Urban pedestrian mobility for mobile wireless …h-yamagu/papers/AHN2009...Urban pedestrian mobility for mobile wireless network simulation Kumiko Maedaa,*, Akira Uchiyamaa, Takaaki

Urban pedestrian mobility for mobile wirelessnetwork simulation

Kumiko Maeda a,*, Akira Uchiyama a, Takaaki Umedu a, Hirozumi Yamaguchi a,Keiichi Yasumoto b, Teruo Higashino a

aGraduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, JapanbGraduate School of Information Science, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara 630-0192, Japan

Received 5 March 2007; received in revised form 29 December 2007; accepted 2 January 2008Available online 17 January 2008

Abstract

In order to perform precise evaluation of MANET applications in the real world, realistic mobility models are needed inwireless network simulation. In this paper, we propose a new method to create urban pedestrian flows (UPF) mobility sce-narios, which reproduce the walking behavior of pedestrians in urban areas. From given densities of pedestrians observedat several points, our method derives a UPF mobility scenario that reproduces the walking behavior of pedestrians con-sistent with the observed densities, using linear programming techniques. We have developed a network simulator Mobi-REAL to design and evaluate MANET protocols and applications with this realistic mobility model. MobiREAL providesvarious functions and tools including a mobility model to describe the behavior of individual nodes, a GUI to assist withautomatic generation of UPF mobility scenarios and a visualization tool. We have conducted some experiments using theMobiREAL simulator. Through the experiments, we have investigated the influence of node mobility on the performanceof MANET protocols and have shown the usefulness of our method and the MobiREAL simulator.� 2008 Elsevier B.V. All rights reserved.

Keywords: Mobile ad hoc network; Simulation; Mobility model; Performance evaluation; Design support

1. Introduction

Mobile ad hoc networks (MANETs) areexpected to be very useful and important infrastruc-ture for achieving a future ubiquitous society. How-ever, designing MANET protocols and applicationsis a very complicated task since it is hardly possibleto build large-scale and realistic testbeds in the real

world for performance evaluation. Thus there arealways demands for methodologies that allow usto design, analyze and validate given applicationsin simple and inexpensive ways. Nowadays networksimulators are mainly used for such a purpose. Sincethe constituents of MANETs are not stationary,mobility models greatly affect the performance ofMANET systems [1–5]. Therefore, in order to eval-uate the performance of MANET protocols andapplications more precisely in the real world, weneed more realistic mobility models. However, thereis a tradeoff between the reality of mobility models

1570-8705/$ - see front matter � 2008 Elsevier B.V. All rights reserved.

doi:10.1016/j.adhoc.2008.01.002

* Corresponding author. Tel.: +81 6 6879 4557; fax: +81 6 68794559.

E-mail address: [email protected] (K. Maeda).

Available online at www.sciencedirect.com

Ad Hoc Networks 7 (2009) 153–170

www.elsevier.com/locate/adhoc

and the cost for their complexity. In particular,modeling movement of real nodes (e.g., pedestrians)with high fidelity for the evaluation of town-widedeployed networks requires detailed observation orsurvey of those people moving from place to placealong streets. Obviously such observation cannotbe easily carried out due to cost, privacy and otherreasons.

In this paper, we propose a method to createurban pedestrian flow (UPF) mobility scenarios.The method targets the reproduction of the walkingbehavior of pedestrians in city sections, shoppingmalls and so on. Given the average densities ofpedestrians on certain streets, which can be easilyobtained by fixed point observations, and a set ofwalking paths pedestrians are likely to follow, themethod determines flows of pedestrians using linearprogramming (LP) techniques. Also, the maximumerror between the observed density and the deriveddensity is minimized to reproduce realistic move-ment of pedestrians. In our experiment, two personshave measured the average densities of pedestrianson 33 streets in a 500 m � 500 m area in front of alarge train station in Osaka for about a half hour.The maximum error between the observed densitiesand the derived densities was only 9.09%. We havedeveloped a network simulator called MobiREALfor simulating MANET systems with UPF mobilityscenarios.

MobiREAL has been developed by extending thenetwork simulator GTNetS [6], developed at theGeorgia Institute of Technology. The main featuresof MobiREAL are twofold. First, MobiREALintroduces an original model called the conditionprobability event (CPE) model to describe thedynamic behavior of pedestrians, such as adjustingwalking speeds and directions to avoid collisionwith neighbors and obstacles, and stopping at atraffic signal. By the CPE model, we can alsodescribe the interaction between pedestrians andnetworks, e.g., we can describe a scenario that amobile node makes a detour when it receives trafficjam information through MANETs or cellular net-works. MobiREAL is developed by integrating theframework to enable the interaction between mobilenodes and network applications. By incorporatingthis CPE model into UPF mobility scenarios, thereality of simulations is considerably improved. Sec-ondly, MobiREAL provides a suite of useful tools.With a visualization tool called an animator, theresults of simulations can be analyzed easily andintuitively. The animator can visually animate the

movement of nodes, network topology, packetpropagation and so on, and can also show statisticalinformation like node density and the packet errorrate observed in each sub-region. Also, the UPF sce-nario generation tool helps to input geographicinformation like obstacles and street structure, andautomatically generates UPF scenarios. We notethat the UPF scenario generator and the mobilitysimulator part of MobiREAL can be used indepen-dently of the network simulator part of Mobi-REAL. Therefore, it can easily be applied tocreating trace-based mobility scenarios supportedby many other network simulators. This featureallows users of the other simulators to receive thebenefit of realistic mobility scenarios generated byour toolset.

We have conducted some simulation experimentsusing the MobiREAL simulator. Through theexperiments, we have investigated the influence ofnode mobility on the performance of MANET pro-tocols and have shown the usefulness of the pro-posed method.

2. Related work

We often use simple mobility models such as therandom way point (RWP) model [7] in free space forsimulating MANET protocols. This is because suchsimple mobility models are commonly available inmany simulators and therefore can be a commonenvironment for comparative experiments. Severalanalytical results have been presented for theRWP model and its variants [8–18]. In [8], Bettstet-ter presents a variant of RWP where nodes canchange their directions smoothly, keeping the sameanalytical properties as RWP. In [9], Chu and Niko-laidis address that the node density of RWP is non-uniform and that the non-uniformity depends onthe speed of nodes. Rojas et al. [10] show that theCauchy distributions and the chi-square distribu-tions can model the deployment of locations andresidual pause times of waypoints more accuratelythan uniform distributions and exponential distri-butions, respectively. Hyytia et al. [11] derive anexpression that represents the nodes’ position distri-bution of RWP in an arbitrary convex domain andpropose the RWPB model that forms contrastivedistribution with RWP. Yoon et al. [12] focus onthe velocities of nodes in RWP and prove that theharmful effects are observed in the performanceevaluation before reaching the steady-state. Yoonet al. also present the ‘‘sound mobility model” based

154 K. Maeda et al. / Ad Hoc Networks 7 (2009) 153–170

on the RWP model in [13]. This model is designed tomaintain the average velocities of nodes to excludethe harmful effects. McGuire [17] derives the sta-tionary distribution of nodes for a general class ofmobility models. Nain et al. [18] analyze the station-ary distribution of nodes in the random directionmodel and describe its usefulness compared withRWP.

There are many methodologies that synthesizemobility models from observed data and geographyinformation to pursue reality [19–24]. In [19], Jard-osh et al. introduce obstacles (i.e., buildings) andpathways in a given simulation field. A pathwaybetween any two obstacles can automatically begenerated using a Voronoi graph computation algo-rithm. The research group also provides plug-ins forGloMoSim [25] and ns-2 [26] simulators to utilizethe proposed mobility. Hollick et al. [20] proposea macroscopic mobility model for wireless metro-politan area networks, where a simulation field isdivided into multiple zones with different attributessuch as workplace, commercial and recreationzones. Also, each mobile node has an attribute ofresident, worker, consumer or student. Given tripswith destinations for user nodes, an existing urbantransportation planning technique is used to esti-mate the user density in each zone. Hsu et al. [21]present the WWP (weighted way point) model.The WWP model defines a set of crowded regionssuch as cafeterias and buildings at a university.Given a distribution of pause times for each regionand a transition probability of nodes for each pairof regions, it uses a Markov model to model nodes’movement between these regions. For vehicular adhoc networks, a technique to reproduce realisticmobility of vehicles in ns-2 is proposed in [22].Group-based mobility models also capture someaspects of realistic movement. Musolesi et al. [23]propose a group-based mobility model based onsocial networks of individual users. Also, severalgroup-based mobility models are proposed in [24].

