MoBAN: A Configurable Mobility Model for Wireless Body Area Networks

Transcription

1 MoBAN: A Configurable Mobiliy Model for Wireless Body Area Neworks Majid Nabi 1, Marc Geilen 1, Twan Basen 1,2 1 Deparmen of Elecrical Engineering, Eindhoven Universiy of Technology, he Neherlands 2 Embedded Sysems Insiue, Eindhoven, he Neherlands {m.nabi,m.c.w.geilen,a.a.basen}@ue.nl ABSTRACT A good mobiliy model is an essenial prerequisie for performance evaluaion of proocols for wireless neworks wih node mobiliy. Sensor nodes in a Wireless Body Area Nework (WBAN) exhibi high mobiliy. The WBAN opology may compleely change because of posure changes and movemen even wihin a cerain ype of posure. The WBAN also moves as a whole in an ambien nework. Therefore, an appropriae mobiliy model is of grea imporance for performance evaluaion. This paper presens a comprehensive configurable mobiliy model MoBAN for evaluaing inraand exra-wban communicaion. I implemens differen posures as well as individual node mobiliy wihin a paricular posure. The model can be adaped o a broad range of applicaions for WBANs. The model is made available hrough hp:// as an add-on o he mobiliy framework of he OMNeT++ simulaor. Two case sudies illusrae he use of he mobiliy model for performance evaluaion of nework proocols. Caegories and Subjec Descripors C.4 [Performance of Sysems]: Modeling Techniques; Performance Aribues; I.6 [Simulaion and Modeling]: Model Developmen General Terms Algorihms, Performance, Theory. Keywords Mobiliy model, Wireless body area nework, Performance evaluaion, Nework simulaor, OMNeT++, MiXiM framework. This work was suppored by he Duch innovaion program Poin-One, hrough projec ALwEN, gran PNE Permission o make digial or hard copies of all or par of his work for personal or classroom use is graned wihou fee provided ha copies are no made or disribued for profi or commercial advanage and ha copies bear his noice and he full ciaion on he firs page. To copy oherwise, o republish, o pos on servers or o redisribue o liss, requires prior specific permission and/or a fee. SIMUTools 2011 March 21 25, Barcelona, Spain. Copyrigh 2011 ICST, ISBN INTRODUCTION In he las decade, an increasing ineres has been observed for wireless sensor nework (WSN) echnology in human body relaed applicaions. Several sensor devices may be deployed on or in a human body o sense he vial biological signals of he body. These sensors hen communicae ogeher forming a Body Area Nework (BAN). Wireless echnology makes he communicaion much easier and more comforable for humans, hus enabling more applicaions for such neworks (WBAN). There are several ineresing applicaion areas of WBANs such as healh care and spors. Coninuous monioring of elderly people or paiens, for example, is really in demand as hese groups are increasing in number. WBANs ogeher wih an ambien nework form a secure environmen for elderly people and paiens o suppor hem in heir daily life, while being moniored by care workers. Body area neworks differ from ypical large-scale wireless sensor neworks in many aspecs. Biosensor devices have o mee special consrains, such as a very small size, ligh weigh, ulra-low power consumpion, and igh performance requiremens of he running applicaion. Moreover, he characerisics of he wireless channel are differen and links are in general low qualiy and ime-varian. Thus, he proper archiecures and proocols for wireless communicaion in hese neworks are sill under acive research. The research is being conduced on various aspecs of differen nework layers (e.g. physical layer, medium access conrol, and link layer). Performance evaluaion of he proposed proocols is an imporan phase of such research. Performance evaluaion is ypically done using simulaions or real experimens. Alhough real experimens provide more reliable and precise resuls, simulaion is sill of grea imporance because i is less expensive, exensible o larger scale neworks, and can be done in a shorer ime. Simulaion is also imporan because we can beer observe he behavior of he proocol and deec poenial bugs in he design and implemenaion process. Mobiliy models have a big impac on he accuracy of simulaions for wireless ad hoc and sensor neworks wih mobiliy in he nework. Mobiliy models ry o mimic he behavior of mobile nodes in realiy by characerizing sochasic paerns of node movemen. The righ mobiliy model srongly depends on he applicaion scenario. Since he sensors are deployed on he body in a WBAN, here is high node mobiliy and he nework opology varies frequenly.

