Autonomic Cognitive-based Data Dissemination in Opportunistic Networks

Transcription

1 Auonomic Cogniive-based Daa Disseminaion in Opporunisic Neworks Lorenzo Valerio, Marco Coni, Elena Pagani and Andrea Passarella IIT-CNR, Pisa, Ialy Compuer Science Dep., Universià degli Sudi di Milano & IIT-CNR, Ialy Absrac Opporunisic Neworks (OppNes) offer a very volaile and dynamic neworking environmen. Several applicaions proposed for OppNes - such as social neworking, emergency managemen, pervasive and urban sensing - involve he problem of sharing conen amongs ineresed users. Despie he fac ha nodes have limied resources, exising soluions for conen sharing require ha he nodes mainain and exchange large amoun of saus informaion, bu his limis he sysem scalabiliy. In order o cope wih his problem, in his paper we presen and evaluae a soluion based on cogniive heurisics. Cogniive heurisics are funcional models of he menal processes, sudied in he cogniive psychology field. They describe he behavior of he brain when decisions have o be aken quickly, in spie of incomplee informaion. In our soluion, nodes mainain an aggregaed informaion buil up from observaions of he encounered nodes. The aggregae saus and a probabilisic decision process is he basis on which nodes apply cogniive heurisics o decide how o disseminae conen iems upon meeing wih each oher. These wo feaures allow he proposed soluion o drasically limi he sae kep by each node, and o dynamically adap o boh he dynamics of iem diffusion and he dynamically changing node ineress. The performance of our soluion is evaluaed hrough simulaion and compared wih oher soluions in he lieraure. Keywords-opporunisic neworks; conen diffusion; cogniive heurisics; I. INTRODUCTION Cogniive psychology sudies he way he human brain works and reacs o exernal simuli. Several sudies show ha he brain ofen perceives he observed evens as binary sequences occurring over ime [18] and use hem o make decisions according o a frequency-based reasoning. In paricular, cogniive heurisics are funcional models of he menal processes [12], [13] on which he humans rely o quickly ake appropriae acions also in presence of incomplee knowledge of he siuaion. They do no aim a reproducing he deailed physiology of he brain s processes (as neural neworks), bu model heir funcionaliy. Heurisics can hus be seen as mehods used by he brain o quickly find a soluion o a problem, when he exhausive search of he opimal soluion is impracical or infeasible. Cogniive heurisics have been applied in various fields, such as financial decision making [8], forecasing purchases [10], resuls of spor evens [5], oucomes of poliical elecions [9]. Usually, he soluion supplied by heurisics well approximaes he opimum. The capabiliy of heurisics o work in a fas and frugal way makes hem an ineresing approach o be adoped in OppNes.Opporunisic neworks are self organising mobile neworks where he exisence of simulaneous end o end pahs beween nodes is no aken for graned, while disconnecions and nework pariions are he rule. Neverheless, opporunisic neworks suppor muli hop communicaion by emporarily soring messages a inermediae nodes, unil he nework reconfigures and beer relays (wih respec o he final desinaions) become available. Due o he scarciy of resources, he impossibiliy of building a global sysem knowledge, and he possibly shor ime a disposal of he nodes when a conac occurs o exchange informaion and carry on daa disseminaion, using cogniive heurisics in such an environmen looks in principle a sensible approach. I is worh noing ha his approach is no ye anoher bioinspired proocol. In our scenario, nodes are acual proxies of heir human users in he cyber world. By using he same cogniive processes of heir users nodes behave very similar o how human counerpars would behave if facing he same problem in he real world. In his sense nodes play heir role of proxy. Among he various cogniive heurisics, in his paper we consider in paricular he recogniion heurisic [12], [13]. In a single senence, i saes ha, when confroned beween wo possible alernaives, he brain selecs he one ha i recognises. The behaviour of his heurisic can be explained hrough he following example: a person asked o o indicae which universiy is more endowed wihou having any direc informaion abou he real eniy of endowmens will make his selecion according o oher indirec informaion like how ofen a universiy name comes o his aenion. The more ofen he hears a universiy name he more likely he will indicae he recognized universiy name as more endowed. In his work, we exploi he recogniion heurisic for daa disseminaion in opporunisic neworks. We assume a scenario characerized by he presence of conens daa iems organized in specific opics channels of ineres and nodes ineresed in some of hose opics. Moreover, nodes ac as boh conens generaors and daa

