGeocast over an unaltered IP stack is problematic. This isbecause once one commits a packet to the IP system, itcannot be canceled. Immediately after receiving the firsttransmission, having not had time to hear any others, allnodes in an area would naively commit retransmissions toIP, resulting in all of them retransmitting. This leads tounacceptable flooding of every packet. To counter this, one

introduces a randomized transmit delay; that is, each nodewaits a random amount of time before committing theretransmission. This allows latter nodes an opportunity tohear earlier retransmissions and avoid committing theirown. While this works to reduce transmissions, theserandom delays must be on the order of many packettransmission times (e.g., between 2 and 100 ms), leading tosignificantly larger average latency than is possible with acustom implementation. Note also that once the packet iscommitted to IP, there is no way to recompute its priorityon hearing other transmissions. This leads to an approx-imation to CD-P that can make it less effective.

In previous work, a colleague and I have simulated thisapplication layer geocast implementation (M and T heuristicsonly) and shown that the key transmissions per geocaststatistic rises, placing much stronger limitations on scaling.In addition, a set of first prototypes using this implementa-tion were field-tested [8] and shown to bear out thesesimulation results: while flooding is avoided, the transmis-sions per geocast rises by a factor of 2 or more for scenariosinvolving up to 70 nodes. End-to-end latency also increases.

These studies motivate a tighter implementation. Thepreferred implementation, and the one simulated below, isto alter the standard 802.11 MAC layer (or indeed whateverMAC layer one is interested in using) to provide a latecancelation hook: this is a callback that is executed immedi-ately prior to start of transmission; if the callback returns 0,the transmission is canceled. We use this hook to call ourheuristics check (Pass(p) above). Priority queuing requiresanother alteration to the standard IP stack. As defined, thepriority is recomputed as each packet is about to leave thequeue, because priority values depend on how close to CGRall prior transmissions heard took place. Thus, the normalIP queuing code must be altered to do the priority-basedselection specified above. A new prototype embodying thispreferred implementation is currently being built.

4 EVALUATION

To evaluate scaling and reliability, and to compare to othergeocast approaches, we have implemented a simulation


Fig. 8. The example of Fig. 6 showing CD-Ps transmission order: againA; . . . ; E originate one geocast each, but CD-P uses fewer transmissionsthan CD.

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model of wireless MANET nodes running Classic Geocast [4]and both the CD and CD-P heuristics. This model runs in theQualNet simulator (scalable-networks.com), usingQualNets 802.11b PHY layer model with a simulated 2 Mbpschannel. QualNets 802.11b MAC layer model has beenaltered to implement the Geocast framework as describedabove, including late cancellation and prioritization.

4.1 Scenario Selection

We have selected 14 scenarios covering a range of complex-ity measures in numbers of nodes, terrain complexity, andtraffic load. These scenarios represent realistic battlefieldtraining scenarios envisioned for the OneTESS system, aswell as more abstract random node placements and traffic.The geocasts in the training-like scenarios are used toimplement engagement simulation of long-range shots, aswell as geographic command and control messaging. Ratherthan describing each scenario in detail, we summarize themin Fig. 9. In the figure, each scenario (numbered 1 . . . 1 4) is anumbered box, with number of mobile nodes in the scenarioin parentheses. The position of the box semiquantitativelyrepresents its position in two other complexity dimensions.The horizontal position indicates its terrain complexity.

This includes the number of terrain obstacles as well asgeometric relationships among nodes. That is, a scenarioappears farther to the right if it has more radio-blockingobstacles, and/or less connectivity between nodes due todistance separation. The vertical axis represents offered load(geocasts/s). A conscious attempt was made to cover allcombinations of the complexity dimensions.

Each of the 14 scenarios was based on one of the fivegeographic layouts depicted in Fig. 10. In all the figures, thedark gray rectangles (or squares) represent radio-blockingterrain obstacles, such as buildings or hills. The dotsrepresent wireless nodes. Thick dark arrows indicate

general direction of node motion during the scenario. (Inlayouts d and e, node motion is random, which is notshown.) The thin arrows that point to circles show typicalgeocasts with geocast regions, for illustration; there are far

more geocasts in the scenario than are shown. Areadimensions are given as width height.

