J. Parallel Distrib. Comput. 71 (2011) 1189–1200

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J. Parallel Distrib. Comput.

journal homepage: www.elsevier.com/locate/jpdc

The impact of mobility on Mobile Ad Hoc Networks through the perspective ofcomplex networks✩

Cristiano Rezende a,∗, Azzedine Boukerche a, Richard W. Pazzi a, Bruno P.S. Rocha b,Antonio A.F. Loureiro c

a PARADISE Research Lab, University of Ottawa, Ottawa, Canadab Eindhoven University of Technology, Eindhoven, The Netherlandsc Universidade Federal de Minas Gerais, Belo Horizonte, Brazil

a r t i c l e i n f o

Article history:Available online 15 January 2011

Keywords:MANETVANETComplex networkMobility

a b s t r a c t

Mobile Ad Hoc Networks (MANETs) are wireless networks where nodes’ exchange of messages doesnot rely on any previously deployed infrastructure. Portable devices that are capable of wirelesscommunication have become extremely popular making possible the establishment of wide ubiquitousnetworks. Users connected to such networks can access the provided services anywhere and anytime.

Nevertheless, this architecture suffers from a highly unstable topology since links between nodesbreak constantly due to users’ movement. Mobility has a paramount influence on the network topology.Therefore, it is of utmost importance to understand the impact of mobility in MANETs. In this work, weperform a thorough analysis on how mobility shape the behavior of MANETs. Our range of observationvaries from general MANETs composed of walking users to a next generation of MANETs formed byvehiclesmoving either in a city environment or in a highway scenario, namely Vehicular AdHocNetworks(VANETs). Our analyses are performed observing the networks through the perspective of complexnetworks. We were able to identify underlying characteristics of these networks and showed how theseobservations can be used to improve the performance of MANETs.

© 2011 Elsevier Inc. All rights reserved.

1. Introduction

We are currently witnessing an intense evolution on the tech-nology of portable computational devices and wireless communi-cation. Such development provided the necessary equipment forthe formation of a newnetworkmodelwhere devices are no longerstatic but rather able to move freely. Furthermore, it is possible tomaintain communications capabilities evenwithin regionswith nopreviously deployed infrastructure. These networks are known asMobile Ad Hoc Networks (MANETs) and communication is estab-lished in a multi-hop fashion betweenmutually reachable devices.

In this scenario, nodes’ mobility presents itself as a complexchallenge since it causes frequent topology modifications and,consequently, paths’ disruptions. Mobility does not only pose asa challenge to keep an ongoing communication but it also makes

✩ This research is partially sponsored by grants fromNSERC, the Canada ResearchChair Program, the Ontario Distinguished Researcher Award, the Ontario ResearchFunds (ORF), and the Ontario Centres of Excellence (OCE).∗ Corresponding author.

E-mail addresses: creze019@site.uottawa.ca (C. Rezende),boukerche@site.uottawa.ca (A. Boukerche), rwerner@site.uottawa.ca (R.W. Pazzi),B.P.S.Rocha@tue.nl (B.P.S. Rocha), loureiro@dcc.ufmg.br (A.A.F. Loureiro).

0743-7315/$ – see front matter© 2011 Elsevier Inc. All rights reserved.doi:10.1016/j.jpdc.2010.12.009

difficult the initial establishment of communication since thelocation of a specific destination is not as easily obtained as in staticnetworks.

Mobile AdHocNetworks behave distinctly fromother networksand a useful way to study them and to determine their intrinsic at-tributes is through the perspective of complex networks.Whenweobserve MANETs as complex networks, we can identify non-trivialfundamental aspects through the analysis of the attributes of theformed complex networks. Therefore, we are able to understandclearer how these networks behave and propose more suitable so-lutions.

Mobility plays an utmost important role in the analysis ofMANETs as complex networks. Nodes’ movement dictates howlinks among them are created. Whenever a node in the networkmoves to a different region, it provides a shortcut connectionbetween the original region and the new one.

In this work, we study broadly the role of mobility in MANETsin the formation of different complex networks. Our goal is,through this analysis, to understand the behavior of varied forms ofMANETs and to determine important features that may guide thedevelopment of more efficient and more effective protocols. Thevaried forms of MANETs are differentiated from each other basedonwhichway users aremoving, specially regarding the speed theymove.

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The most general MANET instance considered in the existingliterature is the one formed by pedestrians that move within aregion (e.g. hospital, campus, building) carrying a computationalportable device equipped with a wireless radio. In this scenario,we can consider either devices that are constantly online andcan communicate while moving, such as PDAs and cellphones, ordevices that have discontinued operations, such as laptops. Themobility in this case is restricted to low speed movements, sinceusers walk from one location to the other.

In order to further understand the role of mobility in MANETs,we investigate a different kind ofMANETwheremovement ismoreintense and at higher speeds. These MANETs are composed byvehicles and the network model they follow is named VehicularAd Hoc Networks (VANETs). This environment is further dividedinto two scenarios: urban and highway. An urban VANET is formedby vehicles moving along streets in a city. They are subject tostreet boundaries; thus their movement is not so erratic as ingeneral MANETs. Themovement behavior in an urban scenario haspeculiarities that are described further in this work. The highwayVANET is also composed of vehicles butmoving in a road instead ofstreets. This scenario differs from the previously described urbanscenario as vehicles move at higher speeds and their movementsis more restricted since they follow the same direction for longerperiods of time.

Therefore, we analyze these different MANETs scenarios interms of howmobility affect their topology.With such analysis, wewere able to identify characteristics that can certainly be used toimprove the performance of protocols and mechanism in a varietyof MANETs’ aspects (e.g. routing, QoS, localization).

In the following section, we describe some of the works in theliterature that have also looked into MANETs’ and VANETs’ issuesas complex networks. Furthermore, in Section 3, the main prop-erties of complex networks are explained and their connection tothe above mentioned networks. The analysis of MANETs as com-plex networks is shown in Section 4 and of VANETs in Section 5.Finally, we present our final remarks in Section 6.

2. Related work

In this section, we list some of the works that have also con-sidered either MANETs or VANETs as complex networks. These pa-pers also point the importance ofmobility in the network topology.Although suchworks exist, to the best of our knowledge, there is noother work that analyzes the impact of mobility on the propertiesof the complex networks that represent MANETs or VANETs.