2 carriers, indeed, conacs beween nodes are he only way o disseminae daa iems in he sysem. A key problem every node par of a daa disseminaion sysem for opporunisic nework has o face is dynamically deciding when specific daa iems mus be diffused more or less aggressively. In his paper we exploi he recogniion heurisic o address boh hese aspecs: (i) in order o decide wheher diffusion has o be boosed for a cerain iem, nodes in our sysem recognise which iems are of ineres for several nodes; (ii) in order o decide wheher an iem is already sufficienly diffused, nodes in our sysem are able o recognise ha is already carried by (mos of) he ineresed nodes. The work presened in [6] is a preliminary aemp a invesigaing his approach (see Secion II for more deails). The main focus of [6] was o highligh ha using he recogniion heurisic is a viable opion. In his paper we urn his idea in he definiion of a concree sysem for opporunisic neworks, by invesigaing how cogniive heurisics can be applied aking ino consideraion key resricions of opporunisic neworks, i.e. resource limiaions and dynamic condiions. In paricular, in [6] he recogniion decisions were aken based on puncual informaion abou he single daa iems, and by defining fixed parameers, ha had o be uned depending on he specific neworking environmen. These feaures can resul in significan scalabiliy problems, and when wrongly uned in poor adapaion o dynamic environmens. Clearly, hey represen roadblocks for applying recogniion heurisics in concree cases. In his paper, while we share he overall idea of [6], we drasically modify he acual daa disseminaion algorihms o remove hese roadblocks, by sill keeping he benefi brough by he use of recogniion heurisics. Specifically, we only exploi aggregae informaion for driving he behaviour of he recogniion heurisic, ha is, we invesigae how he cogniive heurisics could be applied by saring from aggregae informaion abou he disseminaion sae of daa iems only. This can be seen as he applicaion of anoher cogniive mechanism aimed a mainaining only few essenial informaion abou he sae of he surrounding environmen and permis o drasically reduce (wih respec o [6]) sae mainained by nodes o implemen he daa disseminaion policies. Our resuls show ha his reducion comes wihou scarifying he performance in erms of delivering daa iems o ineresed users. Anoher key feaure is represened by he inroducion of a sochasic mechanism ha drives he recogniion process. This permis o avoid using fixed hresholds, and makes he sysem adapive o dynamical condiions. More precisely, in his paper we show how he proposed algorihm efficienly reacs o dynamic scenarios where a a cerain ime nodes may change heir ineress abou channels, or when compleely new channels/iems are injeced in he running sysem. II. RELATED WORK A. Conen Disribuion in OppNes In he lieraure, some works appeared ha consider he problem of conen diffusion in mixed fixed/mobile neworks. In [11], a hybrid infrasrucure is considered where hrowboxes i.e. devices wih boh wired and wireless inerface communicae wih one anoher and wih he wireless nodes. Nodes upload held iems when in he communicaion range wih a hrowbox, and possibly download iems ha saisfy local ineress. A similar hybrid infrasrucure is considered in [15]. In boh proposals, caches are mainained in he nodes belonging o he wired infrasrucure wih usual cache replacemen algorihms. Several works deal wih he problem of conen disribuion in pure OppNes. In he PodNe Projec [14], a framework is considered similar o ha of his work. Nodes may subscribe o channels of ineres. Upon each encouner, nodes exchange iems in order o rerieve hose belonging o he subscribed channels. Then, oher iems may be exchanged and loaded in a public cache in order o faciliae heir disseminaion o ineresed nodes. The iems o be mainained in he public cache are chosen depending on he channel populariy, bu blindly o social aspecs. By conras, in ConenPlace [1], nodes aim a filling heir caches in order o maximize boh he local uiliy (i.e. he ineress of he local user) and he communiy uiliy. The laer forces nodes o carry iems ha he local user is no ineresed in, bu ha are of ineres for he users belonging o he same social communiies of he local user. For he aim of iem selecion, wo opposie indexes are considered: he access probabiliy, i.e. he number of users ineresed in he iem and belonging o he communiies of he local user, and he availabiliy, i.e. he number of users in he communiies already owning he iem. Some works consider a publish/subscribe framework. According o his, in [16] some nodes are idenified as brokers, and are in charge o coordinae iem disribuion and o convey iems o ineresed nodes. The brokers are he mos popular nodes in erms of social ies and encouners wih he oher nodes. In SocialCas [7], nodes disribue informaion abou he channels hey are ineresed in. Each node uses his informaion and is paern of encouners o compue is own uiliy for each ineres. When wo nodes n1 and n2 encouner, an iem is sen from n1 o n2 if n2 has greaer uiliy han n1 for he iem channel. This approach uses rouing more han caching in order o deliver conen o ineresed nodes. Moreover, i relies on he assumpion ha nodes belonging o he same social communiy share he same ineress. An exensive survey abou conen diffusion in OppNes can be found in [3].