There is some research analyzing the relationshipbetween network traffic and the behavior of users bytracing user behaviors on wireless networks. Kotzand Essien [27] analyze user behavior patterns bycollecting traces of 2000 users for 11 weeks at 476wireless APs distributed in 161 buildings at Dart-mouth College. They found interesting characteris-tics of traffic depending on time and location. Theauthors of [28–30] have also collected similar tracesfrom wireless network users in a metropolitan areawireless network at the SIGCOMM2001 conference

location and in three large corporate buildings,respectively. Thajchayapong and Peha [31] obtainedtraces from the IEEE 802.11-based system at theCarnegie Mellon University campus. Contrary tomost researchers’ expectations, the cell residencetimes do not form an exponential distribution.McNett and Voelker [32] analyze the mobility pat-terns of users of wireless hand-held PDAs in a cam-pus wireless network and synthesize the ‘‘campuswaypoint model” that serves as a trace-based com-plement to the RWP model. Also, Kim et al. extractuser mobility characteristics from wireless networktraces and present a mobility model based on thecharacteristics in [33]. These research efforts aim atgetting knowledge on capacity planning and AParrangement for building next-generation large-scale mobile network infrastructures. However, theyrequire some wireless network infrastructures and avery large amount of information collection to pro-duce a mobility scenario.

Many realistic mobility models including ourproposed methodology have intended to model theenvironment in the real world. Our proposed meth-odology is closest to the work presented in [19–21],which tries to reproduce the real world’s geographyand movement of nodes. These methodologiesrequire some observation of user behaviors in a tar-get region, but the simplicity of observation is animportant factor because a large amount of humanand system resources are required to collect com-plete information about users’ behavior. Therefore,we propose a methodology to reproduce urbanpedestrian flows from the density of nodes. Densi-ties of pedestrians can be obtained by fixed-pointobservation using cameras and therefore it is easierthan measuring transition probabilities among hot-spots, which is assumed by the methods presented in[20,21]. The UPF model considers the node densityto reproduce the nodes’ moving flows over streets,while many existing models use the node densityat the hotspots (i.e., major points) to reproducethe nodes’ transitions among them. The UPF modelpossibly fails to accurately reproduce the transitionprobability among the major points, but can repro-duce realistic flows on streets in the city section,which are abstracted in those existing models.

2.1. Our contributions

One of the advantages of the MobiREAL is thatwe can dynamically change the behavior of nodesdepending on the information received through the

K. Maeda et al. / Ad Hoc Networks 7 (2009) 153–170 155

network. The dynamic control of pedestrians’ behav-ior can be written simply using the CPE model.Another advantage is the powerful GUI tools (Ani-mator and UPF scenario generator). From simplefixed-point observations on roads, we can automati-cally calculate the flow rate of each pedestrian flowfrom a starting point to a destination. As a total,multiple pedestrian flows reproduce rather realisticpedestrian flows. We can also use QualNet or OPNetas a network simulation part in the proposed frame-work. Details are described in Section 5.4.

Compared with our preliminary studies [5,34]that presented the basic concept of the UPF mobil-ity generation method and the basic design of theCPE model, respectively, this paper gives a moreformal and precise description of their design andimplementation. Also, we added two experimentsthat evaluated the accuracy of mobility (Section6.2) and two case studies that evaluated realisticapplications and protocols with UPF mobility sce-narios using the MobiREAL simulator (Section6.3). Through the case studies, we could confirmthat realistic (thus non-uniform) node movementand position distribution have a significant influenceon performance of mobile wireless network systems.This fact encourages us to address the effectivenessof our UPF mobility and MobiREAL toolset pre-sented in this paper. Also, we made several minorbut important modifications to the MobiREALsimulator to enhance its capability. For example,we refined and simplified the CPE model to be har-monized with the UPF mobility scenarios andenhanced the facility of the MobiREAL animatorto support statistical data representation. As a con-sequence, the contribution of this paper is that wepresent a complete toolset for the development ofprotocols and applications and prove its usefulnessthrough case studies.

3. Deriving urban pedestrian flows from simple

observations

To generate a UPF scenario, we give a street mapof the simulation field as a graph called a streetgraph, and possible paths of pedestrians on thestreet graph. We also give average densities ofpedestrians on some streets. These densities can besimply obtained by fixed-point observations andso on. Then for each path, we calculate the numberof pedestrians that come into the path per unit oftime (this number is called flow rate) using a linearprogramming (LP) technique, where the maximum

error between the density obtained from the obser-vation and that derived from the LP solution is min-imized. Using the derived flow rates, the UPFscenario generation tool generates a UPF scenariothat can be used in the MobiREAL simulator. Thiswill be described later.

3.1. User inputs

3.1.1. Simulation field

A simulation field may contain polygons thatrepresent obstacles such as buildings and an undi-rected street graph G ¼ ðV ;EÞ where V and E repre-sent geographic points and streets, respectively(Fig. 1). The geographic points are set at intersec-tions and entrances to buildings, stations, terminals,underpasses, shopping centers and so on. For eachedge eij 2 E, the width W ij of the street is also given.

3.1.2. Pedestrians’ paths

In urban areas, many types of pedestrians arewalking around for different purposes. Commutersusually get off trains and go straight to their transferpoints or offices. Shoppers visit their favorite shopsand remain for a while. We estimate the possiblewalking paths of pedestrians and represent themas a set R of acyclic paths on street graph G. Eachpath starts and ends at vertices that can be startingpoints and destinations like stations and buildings.We note that if a path contains a loop, we mayregard this loop as another path and separate itfrom the original path. Then we can avoid loopseven though the given paths contain them.

It may be difficult to enumerate all of the possiblepaths. Therefore, we may use the following simpleenumeration. In general, pedestrians are consideredto walk along the shortest paths. So, we specifysome points where many people appear or disap-pear (e.g., stations and shops) and compute theshortest path for every pair of these points. For eachpath computation, we can choose one of several

Fig. 1. Simulation field and street graph.


options case by case. For example, considering thefact that people tend to walk along major streets,we put some weights that prioritize these streets inthe shortest path construction algorithm.

3.1.3. Observed density of pedestriansNext, we have to determine a flow rate of each

path, that is, the number of pedestrians that comeinto the path per unit of time. We give the densitieson some streets measured in the real world andderive flow rates based on these values. We letEMð� EÞ denote the set of edges on which densitiesof pedestrians are observed. For each edge eij 2 EM ,we let Dij denote the observed density on the edge.

In general, for the node density d on an edge, thepedestrian rate g on the edge (i.e., the number ofpedestrians going into the edge per second), thespeed v of pedestrians on the edge and the widthw of the edge, the following equation holds:

dðperson=m2Þ ¼gðperson=sÞ

wðmÞ � vðm=sÞ: ð1Þ

It is known that the speed of a pedestrian followsthe negative increase of density. Therefore,

v ¼ �k � d þ v0; ð2Þwhere k and v0 are positive constants. Here, v0 is theaverage speed of the pedestrian in a sparse area.Also on any (very) crowded street, a particular den-sity (say Dmax) was observed when the speeds ofpedestrians are very close to 0. Therefore, from(2), we obtain

�k � Dmax þ v0 ¼ 0: ð3ÞFinally, from Eqs. (1)–(3), we obtain the followingpedestrian rate calculation function G from the den-sity d and width w of the edge:

g ¼ Gðd;wÞ ¼ v0 � w � d � 1� dDmax

� �: ð4Þ

We should select streets to observe by consideringthe characteristics of the field geography and the de-sired preciseness of the reproduction. The solutionwill become more precise if we obtain more infor-mation about densities. We evaluate the relation-ship between the number of streets and thepreciseness of the result in Section 6.2.1.