2 The channel qualiy and connecion beween nodes srongly depends on he relaive posiion of sensor nodes. I makes he accuracy of he mobiliy model for WBANs more criical. In fac, simulaing a proocol for WBANs wihou uilizing an appropriae mobiliy model is no reliable a all. There is individual mobiliy of sensor nodes deployed on differen posiions of he body as well as global movemen of he whole body in he surrounding environmen. A mobiliy model should capure all hese aspecs. Furhermore, i should provide sufficien flexibiliy for researchers o adap i o heir specific applicaion scenarios wihin he WBAN domain. This paper presens MoBAN (Mobiliy Model for BANs), a comprehensive and configurable mobiliy model for simulaing wireless body area neworks. The model is useful for simulaing boh inra- and exra-wban proocols. The former considers he communicaion beween he sensor nodes wihin a WBAN on a body. Exra-WBAN proocols ake care of communicaions beween a WBAN and is environmen, wih poenially several body area neworks as well as an ambien nework. The model is configurable, which makes i usable for a large variey of applicaions. There are several open source frameworks well srucured for simulaing proocols for WSNs and WBANs. For insance, he MiXiM framework [13] on op of he OMNeT++ [2] discree even simulaor has a powerful library for simulaing such kinds of wireless neworks. The mobiliy framework of OMNeT++ makes i furher suiable for mobile nework simulaion. Casalia [1],[19] is anoher framework on op of OMNeT++ which is more specific for simulaing WBAN proocols. I has an advanced channel model based on empirically measured daa for he human body as he propagaion medium as well as a radio model based on real radios for low-power communicaion. There are also several frameworks on op of oher nework simulaors like NS-2/3 [4]. However, here is no freely available WBAN mobiliy model for simulaing WBANs in any of hese simulaion frameworks. We have implemened our mobiliy model as an add-on o he mobiliy framework of he OMNeT++ nework simulaor so ha i can be used for research on WBAN proocols. The res of he paper is organized as follows. The nex secion presens a survey on mobiliy models for wireless ad hoc and sensor neworks exising in he lieraure. Our mobiliy model MoBAN is presened in Secion 3. Secion 4 describes implemenaion issues of MoBAN. Two case sudies are discussed in Secion 5, showing he need for a mobiliy model and he usabiliy of MoBAN for performance evaluaion of differen communicaion proocols. Secion 6 concludes. 2. MOBILITY MODELS REVIEW Several mobiliy models have been presened in he lieraure of wireless ad hoc and sensor neworks. A deailed survey of early proposed models can be found in [6]. This secion firsly classifies mobiliy models and hen briefly reviews some exising models of each class. Since here are many applicaion-specific mobiliy models in he lieraure, we jus review general and commonly used models. Finally, he only exising mobiliy model for wireless body sensor nodes is reviewed. In general, we can classify mobiliy models ino wo major classes, namely singular node and group mobiliy models. In he former class, here is no correlaion beween he movemen of differen nodes and i models individual node mobiliy paerns regardless of he mobiliy of he oher nodes in he nework. The laer one akes a group of nodes ino consideraion which poenially have a paricular relaionship, which inroduces correlaion beween heir posiions. Social aciviies of human beings are an example of group mobiliy for ad-hoc wireless mobile devices. 2.1 Singular Node Mobiliy Models The Random Walk Mobiliy Model (RWMM) [27] is a commonly used singular node mobiliy model in which a node randomly selecs a direcion and a velociy value from a given range. The node hen moves eiher wih a consan ime inerval or unil a consan disance is raveled. The movemen akes place wihin a given recangular space which is he simulaion area. The node hen repeas he random selecion and movemen process. The Random Waypoin Mobiliy Model (RWPM) [10] is an adaped version of RWMM in which a pause ime is insered beween he changes in direcion and speed. The pause ime is seleced randomly from a given range. In RWPM, a desinaion is seleced randomly from he simulaion area and a node moves oward ha posiion wih a randomly chosen speed. A specific relaion beween he pause ime and he speed can be applied based on a specific applicaion, o fi he model owards eiher a more sable nework or a nework wih frequen opology changes. Boh he RWMM and he RWPM suffer from he node concenraion problem. The probabiliy of a node being in he cener of he simulaion area is higher and node clusers form around he cener. The Random Direcion Mobiliy Model (RDMM) [22] ries o alleviae his problem by forcing he nodes o mee a border in each movemen sep. Moreover, a probabilisic version of a random walk is proposed in [7] ha uses a probabiliy marix for deermining he arge posiion of he nex sep. The marix can be se based on a specific applicaion. All models menioned so far are memory-less. This means ha he compleed movemen sep does no have any impac on he decision abou he movemen parameers of he nex sep. In [15], he Random Gauss-Markov Mobiliy (RGMM) model is proposed in which he value of speed and direcion are calculaed using a combined Markovian and Gaussian disribuion. By he Markovian propery, he values of speed and direcion a he n h sep are calculaed according o heir value a he (n 1) h sep. On he oher hand, using a Gaussian disribuion wih he given mean values for he direcion and speed, randomness is insered ino he selecion process. The impac of hese wo pars is conrollable by seing jus one uning facor. 2.2 Group Mobiliy Models In he wireless neworks domain, here are many siuaions in which here are dependencies beween he movemen paerns of differen nodes. Human mobiliy paerns mosly show such group behaviors (for insance, because of social aciviies). Accordingly, several mobiliy models have been proposed in he lieraure ha each ry o model he