In different approaches [21,12], researchers have proposed theuse of social networks’ aspects in order to design mobility modelsfor MANETs to be used in simulations. Both works support theidea that, by this manner, simulations can be executed on thetop of a scenario with higher fidelity to reality. A more precisemobilitymodel has a stronger impact on the network topology andoverall performance than concerns regarding signal attenuation orhow obstacles affect nodes’ communication [19]. Perisa et al. [28]also analyze the impact of mobility in simulations for MANETs’applications aimed towardwar scenarios. They have used complexnetworks’ aspects to study and improve mobility models for thisenvironment.

Hui et al. [14] investigate MANETs through the perspective ofhow it behaves as a social network. The idea is that the socialinteraction between users can be studied and used as premisesto the deployment of a better forwarding protocol. They takeinto consideration data sets of real interaction between peopleunder different scenarios. Their solution consists in the formationof communities (clusters) and identification of nodes with highcentrality (high betweenness) within these communities. Thesenodes would then be used as hubs, thus paths would be shorter.

The authors have used these ideas to develop a Publish/Subscribemechanism for MANETs [36] where brokers are nodes with highcentrality.

Yen and cheng [35] make an interesting correlation between awireless network clustering coefficient and the occurrence of hid-den terminals. In this paper, they conclude that such phenomenonis related to the complex network property of clustering coeffi-cient, in the sense that with higher clustering coefficients less col-lisions due to hidden terminals take place.

A study of the evolution of VANETs’ topology is presentedby Pallis et al. [26]. Although they show the variance of some pa-rameters over time, they have lacked to analyze the impact ofmobility on these parameters. Another approach to the study ofVANETs’ topology is using a non-Ad Hoc communication, insteadof mobility, for random links connecting distant nodes [9]. Similarwork is presented by Dixit et al. [8], where they investigate cellularwireless networks and how mobility or communication throughmechanisms different than the one between themobile and a BaseStation (e.g. Ad Hoc, secondary infrastructure) can impact the net-work’s topology changing it into a small-world or scale-free net-work.

3. Complex networks

Complex network is an area of science that analyzes the be-havior of any network through graph properties and tries to re-late them to the real aspects of these networks. These identifiedproperties are not trivially observed and they are usually the resultof recurrent behaviors in the network. By this manner, the studyof networks through the complex networks’ perspective provideinsightful information to a deeper understanding of their charac-teristics. Faloutsos et al. [11] and Albert and Barabàsi [1] provideseveral definitions and applicabilities of complex networks.

The networks studied through complex networks do not haveto be necessarily of technological nature. They can be the repre-sentation of any behavior or phenomenon as long as vertices andconnections among these vertices are clearly defined. Therefore,complex networks can be used to analyze details of a wide diver-sity of interactions.

Although complex networks are also used to infer conclusionsin a variety of natural science studies, like the biological advancesin the work by Wagner and Fell [32], in this work, we investigatehow MANETs’ topology is affected by the social behavior oftheir users (particularly through mobility). For this purpose, it isimportant to explain some principles of networks formed by socialinteraction.

Social networks consist on the interaction between peopleand they have been widely studied through the perspective ofcomplex networks [31,17,18]. The interaction considered can beof different kinds such as friendship, romantic relationship or co-authoring. Boyd and Ellison [6] have described and analyzed theevolution of social networks in theWeb. These services allow usersto fill a profile and to inform friendship relations through virtualconnections on the websites. There are several services like this inwhich the most prominent have been widely studied as complexnetworks, for example: Facebook [23], Youtube [27], MySpace [7]and Twitter [13].

In order to understand properly the behavior of these complexnetworks, we analyze how different social interactions impactseparately and distinctively in the formation of specific networkmodels. Furthermore, we study the network models when theseinteractions are considered together.

A classical network model known as regular networks (alsoreferred as lattices) defines topologies composed by nodes thatare strongly connected among local groups but these groups arenot strongly connected among themselves. Lattices are usually

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represented graphically with vertices placed in a circle andwith links connecting nearby nodes (see Fig. 1(a)). This modelrepresents with fidelity aWireless Static Ad Hoc Network since thecommunication would be solely possible among mutual reachableradios.

Random networks are formed by nodes that are randomlyconnected to each other. These networks are also known asErdős–Rényi networks due to their contribution in studying thismodel in the late 50’s and early 60’s [10]. If a random networkis moderately dense in the number of links among vertices, thereare many distinct paths between nodes, thus, leading to a scenariowith not only redundant paths available but also with usually anoption of short length (see Fig. 1(c)). The occurrence of a MANETwith a topology similar to a random network would occur in a sce-nario with devices with different radio ranges and nodes movingintensively and erratically.

One well-known model of complex network is called SmallWorld [20,34]. This model includes two important graph proper-ties: a strong clustering coefficient, and a small diameter. The clus-tering coefficient of a vertex in a graph quantifies how close thevertex and its neighbours are from being a clique, while the clus-tering coefficient of a network is the simple mean of all vertices’values. It is related to how often there is a link between two dis-tinct vertices that are both connected to a third one. The diametercan either be the average or the longest of all the shortest pathsbetween any two vertices. As proven by Watts and Strogatz [34],a small world is in the middle of the process of rewiring edges be-tween vertices in a regular graph (i.e. a lattice) changing it into arandom graph (see Fig. 1(b)).

The connection between smallworlds andMANETs is due to thefact that a high clustering coefficient is an expected result of thebroadcast nature of communications in a wireless environment.Therefore, due to the proximity required for establishing a connec-tion between any two nodes, there is a high probability that twoneighbours of a specific node will also be mutually reachable. Be-sides that, mobility creates random links between nodes which actas shortcuts between them.

Newman [24] studies the evolution of regular networks to ran-dom networks through the process of randomly rewiring linksfrom lattices. Newman shows how the mean path lengths and theclustering coefficient evolve in such process with different prob-abilities of a link to be rewired. The interesting conclusion is thatlattices and random networks would occur in distinct extremes,with probability zero and one respectively, and somewhere in themiddle there would be graphs that represent small worlds. Fig. 1illustrates the topology evolution through the rewiring process.