3.2. Determining flow rates

Next, we describe the algorithm to derive flowrates from the observed pedestrian density. Given

an undirected graph G, the width W ij of each edgeeij 2 E, a set R of paths, and the observed densityDij on each edge eij 2 EM , the algorithm derivesthe flow rate of each path in R, minimizing the max-imum error between the derived and observed den-sities on the edges.

To formulate this problem as an LP problem, weintroduce a variable fk representing the flow rate ofpath rk 2 R and a variable gij representing thepedestrian rate on edge eij 2 E. We also introducea variable d, which means the maximum error ofthe derived pedestrian rate from the correspondingpedestrian rate given by Eq. (4).

For each edge eij, its pedestrian rate is the sum offlow rates on eij. Therefore, the following equationholds:

8eij 2 E; gij ¼X

rk2R^eij2rkfk: ð5Þ

The following inequality limits the errors of derivedpedestrian rates by d, where function G is given byEq. (4):

8eij 2 EM ; �d 6GðDij;W ijÞ � gij

GðDij;W ijÞ 6 d: ð6Þ

Also pedestrian rate gij has an upper limit derivedfrom the nature of function G. From the definitionof G in (4),

gij ¼Gðdij;W ijÞ;

¼ v0 � W ij � dij � 1� dij

Dmax

� �;

¼� v0 � W ij

Dmax

d2ij þ v0 � W ij � dij;

¼� v0 � W ij

Dmax

dij � Dmax

2

� �2

þ v0 � W ij � Dmax

4

is obtained. Knowing that v0, W ij and Dmax are allpositive constants, the following inequality isobtained:

gij 6v0 � W ij � Dmax

4: ð7Þ

Our objective is to minimize the maximum error d.Under constraints (5)–(7), the objective function isdefined as follows:

min d: ð8ÞBy solving this LP problem, we obtain the flow ratefk for each path rk 2 R minimizing the maximum er-ror d of the derived pedestrian rates. Using the de-rived flow rates, a UPF mobility scenario that can


be used in MobiREAL is generated automaticallyby the UPF scenario generation tool. The detailsof this tool are given in Section 5.1. In the scenario,we generate node instances following the derivedflow rate fk, and the nodes walk along the path rk.A path with flow rate 0 means that there is no pedes-trian flow along the path.

4. Condition probability event model

The UPF model focuses on macroscopic move-ment of nodes, while the condition probability event(CPE) model focuses on the microscopic behaviorof the individual nodes. The CPE model allows usto dynamically and more precisely control thebehavior of nodes on pedestrian flows determinedby the UPF model. With this model, we maydescribe such behavior that a mobile node makesa detour (changes to another pre-scheduled route)when it receives traffic jam information throughMANETs or cellular networks.

We would like to note that general-purpose pro-gramming languages such as C or Perl allow a widevariety of descriptions, and therefore can be used todescribe the detailed behavior of nodes. However,this generality may confuse designers. Thus we pro-pose the CPE model for simulator users to simplydescribe the behavior of nodes (simulation scenar-ios). In the CPE model, the next action of a nodeis determined by its current status (position, direc-tion, velocity, scheduled route and so on) and envi-ronmental factors (global time, information fromnetwork systems and so on), and this decision canbe probabilistic. This model captures well humans’decision-process of actions (action is probabilisti-cally determined by current status and environ-ment), allowing reasonable abstraction ofunnecessary behavior for the fidelity of modeling.

Thus it has much better applicability for behaviormodeling than general languages.

We depict our concept of modeling pedestrianbehavior in Fig. 2. Formally, the CPE model con-sists of a list of rules where each rule is a tuple ofa condition, a probability and an action, and node

state variables, which represent the status of eachnode and are introduced later. The rules can referto and update node state variables like velocity vec-tor and position of the node. We may specify thesame set of rules to all nodes or a different set ofrules to each group of nodes. We specify a logicalformula as a condition, a value in the range of[0, 1] as a probability and a set of substitution state-ments that may update values of the node state vari-ables as an action. For each increment of thesimulation clock (referred to as T), all rules satisfy-ing the given conditions are executed one-by-onefrom the top of the list with the specifiedprobabilities.

Each node has six node state variables: currentposition P, scheduled route ROUTE (sequence ofvertices), base velocity V f , actual velocity vector ~V ,input data AI of the node to the network system(application input) and output data AO from thenetwork system to the node (application output).These variables are referred to and updated by therules. When a node is generated according to theUPF scenario derived in Section 3, an instance ofthe CPE model of the node is initialized as follows.The path rk 2 R is set to the initial value of routeROUTE, and the velocity v0 in Exp. (2) is set toV f , which represents the velocity in a sparse area.Then normal descriptions in the CPE model containsuch rules that update ROUTE and V f followed bythe rules that update current position P and actualvelocity vector ~V accordingly. These rules determinethe basic behavior of nodes that walk along the

Node

Variables Network Applicationvoid TestApplication::StartApp(){l4Proto->Send( defaultSize);…CancelTimer( pendingEvent);pendingEvent = new TimerEvent( SEND_PKT);timer.Schedule( pendingEvent, nextSendTime, this);

}

RulesCondition1, Prob.1, Action1Condition2, Prob.2, Action2Condition3, Prob.3, Action3

…ConditionN, Prob.N, ActionN

Refer to, Update

VP

Vf , ROUTEAOAI

AOAIRefer to

CPE

Other NodesV, P

Fig. 2. Interaction between CPE instance and network application.


routes. The actual velocity j~V j cannot be greaterthan the base velocity V f , but may be smaller whenthe node cannot walk smoothly in a crowd.

The CPE model assumes that an application pro-gram of the network simulator is notified of thevalue of application input AI, and updates the valueof application output AO. In the rules of the CPEmodel, we can set any value to application inputAI but can only read application output AO. Thevariables AI and AO enable simulator users todescribe interactive behavior of nodes with theapplication program. We note that in order toenable the application program and the CPEinstance to interpret AI and AO, several librariesare offered to mitigate the developers’ effort.

We offer many pre-defined functions to help thedevelopers to specify realistic behavior of nodes inthe CPE model. For example, we provide a functionthat returns the positions and actual velocity vectorsof nodes within eyesight. A part of them are pre-sented in the following example.

4.1. Example description in CPE model

Here, we consider the following MANET system.Shoppers holding information terminals with shortrange radio communication devices exchange usefulinformation (e.g., sales information) when theyencounter each other.

We present a description written in the CPEmodel for the shoppers in Table 1. Rule E1describes the behavior that changes the scheduledroute ROUTE dynamically, according to an appli-cation output AO. If the shopper receives informa-tion ‘‘SHOP_A” given by the application, he/shechanges his/her destination to the vertex‘‘SHOP_A” with probability 0.3. This is done byupdating the ROUTE variable to the shortest pathfrom current position P to the vertex specified by

Point(SHOP_A). Rule E2 describes the behaviorthat gives an input to the application dependingon the position of the given node. More concretely,the node gives an application input ‘‘SHOP_A” tovariable AI with probability 0.1 when he/shereaches the vertex specified by Point(SHOP_A).We note that the application is programmed to dis-tribute the given input to the neighbor nodes. RuleE3 implements the behavior that follows a trafficsignal at intersection INT_1. A function ‘‘stop( )”used in the action part sets 0 to the node’s basevelocity V f temporarily. The condition of E3becomes true if the simulation time T (min.) is even,which means the period of the red light.