3 B. Recogniion heurisic in opporunisic neworks In [6], a preliminary version of he approach presened in his work is proposed. For he sake of self-conainmen, we summarize here is characerisics. The caching mechanism is based on wo concurren algorihms: Recogniion and Modified-Take-The-Bes (in he following, for shor, MT 2 B). The former aims a deermining wha channels and iems are popular. A channel is popular when many nodes are subscribed o i. An iem is popular when i is held by many nodes. Upon an encouner beween wo nodes, he nodes exchange he se of channels hey are subscribed o, and he lis of iems hey hold. For every channel o which he oher node is subscribed, and every iem i holds, a couner is incremened. When, a channel/iem couner is greaer han a hreshold θ, hen he respecive channel or iem is deemed as popular. Two differen hresholds, θ C and θ I can be used for channels and iems respecively. MT 2 B aims a deermining wha iems are useful and should hen be kep in he local cache. The uiliy of an iem grows wih he populariy of he channel i belongs o, and decreases as i becomes more diffused. According o he saus informaion mainained by Recogniion, MT 2 B ranks he iems owned by an encounered node for decreasing uiliy. In paricular, he following rules are used: (i) iems belonging o unpopular channels are considered useless; (ii) already diffused iems are considered useless. Then, subjec o he local memory availabiliy, a node selecs he mos useful iems and uploads hem in is own local cache. In his sense, channel populariy booss he caching of (currenly) unpopular iems, while iem diffusion sops replicaion in furher nodes. This approach has wo main drawbacks. On he one side i relies on fixed hresholds o be uned according o he environmen, he node mobiliy and heir encouner paern. Moreover, in presence of highly dynamical scenarios where new iems are coninuously creaed, his saiciy of parameers becomes even more limiing. On he oher side, he amoun of puncual sae informaion every node has o keep in order o ake decisions abou he diffusion sae of daa iems can become inracable w.r.. he memory consrains o which nodes are subjec o (we provide a quaniaive analysis of his poin in Secion IV). These characerisics harm he acual suiabiliy of his approach for is successful applicaion in real world scenarios. III. OBLEM STATEMENT AND SYSTEM ASSUMPTIONS We consider a sysem composed by N nodes. Nodes can subscribe o one or more channels of ineress. We assume ha here are K channels available. Every node can generae conen iems. Each iemiis labeled wih he idenifier of he channel of ineres i belongs o, i.ch. A node can generae iems also for channels i is no subscribed o. There is no global knowledge of he channel subscripions, nor of he paern of encouners among nodes. Nodes have finie memory availabiliy, hus being unable o sore an unlimied number of iems. Iems have an infinie lifeime. Ye, new channels may be creaed dynamically, nodes can subscribe o hem, and iems for hem may sar o appear. Due o he lack of global knowledge, nodes have o discover he sysem saus, and ake decisions abou wha iems o cache accordingly. Caching permis o carry iems around he nework ill encounering nodes ineresed in hem. As he primary goal, for each iem i belonging o a channel ch, he diffusion procedure mus maximize coverage, i.e., maximize he probabiliy ha all nodes subscribed o ch will evenually receive i. Taking ino accoun he characerisics of he OppNes, a secondary goal is o also consider energy saving and (more in general) resource consumpion, by limiing communicaion when his does no jeopardize he coverage. IV. OBABILISTIC RECOGNITION In [6], puncual informaion for each iem and channel are mainained in order o recognize heir populariy. This leads o a non-negligible amoun of memory used ha limis he usabiliy of his approach in real scenarios. In order o improve he previous approach and make i suiable for large scale scenarios, we have o reduce he amoun of informaion a node mainains abou is environmen by minimizing he loss of