In this work, we study and evaluate how mobility acts simi-larly to the rewiring process in the topology of MANETs. A Wire-less Static Ad Hoc Network poses as a regular network. If nodesin this network are no longer static and move intensively andfreely around the network area, the resulting topology would bean Erdős–Rényi network. For this reason, we intend to analyze dif-ferentmobility scenarios and identifywhether the network resem-bles better a lattice, a small world or a random network.

4. MANETs as complex networks

Mobile Ad Hoc Networks connect wireless devices capableof moving in an environment where there is no underlyinginfrastructure and communications between nodes are performedonly by mutually reachable radios. When a node needs to contacta distant node that is beyond its radio range, intermediary nodesbetween themmust be used. Even thoughwired networks also relyon multiple hop paths, in MANETs this is a more complicated tasksince finding a specific destination and keeping communicationwith it is a challenging issue due to the highly dynamic topology.

We consider as a general MANET an environment composed byusers moving on foot through a region carrying portable deviceswithwireless communication capabilities. An illustration of the in-teractions among users in MANETs is shown in Fig. 2. This scenariopresents challenges other than a dynamic topology, such as com-putational restrictions and battery limitations. However, mobilityis still themost significant factor in building the network topology.

Mobility in these MANETs has specific characteristics, nodes’movements are erratic, short and reasonably slow. They are erraticsince there are no severe path restrictions such as streets and roads.Their short length and slow speed are due to the limitations ofhuman movement.

General MANETs are very useful in a diversity of scenarios pro-viding several appealing services. Students in a University campuscould take advantage of this network through ad hoc connectionof their PDAs, mobiles or notebooks and share information such asnews, grades or class schedules. The communication among emer-gency response agents in a region after a natural disaster may beperformed through a MANET (see [2]). Physicians, nurses or anyhealthcare provider could carry a PDA that would promptly pro-vide accurate and updated informations in order to assist decisionsor medication regulation (see [33]).

We have considered the mobility aspect and the communica-tion mechanism in these MANETs and we came to the conclu-sion that such scenario would be represented with more fidelityby a small world. The short-range wireless communication basedon broadcast transmissions creates a strong locality in packageexchange in the sense that a group of nearby users are able tocommunicate with each other. This leads to a consistently highclustering coefficient. Although the mobility is not extremely in-tense, it is enough to create random links betweendistant groups ofusers which already decreases significantly the path length amongnodes.

Taking these considerations, we have tackled the issue of dis-seminating information to interested parties in a MANET. We havedeveloped a Publish/Subscribe architecture to perform this taskand we have analyzed its performance through the complex net-works’ perspective.With our conclusions, wewere able to improvean initial proposal (see [30]) providing amore suitable and efficientversion (see [29]). In the next subsections, we summarize our find-ings and discuss it for the purpose of this work.

4.1. PSAMANET+

Publish/Subscribe (Pub/Sub) is a communication paradigm thatfits these general MANETs very conveniently. Pub/Sub exchangesinformation based on its content as well as users’ interests. Its goalis to deliver all information to all users that have described an in-terest in it. It is particularly convenient forMobile AdHocNetworksdue to Pub/Sub’s decoupling nature, which allows entities to com-municate even if they are not connected simultaneously or do notknow each other’s addresses.

We have used mobility to notify interested subscribers aboutmessages sent by publishers in a Mobile Ad Hoc Network. The goalis to deliver publications to all interested parties using the mini-mum amount of broadcasts (messages). Even though expressive-ness, filters, merging techniques, and other factors are of majorimportance to event notification services, the proposed solutionfocuses solely on the delivery task of these services. An importantassumption is that mobile devices are able to know whether theyare moving [16,22].

The idea is that publications received locally can be forwardedto distant regions of the network after a node moves. Nodes alsokeep a knowledge of their neighbourhood’s interests based onsubscriptions and forward incoming publications that match theseinterests. In this way, we use the carry-and-forward paradigm

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Fig. 1. Evolution from a regular network, through a small world to a random network.

Fig. 2. An illustration of a MANET in a University campus.

where nodes receive and store packages in a location and carrythem physically moving to a different region of the network wherethey forward the stored packages.

Therefore, we can detect the potential of both small-worldproperties in such an approach. The neighbours’ awareness ofnodes’ interests, and consequently the resulting exchange ofmessages, incurs a high clustering coefficient in the dissemina-tion graph. Besides that, since nodes move, carry-and-forwardpackages in other regions, random links are also created whichdecreases the length of paths between publishers and their sub-scribers.

Our proposed protocol is called the Publish/Subscribe Architec-ture for Mobile Ad Hoc Networks (PSAMANET+). This is the secondversion of our protocol1 and it is improved by understanding thesmall-world nature of the architecture.

Subscriptions are sent to a node’s neighbours to inform itsneighbourhood about its interests. They are sent either when thesubscription is created or after a nodemoves in order to update thenew neighbourhood. Publications are broadcast by the publishernode to all of its neighbours. After this step, every time any reachednode moves, it will potentially broadcast this received publicationin the new region to which it has moved. Publications are also

1 The first version was called PSAMANET; see [30].

forwardedwhen amatch is found,which occurswhen an incomingpublication matches a previously stored subscription or when anew subscription matches stored publications.

Therefore, a publication can run through the network in thefollowing steps: it is first broadcast by the publisher itself, then allneighbours check whether they have any matching subscription(if they do, they forward it instantly). Afterwards, if any of theseneighbours move, as soon as they stop they will broadcast thepublication to a different area of the network. In this way, nodes’movement helps to disseminate a publication to the entire areacovered by the network. Algorithm 1 describes the steps followedby a node when it receives a publication.

The dissemination of publications is shown in Fig. 3. It startswith the publisher broadcasting the publication to its neigh-bours (a). Then, if an aware neighbour is reached, it immediatelyforwards the incomingpublication (b). After a reachednodemoves,it broadcasts the publication into the new region inwhich it has ar-rived (c). In this way, interested nodes or even other aware neigh-bours can be reached (d).

PSAMANET (the first version) already takes advantage of con-sidering MANETs as small worlds since it uses mobility to createa link between a node that broadcasts a publication in one re-gion and a distant node that receives this publication forwardedby themobile node. A high clustering coefficient is also handled, assubscriptions are used to notify neighbours about other nodes’ in-

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(a) (b) (c) (d)

Publisher InterestedNode

ReachedNode

AwareNeighbour

InterestedReached Node

Fig. 3. PSAMANET+ publication dissemination.

terests, but it tries to avoid multiple broadcasts of the same pub-lication (which would happen in a network with a high clusteringcoefficient) using the probability of accepting a subscription.