Rules E4 and E5 define fundamental behaviorbased on the UPF mobility. Rule E4 calculatesvelocity vector ~V from current position P, routeROUTE, base velocity V f and the position of theneighbor nodes neighborsðP ; V fÞ. We note that wehave modeled evasive actions (turn or slow down)against the approaching people. The UPFvector( )in Rule E4 calculates velocity vector ~V based on thismodel to make the behavior of the node more real-istic. Rule E5 updates current position P based onvelocity vector ~V calculated by Rule E4.

5. MobiREAL

We have developed a network simulator calledMobiREAL to simulate MANET systems with therealistic behavior of nodes based on the UPF modeland the CPE model.

The architecture of the MobiREAL simulator isshown in Fig. 3. MobiREAL is composed of a net-work simulator that simulates network systems, abehavior simulator that simulates the movementof nodes, a UPF scenario generator and an anima-tor. The behavior simulator takes three inputs, asimulation field, a UPF scenario and a set of CPE

Table 1Example behavior description of shoppers in CPE model

Condition Prob. Action

E1[exp.] AO == ‘‘SHOP_A”[receives string ‘‘SHOP_A” from theapplication]

0.30 ROUTE = shortest_path(P, Point(SHOP_A)); [changedestination to vertex SHOP_A]

E2 P == Point(SHOP_A) [arrives at vertex SHOP_A] 0.10 AI = ‘‘SHOP_A”; [give an application input‘‘SHOP_A”]

E3 (P == Point(INT_1)) ^ ((T%2 min) < 1 min) [stops atvertex INT_1 with traffic light which changes every min.]

1.00 stop( ); [wait until the light changes to green]

E4 – [Execute always] 1.00 V = UPFvector(Vf, P, ROUTE, neighbors (P, Vf));[calculate velocity vector from state various variables]

E5 – [Execute always] 1.00 P = UpdateLocation(P, V); [update position]


scenarios. As a simulation field, we specify the struc-ture of streets and buildings that interfere with radiopropagation. As a UPF scenario, we specify theroutes of nodes and their node generation rates(flow rates). According to the UPF scenario, Mobi-REAL generates node instances that move based onthe CPE scenarios. With the UPF scenario genera-tor, we can generate a simulation field and the cor-responding UPF scenario graphically andintuitively. For the network simulator, we specifyapplication programs. In each application compo-nent running on a mobile node, interaction withthe corresponding CPE instance may be described.

Since the simulator part of MobiREAL is com-posed of two independent programs (i.e., behaviorsimulator and network simulator), we have imple-mented MobiREAL according to the following con-cept. First, both simulators hold the positions andspeeds of nodes independently and synchronize theprogress of simulations. The behavior simulator cal-culates the latest positions, directions and speeds ofnodes, and sends them to the network simulator(arrow (a) in Fig. 3). In response, the network sim-ulator updates its information about the nodesaccording to the received data, sends outputs ofthe application to the behavior simulator (arrow(b) in Fig. 3) and continues the simulation. Thebehavior simulator can perform dynamic behaviorchange according to this information.

5.1. UPF scenario generator

We have developed the MobiREAL UPF sce-nario generator. Showing a BMP graphic map filelike Fig. 4, simulator users can specify a street graphthrough the GUI with their mouse devices. We spec-ify two attributes (width and observed node density)for the street. This tool also assists in generatingpotential candidate routes as described in Section3.1.2. Then this tool generates a program code ofthe UPF scenario that can be run in MobiREAL

where the linear programming problem solverlp_solve [35] is used to calculate the flow rate oneach route.

5.2. Network simulator

The network simulator needs to synchronize withthe behavior simulator to exchange information,update the positions and the speeds to follow thebehavior simulator, and process application inputsand outputs. We have developed the network simu-lator by extending and modifying GTNetS [6] devel-oped at the Georgia Institute of Technology.GTNetS aims at improving the scalability of large-scale simulation, so it has a mechanism for parallelsimulation of wired networks. For simulation ofwireless networks, GTNetS also supports routingprotocols like DSR, AODV and NVR (wirelessform of Nix-Vector routing) and standard MACand physical layer protocols like IEEE 802.11.

In GTNetS and many other simulators, themovement of nodes is bounded by boundaries, ora node moving through a boundary appears fromthe other side. This means that the number of nodesis fixed throughout simulations. On the other hand,

Fig. 3. MobiREAL simulator overview.

Fig. 4. GUI support for map and UPF scenario generation.


our mobility requires dynamic generation and dele-tion of nodes. In our MobiREAL network simula-tor, we have modified the node managementfunction of GTNetS to implement the dynamic gen-eration and deletion of nodes. Also, we have modi-fied the simulation module of the physical layer toimplement radio propagation attenuation underthe existence of obstacles in order to achieve morerealistic wireless network simulation. In this radiopropagation model, attenuation when the radiopenetrates obstacles is considered. For this purpose,simulator users can specify an attenuation coeffi-cient for each obstacle.

5.3. Animator

The MobiREAL Animator visualizes the tracesof the simulation. Visualization of the simulationresults is very useful for designing, debugging andpresenting network systems. In particular, visualiz-ing mobility of nodes is mandatory to confirm theirrealistic behavior.

MobiREAL Animator runs on the MicrosoftWindows platform, and has been developed usingDirectX. Animator can animate the movement ofnodes, the topology of wireless links, packet propa-gation and so on. We can choose whether theseobjects are to be displayed or not. Each mobile nodeis represented as a small circle, and a semi-transpar-ent concentric circle represents its radio range. Eachpacket transmission is drawn by concentricallygrowing circles with colors. First, a circle with 1pixel radius is drawn when a packet is sent. Thenthe radius of the circle gradually grows until itreaches the radio range of the node sending thepacket. The color of nodes can be freely set in a net-work application program. For example, Animatorcan show the nodes that received certain packets in

red and others in blue. A snapshot from Animator isshown in Fig. 5a.

Animator can also show some measured metricslike node density and packet loss rate as shown inFig. 5b. In this function, a simulation field is sepa-rated into grid cells, and they can be color-codedaccording to the measured values. Thus users cananalyze, for example, the influence of geographyon the density of nodes, and the relationship betweenthe density of nodes and network performance.

For interested readers, several movies and snap-shots of Animator are presented on the MobiREALweb page [36].

5.4. Tool support for existing simulators

The MobiREAL behavior simulator, Mobi-REAL Animator and MobiREAL UPF scenariogenerator can be used as independent programs.Therefore, instead of GTNetS, other simulatorssuch as ns-2, Qualnet and GloMoSim may be usedas the network simulator part of MobiREAL simu-lator. If the interaction between the behavior simu-lator and the network simulator is disabled, we canuse the trace file generated by the behavior simula-tor by converting it into the corresponding inputsfor the trace mobility of those simulators. But in thiscase, we cannot use application input/output (AI/AO in Section 4 in the CPE model.

If we wish to let the behavior simulator interactwith those network simulators, we must add suchan interaction mechanism to the network simula-tors. Usually, wireless network simulators includingGTNetS have a ‘‘mobility class”, in which behaviorof mobile nodes is described. In the case of theMobiREAL simulator, we have enhanced themobility class of GTNetS so that the networksimulator can periodically replace the speeds and

Fig. 5. Snapshots from MobiREAL Animator.


directions of nodes with the latest ones receivedfrom the behavior simulator. According to this con-cept, we have accomplished cooperative executionof GTNetS and the MobiREAL behavior simula-tor. In the case of GloMoSim, MobilityTrace classcan be modified in the same way. Similarly,MobileNode class and PositionHandler class shouldbe enhanced in the case of ns-2.