accuracy in erms of acquired knowledge. In [6], he recogniion hresholds for iems and channels (θ I and θ C, respecively) have a differen impac in erms of diffusion performance. The former plays a more imporan role because i regulaes he replicaion level a which a daa iem is deemed as recognized and no disseminaed furher. Moreover, i is reasonable o hink ha he number of iems in he sysem largely exceeds he number of channels. This means ha, in erms of scalabiliy, i is criical o reduce he overhead relaed o keep deailed informaion abou iems diffusion (while keeping deailed informaion abou channels populariy is far less a concern). Hence, in his work we focus our effors on he problem of minimizing he sae informaion mainained abou iem diffusion, while leaving unchanged he recogniion procedure for he channels. We reduce he sae mainained a nodes by compressing he knowledge abou iems diffusion ino an aggregae measure ha les idenify, in erms of probabiliy, if he iems belonging o a given channel of ineres are spread enough, so as o sop heir diffusion in favor of oher less diffused iems. Le us focus on a generic node, and le S ch be he se of iems belonging o a cerain channel ch, received during an encouner e a ime wih anoher node. Le us finally denoe wih Snew ch S ch hose iems ha are definiely new w.r.. he node experience, i.e. iems ha a node has never seen before. We define he measure of novely a node observes upon he encouner e as: N() = Sch new S ch (1)

4 Figure 1. p ch Increasing rend of p ch during he sysem evoluion Informally,he idea behind probabilisic recogniion is as follows. The more imes a node receives almos he same kind of informaion, he sronger he belief ha here is nohing more o know for ha channel. Thus we are ineresed in he complemen of (1): 1 N() = 1 Sch new S ch Equaion (2) measures he amoun of novely in he informaion received from an encounered node w.r.. a given channel, ha we use as an insananeous indicaor of he diffusion of he iems in ch. Noe ha, as explained in deail in Secion IV-B, S ch and Sch new can be compued by keeping he sae informaion mainained a nodes consan, irrespecive of he number of daa iems in he sysem. Le p ch () be he esimaed degree of diffusion of he iems in ch, a he ime. We aggregae he insananeous informaion colleced during encouners wih nodes in a unique index, as follows (assuming ha is a discree variable incremened a each encouner): (2) p ch () = α p ch ( 1)+(1 α) (1 N()) (3) where 0 α 1 regulaes he balancing beween he pas experience and new informaion. Figure 1 shows he ypical rend of p ch we have observed in our simulaion (deails on he simulaion seings are provided in Secion V). I shows ha as ime passes, iems become more and more spread, and he probabiliy of observing new iems goes o zero bringing he diffusion probabiliy close o 1. The index defined by (3) is used o deermine when iems of channel ch are recognized, as described in deail in he following secions. A. Preliminaries on he Sochasic Mechanism In order o auonomically recognize he iems diffusion, nodes exploi he diffusion probabiliy defined in (3). More precisely, for every known channel ch a node deems he corresponding iems as diffused or no diffused, according o a Bernoulli rial wih parameer p ch (): { 1 Iems are diffused B(p ch ()) = (4) 0 Iems are no diffused In his way, as long as a node does no receive any new informaion abou a channel ch, he corresponding value of p ch (one for each channel and differen for each node) ges increasingly close o 1, sraighening over ime he belief ha he iems of ch are diffused. The drawback of using in he recogniion process an aggregae measure ogeher wih he sochasic approach is ha his resuls in a loss of granulariy w.r.. he informaion abou he single iems diffusion. However, he benefi is wofold: (i) he nodes can auonomically adap o he local scenario, and do no need o rely on a predefined hreshold o be uned, and (ii) he randomness of he decision process permis o sporadically resar he diffusion of almos spread iems hus increasing he probabiliy of