3 Cluser i RM ij Node j GM i RMij RP(-1) GMi RP() Figure 1: Node movemen in he RPGM model [18] movemen paern of humans in specific scenarios. The column mobiliy model, he pursue model, and he nomadic communiy model, all presened in [23], are some early approaches o model such correlaed mobiliy paerns. Recen aciviies are mosly based on he concep of higher node densiy in more popular locaions such as he home locaion. For insance, he Small World In Moion (SWIM) model [16] presens a mobiliy model based on he fac ha humans go more ofen o locaions near heir home and o locaions in which hey can mee many oher people. The N-Body model presened in [26] uses some real human movemen races and ries o capure social informaion merics from hem. The model hen synhesizes ha informaion o make oupu races reproducing he heerogeneiy of he inpu races. Among all group mobiliy models, he Reference Poin Group Mobiliy model (RPGM) [9] is a general model ha can be uned o model many scenarios. In fac, many recen proposed models are in some way special cases of he RPGM model. As we are using his model for our mobiliy model, i is explained in deail. In he RPGM mobiliy model, a Logical Cener (LC) is defined for he group of nodes, he moion of which defines he enire group movemen. Every group i has a group moion vecor GM i ha deermines he moion of he group s logical cener (LC i). A Reference Poin (RP) is assigned o each node in he group and deermines he iniial posiion of he node on he body. The reference poin of a node is a fixed poin relaive o he logical cener of he group. Therefore, he reference poin of every node wihin he group i moves wih he group moion vecor GM i. Fig. 1 illusraes he siuaion for a group. Moreover, every node j moves wihin a predefined area (a circle or sphere wih radius r ij) around is reference poin wih a random moion vecor RM ij. So here is independen moion of individual nodes in he group while he logical cener provides he whole group movemen. The locaion of every node afer each ime sep is defined by he sum of he group moion and he random moion vecors. The definiion of RPGM is quie general and defines he correlaion beween nodes in he group. Noice ha he RPGM does no prescribe a mobiliy paern for moving he LC or for individual movemen wihin he group. The appropriae paerns should be designed according o he requiremens of he exac scenario. By selecing a proper group moion behavior, we can model human movemen, while seing he random moion vecors ( RM ij) o define he moion of individual sensors insalled on various posiions on he body. r ij The RPGM model has been uilized in [21] o make a model of he mobiliy of body sensor nodes. In his model, he global movemen of he LC (human movemen) is done using he Random Gauss-Markov Mobiliy model. A swarm behavior inspired model presened in [11] and [12] is uilized for he individual movemen of sensor nodes on he body. In his mehod, if he node is close o is reference poin, he model forces i away from ha posiion. On he oher hand, if he node is far from is reference poin, he swarm inspired mehod aracs he node owards is reference poin. These movemens ake place wihin he given range around he reference poin of each node. To he bes of our knowledge, he model of [21] is he only mobiliy model for wireless body sensor neworks described in deail in he lieraure. However, here are several limiaions in his model. Firs, he model uses cerain mobiliy models for moving he logical cener of he group and individual node mobiliy. I is no clear why hese models should be proper choices for a WBAN. Furher, i makes he model unadapable for differen applicaions and someimes he model is far from real world mobiliy paerns. Second, he model does no specifically implemen differen posures of a human body. Posures are of grea imporance in WBANs as he nework opology may enirely change due he posure changes. Furher, in a real WBAN, movemen parameers of he model such as he speed and arge posiions of he human srongly depends on he posure. Third, he model as i has been presened only considers posiion and moion in wo dimensions which is unrealisic for WBANs. In his paper, we presen he MoBAN mobiliy model for evaluaing inra- and/or exra-wban communicaion wih a focus on compleeness, configurabiliy, and availabiliy of he model. I can be configured o model he mobiliy paerns in various applicaion scenarios of wireless body area neworks. We have used earlier versions of he model in wo earlier publicaions, [17] and [18], o evaluae he proocols proposed in hose papers. The curren paper describes he model in deail, and makes i available o he scienific communiy. 3. MOBILITY MODEL FOR WBANS A mobiliy model for mobile neworks deermines he node posiions a any insance of simulaion ime and so influences he nework opology and link properies. Consequenly, he precision of he model has a major impac on he precision of he evaluaed nework performance. A proper mobiliy model for WBANs should be able o saisically model he righ movemen paerns of he individual nodes insalled on he body as well as he whole body movemen. A he same ime, i should be adapable for various applicaion scenarios in which he movemen paerns, he human aciviies, and he surrounding environmen may differ. We firs explain he general srucure of our mobiliy model (MoBAN) and hen presen is consiuen blocks in more deail. 3.1 Model Srucure The RPGM [9] model consrucs he basic plaform for modeling a WBAN in our model. In fac, he RPGM model deermines he grouping sraegy of MoBAN. We exend he RPGM model by inroducing posures which have individ-

4 Algorihm 1: The MoBAN srucural process. Daa: π : WBAN posure a sep A : Simulaion area ype a sep 1 while True do 2 π = SelecPosure(π 1,A 1 ); 3 if IsSable(π ) hen 4 T s=selecduraion() ; 5 wai for T s seconds ; 6 else 7 Des = SelecDesinaion(); 8 V = SelecSpeed(π ); 9 Move he LC oward Des wih velociy V ; 10 Wai unil Des is reached ; 11 end 12 end ual mobiliy parameers. The MoBAN iself is consruced by wo basic conrol unis which are he posure selecor and he global movemen module. On he one hand, he posure selecor process deermines he curren posure a any insance of ime. The individual movemen (he random moion vecor) of every sensor node is subsequenly deermined according o he seleced posure. On he oher hand, he global movemen process is responsible for conrolling he mobiliy of sensor nodes on he body as a whole (i.e. moving he logical cener of he group). The parameers of his movemen are of course no independen from he seleced posure and he exac applicaion scenario. A mobiliy paern sars wih he