6. Experiments

We have carried out several experiments. Ourobjective is threefold. First, we would like to con-firm that our UPF methodology and the relatedMobiREAL toolset can model the pedestrians’flows in the real world with high fidelity, withouta great deal of manpower. For this purpose, we con-ducted fixed-point observation in downtown Osakaand derived the pedestrians’ flows. The result of thisexperiment given in Section 6.1 has shown that ourscheme could reproduce a position distribution verysimilar to the observed ones. Second, we would liketo evaluate the trade-off between the fidelity and thecomputation cost of mobility by two experiments inSection 6.2. One evaluation is for the impact of theamount of given density information in the deriva-tion of the UPF scenario, and another one is forthe impact of the synchronization overhead betweenthe behavior simulator part and the network simula-tor part of MobiREAL on the accuracy of simula-tion. Finally, we would like to show that the non-uniform distribution of the node movement andposition affects performance of mobile wireless net-work systems. For example, in the real world, thelocation of wireless base stations considerablyaffects the quality of multi-hop wireless network ser-vices while it will have little impact with uniformmovement and position distribution of nodes. Thisfact encourages us to assert the effectiveness of ourUPF model presented in this paper. Thus, to provethe fact, we conducted performance evaluation ofthe following application and protocol: (i) real-timedata streaming from a stationary base station to amobile client via MANET; and (ii) a mobility man-agement protocol for wireless mesh networks usingthe mobility prediction as in [37,38]. The resultsare given in Section 6.3.

6.1. Fidelity of reproduced pedestrian flows

We have measured the node density on eachstreet in downtown Osaka. Using the obtained data,

we have determined pedestrian flows based on ourUPF technique described in Section 3 to see the sim-ilarity of the derived and observed density ofpedestrians.

Two students observed the node density on thestreets beginning at 14:00 on Sunday. At each obser-vation point, we just took a photo with a digitalcamera, so the observation period itself was aninstant. Next, we measured the width and lengthof the street captured in the photo. Finally, we cal-

Table 2Measured node density on streets in downtown Osaka

Edge Width Density

9–8 7 0.0338–7 8 0.0597–34 8 0.04134–5 8 0.0415–4 4 0.0254–2 10 0.01315–13 6 0.00513–10 6 0.00510–6 8 0.03014–15 12 0.00815–16 8 0.00616–32 12 0.01232–17 12 0.01217–18 12 0.01324–19 12 0.03219–20 12 0.03520–21 12 0.02321–22 12 0.0278–14 8 0.04514–19 12 0.02619–23 8 0.0583–7 14 0.0607–10 6 0.06510–31 8 0.04631–16 8 0.04516–20 12 0.00810–12 12 0.01518–22 12 0.02612–17 12 0.01217–21 12 0.01211–30 8 0.04030–12 8 0.0400–5 14 0.0345–6 7 0.0506–11 12 0.06011–18 12 0.0211–4 12 0.0024–6 5 0.03030–26 3 0.01330–27 3 0.06628–31 3 0.02532–29 3 0.01234–33 3 0.053

width: m, density: person/m2.


culated the density of pedestrians using those valuesand the number of pedestrians in the photo. Theobtained data is shown in Table 2. The map ofdowntown Osaka with the corresponding streetgraph is shown in Fig. 6. The size of the field is500 m � 500 m and the graph includes 35 verticesand 44 edges. It took two students only about a halfhour to obtain the average densities on the 43streets.

As candidates for nodes’ source or sink points(destination points), we selected 21 points locatednear shops, major intersections with subwayentrances and the border of the field. We calculatedthe shortest path for each pair of the 21 destinationpoints, and thus 210 routes were found as atotal. We assumed that v0 ¼ 1:39 m=s and Dmax ¼1:4 person=m2. Then we constructed 130 linear con-straints that included 253 parameters (variables).lp_solve [35] was used to solve the given LP problemand the solving time was less than 1 s.

We show the result in Table 3. When we obtainedthe solution of the LP problem, the maximum error,which was minimized in the LP problem was 9.09%(0.102 person/s) and the mean error was 8.74%(0.030 person/s). We would like to note that sincethere is no absolute benchmark to compare with,there is no way to thoroughly show the fidelity ofthis result. However, in the general consensus wecan say that this error is in the acceptable range.

These simulation settings and the obtained UPFmobility were also used in the experiments describedin the following subsection.

6.2. Accuracy of mobility

Modeling mobility is to pursue the balancebetween fidelity and simplicity. In this section, wewillshow two experiments that evaluate the trade-off

between accuracy and the computation cost ofmobil-ity. In Section 6.2.1, we evaluated the impact of theamount of given density information on the accuracyof UPF mobility. In Section 6.2.2, we evaluated theimpact of the synchronization interval between thenetwork simulator part and the behavior simulatorpart of MobiREAL (i.e., simulation overhead) onthe accuracy of the simulation. We hope our experi-mental results are helpful for modeling work.

6.2.1. Density specified ratio and reproduction quality

of derived flows

In this experiment, we evaluated the impact ofthe amount of given density information on the

Fig. 6. Downtown Osaka.

Table 3Derived flow rates

Routes Flow rate

g0_11 0.375g0_20 0.121g0_24 0.036g0_3 0.069g1_18 0.026g1_23 0.010g1_33 0.005g2_3 0.110g2_11 0.057g3_9 0.286g3_11 0.105g3_16 0.046g3_23 0.326g3_28 0.078g4_11 0.108g6_11 0.176g6_16 0.258g6_18 0.090g6_23 0.048g9_16 0.053g9_25 0.006g11_16 0.146g14_21 0.002g18_21 0.174g18_23 0.027g18_25 0.398g18_27 0.145g18_33 0.052g20_23 0.101g21_23 0.026g23_24 0.027g24_25 0.396g24_28 0.002g24_33 0.022g26_29 0.029g26_33 0.020g27_33 0.096g28_29 0.016

Flow rate: person/s, flow rates of all the other routes were zero.


accuracy of UPF mobility. We observed the nodedensities on the 43 streets as mentioned in Section6.1. In this section, we used the part of the observed43 density information (from 22 up to 39 chosenrandomly from the 43 density information) as aninput of LP and evaluated the error of the densityon the derived UPF flows. Table 4 shows the aver-age/maximum error between the observed pedes-trian rate and the derived one (jobserved�derivedj) and its ratio (jobserved�derivedj

observed� 100). Here,

‘‘specified” is the error only on the streets wheredensity was given, but ‘‘all” is the error on allstreets. For the comparison we also simulated amodified version of the random walk mobility calledrandom walk with obstacles (rwalk/ob) and countedthe pedestrian rate of the model. In rwalk/ob, eachnode moves along the given street graph. At eachcorner, the node selects the neighboring corner atrandom and moves toward it. We used the samestreet graph in both UPF and rwalk/ob. We alsoshow the error between the pedestrian rate of therwalk/ob and the observed rate.

The number of LP constraints decreases as theamount of given density information (the numberof specified streets) decreases, but the number ofLP variables is the same for all cases. The erroron the specified streets decreases as the number ofspecified streets decreases because the constraintsare relaxed, but the error on the unspecified streetsincreases. The error on the unspecified streets variesfrom 0 to 900. The result shows that losing littledensity information tremendously increases errorsat specific points. With more detailed analysis, wefound that the observed density that extremely dif-fers from the neighbors tends to cause large errorif such density information is not given to the LPproblem. Consequently, we should observe as manystreets as possible, being especially careful about the

streets with high or low node densities. As futurework, we will study a method to fill in the unknowndensities from the known densities by using empiri-cal knowledge.

6.2.2. Time for interaction and position errorIn this experiment, we evaluated the trade-off

between the accuracy of the node position and thesynchronization cost. In the simulation of Mobi-REAL, the behavior simulator holds accurate posi-tions of nodes. The network simulator periodicallysynchronizes with the behavior simulator, receivesthe positions and velocity vectors of nodes, andcalculates current nodes’ positions using receivedvalues until the next synchronization. If the syn-chronization interval is too long, the velocity vectorchanged by the behavior simulator may cause alarge error between the positions held by two simu-lators. However, if the synchronization interval istoo short, the computation cost for the synchroniza-tion increases.