reaching hose few nodes ha for some reason are no aligned wih he mean condiion of he sysem. B. Resuling Algorihm In his secion, we presen how he described approach can be pracically implemened in order o fuse he recogniion heurisic wih he probabilisic approach and exploi i in an opporunisic neworking scenario. Before doing so, le us briefly recall he srucure we assume abou each node s memory space. This is he same used in [6], and is repored also here for he reader s convenience: Daa Caches: Local Iems cache (LI) conains he iems generaed by he node iself; Subscribed Channel cache (SC): conains he iems belonging o he channel he node is subscribed o and obained by encouners wih oher nodes; Opporunisic Cache (OC): conains he mos useful iems from a collaboraive informaion disseminaion poin of view. These iems are obained by exchanges wih oher nodes and belong o channels he node is no subscribed o. Recogniion cache: Channel Cache (CC): whenever a node mees anoher peer subscribed o a given channel, he channel ID is pu in his cache, along wih a couner. Iems Channel Cache (ICC) : conains he channel IDs and he aggregae informaion abou he diffusion probabiliy of iems. Iem Hash (IH): a Bloom filer, used o remember which iems a node sees along meeings. Channel Hash (CH): a Bloom filer, used o remember recognized channels no longer presen in CC. The main logical seps of he daa disseminaion algorihm based on probabilisic recogniion are as follows (upon encounering wih anoher node): 1) recognise which channels are popular 2) recognise if he iems of a channel are spread 3) fill up he shared memory wih he less spread iems for redisribuion

5 Sep 1. For every conac beween wo nodes, each of hem incremens he couners associaed o he oher node s subscribed channels unil a given hreshold θ C is reached, afer ha he channel is marked as recognized. If he number of enries in CC exceeds he maximum capaciy, hen he oldes enry is dropped. In his case, if i was marked as recognized, he channel ID is recorded in a Bloom Filer (CH). In his way, he nodes can disinguish beween channels ha are no in CC because hey have never been seen (in his case hey are no in he BF), and channels ha have been replaced. Once concluded he recogniion phase for channel populariies, he second sep begins. Sep 2. We will now refer o Algorihm 1. Upon a meeing, wo nodes exchange he conen summary of heir caches (LI + SC + OC). Le us consider he se of iem IDs received and belonging o a same channel (line 9). By querying a Bloom Filer (IH) ha conains he informaion abou all he iems received during pas encouners, we coun how many of hem are definiely new (lines 11 14) and updae he diffusion probabiliy (line 19) corresponding o ha channel according o equaion (3). I is worh noing ha he decision of couning he new iems insead of he replicas is driven by he inrinsic characerisics of he Bloom Filer. Due o he probabilisic naure of a Bloom Filer, here is a non-null probabiliy of obaining a false posiive when querying if an iem is presen in he daa srucure. By conras, he negaive answer is always rue, hus we rely only on definiely negaive answers, which may lead, in principle, in a sligh under esimaion of he number of new iems, and hus in sopping he diffusion process oo early. Or simulaion resuls show ha his has, in pracice, no impac on he effeciveness of he disseminaion process. Once updaed he diffusion probabiliy we use i o decide wheher he daa iems of ha channel are recognized or no, according o a Bernoulli rial wih probabiliy p ch (lines 20 25). In principle, from a echnical poin of view he size of he Bloom filer (IH) should be defined a priori based on he number of elemens o be sored and he desired false posiive probabiliy, being impossible o sore exra elemens wihou increasing he false posiive probabiliy. In his work, we explore wo possibiliies. On he one hand, we use a Scalable Bloom filer, a varian of Bloom Filers ha can adap dynamically o he number of elemens sored, while assuring a maximum false posiive probabiliy [17]. This soluion guaranees