However, there is another important characteristic of smallworld networks, which is the small diameter of such networks.2Assuming this, we can limit the number of hops a publication trav-els through the network and thus avoid unnecessary broadcastssince, theoretically, every node would be reached with just a smallnumber of hops. The new architecture with this modification iscalled PSAMANET+.

Algorithm 1 Receiving Publication Pubif Number of hops that Pub has travelled is bigger than themaximum number of hops allowed then

Drop Pubelse

if Pub matches any of mine Subscriptions thenHandle Pub by proper application

end ifif Pub matches any neighbour’s Subscription then

Broadcast Pubend ifStore Pub into Publication Buffer

end if

4.2. Mobility model — random waypoint

The movement in MANETs composed by walking users has itsown peculiarities. In order to clarify this mobility behavior, wehave considered the scenario of a university campus. Studentswould move among bus stops, residence buildings, different de-partments and local cafeterias and restaurants. These movementsare characterized by being of short distance and with speeds feasi-ble to humanwalking. Although there are still physical limitations,a campus provide a wide area for pedestrians’ movement, thus,they are more erratic than the movements of vehicles in streetsand roads. Another important consideration is that students wouldmove from one point to another, remain still for some time andthan move to a different location.

The most widely accepted and used mobility simulation modelfor this scenario is the Random Waypoint (RWP). This model isan abstract representation of thementionedmobility environmentbut it succeeds in providing reasonable fidelity to real movementpatterns. Johnson and Maltz [15] were the first to propose theRWP. In this initial version, a node would choose randomly a

2 Watts and Strogatz [34] show that it is proportional to the logarithm of thenumber of vertices in the graph.

Table 1Experimental scenario.

Parameter Value

Number of nodes 200Radio propagation model Free spaceRXthresh_ 7.69113e−08Routing DumbAgentRadio range 50 mArea 600 m × 600 mSimulation time 1 hNodes’ speed [1.5, 4.0] m/s (normally)Nodes’ pause time [0, 600] s (uniformly)

destination, a speed and a pause time that it would remain still atthe destination.

Several concerns regarding RWP issues that would diminish itsfidelity to real life movement were pointed out in the literature.Yoon et al. [37,4] have discussed these issues. The main problemis that if the lower extreme of speed limits was zero, the aver-age speed of the network would never converge but instead itdecreases continually. This happens, since, whenever a far destina-tion is chosenwith a speed close to zero, the respective nodewouldremain at this low speed for all the remainder of the simulation.

In order to overcome these issues, an improved version of RWPwas used. In this version, the lower speed limit cannot be too small.Besides that, the speed is chosen randomly from the range butfollowing a normal distribution. By this manner, extreme speedsare less frequent. This has been proven to solve the problemof non-converging speed averages.

4.3. Experiments and results

Throughout this work, our evaluations are performed throughsimulations using the network simulator 2.31 [25]. This simulatoris the most widely used and represents with high fidelity realwireless communication.

As mentioned, an important property of small-world networksis that the diameter of these networks is small. Assuming this, wecan limit the number of hops a publication travels through the net-work and thus avoid unnecessary broadcasts since, theoretically,every node would be reached with just a small number of hops.This is the improvement in PSAMANET+ compared to the originalversion.

We want to analyze the performance of PSAMANET+ for differ-ent limits on the number of hops.We used the same statistical pro-cess as in previous experiments and varied the maximum numberof hops from one to twenty. The scenario is the same as that de-fined in Table 1. Results regarding PSAMANET+ for different valuesof the maximum number of hops are depicted in Fig. 4. The lastplotted value in each line is the result of the original PSAMANET,thus, an infinite HM . The most obvious observation is that smaller

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(a) Maximum number of Hops × messages sent. (b) Maximum number of Hops × match rate.

Fig. 4. Experiments varying number of maximum hops.

(a) Pause time × match rate. (b) Pause time × match rate.

Fig. 5. Match rate varying mobility intensity for different configurations.

values for HM lead to a reduced traffic, as shown in Fig. 4(a). Thenumber of messages sent by PSAMANET is not much bigger thanfor the largest values of HM analyzed in PSAMANET+.

Regarding the match rates, Fig. 4(a) shows intriguing results.From the smallest values of HM , small increases resulted in muchhighermatch rates. However, further increases ofHM led to slightlylower match rates. Nevertheless, the original PSAMANET has asignificantly higher match rate (for some of the configurations)than the largest evaluated values of HM . Therefore, even thoughthe number of messages sent showed a monotonic increasingbehavior, the match rate has shown a more erratic behavior. Thishappens because the maximum number of hops for publicationshas a strong effect on the publications that remain in the nodes’Publication Buffer and also on the propagation through neighbourinterests awareness.

PSAMANET+ was able to achieve match rates above 90% withmuch fewer messages sent. The tuple configuration (500, 50, 0.4)achieve match rates of 90%, 97% and 99%, sending 94 k, 129 k and156 kmessages, respectively. These results, when compared to theoriginal PSAMANET, show that high match rates could be achievedwhile sending substantial fewer messages. The original solutioncould only achieve match rates above 90% by sending twice thenumber of messages.

These results of PSAMANET+ consider only one movement sce-nario. It is interesting to analyze the performance of architectures

designed for MANETs in scenarios with different mobility intensi-ties. The random waypoint parameter that has the strongest im-pact on the mobility intensity is the mean pause time. Thus, wenow analyze the impact of mobility intensity on some configura-tions of PSAMANET+while varying themean pause time. Each con-figuration has one more dimension than the original PSAMANET:the maximum number of hops HM . Thus, a configuration is aquadruple (PBs, PBM , STp, HM ) where PBs is the maximum numberof publications stored at each node, PBM the maximum number ofpublications broadcast at any moment and STp the probability of asubscription to be forwarded to further hops.

The chosen configurations were based on the seven configu-rations of the initial results and the results in Fig. 4. We havechosen HM values that achieve high match rates using few trans-missions. The maximum number of hops varied mostly between 2and 6, with the exception of the configuration I for which we useda higher HM of 10. Results were divided into two different groupsthat show similar results. Figs. 5 and 6 show the average of 30 runswith confidence intervals of 95% using z-distribution.