With reference to the horizontal axis of Fig. 9, layout e isof low complexity, because there are no radio obstacles andconnectivity is high. Layouts a, b, and d are of highcomplexity for those scenarios in which geocasts musttraverse long distances, because such geocasts must berouted around many obstacles and around connectivitygaps. However, scenario 8 uses layout b and scenario 7 usesd but these are only of medium complexity, because theirgeocasts are biased toward shorter distances. Finally,layout c is of high complexity, because the geocasts musttraverse physical gaps via long paths around emptyregions. Note that while packets cannot reach horizontallyacross the large gaps, the nodes are still completelyconnected, because those at the top are within radio rangeof some near the top of the central group.

For each geocast, the sender and targeted geocast regionswere chosen in one of two ways. For scenarios 2, 3, 8, 9, 10,11, 12, and 14, geocasts were those sent during a realisticOneTESS (simulated) training scenario. These were basedon layouts a, b, and c. The mobility model for thesescenarios was produced by domain specific behaviorgenerators [9] that modeled realistic movement for thescenario type. In particular, they attempted to representrealistic node motion seen in military training; nodesrepresenting dismounted soldiers executed biased randomwalks at an average speed of 2 m/s, while vehicles followedscripted paths at speeds from 0 m/s to 40 m/s. For the othersix scenarios (based on layouts d and e), geocasts weregenerated randomly in two ways: some were generateduniformly randomly among nodes, while others were

random but biased toward longer (longer than 500 m)distances. In general, geocast regions were selected tocontain at least one node. Node motion in these scenarioswas a biased random walk between 0 and 2 m/s.

In summary, eight scenarios were based on realisticusage of real-world, high-scale MANET application (mili-tary training), while the others are intended to representother high-scale applications, such as emergency commandand control messaging, commercial gaming, advertising, ortraffic services. The six nonmilitary scenarios take placeeither in a modeled urban area (layout d) or in an open areasuch as a marketplace, sporting event, or fairground

(layout e). The geocast patterns in the military scenariosare chosen to be realistic for the training application, withboth light and heavy traffic rates represented. The geocastpatterns in the other scenarios are either uniformly randomor else random but biased toward longer distances(increasing the effective complexity of the terrain).

Each scenario was run 36 times (36 separate parametersettings), for a total of 504 simulation runs. The 36 representthe cross product ofM 2 f0; 2; 4g, T 2 f1; 40%; 20%; 10%g,CD 2 fon; offg, and CD-P 2 fon; offg, except that CD andCD-P were never both on at the same time. M and T arethe Classic Geocast MinTrans and Threshold parameters. T

values are expressed as a percentage of the modeled radiorange. Modeled radio range was 500 m for these runs. Inwhat follows, the notation m;t;c refers to the parametersetting with M m and T t, and c indicating which CD


Fig. 9. Properties of scenarios used in case study; each box representsone scenario, with the box containing the scenario number above thenumber of mobile nodes.

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heuristic is active: c NoCD ) both are off; c CD ) CDis on and CD-P is off; and c CD-P ) CD-P is on and CDis off.

4.2 Metrics

In the study, we measured Success% of Feasible (Success%)and Latency. Success% measures the fraction of feasiblegeocasts that reached the target node. For this study, wedesignated the node closest to the center of the geocast

region as the target node for the geocast. As mentionedabove, each geocast has at least one node in the geocastregion, so the target node is well defined for all geocasts. Ageocast is feasible if and only if a network path exists fromsender to target node at time of sending. Latency measuresaverage elapsed time from first transmission to reception bythe target node for successful geocasts. The goal of a geocastimplementation is high Success% with low Latency.