We used the mobility scenario derived in Section6.1. The speed of the node followed uniform distri-bution between 1.0 m/s and 2.0 m/s. The simulationtime was 100 s and the simulation granularity of thebehavior simulator was set to 0.2 s. Results areshown in Table 5. Here, the error of the node posi-tion is defined as the error between the positionwhich the behavior simulator holds and that whichthe network simulator holds. The error was sampledjust before the network simulator updated its nodeposition by the synchronization.

Obviously, there is a trade-off between the accu-racy of the node position in the network simulatorand the total computation time for the synchroniza-tion (‘‘synchronization time” in Table 5). The errorreaches zero when the synchronization interval isequal to the simulation granularity of the behavior

Table 4Density specified ratio and error of pedestrian rate

Number of specified streets 43 39 34 30 26 22 rwalk/ob

Number of constraints 130 122 112 104 96 88 –

Specified(ave.)

Person/s(%)

0.0304 (8.74) 0.0273 (7.81) 0.0204 (5.91) 0.0139 (3.98) 0.0112 (3.25) 0.00688 (2.04) –

Specified(max)

Person/s(%)

0.102 (9.09) 0.102 (9.09) 0.102 (9.09) 0.101 (9.00) 0.0984 (8.82) 0.0938 (8.82) –

All (ave.) Person/s(%)

0.0304 (8.74) 0.0445 (13.7) 0.0645 (21.7) 0.0861 (31.2) 0.111 (43.7) 0.144 (59.5) 0.270 (253)

All (max) Person/s(%)

0.102 (9.09) 0.449 (135) 0.626 (265) 0.738 (408) 0.801 (622) 0.933 (901) 0.825 (2170)


simulator. For the maximum speed V of nodes andthe synchronization interval T, the error must beless than 2VT (theoretical limit of the maximalerror). So the synchronization interval is consideredshort enough if 2VT is far smaller than the commu-nication range of the node. In our simulations, weset the synchronization interval to 1 s.

6.3. Performance evaluation of mobile wireless

networks with UPF mobility

6.3.1. Application example 1: data streaming on

MANETs

In this experiment, we carried out simulation ofreal-time data streaming with the assistance ofMANETs. In the simulation scenario, real-timedata such as movies are delivered by multi-hop relayfrom a short-range base station such as an Internetgateway to mobile clients. A route is established bythe DSR protocol from the base station to each cli-ent. Through this scenario, we examined our expec-tation that different placements of base stationscause considerable performance distinction if realis-tic (thus non-uniform) position distribution isassumed, but little difference under uniform ones.If this is true, this fact emphasizes the necessity ofrealistic mobility.

Fig. 7a shows the simulation field. In the simula-tion, each client receives a 56 kbps movie for 180 s.We chose 120 clients at random at every 180-s inter-val. We compared our UPF mobility scenario mod-eled in Section 6.1 and random walk with obstacles(rwalk/ob). We used the same street graph in bothUPF and rwalk/ob. We used IEEE802.11 DCF withthe RTS/CTS mechanism as the MAC protocol.The radio range was set to 100 m. In the simulation,we used a simple radio attenuation considering line-off-sight propagation only. The obstacles wereplaced as shown by the grayed polygons in Fig. 7a.

We chose three candidate points for the locationof the base station where node densities are quitedifferent from each other in the UPF model. Tomeasure the node density in the field, we mayexploit the capability of the Animator, which canshow the node density in each square section asshown in Fig. 7b. In this figure, the depth of grayin each section represents the degree of node con-centration (i.e., deeper gray means higher density).Then we selected the three points A, B and C inFig. 7a as the candidate positions for a base stationplacement. A, B and C correspond to high-, low-and middle-density sections, respectively. Then wemeasured the packet arrival ratio and the averagepath length to see the performance difference.

Table 5Time for interaction and position error between network and behavior simulators

Synchronization interval (s) 0.2 0.4 1.0 2.0 4.0 10.0 20.0

Synchronization time (s) 100.3 58.5 26.2 13.5 7.0 3.2 1.9Average error (m) 0 0.004 0.028 0.090 0.250 1.005 3.114Maximum error (m) 0 0.789 2.816 5.722 13.497 35.765 66.372Theoretical maximum (m) 0.8 1.6 4.0 8.0 16.0 40.0 80.0

Fig. 7. (a) Simulation field and candidate points for location of base station. (b) Graphical presentation of node density (snapshot fromthe MobiREAL animator).


Fig. 8 shows packet arrival ratio at the destina-tion, and Fig. 9 shows the average hop length tothe destination. As expected, the packet arrival ratioof each case with rwalk/ob is almost the same. Onthe other hand, we can see the difference in UPFmobility. The difference becomes small as the num-ber of nodes increases, but still remains in the caseof 80–90 nodes. The average of the hop length inFig. 9 is small when the number of nodes is alsosmall because it is hard to establish a route withmany hops. When the number of nodes is largerthan 50, base station C placed at the center of thefield provides the shortest length of all the place-ments in rwalk/ob. Although, in UPF, there is a lit-tle difference between the hop length in the case ofthe base station C and that of the base station Aplaced at the upper right. It is known that as thedensity becomes higher, a route is shorter becauseone hop length can be longer in high node densitysituation. In addition, there are many nodes nearthe base station A so that the randomly chosen cli-ent walks near the base station A with a higherprobability in UPF than in rwalk/ob. From theseresults, we found that base station C in the mid-

dle-density section could achieve almost the samepacket arrival ratio as that of base station A inthe high-density section, and that base station Acould provide similar hop length with base stationC in the center of the field.

Consequently, distribution of the density mayinfluence the performance of the location-dependentapplications such as location planning of base sta-tions. It is necessary to use a mobility model thatcan reproduce realistic position distribution likethe UPF model to evaluate the performance consid-ering the characteristics of the target field.

6.3.2. Application example 2: prediction-based

mobility management in wireless mesh networkAs another example, we deal with mobility man-

agement for wireless mesh networks composed of aset of base stations. Each client connects to one ofthe base stations (mesh routers) to utilize the net-work. The radio ranges of a client and a router were100 and 200 m, respectively. The placement of meshrouters is shown in Fig. 10 where polygons representobstacles and others are walkways. The mesh rou-

0

0.2

0.4

0.6

0.8

1

20 40 60 80 100

rece

ived

pac

ket r

atio

# of nodes

ABC

Urban Pedestrian Flows (UPF)

0

0.2

0.4

0.6

0.8

1

20 40 60 80 100

rece

ived

pac

ket r

atio

# of nodes

ABC

Random Walk with Obstacles

Fig. 8. Packet arrival ratio.

1 1.5

2 2.5

3 3.5

4 4.5

5 5.5

6

20 40 60 80 100

aver

age

hop

leng

th

# of nodes

ABC

Urban Pedestrian Flows (UPF)

1.5

2

2.5

3

3.5

4

4.5

5

20 40 60 80 100

aver

age

hop

leng

th

# of nodes

ABC

Random Walk with Obstacles

Fig. 9. Average hop length of DSR route.


ters were placed to cover almost all the pedestrians’walkways.

The basic concept of mobility managementschemes developed for cellular or mobile IP net-works can be applied to wireless mesh networks[39]. In this experiment, we evaluate the efficiencyof the prediction-based mobility management pro-tocol that reduces the location updates by predictingthe current location of mobile nodes from past loca-tions. There are many protocols that utilize mobilityprediction for mobility management [37,40,41]. Theprotocol of this experiment predicts mobility by theapproach based on data mining like [38].

Each client holds its movement log as a sequenceof neighbor routers like ‘‘r1; r2; . . . ; rn”. We assumethat the client can recognize the neighbor routerby a hello message or traffic for other clients. Wealso assume that each mesh router has enough

movement logs of clients collected beforehand inits database. In this protocol, the client sends itsmovement log to the router in the location updateprocess. The router predicts the movement of theclient using its database. The client and the routerutilize the prediction result for the next locationupdate and paging, respectively.