The division of the evaluated configurations into two differ-ent groups is based on the following observation. Configurationslimited by small values of HM have stronger impact due to dif-ferent mobility intensities regarding match rates (Fig. 5(a)) thanconfigurations with bigger values of HM (Fig. 5(b)). This happensbecause, with smaller HM , the dissemination of publications relies

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(a) Pause time× message sent. (b) Pause time× message sent.

Fig. 6. Number of messages sent varying mobility intensity for different configurations.

more on the movement propagation method than on the neigh-bours’ interests. Configurations with higher HM values are also im-pacted by mobility intensity, although in a weaker manner. Thenumber of messages sent also behaves differently in each group,but not as differently as the match rates. Although all config-urations have shown an exponential growth in the number ofmessages with higher mobility intensities, the match rate had anexponential growth with HM equal to two, and a linear growth (oreven logarithmic) with bigger values of HM .

For this reason, in more static scenarios PSAMANET+ configu-rations with high HM (or even the original PSAMANET) are moresuitable than ones with a small HM . However, as the mobility in-tensity grows, PSAMANET+ configurations with small HM exhibitoutstanding performance, achieving high match rates with smallnumbers of sent messages. Thus, it can be seen that HM is anotherdimension that add flexibility to an application designer to choosethe configuration most suitable to the environment in which theapplication will be deployed.

4.4. Conclusion

We have studied the topology of general MANETs through theperspective of complex networks and we have identified interest-ing properties that can be used to guide the implementation of anefficient and effective Publish/Subscribe architecture. The topologywas considered to behave similarly to a small world and, thus, weused the high clustering coefficient and the short path length prop-erties in the design of our solution.

PSAMANET+ disseminates publications over the network usingthe carry-and-forward paradigm in order to take advantage of therandom links in the network. Subscriptions were used to notifynearby neighbours so that the high clustering coefficient could beexploit.

The results have shown that by using these assumptions wecould achieve a high matching rate while sending fewer messagesthan other approaches. This is a demonstration that a deeper un-derstand of the network topology through the perspective of com-plex networks can lead to significant improvements in generalMobile Ad Hoc Networks.

5. Vehicular Ad Hoc Networks as complex networks

Vehicular Ad Hoc Networks (VANETs) are a next generation ofMANETs, they are composed of vehicles as nodes thatmove at highspeeds through restricted paths (streets or roads). These networksare formed by vehicles equipped with computational devices ca-pable of performing wireless communication among themselves.

This network model is a specific case of a Mobile Ad Hoc Net-work (MANET) with characteristics distinct from those of generalMANETs, such as an unlimited energy source and extremely dy-namic topology resulting from the much higher speeds at whichnetwork nodes move. These peculiarities make it necessary thatprevious solutions for general MANETs need to be re-evaluated inorder to ensure their suitability to this scenario.

Several applications can be developed on top of these net-works, and they are usually aimed at either enhancing safety,improving emergency response or providing general services fordrivers/passengers. Examples of applications that increase thesafety of drivers and passengers are emergency brakes activatedwhen a nearby accident is detected, or an automatic way to en-sure a minimum distance between cars. Proper healthcare in anemergency response can be given faster if, for instance, a live videostream is provided to a paramedic inside of an ambulance on theway to the accident site. VANETs can also be used to provide Inter-net access inside the vehicle or even videoconferencing betweendrivers or passengers in different vehicles.

VANETs can be divided into two different groups based on thescenario in which vehicles are moving. The first consists of the ur-ban environment where vehicles move along streets and avenuesin a city. The second is the highway environment with vehiclesmoving along freeways. They have distinct movement behaviorsthat lead to different topology scenarios.

In this section, we describe in detail both scenarios and thor-oughly evaluate the impact of mobility in the network topologies.Our methodology consists of assembling a graph that representsthe connectivity of the network through time. A vertex in thisgraph represents a vehicle and an edge between any two verticesindicates a communication capability between the respective ve-hicles. Consequently, the considered graph represents all the com-munication among vehicles during the defined period of time.

As aforementioned, we used the network simulator 2.31 forour evaluations. Table 2 shows the value for the most importantparameters. We have opted to evaluate the performance of thenetwork for 30 min. However, in order to avoid the influenceof cold start issues in the results, we have included 10 min ofsimulation where the only event is node movement and in thefollowing 10 min messages are exchanged but not considered inour results. By this manner, a steady scenario is reached beforeevaluation starts.

In order to determine if two vehicles are capable of commu-nicating, each vehicle broadcasts a beacon package periodically.The interval between consecutive beacons is randomly chosen be-tween 0.8 and 1.2 s (thus, an average of 1 s between beacons).

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Table 2Simulation parameters.

Parameter Value

Radio propagation model Two raygroundMac layer IEEE 802.11RXThresh_ 1.92278e−08(100m)Antenna OmniAntennaTotal simulation time 50 minSimulation time analyzed 30 min

Table 3Statistics analysis.

Parameter Value

Number of samples 32Confidence level 95%Distribution Gaussian

When a beacon is received, the triple consisting of timestamp,source vehicle and receiving vehicle is stored in a separate file.

Every plotted result is an average of the evaluation of 32 graphs,each taken from distinct execution instances. Therefore, eachgraph is assembled from the information gathered from uniqueinstances with different seeds for random number generation,different vehicles’ movements and different beacon broadcasts.Besides the average, confidence intervalswere also plotted. Table 3summarizes the statistical analysis in the following experiments.

An important concept in the graph definition is the length of thetime window T during which connections are considered. A graphGT is formed by links between vehicles through a time windowof T s. Different values of T are evaluated and so that we discussthe impact of mobility in the topology of VANETs. The majorityof values considered for T are smaller than the duration of thesimulation t . For this reason, the starting time ts of the windowconsidered is randomly chosen between 0 and t − T .

Sections 5.1 and 5.2 describe in detail the simulation mobilitymodels and specifics of the experimental setup of urban and high-way scenarios, respectively. In each of these sections, the resultsand conclusions are discussed.

5.1. Urban scenario

The urban scenario presents itself as an environment for thedeployment of interesting services on the top of VANETs. It ispossible to use this architecture to provide on board Internetaccess. Emergency response can be improved in several fronts suchas alerting vehicles of incoming emergency vehicles or provide theinfrastructure for live streaming directly from the accident sceneto hospitals and paramedics in ambulances.