4.3 Results

Fig. 11 shows Success% over all geocasts and all scenarios.Each data point is the result for a particular parameter

setting. The horizontal axis shows variation in settings of Mand T. The middle curve (with diamond-shaped markers)groups settings with neither CD nor CD-P on (notatedNoCD). The upper curve (squares) groups all settings

with CD-P on. The lower curve (triangles) connects settingswith CD on.

This graph clearly shows that 0; 1; CD-P performsbest. Its result (93.3 percent) is 21.8 percent above the bestNoCD setting 2; 40; NoCD and 12.7 percent above the bestCD setting, 0; 1; CD. Moreover, as M and T are varied bymoving to the right, increasing redundancy, the CD-Psettings are consistently more than 10 percent higher than


Fig. 11. Success% of all geocasts in all scenarios for distinct parametersettings; uppermost curve is for CD-P.

Fig. 10. Geographic layouts of the five terrain types underlying the 14 scenarios used in the evaluation study.

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the CD settings and 20 % higher than all CD settings butthe first. This suggests that CD-P should be on by default.

Onemay wonder why Success% of CD-P and CD decreasewith decreasing T or increasing M (e.g., 0; 1; CD-P versus(0, 40, CD-P)). One might think that by increasing redun-dancy, one should increase success. This need not always betrue, however, because increased redundancy leads to morecollisions and medium contention, which can reducesuccess. A related puzzle surrounds CDs behavior. Sincepriority queuing is added primarily to improve scalingbehavior, why does adding it also improve reliability? Theanswer is the same as above: CD results in so much extra

traffic that collisions and medium contention lead to morelost geocasts. This is where our evaluation study benefitsfrom using a high fidelity simulator capable of modelingrealistic MAC and queuing behavior. These effects are lesslikely to be detectable in contention-free perfect-MACsimulations.

While encouraging, these aggregate statistics could bemisleading, if performance is erratic across scenarios. Toinvestigate this, Fig. 12 graphs Success% versus scenarionumber for six selected individual parameter settings. Thethree groups connected by solid lines are settings with CD-Pon. The three dashed curves are for NoCD settings. Here

again, even though it is not the best for all scenarios,0; 1; CD-P is consistently high for all 14 scenarios. TheNoCD settings are high for some, but dramatically poor forothers. In order to rank settings relative to one another, wecomputed the deficit score for each. The deficit score for aparameter setting P on a scenario S is the average differencebetween the best Success% on S among all settings and theSuccess% of P on S, averaged over all scenarios S where Pdoes not score the highest score. Table 1 shows the rankingsof selected parameter settings. The table shows that0; 1; CD-P has highest Success% on 8 of 14 scenarios,while it averages only 2.15 percent below the highest on the

other 6. The best NoCD setting here is 0; 40; NoCD whichties for best on only two scenarios and averages 19.82 percentbelow the best on the other 12. Note that all CD-P settingsrank higher than all NoCD settings. This is further evidence

that CD-P should be used by default. Note, however, thatthere do exist scenarios, such as #7, where a NoCD settingslightly outscores all CD-P settings. CD-P does compara-tively better than NoCD in more complex terrain, because ituses the hill-climbing heuristic to add valuable transmis-sions that the NoCD heuristics do not add, while the priorityqueuing acts to avoid the scalability problems that can arisefrom pure CD. NoCDs tend to do well in lower terraincomplexity and under higher load. Lower terrain complex-ity means that geocasts require fewer relays to complete,whereas high load means that the extra transmissions CD-Padds tend not only to be superfluous but to cause a smallamount of collision loss.

The combination of M and T with CD-P (2, 40, CD-P)outperformed CD-P alone on scenarios 8, 9, and 10. Asterrain complexity increases, hill climbing tends to get stuckin local minima; as long as traffic is not too high, M and Tcan help avoid such problems.

Fig. 13 is similar to Fig. 12, replacing NoCD curves withCD curves. CD performs better than NoCD did, but CD-Psignificantly outperforms CD on 7 of 14 scenarios (2, 6, 7, 8,9, 12, 13). Referring to Fig. 9, these are precisely the higher


Fig. 12. Success% of geocasts in each scenario, with each curveshowing performance of a single parameter setting; dashed curves areNoCD settings and solid curves are CD-P settings.