posure selecor process. Once he posure is seleced for he nex mobiliy phase, informaion relaed o he seleced posure will be rerieved. If he posure has been defined as a sable posure, such as lying down, he posure selecor process keeps he conrol and wais ill he seleced ime duraion expires. I hen selecs anoher posure based on is sraegy. In he case of a mobile posure, such as walking, he global movemen conrol module sars moving he whole WBAN aking he parameers relaed o ha specific posure ino consideraion. The movemen behavior, like he desinaion, speed, and he pah o ha desinaion, depends on he specific sraegy for he running applicaion scenario. Once he WBAN reaches he desinaion poin, he posure selecor module ges he conrol o selec he nex posure and he process is coninued. Algorihm 1 presens his process. The res of his secion explains he differen funcions in Algorihm Posure Specificaion Le S = {s 1, s 2,..., s Ns } be he se of N s sensor nodes in he WBAN. Moreover, in he mobiliy model, le here be N p differen posures π = {π 1, π 2,..., π Np }. Each posure is defined by a se of specificaion parameers which are applicaion dependen and should be specified by he user. The specificaion of each posure includes relaive node posiions (reference poins), he radius of a sphere around he reference poin of any node, he velociy value of he local movemen wihin he sphere, and he speed range of he whole body movemen (global movemen), using he noaions in Table 1. Table 1: The basic specificaion of he posures π j Noaion Descripion (1 i N s,1 j N p) RP ij Relaive reference poin of node s i V ij Velociy value of node s i r ij Radius of movemen sphere of node s i V minj Minimum velociy of he WBAN Maximum velociy of he WBAN V maxj A posure can be mobile or sable, based on he speed range of is global movemen (V max) which will be given based on he applicaion scenario. In general, ypical mobile posures are walking and running. Bu in specific applicaions such as a hospial siuaion, siing or even lying down can be hough of as mobile posures when some sor of carrier like a wheelchair or mobile bed is used. A posure can also include more deail abou he physical channel characerisics as discussed laer. 3.3 Local Mobiliy Individual movemen of any sensor node (say node s i) wihin he WBAN srongly depends on he seleced posure. Based on he RPGM model, every node moves wihin a given sphere around is reference poin. The movemen parameers which are he speed (V ij) and he radius (r ij) of he movemen sphere around he reference poin are obained from he specificaions of he seleced posure π j shown in Table 1. The values are given based on he expeced movemen behavior of he node deployed on ha specific posiion in he curren posure. A node on an arm can have a very high mobiliy in he running posure whereas he node deployed on he ches is fixed and has no local mobiliy (zero velociy). However, even a node on he arm has a very low mobiliy in he lying down posure. We use he random walk mobiliy model (RWMM) [27] in 3D space o deermine he random moion vecor RM ij in each local movemen sep. In each sep, a poin wihin he sphere space is seleced uniformly randomly as he desinaion. The node hen moves oward he desinaion wih he given velociy value V ij. 3.4 Posure Selecion Sraegy Posure selecion is a very imporan par of he model as he posure srongly influences he nework opology and node conneciviy. The seleced posure also deermines he local movemen of sensor nodes and he global mobiliy of he whole WBAN. Therefore, i affecs he connecion beween he nodes in he WBAN and he exernal nework like oher WBANs or he surrounding ambien sensor nework. Experimenal measuremen resuls in [20] show how Packe Delivery Raio (PDR) and link conneciviy beween differen sensor nodes deployed on a human body vary over differen posures. As he communicaion proocols are expeced o suppor such changes in he nework opology and link qualiy, he mobiliy model should model posure changes carefully o evaluae he proocol correcly. A posure paern is a sequence of seleced posures in a cerain period of ime (for example, he whole simulaion ime). Saisically, in realiy, some posure paerns ake place wih a higher probabiliy han oher paerns. For

5 RUN WALK 0.4 STAND SIT LYING (a) RUN WALK 0.37 STAND SIT LYING (b) Figure 2: a) A ypical Markov model for posure paern selecion used as he iniial model. b) The updaed Markov model based on he given seadysae vecor of posure disribuion Π A 0 for a cerain area ype A example, changing he posure from lying down o running is very rare whereas changing o he siing posure is very likely. We use a one-level Markov model o model paern sequences while mainaining randomness of he posure selecion. We may use muliple Markov models o differeniae beween area ypes (see below for deails). Using such a Markov model, he curren posure a sep (π ) is aken ino consideraion for selecing he nex one (π +1 ). The Markov model is described by a ransiion probabiliy marix P in which P ij sands for he probabiliy of he ransiion from posure π j o π i. In a Markov model, ransiions originaing from a specific sae (posure) should add up o one ( N p i=1 Pij = 1, 1 j Np). The ransiion marix (P) can be obained from real human posure races. Fig. 2(a) shows a ypical Markov chain including five differen posures, namely running, walking, sanding, siing, and lying down (he figure uses abbreviaed erms for he posures). The edges are labeled wih he ransiion probabiliies. An imporan decision ha should be made is he ime duraion of posures. In he case of a mobile posure, he ime duraion is indeed buil-in as he nex posure selecion is done upon reaching he seleced desinaion. For a sable posure, he posure selecor process wais for a cerain duraion (T s) and hen selecs he nex posure according o he Markov model. As a posure change may influence he opology of he WBAN, several adapaion mechanisms have been proposed in proocols o configure and adap he nework, accordingly. Reconsrucing he rouing srucure in [5] and ransmi power adapaion in [17] can be menioned as examples. These adapaions may ake some ime depending on he mehod. Therefore, having realisic posure duraions is imporan for evaluaing he applicabiliy of a proocol. Using realisic duraions, one is also able o find he righ values for he parameers of he proocol o realize he expeced behavior in he arge applicaion. In our WBAN mobiliy model, we ask he user o specify a desired disribuion for he ime duraion of each posure according o he applicaion scenario. This can simply be a consan ime duraion or a uniform disribuion, or a more precise disribuion closer o real-life posure duraions. The funcion SelecDuraions() in Algorihm 1 uses he given disribuion o selec he posure ime duraion (T s) upon he selecion of a sable posure. 