An efficient architecture in this scenario would provide thetechnology for the development of a wide variety of additionalfacilities and marketing opportunities for companies in diverseniches. A service discovery mechanism could provide a searchengine based on the vehicle’s current location offering a muchvaluable information to the users. Shopping centres and mallsmay use such infrastructure to offer their customers directionsfor the closest available parking space or even the capability ofpreviously reserving a spot. Another appealing example is that fastfood restaurants with a drive-through facility could provide anordering mechanism so that customers would have an even moreconvenient experience.

In order to provide the necessary infrastructure for the deploy-ment of these applications, it is of utmost importance to first un-derstand the underlying characteristics of the network topology. Inthe following subsections we provide a detailed explanation of ourexperiments and the analysis of the results we have observed.

Table 4Urban mobility model parameters.

Parameter Value

# Density 25 vehicles/km (550 vehicles in total)Area 1 km × 1 kmSegment length 100 mNumber of blocks 10 × 10Speed range 5–30 m/sSegment maximum capacity 20 vehiclesReducing factor 5−[(#vehicles/5)−1]

5.1.1. Mobility model — urban mobility modelVehicles’ movement within a city environment are not as free

of constraints as the movement of pedestrians in a Universitycampus. Vehicles move along streets and avenues which restrictthe direction of the movement. Although vehicles are not able tochange direction anywhere in a city, abrupt changes of directionsare possible at intersections. Therefore, there is still some erraticbehavior in their movement. Furthermore, vehicles movement inthis scenario differ from pedestrians in a general MANET in termsof their speeds. Vehicles in a city move much faster than walkingusers in the previous scenario.

Bai et al. [3] describe a mobility model for the city environmentnamed Manhattan Model based on streets placed as in a grid (asthe New York borough of Manhattan). In this model, each streethas two lanes (one for each direction) and vehicles move at arandom speed within a predefined range. When a vehicle reachesan intersection it randomly chooses which direction to pursue.Vehicles have a 50% chance of continuing straight and 25% chanceof turning left and the remaining 25% probability of turning right.

In this work, we have designed the Urban Mobility Model(UMM) which extends Bai’s Manhattan Model taking into consid-eration traffic conditions. In real life, the speed of vehicles in a cityis not only subject to speed limits but greatly by traffic conditionsas well. For this reason, we have used a reducing factor that isproportional to the number of vehicles in the street segment thevehicle is moving to. Besides that, there is a maximum capacity re-garding the number of vehicles in a segment at any given time. Thiscapacity is calculated based on an estimate vehicle length and thesegment length.

Traffic plays an important role in themovement behavior of thenetwork since it affects not only the movement speed but the den-sity around the network. We believe that, with traffic awareness,the UMM represents with higher fidelity the movement aspects ofreal life scenarios.

Table 4 summarizes the values of the main parameters forthe generation of the 32 distinct instances of the Urban MobilityModel. It is important to point out that, although the speed isinitially randomly chosen with the defined range, vehicles maymove slower than the lower limit since the final speed is subject totraffic conditions. The reducing factor is a function on the currentnumber of vehicles in a segment and it is only used if there is morethan 5 vehicles in such segment.

5.1.2. Results and conclusionsFig. 7 shows the results of the data we have gathered in these

simulations on the urban scenario. In Fig. 7(a), we show howthe clustering coefficient and network diameter vary for differentlengths of the time window T . The degree distribution for vehiclesgrouped based on their average speed is shown in Fig. 7(b).

The first observation in Fig. 7(a) is that the network diameterdecreases extremely fast with increments of T . This means thatvehicles carrying packages to forward in different regions of thenetwork for a short period of time would be enough to reach thewhole area with few transmissions. After 30 s, the diameter of thenetwork has already reached the natural logarithm of the number

C. Rezende et al. / J. Parallel Distrib. Comput. 71 (2011) 1189–1200 1197

of nodes’ level. Another interesting conclusion is that for delay-tolerant applications, a network region with the same dimensionsusing one single intermediary node (for T greater than seven min-utes) is possible.

In Fig. 7(a), we see that mobility initially decreases the highclustering coefficient of a static scenario. Nevertheless, this de-crease is not substantial and, with time, mobility starts to increaseagain the clustering coefficient since vehicles continue meetingand forming more cliques among neighbours.

Regarding Fig. 7(b), it is important to explain that consideringlonger windows, the vehicles average speed converges more tothe speed range mean (for this reason, for T greater than twominutes, not all speeds are plotted). The expected result would beof increasing degrees for faster vehicles since they would coverlarger portions of the network at the same time. However, thisis only observed for average speeds up to 15 m/s. The reason forthe degree’s decrease after that is due to the fact that in a cityenvironment vehicles are subjected to traffic which significantlydecreases their speeds. A vehicle in a congested street would besurrounded by several other vehicles and a vehicle with a highspeed average has not got stuck into traffic.

With these results, we can see that with mobility, the topologysimilar to a lattice, when nodes are static, move to a scenario moreaccurately represented as a small world, but does not lead to arandom network since the communication among nearby nodesstill prevails. These conclusions can be used for future design ofmore suitable solutions to this scenario.

5.2. Highway scenario

The behavior of vehicles changes notably from a city environ-ment to a highway scenario. Highways are used to connect fartherlocations which are either different cities or simply distant regionswithin a large city. Therefore, they have to provide the means forvehicles to move faster in more straight directions and vehicles.

Highways differ from cities’ streets and avenues by being usu-ally broader, having higher speed limits, fewer traffic lights andfewer intersections that require a full stop. Vehicles’ movement ina highway scenario is less erratic than in an urban one since roadsdo not have abrupt changes of direction as paths followed in a city.

In the following subsection, we discuss themobilitymodel usedto simulate this environment. It is an abstract representation thatoffers reasonable fidelity to the real life scenario.

5.2.1. Mobility model — freeway+The most commonly used model for a highway scenario is the

freeway model [3]. However, an unrealistic aspect of this model isthat vehicles do not cross other vehicles once they reach a slowervehicle in front of them; instead they reduce their speed oncethey are within a certain threshold distance from this vehicle. Theresulting scenario would be characterized by a synchronization ofspeeds and a constant distance between vehicles in the same lane— that is, more stable topologies. This unrealistic behavior wouldlead to biased and inaccurate analysis of the network topology.