TABLE 1Rankings of Parameter Settings (See Text)

Fig. 13. Same as Fig. 12, but dashed curves represent three CDsettings.

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load scenarios. The best CD setting, 0; 1; CD, has a deficit

score of 14.35 percent. These data show that CD does notscale well under load, and that CD-P significantly improvesupon it.

Finally, Fig. 14 graphs average latency for three settings,each, respectively, having the best Success% among eitherNoCD, CD, or CD-P. With only one exception (scenario#12), CD-P has lower or equal latency than the others. CDhas by far the worst latency. Averaging these across allgeocasts of all scenarios for each of the three settings, weobtain 774 ms for 2; 40;NoCD, 780 ms for 0; 1; CD-P,and 8,724 ms for 0; 1; CD. Note that CD-P manifests a91 percent decrease compared to CD, illustrating another

way in which CD-P scales better than CD.Note that node mobility does not significantly affect theperformance of CD-P, as shown by the consistently highSuccess% and low Latency across all scenarios in Figs. 12and 14. This is not surprising, given its stateless design.Unlike many geographic routing approaches (Section 5), itdoes not depend on cached topology information thatbecomes inaccurate as nodes move.

4.4 Summary: Why Does It Work?

This study suggests that CD-P outperforms both NoCD andCD. In particular, the 0; 1; CD-P setting is ranked highestunder the criterion of Table 1. In addition, it also

demonstrates that CD is inefficient as scenario complexityscales up, whereas adding priority queuing to form CD-Pfixes that problem. As can clearly be seen from Figs. 12 and9, the dominance of CD-P over NoCD is large forscenarios 8-14, which are precisely the set with higherterrain complexity, meaning that for geocasts to complete,they must avoid many obstacles (or coverage gaps) byfollowing the right relay chains. NoCD has no built-inrouting knowledge that could enable it to find pathsthrough obstacles, whereas CD-P (and CD) uses a hill-climbing style of search that does much better. ComparingFigs. 12 and 9, the dominance of CD-P over CD is large for

scenarios 2, 6, 7, 8, 9, 12, and 13, which are precisely thosewith highest offered loads. (Note that these are also the oneswhere CDs latency becomes a problem as well; see Fig. 14.)Here, priority queuing reduces traffic redundancy (from n

for CD to lg n per geocast for CD-P in relay chains) andconcomitant contention-related losses resulting from highloads. This bears out the scalability behaviors illustrated inSection 3, so the study has borne out that this problem doesarise commonly, does lead to scalability problems for CD,and is solved by priority queuing (and even though not allgeocasts go to the same place).

4.5 Threats to ValidityAn empirical study is limited by its selection of scenarios. Wehave attempted to span wide ranges of complexity in at leastoffered load, terrain, numbers of nodes, and selection ofendpoints. Of course, as a prediction about the real world, asimulation study is limited by its chosen level of abstraction.While this papers QualNet-based simulations are relativelyrealistic, Geocast implementation, nevertheless, must bedone carefully in order to achieve similar results.

5 RELATED WORK

Geographic routing protocols fall into two broad classes:those that do not require current neighbor-topology in-formation and those that do. Generally, topology-basedapproaches suffer from three drawbacks in the high-scalescenarios of interest in this paper. First, we seek scaling tohigh geographic density; since topology-packet traffic growsin proportion to density, this overhead can becomeprohibitive. Second, we seek scaling to medium and highlevels of node mobility. This leads to topology informationrapidly becoming stale, which tends to mislead thealgorithms relying on it. Finally, topology-based approachesdepend on link symmetry: if a node hears a packet from a

neighbor, the assumption is that it can be heard by theneighbor as well. However, this assumption is often notsatisfied due to differences in equipment characteristics(e.g., antenna), differing battery levels, and radio propaga-tion phenomena like multipath effects. Several of ourapplications of interest (such as military ones) requirerobust propagation even under such conditions. Thus, theprimary competitors to our CD-P with Classic frameworkare the topology-free approaches. These are most likely to beapplicable to our high-scale application areas of militarysimulation, geographic command and control, emergencymessaging, and commercial applications. However, forcompleteness, we review several of the others as well.