3.5 Locaion and Posure Paern Coherency Posure selecion based on a Markov model is he basic absracion in our model. Given he Markov model, we decide abou he posure a any ime considering he curren posure and he ransiion probabiliy marix. However, for many applicaions, he likelihood of posure paerns may depend on he locaion in he area of simulaion. To ake his correlaion beween he WBAN posiion and he posure paern ino accoun, we provide he user of he model wih differen absracion levels for modeling. Le A = {A 1, A 2,..., A Nd } be a pariioning of he simulaion area ino N d differen area ypes. So, in any ype of locaion, we may have differen posure paerns. As an example, differen rooms in a building can be hough of as having saisically differen posure paerns. The posure paern in a bedroom is surely differen from he paern in he living room or he kichen. For he mos precise level of specificaion, a user can define a dedicaed Markov model for any locaion of he area. In his case, N d differen ransiion probabiliy marices should be given. A a coarser level of specificaion, we jus ask a user o specify a Markov chain wih an iniial ransiion marix (P) for posure changes plus he seady-sae probabiliy vecor (Π A k ) ha deermines he disribuion of posures in each area ype. Thus Π A k i sands for he seady-sae probabiliy of posure π i in area ype A k. Then he Markov model wih iniial ransiion probabiliies can be auomaically updaed o ake his probabiliy vecor ino consideraion. Consequenly, he ransiion marix P A k for any area ype A k is exraced by adaping he iniial ransiion marix P o saisfy he desired seady-sae vecor Π A k. So, on he one hand, he goal is o find a ransiion probabiliy marix (Markov chain P A k ) wih he seady-sae probabiliy vecor Π A k, which should saisfy P Ak Π A k = Π A k. On he oher hand, he resuling ransiion marix should be as much as possible similar o he iniial ransiion marix P. We ranslae his o minimizing P A k P F where F sands for he Frobenius norm (which is a sandard disance meric for marices). In oher words, if we le D = P A k P, he aim is o minimize D F such ha D Π A k = P Ak Π A k P Π A k = Π A k P Π A k (1) Since Π A k T is he pseudo inverse of Π A k, he minimum norm

6 soluion for D will be as follows. D = (Π A k P Π A k ) Π A k T (2) As we supposed ha D = P A k P, hen he soluion for he new ransiion marix is calculaed by Eqn. 3. P A k = P + (I Np P) Π Ak Π A k T (3) where I Np sands for he ideniy marix of size N p. The resuling ransiion marix can be proven o be Markovian by showing ha every column of he marix P A k adds up o one. Noe ha all enries of he ransiion marix should be a probabiliy value (0 P A k ij 1). We ake ha ino accoun for compuing he marix. To do so, i may be necessary o modify he resuling marix and repea Eqn. 3 in a recursive manner ill we obain a proper ransiion marix saisfying he desired seady-sae probabiliies. As an example, consider he iniial Markov model of Fig. 2(a) wih he ransiion marix P given in Eqn. 4. P = Now, suppose ha we specify a seady-sae posure disribuion Π A 0 for he area ype A 0 as in Eqn. 5. This can be hough of as a disribuion for a bed room for example. Π A 0 = The new Markov model P A 0 for his area ype is calculaed using Eqn. 3 as follows P A 0 = (6) Fig. 2(b) shows he resuling Markov chain for he area ype A 0 (he probabiliy values in he figure have been rounded). This model saisfies he given seady-sae posures disribuion and i sill akes he more likely ransiion ino accoun based on iniial Markov model P. Thus, he oupu posure paern will be more realisic. As i is observed, he model can be configured o be very specific for a paricular scenario by providing one Markov chain for each area ype of he simulaion area, or i can be more general hrough uilizing a less precise model specificaion which will be more convenien for he user. 3.6 Global Movemen When a mobile posure is seleced, a arge posiion should be chosen o sar movemen of he whole WBAN (moving he LC of he group). A uniform random sraegy can be applied as he basic level if he probabiliy of being seleced as he arge posiion for all area ypes of he area is he same in he applicaion. There is also he possibiliy o (4) (5) have a specific non-uniform WBAN posiion disribuion for differen area ypes of he simulaion area. For insance, we may specify a value p i as he probabiliy of he area ype A i being seleced as he arge posiion for he movemen of he WBAN. Doing so, differen WBANs in he nework move independenly from each oher. However, in many applicaions, modeling he social aciviies (e.g., meeing in a room) is of grea imporance as i can change many hings in he nework, like inerference, wave propagaions, and he conneciviy beween WBANs and he WSN infrasrucure. According o he applicaion scenario, a communiy-based mobiliy model in he exising lieraure of mobile ad-hoc wireless neworks (e.g. models presened in [14],[16],[23],[26]) can be chosen o be used on op of our WBAN mobiliy model o include social aciviies as well. 3.7 Temporal Correlaion The spaial correlaion has been aken ino accoun in he MoBAN mobiliy model hrough he possibiliy of specifying locaion-dependen disribuions for he posure paern (Markov chain), and WBAN arge posiion selecion, as described. However, in many applicaions in realiy, ime is also imporan. For insance, he area ypes ha a human (WBAN) may visi during day ime and nigh ime are differen. As a soluion, one can independenly conduc several simulaion runs wih differen parameers and disribuions o check he performance of he nework in differen ime frames. Neverheless, inegraing such a faciliy in he mobiliy model is worhwhile and makes he model more convenien o use. Temporal correlaions can be accordingly inegraed ino he model by performing he space and ime pariioning wih he same mechanism ha was already explained. I means boh he arge posiion and he posure paern selecion processes can be done aking he ime ino consideraion as well. To do so, he simulaion area is pariioned ino N d differen area ypes as explained in Secion 3.5. Each area ype hen is pariioned ino N separae ime frames. By his, we have N d N space-ime pariions in which A i,j is se o be he i h spaial area ype in he j h ime frame. Now, differen disribuions can be specified for differen space-ime pariions based on he applicaion scenario and he required level of precision. 