The location update initiated by the client con-sists of the following steps:

1. First, the client that executes the location updatesends a location update packet including itsmovement log to the neighbor router.

2. The router that receives the location updatepacket holds the client ID (e.g., IP address) andthe current time. Then, the router searches forits holding movement logs that contain the samesequence as that in the received packet. Next, therouter selects up to K routers to which the clientlikely moves (called candidate routers and K isgiven in advance) and sends the IDs of the rou-ters to the client. The details of the router selec-tion algorithm are shown in Fig. 11.

3. The client holds the received candidate routerIDs.

If the client recognizes that he is entering an areacovered by a non-candidate router, then the client ini-tiates a location update. The router also holds thecandidate routers of the client. If a request for a callwas received, then all the areas of the candidate rou-ters of the callee are paged at almost the same time.

In the experiment, we simulated the performanceof this protocol in the UPF mobility and in the

Fig. 10. Placement of mesh routers.

Fig. 11. Algorithm to select candidate routers.


random walk with obstacles (rwalk/ob). Weadjusted the node lifetime of rwalk/ob so that thedistribution of the lifetime can almost be the sameas that of UPF mobility. Other simulation settingsare the same as in Section 6.3.1.

We calculated the total cost per call arrival. Here,U is the cost for paging the area of a router and V isthe cost for a location update. Call-to-mobility ratio(CMR) represents the relative frequency of call rateand mobility and is defined as ‘‘an average stay timein the area that a router covers” divided by ‘‘anaverage interval of the call arrival”. Fig. 12a showsthe cost per call for U = 10, V = 1 and CMR = 0.1as applied in [37,42]. The cost is smaller in UPFthan in rwalk/ob for K 6 4. The possible flow ofthe node is limited in the UPF so we can limit can-didate routers. On the other hand, rwalk/ob hassmaller cost for K P 5. Nodes in UPF go far awayin a short time because they walk along the shortestpath, but nodes in rwalk/ob decide movement ran-domly so some of them may return to the samepoint. This difference seems to affect the total num-ber of the location update. Fig. 12b shows the costper call for U = 5, V = 1, CMR = 0.5. K (the max-

imum number of the candidate routers) that has thesmallest cost differs in the two mobility models.

Consequently, from these results, the movementroutes of the nodes have an influence that may notbe negligible in the performance evaluation, espe-cially of the mobility-aware protocols. We shouldcarefully choose the mobility model considering thisinfluence.

7. Conclusion

In this paper, we have proposed a realistic mobil-ity model called the UPF model and a behaviordescription model called the CPE model, and pre-sented the facility of the MANET simulator Mobi-REAL, which uses our models for realisticsimulation of MANET applications and protocols.By the UPF model, we can reproduce realisticpedestrian flows from data obtained by simpleobservation such as fixed-point observation usingdigital cameras or web cameras. To our best knowl-edge, no existing method considers this researchdirection. Also, through some experiments, weshowed that we could design several MANET pro-tocols and applications and verify their performanceon the network topology, which was created by theUPF model and had never appeared in the existingrandom-based mobility models. In addition, usingthe CPE model, we can enhance the reality of themicroscopic mobility such as stopping at traffic sig-nals and slowing down due to congestion. The dis-cussion about the reality and influence of thismobility still remains for future research, thoughwe believe that our UPF/CPE framework providesreasonable fidelity of mobility modeling to conductthe performance evaluation of MANET systems inrealistic environments.

Our future work includes more detailed valida-tion of the influence and fidelity of our mobilitymodels, comparison of our mobility models and realtraces, and the evaluation of several mobile ad hoccommunication protocols using the UPF/CPEmobility.

References

[1] T. Camp, J. Boleng, V. Davies, A survey of mobility modelsfor ad hoc network research, Wireless Communications andMobile Computing (WCMC) 2 (5) (2002) 483–502 (Specialissue on Mobile Ad Hoc Networking: Research, Trends andApplications).

[2] F. Bai, N. Sadagopan, A. Helmy, The IMPORTANTframework for analyzing the impact of mobility on perfor-

40

50

60

70

80

90

100

110

1 2 3 4 5 6 7 8 9

cost

per

cal

l

K

upfrwalk/ob

(a) U = 10, V = 1, CMR = 0.1

9

9.5

10

10. 5

11

11. 5

12

1 2 3 4 5 6 7 8 9

cost

per

cal

l

K

upfrwalk/ob

(b) U=5,V=1,CMR=0.5

Fig. 12. Cost per call arrival.


mance of routing for ad hoc networks, Ad Hoc NetworksJournal 1 (4) (2003) 383–403.

[3] N. Sadagopan, F. Bai, B. Krishnamachari, A. Helmy,PATHS: analysis of PATH duration statistics and theirimpact on reactive MANET routing protocols, in: Proc.ACM MobiHoc, 2003, pp. 245–256.

[4] B. Ishibashi, R. Boutaba, Topology and mobility consider-ations in mobile ad hoc networks, Ad Hoc Networks 3 (6)(2005) 762–776.

[5] K. Maeda, K. Sato, K. Konishi, A. Yamasaki, A. Uchiyama,H. Yamaguchi, K. Yasumoto, T. Higashino, Getting urbanpedestrian flow from simple observation: realistic mobilitygeneration in wireless network simulation, in: Proc. 8thACM/IEEE Int. Symp. on Modeling, Analysis and Simula-tion of Wireless and Mobile Systems (MSWiM), 2005, pp.151–158.

[6] G.F. Riley, The Georgia tech network simulator, in: Proc.ACM SIGCOMM Workshop on Models, Methods andTools for Reproducible Network Research, 2003, pp. 5–12.

[7] J. Broch, D.A. Maltz, D.B. Johnson, Y.-C. Hu, J. Jetcheva,A performance comparison of multi-hop wireless ad hocnetwork routing protocols, in: Proc. ACM/IEEE MobiCom,1998, pp. 85–97.

[8] C. Bettstetter, Mobility modeling in wireless networks:categorization, smooth movement, and border effects,ACM SIGMOBILE Mobile Computing and Communica-tions Review 5 (3) (2001) 55–67.

[9] T. Chu, I. Nikolaidis, Node density and connectivityproperties of the random waypoint model, Computer Com-munications 27 (10) (2004) 914–922.

[10] A. Rojas, P. Branch, G. Armitage, Experimental validationof the random waypoint mobility model through a real worldmobility trace for large geographical areas, in: Proc. 8thACM/IEEE Int. Symp. on Modeling, Analysis and Simula-tion of Wireless and Mobile Systems (MSWiM), 2005, pp.174–177.

[11] E. Hyytia, P. Lassila, J. Virtamo, Spatial node distribution ofthe randomwaypointmobilitymodel with applications, IEEETransactions on Mobile Computing 5 (6) (2006) 680–694.

[12] J. Yoon, M. Liu, B. Noble, Random waypoint consideredharmful, in: Proc. IEEE Infocom, vol. 2, 2003, pp. 1312–1321.

[13] J. Yoon, M. Liu, B. Noble, Sound mobility models, in: Proc.ACM MobiCom, 2003, pp. 205–216.

[14] J. Yoon, M. Liu, B. Noble, A general framework toconstruct stationary mobility models for the simulation ofmobile networks, IEEE Transactions on Mobile Computing5 (7) (2006) 860–871.

[15] G. Lin, G. Noubir, R. Rajaraman, Mobility models for adhoc network simulation, in: Proc. IEEE Infocom, vol. 1,2004, pp. 454–463.

[16] W. Navidi, T. Camp, Stationary distributions for therandom waypoint mobility model, IEEE Transactions onMobile Computing 3 (1) (2004) 99–108.

[17] M. McGuire, Stationary distributions of random walkmobility models for wireless ad hoc networks, in: Mobi-Hoc’05: Proceedings of the sixth ACM International Sym-posium on Mobile Ad Hoc Networking and Computing,2005, pp. 90–98.