5.1 Topology-Free Approaches

In a previous paper, Hall and Auzins [4] showed thatClassic Geocast far outperforms simple flooding. (Asmentioned previously, simple flooding is easily emulatedin the Classic Geocast framework; see Section 2.) Even formoderately scaled scenarios, smaller than some of thoseused in this paper, the success% of feasible is drastically lessfor simple flooding (e.g., 25 percent on one scenario) thanfor Classic Geocast (85 percent on the same scenario). Forlarger scenarios, the difference (2 percent for flooding and99 percent for Geocast) is much larger. This reliability

difference is due to a drastic difference in (re)transmissionsper message: e.g., 220 for flooding versus 11 for ClassicGeocast on the smaller scenario. (Refer to [4] for full details.)The study in [4] shows that Classic Geocast scales


Fig. 14. Latency comparison for three settings, one CD-P, one CD, andone NoCD; lower values are better.

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significantly better than simple flooding, while the presentpaper shows that CD-P shares similar scaling advantagesand improves reliability over (NoCD) Classic Geocast.

As discussed previously, CD is similar to, but moreefficient than, Ko and Vaidyas Scheme 2 [5], which doesnot allow a closer retransmission to cancel other repetitionsof a geocast. This leads to higher traffic and worse scalingthan CD and, hence, CD-P as well.

Heissenbuttel et al. [10] describe the BeaconLess Routing(BLR) protocol. BLR avoids the scaling problems of con-stantly broadcasting topology information in beacon mes-sages. The protocol is similar to (but not the same as) the pureCD heuristic. After a node transmits its message, nodeswithin a certain forwarding zone respond if they are closer tothe destination. BLRs trick is that each node delays itstransmission by an amount that varies according to howmuch progress it can make toward the destination. The onecomputing the shortest delay transmits first, causing allothers to cancel (as in CD). This is better than Ko/VaidyaScheme 2, because it uses late cancellation as in our

approach, but it suffers from the scalability problemdescribed in Section 3: as traffic increases, a potential relaynode will be unable to respond in time to cancel (extra)transmissions of others, resulting in the quadratic behaviordocumented earlier.

Probabilistic Flooding (PF) [11] is another geocastheuristic that can be replicated within the Classic Geocastframework. We have modeled a variant that a) counts thenumber Nof nodes that are neighbors (i.e., recently heardfrom), and b) decides whether to retransmit by a randomcoin-flip with success probability minf1;P=Ng, where P is aparameter. Applying this papers evaluation method, P

12 gave the best performance, with an overall Success% of46.9 percent, and a deficit score of 28.8 percent with two tiesfor best score. Recall that the best CD-P setting had overallSuccess% of 93.3 percent and deficit score of 2.15 percentwith eight best scores. PFs deficit score ranks it below allsettings with CD-P on, and below 7 of 12 NoCD and 6 of12 CD settings. It is particularly weak in scenarios with highterrain complexity.

Khan et al. [12] describe a different probabilistic floodingscheme very similar to Classic Geocast using only the Theuristic. Their scheme computes a probability of retrans-mission that varies with distance exponentially, with

probability 0 at distance 0 (from sender) and approaching1 exponentially with greater distance. This is quite similarto Classic Geocast with T heuristic only, except the lattersubstitutes a step function that is 0 out to distance T andthen 1 thereafter. While one can reasonably expect thesetwo approaches to perform similarly, the present work hasshown how to combine other heuristics (e.g., CD-P, M) withT-like ones to increase reliability while maintaining scal-ability. The study in this paper has shown that CD-Poutperforms T (and M) in general, with only a few scenariotypes being exceptions.