3.8 WBAN Radio Model Parameers The human body has a severe influence on radio wave propagaion, and so i affecs he conneciviy and he opology of he WBAN. Thus i is very imporan o ake he body effec on propagaion loss and link qualiy in various posures ino consideraion. As he effec of he body srongly depends on he relaive posiion of nodes and he body siuaion, i is very useful o include ha in he WBAN mobiliy model as he mobiliy model is responsible for he posiion of nodes a any insance in ime. Based on he radio propagaion model which is used for nework simulaion, we can decide abou he channel parameers in he mobiliy model according o he curren siuaion of he body. Provided ha a pah-loss model is used, for insance, we specify for every pair of nodes in he definiion of each posure, he mean (µ α) and deviaion (σ α) of he pah

7 Configuraion File (.xml) Mobiliy Paern Log File (.x) π, RP 1 Node 1 LocalMoBAN Posure Spec. File (.xml) Inpu Mobiliy Paern (.x) Iniializaion phase MoBAN Coordinaor Module π,rp i Node i LocalMoBAN Posures daa base Local mobiliy parameers π, RP N s Node N s LocalMoBAN Figure 3: Block diagram of he OMNeT++ implemenaion of he MoBAN mobiliy model. loss coefficien α. The mean value is specified (in he range from 3 o 7, see [25]) according o he raio of he disance beween he pair of nodes in which radio waves should be propagaed around or hrough he body. The deviaion is se based on he relaive mobiliy of he pair of nodes in he specific posure. The real values for he mean and deviaion parameers can be exraced from he resul of a real experimen. During he simulaion run, a value is seleced for α according o he normal disribuion N(µ α, σ α) a he sar ime of every seleced posure. 4. IMPLEMENTATION ISSUES We implemened he MoBAN mobiliy model as an addon o he mobiliy framework of he OMNeT++ discree even simulaor, o be used for our own research as well as o make i available o he scienific communiy for oher research on WBAN proocols. The implemenaions of he WBAN mobiliy model as well as he pure RPGM group mobiliy model are available hrough he web sie hp:// Any number of WBANs can be easily insaniaed and differen WBANs can include differen numbers of sensor nodes. The ambien nework or non-wban nodes can sill be involved and can have heir own mobiliy using exising singular node mobiliy models exising in he mobiliy framework of OMNeT++. By his, a complee combined nework consising of several WBANs and an ambien sensor nework can be simulaed. 4.1 MoBAN Implemenaion Modules Figure 3 shows he block diagram of he implemenaion of he mobiliy model for a single WBAN. To have a WBAN in he nework, one mobiliy coordinaor module is insaniaed in he op level simulaion seup as well as one mobiliy module inside each node wihin he WBAN. In he case of muliple WBANs, muliple coordinaors should be insaniaed and he mobiliy module in each sensor node specifies he enclosing WBAN coordinaor module. The mobiliy coordinaor is he main module ha provides he group mobiliy and correlaion beween nodes in a WBAN. In he iniializaion phase, i reads several user defined files. Firs of all, i reads he posures specificaion file and makes a daabase of ha informaion o be used during simulaion by he coordinaor module iself or by oher nodes. Any node can access he posure daabase during simulaion o rerieve informaion abou is local mobiliy parameers (curren posure, V ij, and r ij). A configuraion file as well as all required disribuions for specifying differen non-uniform disribuions is also read in he iniializaion phase. Noe ha all WBANs may use a single posure or configuraion file if hey are in he same siuaion. However, differen inpu files can also be specified o have variey beween differen WBANs in a simulaion run. During he simulaion, he mobiliy coordinaor decides abou he posure and he global movemen of he whole WBAN (posiion of he LC) by implemening he posure selecor and global movemen processes of he model based on Algorihm 1. The LC s posiion of he WBAN is an absolue posiion wihin he (hree-dimensional) simulaion area and is deermined according o he mobiliy model which is being used for movemen of he whole WBAN (Secion 3.6). The coordinaor module also knows he reference poin (RP) of each node in he curren posure as well, as a relaive posiion o he LC. Wha a node s i wihin he WBAN needs is acually is absolue reference poin (RPi ) as well as he curren posure (π j). RPi is calculaed by adding he curren posiion of he logical cener (LC ) by he reference poin of he node s i in posure π j (RPi = LC + RP ij). The mobiliy coordinaor sends he new RPi o every node belonging o he WBAN. The mobiliy module of a sensor node s i receives he RPi and he curren posure π j of he WBAN from is coordinaor module. Subsequenly, i rerieves he parameers of he local movemen (V ij, r ij) from he posure daabase. I decides abou he relaive posiion (L i) of he node relaive o he RPi. Finally, i ses L i + RPi as he physical posiion of he node s i a ha ime insance. Noe ha he individual node mobiliy module has nohing o do wih a posure change as is reference poin is properly updaed upon a posure change by he coordinaor module. I only rerieves he local movemen parameers of he new posure.