a fixed false posiive rae, a he cos of a modes linear increase of he sae size wih he number of iems. On he oher hand, we also consider fixed size Bloom filers, dimensioned as a fracion of he heoreical opimal size (compued wih complee informaion abou he number of daa iems in he sysem). This guaranees a consan sae size, irrespecive of he number of daa iems, a he possible cos of an increase of he false posiive rae. Simulaion resuls presened hereafer show ha using fixed size Bloom filers have no significan effec on he performance of he daa disseminaion process. Sep 3. The resuls of he probabilisic recogniion process are hen exploied by he MT 2 B algorihm o selec he less spread iems o be sored for redisribuion. Differenly from he previous version in [6], MT 2 B does no fill he OC by selecing direcly beween he less spread iems bu by selecing beween hose iems ha belong o he less diffused channels. If he curren OC capaciy would no be enough o sore all he iems ha could possibly be seleced for furher disseminaion, he MT 2 B sors he iems by heir p ch value and fills up he OC wih he firs n iems according o is capaciy. Thanks o his approach nodes have o mainain less sae informaion han he one mainained in [6]. Le us assume he Bloom filer size as fixed, and le us denoe wih K he number of channels and I he number of iems per channel. In he novel approach, every node has o keep only he sae informaion abou channels, hus he memory requiremen has an order of magniude of O(K) because i grows linearly wih he number of channels. By conras in [6] every node has o mainain sae informaion for boh channels and iems, which means ha he order of magniude in erms memory is O(K I). The improvemen is very significan, as in real scenarios I >> K. V. PERFORMANCE EVALUATION Hereafer, we evaluae he performance of he Probabilisic Recogniion hrough a series of experimens by which we show ha he proposed soluion auonomically converges o or ouperforms he resuls of he bes fineuned configuraion of he algorihm proposed in [6] wih a significan reducion of resource consumpion. A. Simulaed Environmen Nodes mobiliy is simulaed according o HCMM [2], a mobiliy model ha inegraes emporal, social and spaial noions in order o obain an accurae represenaion of real user movemens. Nodes move in a 6 6 grid corresponding o a m square, and are grouped in very compac communiies placed far from each oher so as o avoid any border effec e.g. involunary communicaion beween groups. Nodes mobiliy is limied inside he groups hey belong o, excep for few of hem called ravelers, ha are allowed o visi oher groups. Wih his configuraion we wan o simulae differen social communiies where usually people say, apar for few of hem ha due o heir social relaionships can mee people from differen social communiies. In his conex he only way o exchange daa is hrough nodes mobiliy, and ravellers play an imporan role because hey are he unique bridge beween communiies. In our scenarios we have as many channels of ineres as groups. For each group, all he channels are presen wih differen populariy degrees and assigned o he nodes according o a Zipf disribuion [4] wih parameer

6 Algorihm 1 Probabilisic Recogniion 1: Le M be he se of iems received from anoher node. 2: Le I ch be he couner for he iems in M ha belongs o he channel ch and are no presen in IH 3: Le C ch be he couner for he iems in M ha belongs o he channel ch 4: Le p ch be he diffusion probabiliy of he iems ha belongs o he channel ch 5: Le B(p ch ) be a Bernoulli random number generaor 6: Le 0 α 1 7: I ch 0 8: C ch 0 9: for all i M do 10: if ICC.conains(i.ch) hen 11: if ( IH.conains(i)) hen 12: IH IH i 13: I i.ch I i.ch +1 14: end if 15: C i.ch C i.ch +1 16: end if 17: end for 18: for all ch ICC do 19: p ch α p ch +(1 α) (1 I ch C ch ) 20: if B(p ch ) = 1 hen 21: Mark iems of ch as diffused 22: else 23: Mark iems of ch as no diffused 24: end if 25: end for 1. Moreover, for each communiy here is a differen mos popular channel. This makes he scenario uniform as far as channel populariy is concerned, as