Therefore, we have altered this model so that it supports fastervehicles crossing slower ones. The enhanced model, Freeway+, iscomposed of straight lanes for both directions (curves do not needto be represented sinceMANETs use short-range communications)and a same number of lanes per direction. Each lane has aminimum and maximum speed, and vehicles’ speeds are chosenrandomly and change every sc s. When nodes reach one of the twoextremes, a reset method is called, and the nodes are replaced onthe other extreme and in the same lane.

Nodes are distributed uniformly on the road — that is, alwayswithin only one lane. Table 5 shows the parameters of themobilityscenario used in this work. It is important to notice that the

Table 5Freeway+ parameters.

Parameter Value

# Density 10 vehicles/kmTotal length 150 kmRegion evaluated length 75 kmLanes per direction 3Lane width 5 mSlowest lane speeds 5–15 m/ssecond Slowest lane speeds 10–25 m/sFastest lane speeds 20–40 m/ssc 60s ± 10%

different speed limits are for the three distinct lanes in eachdirection; thus, for the whole simulation these same limits wereused.

The evaluation of the network topology of this scenario wouldbe compromised if we did not handle the issue that when a vehiclereaches an extreme it is placed on the opposite extreme andcontinue moving. This is done in order to maintain the networkdensity andwhenplaced on the other extreme this node representsa new incoming vehicle. If this issue is disregarded, we wouldconsider links between vehicles that would never be able to existin a real life scenario. In order to avoid this problem, we haveprolonged the simulated road and have considered solely thecommunications performed within a limit region of the road. Thesimulation lasts 30 min, thus, we consider only the results in themiddle 75 km out of the total length of 150 km. In this way, avehicle that reaches any of the extremes of the considered regionwould never reach the opposite extreme of this region within30 min.

5.2.2. Results and conclusionsFig. 8 summarizes the results of our experiments in the highway

scenario. Once again, the results are divided into two figureswherethe first (Fig. 8(a)) shows the evolution of the clustering coefficientand diameter for different values of T , while the second (Fig. 8(b))shows the degree distribution based on vehicles average speed.

We can see in Fig. 8(a) that the clustering coefficient suffersinsignificant variations for the different lengths of windows. Thisis an interesting observation and it reflects that although in ahighway vehiclesmove at faster speeds, the road imposes directionrestrictions that result in more stable topologies. Vehicles movingto the same direction with similar speeds remain close to eachother for long periods (see [5] for a deeper discussion on thisphenomenon).

The network diameter in a highway scenario decreases abruptlywith further vehicles’ mobility, but in a different scale than whatwe have observed in urban environments. In a highway, it takeslonger to observe shorter paths but the natural logarithm levelof number of nodes is still reached (after around 10 min ofmovement).

In Fig. 8(b), it is shown the degrees distributions for vehicleswith different speed averages for different values of T . We canclearly notice that the speeds are grouped in three portions andthey are related to each of the three lanes with their respectivespeed limits. These results are also subject to vehicles havingtheir average speeds closer to the mean of each lane speed range.This happens specially with greater values of T , and in this case,the behavior of extreme speeds (further from the lane average),represents the results of vehicles that were for short periods inthe analyzed section of the highway. For this reason, in thesecases, the degrees of nodes with speed average closer to thelane range mean represents better the behavior of vehicles. Withthese considerations, we observe that degrees increase slightly butconsistently for faster vehicles.

Once again we have noticed that mobility impacts the topolo-gies in thisMANET scenariomaking thembehave similarly to smallworlds.

1198 C. Rezende et al. / J. Parallel Distrib. Comput. 71 (2011) 1189–1200

(a) Time window T× clustering coefficient/diameter. (b) Average speed × average degree.

Fig. 7. Urban scenario results.

(a) Time window T × clustering coefficient/diameter. (b) Average speed × average degree.

Fig. 8. Highway scenario results.

6. Final remarks

We present in this work a thorough analysis of the effect of mo-bility in shaping the network topology in a variety of MANETs’ sce-narios.We havemanaged to identify underlying attributes of thesenetworks which is an information for protocol designers to pro-pose potentially more suitable and more effective solutions.

The more general type of MANET, composed of walking userscarrying portable devices with wireless communication capabil-ities, poses as a scenario with many promising applications. Wehave studied the performance of a previous solution through theperspective of complex networks and we were able to improve itsperformance being more effective and more efficient.

We have analyzed a next generation model of MANET com-posed by vehicles (VANETs). We have divided VANETs into twogroups based on their environment. The first consists of vehiclesmoving within a city that, among other restrictions, are subjectedto traffic conditions. The second group is that of a highway scenariowhere vehicles are moving at faster speeds and usually boundedto a specific direction for long periods of time. We have evaluatedthe communication graphof both these scenarios throughdifferentperiods of time. We have observed how mobility impacts each of

them and we were able to determine some interesting topologiesproperties.

Mobility changes MANETs’ topology by creating random linksbetween nodes previously unreachable, directly. Therefore, withsufficientmovement, the network switches froma regular networknature while static to a small world. The different style of mobilityin the distinct MANETs models studied in this work showsthat depending on movement behavior, a small-world state isreached earlier or later. We have identified important intrinsicproperties of these networks and we hope that this study guidesthe development of future solutions in MANETs.

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Cristiano Rezende is currently a Ph.D. candidate workingat the PARADISE Research Lab at the University of Ottawain the capital of Canada. He received his Bachelor’sDegree in 2005 and his Master’s Degree in 2007, bothfrom the Universidade Federal de Minas Gerais in BeloHorizonte, Brazil. In 2005, he got the highest score onthe Brazilian Students’ Performance National Exam inComputer Science (ENADE). Since his graduation, he hasworked in research in wireless networks. He has startedstudying protocols for data dissemination in WirelessSensors Networks and after he moved his focus to Mobile

Ad Hoc Networks. His current research fields of interest is in Multimedia Supportin Vehicular Ad Hoc Networks.