Zorzi and Rao [13] have proposed Geographic RandomForwarding (GeRaF). In GeRaF, the sender broadcasts its

location and that of the destination in an RTS message.Nodes hearing the message then use an iterative MAC-based resolution protocol to select a unique node, amongthose closer to the destination than the sender, to relay it.

The nodes then exchange CTS/RTS pairs until therandomized resolution protocol selects a unique relay; atthis point, the data are forwarded to the relay. Refer to [13]for complete protocol details. For comparison purposes,GeRaFs most salient characteristics are as follows: first, it isa pure hill-climbing heuristic similar to CD; in particular,GeRaF is stymied when the relaying encounters a situationwhere the direction toward the destination contains a gap(even if relay paths going other directions exist). A GeRaF-based geocast system would be outperformed (as was pure-CD-P) by a mixed Classic/CD-P strategy on several of thescenarios in our study, such as 8, 9, and 10. In ourframework, CD-P is integrated with the T and M heuristicsprecisely to fix this problem: if CD-P does not result in arelay, T or M may lead to a relay around the problem area,as occurred, e.g., in scenarios based on the layout of Fig. 10c,which has large gaps between sender and geocast region.Second, GeRaF is based upon providing prioritized med-ium access to nodes closer to the destination, in contrast toCD-Ps fair-access scheme. This enables the average GeRaFhop to travel a longer distance than that of CD-P (for densescenarios and several-hop messages, %0:77R versus%0:40R, where R is the coverage radius of a transmission).However, this is at the cost of a much higher and lessscalable per-hop overhead and latency. The time cost of aCD-P hop is essentially just the time to transmit one dataframe. However, the cost of a GeRaF hop is not constant: itrequires approximately 1 2 lg R2=8 control messages(RTS or CTS) plus one data frame, where is the averagearea density of nodes. So GeRaFs overhead grows withdensity; e.g., with 32 neighbors, GeRaF needs 5 control plus1 data, while with 128 neighbors, it takes 9 control plus 1

data. In practice, the authors recommend limiting thenumber of RTS/CTS rounds, aborting the message if norelay is selected. This implies that above a certain density,GeRaF will not function at all. CD-P has no such upperlimit. For low node densities, GeRaFs overhead is boundedby 9 control plus 1 data (such as when there is a single relayvery close to the transmitter). Thus, for low traffic,geographically dense scenarios, GeRaFs latency and over-head costs are less scalable than those of CD-P. A fullercomparison of CD-P and GeRaF is beyond the scope of thispaper, but would be quite interesting as future work,particularly for high load scenarios.

Another proposal by Hughes and Maghsoudlou [14] isbased on each node transmitting in the geocast packet anestimate of the area already believed covered by copies ofthe packet. A node retransmits if and only if its coveragearea includes a grid square that is not indicated as coveredin the packet. As shown in [14], this reduces retransmis-sions; however, (also as in [14]) the number of retransmis-sions is still linear in number of nodes, at least for thecritical special case of all nodes having identical radioranges. Thus, this method merely improves simple floodingby a constant factor, so it is still unscalable. The largestscenario they simulated had 70 nodes.

Chen et al. [15] propose an alternative to GPS-basedgeocasting in which they modify simple flooding byanalytically calculating a time-to-live limit for each packet.Their method does simple flooding, but each packet has the


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number of hops it has gone through within it and once thelimit is reached, it is no longer retransmitted. They showthat this improves the number of transmissions over simpleflooding by about 58 percent, for a class of simple radio-obstruction-free random scenarios. Of course, 0.42 times anunscalable level of traffic is still unscalable.

Pleisch et al. [16] describe a method of enhancingflooding algorithms with compensation packets that canefficiently improve reliability. When a node elects not toretransmit a flooded packet, it instead adds it to anexclusive OR of all packets not retransmitted. Once c suchpackets are combined, the node transmits this XOR. Anyother node having heard c 1 of the packets and thecompensation packet can then reconstruct the missing one.While the approach has advantages, it doesnt help in allcases. For example, in the geocast of Fig. 5, the node in thegeocast region only ever hears packets either transmitted byN or compensation packets from N (which never containpackets transmitted by N). Thus, the compensation me-chanism cannot reconstruct any of the missing packets. CD-

P, on the other hand, does succeed in that situation, asshown previously.