8 (a) Average age of daa iems a he gaeway and average ransmi power of a node in simulaion using MoBAN (b) Average age and Tx power level of a node in a real experimen Figure 4: Simulaion resuls for an inra-wban communicaion proocol obained using MoBAN and resuls of a real experimen. 4.2 Recording and Reusing Mobiliy Paerns The mobiliy paern influences he performance evaluaion resuls of a nework proocol when here is some form of mobiliy in he nework. This effec is more imporan in simulaing neworks including WBANs because of he high mobiliy in hese neworks. On he oher hand, someimes we simulae a proocol o compare is performance wih alernaive proocols. In such a siuaion, o have fairer comparison, i is worhwhile o use he exac same mobiliy paern for simulaing differen proocols. In our implemenaion of he WBAN mobiliy model, we have he feaure of logging he mobiliy paern of a WBAN. The coordinaor module logs he seleced desinaions, velociy values and posure paern in a file if he logging funcion is requesed. The coordinaor module can be se o read a previously logged mobiliy paern of an earlier simulaion run by specifying he name of he inpu mobiliy paern file. To simulae differen proocols for a given nework, we may run he firs simulaion and log he mobiliy paern. Then, for he res, we use he logged paern. 5. CASE STUDIES We used MoBAN wih special configuraions o evaluae communicaion proocols ha we have presened in our earlier publicaions [17] and [18]. In his secion, he impac of he mobiliy model on simulaions is invesigaed. Moreover, as hese wo case sudies concern differen proocols (one for inra- and anoher one for exra-wban communicaion), he applicabiliy and configurabiliy of he model for differen applicaions are illusraed. 5.1 Inra-WBAN Proocol Sack In [17], we proposed a muli-hop proocol sack for inra- WBAN communicaion aiming o provide robusness agains nework opology changes due o he posure changes and low link qualiy in WBANs. All sensor nodes need o send heir daa iems o a gaeway node on he body. Because of a very shor ransmission range of wireless devices deployed on he body, nodes may no be able o reach he gaeway in one hop. The proocol suggess a gossip-based [8] rouing mechanism ogeher wih an appropriae TDMA-based Medium Access Conrol (MAC) o reliably deliver informaion o he gaeway node hrough one or more hops. As he proocol does no rely on any specific rouing srucure like a ree, here is no need for reconsrucing he rouing srucure upon nework opology changes (due o he posure changes and node mobiliy). This makes he proocol robus agains hose changes in he nework opology. Moreover, a ransmi power adapaion mechanism is proposed o opimize he ransmi power consumpion of sensor nodes while realizing he proper node conneciviy o mee he qualiy-of-service requiremens. To evaluae he performance and robusness of such a proocol, a WBAN mobiliy model including differen posures is he firs prerequisie as he proocol aims o provide robusness wih respec o such opology changes. We used a paricular configuraion of MoBAN o his aim. Twelve sensor nodes including he gaeway node are supposed o be insalled on differen posiions in he WBAN. Accordingly, he specificaions of five differen posures are defined and he iniial Markov model of Fig. 2(a) is used o generae he

9 posure paern. Since he proocol is designed for inra- WBAN use, jus one WBAN insance is sufficien as he simulaion seup. Since here are random processes in he communicaion proocol, o have saisically more reliable resuls, he simulaion was run for 32 differen seeds for he random generaor. We used he mobiliy paern recording and reusing capabiliy of he model implemenaion o have he same mobiliy paerns for conducing simulaions using differen seeds. Fig. 4(a) shows a par of he simulaion resul in which he average age of daa iems a he gaeway node is shown. The age for daa from a node a he gaeway a any poin in ime is defined as he ime difference beween he curren ime and he sample ime of he las received daa iem of ha node wihin he WBAN. The sensiiviy of he proocol in erms of he average age as well as he reacion of he proocol hrough changing he ransmi power are visible in he figure. I is bes visible in changing from closed posures (e.g., siing) o open posures (e.g., sanding) and vice versa. Abrup changes in he value of he average age reveal he impac of differen posures on he conneciviy of he WBAN nodes. Transmi power adapaion ries o keep he age level wihin a desired range. The figure also includes he ransmi power rend of a node deployed on he fee o show how he posure changes affec he seings of a node. The ransmi power is seleced among four levels, denoed 0, 1, 2, and 3. The graph shows he average over he 32 runs. To confirm ha nework conneciviy and qualiy of services depend on he posure, we conduced a real experimen using nine MyriaNed [24] sensor nodes deployed on a human body. The experimen was done wih he same posure paern as he simulaions. Neverheless, he experimenal seup differs from he simulaion seup in various aspecs (e.g., he number of nodes, radio behavior, and individual mobiliy of nodes). Thus, he resuls are no comparable wih he simulaions. However, he experimenal curve in Fig. 4(b) shows an insance of he effec of posure changes on he qualiy of service, he nework performance, and he seing of a node (ransmi power level, 0, 1, 2, or 3) in real world WBANs. The experimenal resuls show age peaks and ransmi power variaions as are also seen in he simulaion experimens. This provides an indicaion ha MoBAN capures posure changes as required for simulaions, alhough more experimens are needed wih mobiliy races from real applicaions. Such races are no ye available for WBAN-based applicaions. Traces available for ad-hoc neworking [3] are no suiable for evaluaing he WBAN mobiliy paerns. 5.2 Exra-WBAN Communicaion Proocol As he second case sudy, we show he usabiliy of he model for performance evaluaion of an exra-wban communicaion proocol. In he presence of an ambien nework, WBANs behave like clusers of mobile nodes wihin ha nework. A Medium Access Conrol proocol is designed in [18] o suppor cluser (WBAN) mobiliy in large-scale wireless sensor neworks. The proocol is based on he TDMA sraegy for accessing he shared medium. Cluser mobiliy is suppored by dedicaing a specific par of he TDMA frames o he sensor nodes wihin he mobile clusers. Moreover, WBAN1: lying down WBAN4: siing WBAN2: walking WBAN3: walking Figure 5: A snapsho of he simulaion for exra- WBAN communicaion using MoBAN; view from above. a CSMA-based mechanism has been used for accessing his par of he frames by sensor nodes from differen clusers. For performance evaluaion of he proocol, we made a simulaion seup including 100 ambien (fixed) sensor nodes placed around fixed grid poins as well as up o four WBANs each including five sensor nodes. WBANs move independenly wihin he simulaion area. Fig. 5 depics wha he simulaion looks like in OMNET++ a a paricular ime insance. The lines in he figure show poenial wireless links beween sensor nodes. Four WBANs as well as heir curren posures are shown. WBAN2 and WBAN3 are in he poenial inerference range of each oher. So hey may inerfere or hear each oher. This affecs he performance of he sysem and is quie imporan in he invesigaion of he behavior of he proposed proocol in such siuaions. Simulaion resuls presened in [18] show ha he iner-wban collision raio grows wih increasing he number of WBANs in he simulaion area. The collision raio definiely depends on he ime duraions ha differen WBANs are close ogeher. This means ha modeling he WBAN movemen according o he arge applicaion is of grea imporance for decisions abou he applicabiliy of he proocol. 