[18] P. Nain, D. Towsley, B. Liu, Z. Liu, Properties of randomdirection models, in: Proc. IEEE Infocom, vol. 3, 2005, pp.1897–1907.

[19] A. Jardosh, E.M. BeldingRoyer, K.C. Almeroth, S. Suri,Towards realistic mobility models for mobile ad hocnetworks, in: Proc. ACM MobiCom, 2003, pp. 217–229.

[20] M. Hollick, T. Krop, J. Schmitt, H.-P. Huth, R. Steinmetz,Modeling mobility and workload for wireless metropolitanarea networks, Computer Communications 27 (8) (2004)751–761.

[21] W.-J. Hsu, K. Merchant, H.-W. Shu, C.-H. Hsu, A. Helmy,Weighted waypoint mobility model and its impact on ad hocnetworks, ACM SIGMOBILE Mobile Computing andCommunications Review 9 (1) (2005) 59–63.

[22] A.K. Saha, D.B. Johnson, Modeling mobility for vehicularad-hoc networks, in: Proc. 1st ACMWorkshop on VehicularAd Hoc Networks (VANET 2004), 2004, pp. 91–92 (posterpaper).

[23] M. Musolesi, S. Hailes, C. Mascolo, An ad hoc mobilitymodel founded on social network theory, in: Proc. ACM/IEEE Int. Symp. on Modeling, Analysis and Simulation ofWireless and Mobile Systems (MSWiM), 2004, pp. 20–24.

[24] X. Hong, M. Gerla, G. Pei, C.-C. Chiang, A group mobilitymodel for ad hoc wireless networks, in: Proc. ACM/IEEEInt. Symp. on Modeling, Analysis and Simulation ofWireless and Mobile Systems (MSWiM), 1999, pp. 53–60.

[25] X. Zeng, R. Bagrodia, M. Gerla, GloMoSim: a library forthe parallel simulation of large-scale wireless networks, in:Proc. ACM Parallel and Distributeal Simulation (PADS’98),1998, pp. 154–161.

[26] ns-2, http://www.isi.edu/nsnam/.[27] D. Kotz, K. Essien, Analysis of a campus-wide wireless

network, in: Proc. ACM MobiCom, 2002, pp. 107–118.[28] D. Tang, M. Baker, Analysis of a metropolitan-area wireless

network, Wireless Networks 8 (2/3) (2002) 107–120.[29] A. Balachandran, G.M. Voelker, P. Bahl, P.V. Rangan,

Characterizing user behavior and network performance in apublic wireless LAN, ACM SIGMETRICS PerformanceEvaluation Review 30 (1) (2002) 195–205.

[30] M. Balazinska, P. Castro, Characterizing mobility andnetwork usage in a corporate wireless local-area network,in: Proc. ACM MobiSys, 2003, pp. 303–316.

[31] S. Thajchayapong, J.M. Peha, Mobility patterns in micro-cellular wireless networks, IEEE Transactions on MobileComputing 5 (1) (2006) 52–63.

[32] M. McNett, G.M. Voelker, Access and mobility of wirelessPDA users, ACM SIGMOBILE Mobile Computing andCommunications Review 9 (2) (2005) 40–55.

[33] M. Kim, D. Kotz, S. Kim, Extracting a mobility model fromreal user traces, in: Proc. IEEE Infocom, 2006. URL http://www.cs.dartmouth.edu/dfk/papers/kim:mobility.pd%f.

[34] K. Konishi, K. Maeda, K. Sato, A. Yamasaki, H. Yamag-uchi, K. Yasumoto, T. Higashino, MobiREAL simulator –evaluating MANET applications in real environments, in:Proc. 13th IEEE Int. Symp. on Modeling, Analysis andSimulation of Computer and Telecommunication Systems(MASCOTS), 2005, pp. 499–502.

[35] lp_solve, http://groups.yahoo.com/group/lp_solve/.[36] MobiREAL Simulator Web Page, http://www.mobireal.net/.[37] I.F. Akyildiz, J.S.M. Ho, Y.-B. Lin, Movement-based

location update and selective paging for pcs networks,IEEE/ACM Transactions on Networking 4 (4) (1996) 629–638.

[38] G. Yavas, D. Katsaros, Ozgur Ulusoy, Y. Manolopoulos, Adata mining approach for location prediction in mobile


environments, Data Knowledge Engineering 54 (2) (2005)121–146.

[39] I.F. Akyildiz, X. Wang, W. Wang, Wireless mesh networks:a survey, Computer Networks 47 (4) (2005) 445–487.

[40] T. Liu, P. Bahl, I. Chlamtac, Mobility modeling locationtracking and trajectory prediction in wireless networks,IEEE Journal on Selected Areas in Communications 16 (6)(1998) 922–936.

[41] W.-S. Soh, H.S. Kim, Qos provisioning in cellular networksbased on mobility prediction techniques, IEEE Communi-cations Magazine 41 (1) (2003) 86–92.

[42] Y.W. Chung, D.K. Sung, A.H. Aghvami, Effect of uncer-tainty of the position of mobile terminals on the paging costof an improved movement-based registration scheme, IEICETransactions on Communications E86-B (2) (2003) 859–861.

Kumiko Maeda received her ME degreein Information and Computer Sciencesfrom Osaka University, Japan in 2006.She is currently a Ph.D. student in theDepartment of Information Networking,Graduate School of Information Scienceand Technology, Osaka University. Hercurrent research interests include mobilead hoc networks, network simulationand mobility models. She is a studentmember of Information ProcessingSociety of Japan (IPSJ).

Akira Uchiyama received his ME degreein Information and Computer Sciencesfrom Osaka University, Japan in 2005.He is currently a Ph.D. student in theDepartment of Information Networking,Graduate School of Information Scienceand Technology, Osaka University. Hiscurrent research interests include net-work security and localization in mobilead hoc networks. He is a student memberof IEEE and Information ProcessingSociety of Japan (IPSJ).

Takaaki Umedu received his ME degreein Informatics and Mathematical Sci-ences and the Ph.D. degree in Informa-tion Science from Osaka University,Japan in 2001 and 2004, respectively. Heis currently an Assistant Professor atOsaka University. His current researchinterests include design and implemen-tation of distributed systems and mobilesystems. He is a member of InformationProcessing Society of Japan (IPSJ).

Hirozumi Yamaguchi received his BE,ME and Ph.D. degrees in Informationand Computer Sciences from OsakaUniversity, Japan. He is currently anAssociate Professor at Osaka University.His current research interests includedesign and implementation of distrib-uted systems and communication proto-cols, especially protocols and services onmobile ad hoc networks and applicationlayer multicast. Also, he is working on

some issues of wireless network simulations. He is a member ofIEEE.

Keiichi Yasumoto received the BE, MEand Ph.D. degrees in Information andComputer Sciences from Osaka Univer-sity, Osaka, Japan, in 1991, 1993 and1996, respectively. He joined the facultyof Shiga University in 1995. Since 2002he has been an Associate Professor ofGraduate School of Information Scienceat Nara Institute of Science and Tech-nology. His current research interestsinclude mobile computing, ubiquitous

computing, and distributed systems. He is a member of IPSJ,ACM and IEEE/CS.

Teruo Higashino received his BS, MS andPh.D. degree in Information and Com-puter Sciences from Osaka University,Japan in 1979, 1981 and 1984, respec-tively. He joined the faculty of OsakaUniversity in 1984. Since 2002, he hasbeen a Professor in Graduate School ofInformation Science and Technology atOsaka University. His current researchinterests include design and analysis ofdistributed systems, communication

protocol and mobile computing. He is a senior member of IEEE,a fellow of Information Processing Society of Japan (IPSJ), and amember of ACM and IEICE of Japan.


Documents

Urban pedestrian mobility for mobile wireless …h-yamagu/papers/AHN2009...Urban pedestrian mobility for mobile wireless network simulation Kumiko Maedaa,*, Akira Uchiyamaa, Takaaki