5.2 Topology-Based Approaches

In Greedy Perimeter Stateless Routing (GPSR) [17], greedyrelay selection is augmented by first computing a planargraph from gathered topology information and then, whenthere is no relay closer to the destination, forwarding thepacket instead to the first node counterclockwise fromthe sender around the planar-graph face intersecting thedestination direction. While this heuristic can improveSuccess% over pure greedy approaches, it can also select

overly long paths, resulting in high latency or packet dropsdue to reaching time-to-live limits. Two factors lead to thisweakness. One is that in planarizing the graph, thealgorithm drops out cross links and tends to keep shorthops, so where one hop in the original graph would suffice,two or more are needed in the planar subgraph. Second, itslocal search heuristic can be tricked into traversing largedead-end subgraphs that do not lead to the destinationbefore finally finding its way. For example, for the layout ofFig. 10c, if the geocast originates on the right edge and hasdestination either in the central group or in the left edgegroup north of the senders latitude, then GPSR wouldforward the packet first north greedily until it reached or

passed the destination latitude, at which point it wouldenter perimeter mode; it would then proceed south (by theright-hand rule) until reaching the end of the chain and thenback north, passing through the original sender node again.At that point, it may proceed eventually to discover a pathto the destination, but the entire sojourn south of theoriginator is wasted. By contrast, the T heuristic wouldpropagate the packet both directions from the sender,including northward to the upper edge, where the CD-Pheuristic would take over and move the packet to thedestination using far fewer hops. To be sure, there also existscenarios where GPSR would succeed and CD-P with

Classic would fail to find a route. Another potentialweakness of GPSR is its scalability with geographic density.Its graph planarization algorithm is quadratic in thenumber of neighbors. Moreover, this graph must be

recomputed when neighbors change position. Note thatGOAFR+ of Kuhn et al. [18] is very similar to GPSR and sohas similar strengths and weaknesses compared to CD-Pwith Classic. Finally, Heissenbutel et al. [10] have docu-mented a study comparing BLR to GPSR. In it, they showthat beaconless approaches can be expected significantly tooutperform GPSR in success%, latency, and energy perfor-mance as mobility and density increase. The reason for thisis that the topology information in beacons gets stale andmisleads GPSR, while it also uses up bandwidth andenergy. Since my studies above have shown that CD-P (andCD-P with Classic) outperform CD, and hence, also BLR,we can infer evidence that CD-P outperforms GPSR forscenarios of these types.

In Trajectory-Based Forwarding (TBF) [19], the senderspecifies a parameterized geometric curve (or other one-dimensional continuously parameterized structure such as atree) as a kind of generalized route to the destination(s).TBF then operates at each node greedily by choosing a relayclosest to this curve but farther along it. In this way, thealgorithm chooses a one-dimensional subset of all nodesfrom a dense (2D) scenario to relay a given message. This hasobvious scale advantages over flooding and related techni-ques, which choose the full 2D neighbor set. Being ageneralization of linear hill climbing (as used in CD-P, GPSR,et al.), TBF can get stuck when the trajectory encounters agap or obstacle. In contrast to GPSR, it is not clear how to fixthis in general for TBF, as discussed in [19]. CD-P with M andT can deal with this case, as previously described.

Face Tracing [20] is a generalization of face routingapproaches such as GPSR and GOAFR+ that does not firstplanarize the graph. It suffers from similar scalability

problems they do while improving Success% and latencyby eliminating fewer links from the graph. Its scalability isin question, however, as it requires a preprocessing step ofcluster graph construction that can be problematic in highlymobile networks, and it also requires topology packetsupdating nodes with locations of neighbors.