6. CONCLUSION This paper presens MoBAN, a Mobiliy model for wireless Body Area Neworks. The model has been specifically designed so ha i can be configured for being used for performance evaluaion of a broad range of applicaion scenarios including WBANs. Boh global movemen of he WBAN and he individual node mobiliy wihin he WBAN have been aken ino consideraion. The model can be used in simulaing boh inra- and exra-wban proocols. Two case sudies are shown ha use he specific configuraions of he model for differen proocols. The implemenaion of he model as an add-on o he mobiliy framework of he OM- NeT++ even simulaor makes he model available o he scienific communiy o be used for research on WBAN communicaion proocols and applicaions. The implemenaion of MoBAN as well as he RPGM group mobiliy model on

10 op of he OMNeT++ simulaor can be obained hrough hp:// As fuure work, we plan o perform addiional experimens wih real WBAN deploymens o validae and une MoBAN. Oher researchers are invied o do so as well. Currenly, no real life WBAN mobiliy races for realisic applicaion scenarios are available for research purposes. Wih real races, like hose available for ad-hoc neworking [3], i would be possible o furher es and une he MoBAN mobiliy model. 7. REFERENCES [1] Casalia simulaor websie. hp://casalia.npc.nica.com.au. [2] OMNeT++ Nework Simulaor websie. hp:// [3] SUMATRA: Sanford Universiy Mobile Aciviy TRAces. hp://infolab.sanford.edu/pleiades/sumara.hml. [4] The Nework Simulaor-NS2/3 websie. hp:// [5] B. Braem e al. The wireless auonomous spanning ree proocol for mulihop wireless body area neworks. In Proc. 3rd In l Conf. on Mobile and Ubiquious Sysems (MobiQuious), pages IEEE, [6] T. Camp, J. Boleng, and V. Davies. A survey of mobiliy models for ad hoc nework research. Wireless Communicaions and Mobile Compuing (WCMC): Special Issue on Mobile Ad Hoc Neworking: Research, Trends and Applicaions, 2(5): , [7] C. Chiang. Wireless Nework Mulicasing. PhD hesis, Universiy of California, Los Angeles, [8] D. Gavidia and M. van Seen. A probabilisic replicaion and sorage scheme for large wireless neworks of small devices. In Proc. 5h IEEE In l Conf. Mobile and Ad Hoc Sensor Sysems (MASS). IEEE, [9] X. Hong, M. Gerla, G. Pei, and C. Chiang. A group mobiliy model for ad hoc wireless neworks. In Proc. 2nd ACM In l Conf. on Modeling, analysis and Simulaion of Wireless and Mobile sysems (MSWiM), pages ACM, [10] E. Hyyiä and J. Viramo. Random waypoin mobiliy model in cellular neworks. Wireless Neworks, 13(2): , [11] B. Kadrovach and G. Lamon. Design and analysis of swarm-based sensor sysems. In Proc. 44h IEEE Midwes Symposium on Circuis and Sysems (MWSCAS), pages IEEE, [12] B. A. Kadrovach and G. B. Lamon. A paricle swarm model for swarm-based neworked sensor sysems. In Proc. ACM Symposium on Applied Compuing, pages ACM, [13] A. Köpke e al. Simulaing wireless and mobile neworks in OMNeT++ - he MiXiM vision. In Proc. Conf. on Simulaion Tools and Techniques (SIMUTools). ICST, Brussels, [14] L. Kyunghan, H. Seongik, J. Seong, R. Injong, and C. Song. SLAW: A new mobiliy model for human walks. In Proc. 28h IEEE Conference on Compuer Communicaions (INFOCOM), pages IEEE, [15] B. Liang and Z. Haas. Predicive disance-based mobiliy managemen for PCS neworks. In Proc. 8h annual join Conf. of he IEEE Compuer and Communicaions Socieies (INFOCOM), pages IEEE, [16] A. Mei and J. Sefa. SWIM: A simple model o generae small mobile worlds. In Proc. 28h IEEE Conference on Compuer Communicaions (INFOCOM), pages IEEE, [17] M. Nabi, T. Basen, M. Geilen, M. Blagojevic, and T. Hendriks. A robus proocol sack for muli-hop wireless body area neworks wih ransmi power adapaion. In Proc. 5h In l Conf. on Body Area Neworks (BodyNes). ICST, [18] M. Nabi, M. Blagojevic, M. Geilen, T. Basen, and T. Hendriks. MCMAC: An opimized medium access conrol proocol for mobile clusers in wireless sensor neworks. In Proc. 7h annual IEEE Communicaions Sociey Conf. on Sensor, Mesh and Ad Hoc Communicaions and Neworks (SECON), pages IEEE, [19] D. Pediadiakis, Y. Tselishchev, and A. Boulis. Performance and scalabiliy evaluaion of he Casalia wireless sensor nework simulaor. In Proc. Conf. on Simulaion Tools and Techniques (SIMUTools). ICST, Brussels, [20] M. Quwaider and S. Biswas. Probabilisic rouing in on-body sensor neworks wih posural disconnecions. In Proc. 7h ACM MobiWAC, pages ACM, [21] H. Ren and M. Meng. Undersanding he mobiliy model of wireless body sensor neworks. In Proc. IEEE In l Conf. on Informaion Acquisiion, pages IEEE, [22] E. Royer, P. Melliar-Smih, and L. Moser. An analysis of he opimum node densiy for ad hoc mobile neworks. In Proc. IEEE In l Conf. on Communicaions (ICC), pages IEEE, [23] M. Sanchez and P. Manzoni. ANEJOS: a java based simulaor for ad hoc neworks. Fuure Generaion Compuer Sysems, 17(5): , [24] F. van der Waeren. The ar of developing WSN applicaions wih MyriaNed. Tech. repor, Chess Company, he Neherlands, [25] T. Zasowski e al. UWB for noninvasive wireless body area neworks: Channel measuremens and resuls. In Proc. Conf. on Ulra Wideband Sysems and Technologies (UWBST), pages IEEE, [26] C. Zhao and M. L. Sichiiu. N-Body: Social based mobiliy model for wireless ad hoc nework research. In Proc. 7h annual IEEE Communicaions Sociey Conf. on Sensor, Mesh and Ad Hoc Communicaions and Neworks (SECON), pages 1 9. IEEE, [27] M. Zonoozi and P. Dassanayake. User mobiliy modeling and characerizaion of mobiliy paerns. IEEE Journal on Seleced Areas in Communicaions, 15(7): , 1977.