Azzedine Boukerche is a full Professor and holds aCanada Research Chair position at theUniversity of Ottawa(uOttawa), Ottawa, ON, Canada. He is a Fellow of theCanadian Academy of Engineering and the FoundingDirector of the PARADISE Research Laboratory, School ofInformation Technology and Engineering (SITE), uOttawa.Prior to this, he held a faculty position at the Universityof North Texas, Denton, TX, and he was a Senior Scientistat the Simulation Sciences Division, Metron Corporation,San Diego, CA. He was also employed as a Faculty Memberin the School of Computer Science, McGill University,

Montreal, QC, Canada, and taught at the Polytechnic of Montreal, Montreal, QC,Canada. He spent a year at the JPL/NASA, California Institute of Technology,Pasadena, CA, where he contributed to a project centered about the specificationand verification of the software used to control interplanetary spacecraft operatedby JPL/NASA Laboratory. He has published several research papers in these areas.His current research interests includewireless ad hoc and sensor networks,wirelessnetworks, mobile and pervasive computing, wireless multimedia, QoS serviceprovisioning, performance evaluation and modeling of large-scale distributedsystems, distributed computing, large-scale distributed interactive simulation,and parallel discrete-event simulation. He served as a Guest Editor for theJournal of Parallel and Distributed Computing (special issue for routing forMobile Ad Hoc, special issue for Wireless Communication and Mobile Computing,and special issue for Mobile Ad Hoc Networking and Computing), ACM/KluwerWireless Networks, ACM/Kluwer Mobile Networks Applications, and the Journalof Wireless Communication and Mobile Computing. He serves as an AssociateEditor of IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, IEEETRANSACTIONS ON VEHICULAR TECHNOLOGY, Elsevier Ad Hoc Networks, WileyInternational Journal of Wireless Communication and Mobile Computing, Wiley’sSecurity and Communication Network Journal, Elsevier Pervasive andMobile Com-puting Journal, IEEE WIRELESS COMMUNICATION MAGAZINE, Elsevier’s Journalof Parallel and Distributed Computing, and SCS Transactions on Simulation. He isthe Cofounder of the QShine International Conference on Quality of Service forWireless/Wired Heterogeneous Networks (QShine 2004). He served as the GeneralChair for the Eighth ACM/IEEE Symposium onModeling, Analysis, and Simulation ofWireless and Mobile Systems, and the Ninth ACM/IEEE Symposium on DistributedSimulation and Real-Time Application (DS-RT), the Program Chair for the ACMWorkshop on QoS and Security for Wireless and Mobile Networks, ACM/IFIPSEuropar 2002 Conference, IEEE/SCS Annual Simulation Symposium (ANNS 2002),ACMWWW2002, IEEEMWCN2002, IEEE/ACMMASCOTS 2002, IEEEWireless LocalNetworks WLN 03–04; IEEE WMAN 04–05, and ACM MSWiM 98–99, and a TPCmember of numerous IEEE- and ACM-sponsored conferences. He served as theVice General Chair for the Third IEEE Distributed Computing for Sensor Networks(DCOSS) Conference in 2007, as the Program Cochair for GLOBECOM 2007–2008Symposium on Wireless Ad Hoc and Sensor Networks, and for the 14th IEEE ISCC2009 SymposiumonComputer and Communication Symposium, and as the FinanceChair for ACM Multimedia 2008. He also serves as a Steering Committee Chairfor the ACM Modeling, Analysis, and Simulation for Wireless and Mobile SystemsConference, the ACM Symposium on Performance Evaluation of Wireless Ad Hoc,Sensor, and Ubiquitous Networks, and IEEE/ACM DS-RT. He was the Recipient of

1200 C. Rezende et al. / J. Parallel Distrib. Comput. 71 (2011) 1189–1200

the Best Research Paper Award at IEEE/ACM PADS 1997, ACM MobiWac 2006, ICC2008, ICC 2009, and IWCMC 2009, and the Recipient of the Third National Award forTelecommunication Software in 1999 for his work on a distributed security systemson mobile phone operations. He has been nominated for the Best Paper Award atthe IEEE/ACM PADS 1999 and ACM MSWiM 2001. He is a Recipient of an OntarioEarly Research Excellence Award (previously known as Premier of Ontario ResearchExcellence Award), Ontario Distinguished Researcher Award, and Glinski ResearchExcellence Award.

Richard W. Pazzi is currently the NSERC DIVA ResearchNetwork Manager. He is also a Research Associate atthe PARADISE Research Laboratory at the University ofOttawa. Dr. Pazzi received his B.Sc. and M.Sc Degrees inComputer Science from the Federal University of Sao Car-los, Brazil, respectively in 2002 and 2004. He received hisPh.D. Degree from the University of Ottawa, Canada, in2008. He was the recipient of Best Research Paper Awardsfrom the IEEE International Conference on Communica-tions (ICC 2009) and the InternationalWireless Communi-cations andMobile Computing Conference (IWCMC 2009),

and the recipient of Elsevier’s Top Cited Article (2005–2010) for his work pub-lished in the Journal of Parallel andDistributed Computing (JPDC 2006). He has beenworking with fault-tolerant protocols for Wireless Sensor Networks and MobileComputing. His research interests include Vehicular Ad Hoc Networks, multimediacommunications and networked 3D virtual environments.

Bruno P.S. Rocha is currently a Ph.D. Candidate onthe computer security group (SEC) of the EindhovenUniversity of Technology, in the Netherlands. He hasreceived both his B.Sc. and M.Sc. in Computer Sciencein the Universidade Federal de Minas Gerais (UFMG), inBelo Horizonte, Brazil, in 2005 and 2007, respectively.With a background in Mobile Computing and Security, henow performs research on software analysis, specificallylanguage-based information flows. His research fields ofinterest range between computer security and mobilecomputing.

Antonio A.F. Loureiro holds a Ph.D. in Computer Sciencefrom the University of British Columbia, Canada, 1995.Currently, he is a Professor of Computer Science at the Fed-eral University of Minas Gerais (UFMG), Brazil. His mainresearch areas are wireless sensor networks, computernetworks, distributed systems, anddistributed algorithms.In the last 10 years he has published over 100 papers in in-ternational conferences and journals, and presented sev-eral tutorials in international conferences. He was the TPCChair for LANOMS 2001 (Latin American Network Oper-ations and Management Symposium, sponsored by IEEE

Communications Society) and for the 2005 ACMWorkshop onWirelessMultimediaNetworking and Performance Modeling.