Lee et al. [21] describe NADV where, instead of usingcloseness to destination, they weight it by dividing by a costmetric for each link. For example, a node can compute a costproportional to link loss rates. This metric is then used ingreedy hill climbing (such as CD-P). Zamalloa et al [22] usethis approach in sensor networks having lossy links.

Obviously, given a way to measure link costs, CD-P coulduse this metric as well.

Maihofer [6] surveys several geocast protocols. Amongthose applicable within dynamic MANETs, the mostrelevant is unicast routing with area delivery (URAD),which uses a unicast-based search approach (such asposition-based hill climbing) to reach a node near thegeocast region. This node then initiates a flood of thegeocast region. This reduces the long-range transportcomplexity to n

12, the diameter of the network, but depends

on extensive topology information and still involves flood-ing level complexity near the geocast region.

Lou and Wu [23] describe a method (PDP) for achievingarea coverage with fewer retransmissions than flooding.PDP sends two-hop neighbor topology information. Eachnode then computes a spanning tree in its neighborhood,


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determining if it is a part of the tree, and inhibitingretransmission if not. The algorithms reliability (and thepapers analysis) depends on two idealized assumptions:that neighbor topology does not change during propagation,and that the MAC layer is perfect so there is no mediumcontention at all. In a real-world system (such as thatmodeled in this paper), queuing, MAC delays, and collisions

can disrupt this idealization, leading to unreliability andhigh overhead. By contrast, the simulation study in thepresent work modeled realistic queuing and MAC behavior.

6 CONCLUSIONS

CD-P is a novel heuristic designed to support geocast inhigh-scale MANET applications and integrated into theClassic Geocast framework, allowing it to complementother heuristics. It is based upon three key ideas. First, anode retransmits if it is closer to the center of the geocastregion than all other copies it has heard transmitted.Second, it listens to other retransmissions continuously

prior to its own retransmission and cancels its own if ithears another node transmit closer to the center first. Andthird, scalability relies on each node prioritizing its sendqueue to send soonest those packets that make the mostprogress toward the center of their geocast regions. All ofthese elements are necessary for scalability; e.g., eliminatingqueue prioritization yields CD, which has drastic scalingfailures on many scenarios. Moreoever, the three togethersignificantly improve geocast performance (21.8 percentreliability improvement with no significant increase inlatency over NoCD; 12.7 percent reliability improvementand 91 percent drop in average latency compared to CD andrelated approaches). The study has suggested that

0; 1; CD-P is the best parameter setting to use by default,providing top or near-top performance in all scenarios. Itsintegration in the Classic Geocast framework allowscombining it with other heuristics for increased perfor-mance in more situations. For example, performance can beincreased in complex terrain (which tends to defeat greedyhill climbing) by varying T and M to increase redundancy,as in scenarios 8, 9, and 10 (Fig. 12). How to dynamicallyvary parameters when conditions change is an interestingarea for future work.

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[16] S. Pleisch, M. Balakrishnan, K. Birman, and R. van Renesse,Mistral: Efficient Flooding in Mobile Ad-hoc Networks, Proc.

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[23] W. Lou and J. Wu, On Reducing Broadcast Redundancy inAd Hoc Wireless Networks, IEEE Trans. Mobile Computing, vol. 1,no. 2, pp. 111-122, Apr.-June 2002.

Robert J. Hall received the PhD degree inelectrical engineering and computer sciencefrom the Massachusetts Institute of Technology.Since then, he has been a principal investigatorat AT&T Laboratories Research, working in the

areas of automated software engineering, re-quirements engineering, modeling and simula-tion, and MANET protocols. He is a fellow ofautomated software engineering and serves asa chairman of the steering committee for the

IEEE/ACM International Conferences on Automated Software Engineer-ing. He is the editor-in-chief of the Automated Software EngineeringJournal, an ACM Distinguished Scientist, and a member of the I.F.I.P.Working Group 2.9 on requirements engineering. He is a member of theIEEE Computer Society.

. For more information on this or any other computing topic,please visit our Digital Library at www.computer.org/publications/dlib.


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