he same number of nodes is subscribed o each channel, while he populariy of channels wihin individual groups is skewed according o a convenional model (Zipf law). Every channel has he same number of iems which are iniially assigned o nodes according o a uniform random disribuion. The deailed scenario configuraions can be found in Table I. Paramener Value Node speed Uniform in [1, 1.86m/s] Transmission range 20m Simulaion Area m Number of cells 6 6 Number of nodes 200, 600 Number of channels 8 Number of iems 200(25 per channel) Number of groups 8 Number of ravellers 56(7 per group) Simulaion ime 25000s Table I DETAILED SCENARIO CONFIGURATION B. Simulaion Resuls For he sake of simpliciy, from now on he acronyms and will refer o he Probabilisic Recogniion case and Saic Recogniion, i.e. he algorihm presened in [6], respecively. All he resuls presened in his paper are mean values obained on 10 runs where he iniial configuraion of iems and channels were randomly reiniialized. We evaluae he performance of boh approaches in erms of hi rae, convergence ime and nework overhead. The hi rae a a given ime is defined as he mean value over nodes of he raio beween he number of iems acually presen in he SC of each node w.r.. he oal number of daa iems of he channel o which he node is subscribed. Convergence ime is defined as he ime insan when he hi rae exceeds 99%. The insananeous nework overhead is measured as he mean number of iems exchanged a a given ime insan. Le us recall ha o regulae he disseminaion process relies on saic recogniion hresholds, hus in order o have a fair comparison, we fine uned parameers for every scenario. Wih, he nodes exploi he local informaion hey receive from he surrounding environmen o build up heir own represenaion abou he diffusion process ha hey use o decide which iems are more profiable for redisribuion. This kind of awareness has a grea impac when he OC size is small. Indeed Figure 2a shows ha in a nework composed by 200 nodes wih an OC size of 10 iems, reaches a hi rae greaer han 99% more quickly han. The same behavior holds for a more crowded nework also: Figure 2b highlighs he disribuion abiliy of in a scenario configured wih a nework of 600 nodes, an OC size of 10 iems, and a number of iems significanly smaller han he nework size (200). In his configuraion, a he beginning of he simulaion, he wo hird of nodes are compleely unaware abou he conens acually presen in he scenario. However, also in his case he auonomic approach is able o quickly adap o he siuaion reaching complee coverage faser ha. By conras, he wo approaches become equivalen when he OC size is sufficienly large (OC size 50 ) o make he iem selecion a less criical ask, as shown in Figure 2c. To have a quaniaive undersanding abou convergence velociy we measure he converge imes of he wo approaches, which are in Table II. As we can see he probabilisic approach ouperforms wihou relying on any parameers fine uning. Compared o he probabilisic approach is less de- Experimen Ne. Size 200, OC size s 3800s Ne. Size 600, OC size s 5800s Ne. Size 200, OC size s 1200s Table II CONVERGENCE TIME FOR A COVERAGE 99% manding in erms of resource consumpion. Figures 3a,3b,3c

7 Hi rae Hi rae (a) 200 nodes, OC size = 10, (:θ I = 10,θ C = 10) (b) 600 nodes, OC size = 10,(: θ I = 25,θ C = 25) Hi rae Figure (c) 200 nodes, OC size = 50,(: θ I = 10,θ C = 10) Hirae rends of (black curve) and (gray curve) wih differen nework size 200 (a)-(c) and 600 (b). Iems (a) Mean number of iems exchanged by Iems (b) Deailed view of he firs disseminaion phase Iems Iems (c) Deailed view of he second disseminaion phase (d) Comparison beween and nework overhead Figure 3. Mean number of iems exchanged on a nework of size 200 give, a differen scales, an insigh of he mean number of iems exchanged by nodes during he simulaion on a nework of200 nodes. As we can see here are wo separaed phases in conen disribuion, he firs one (Figure 3b) refers o he disseminaion process inside groups before he arrival of he ravellers in he communiy. Afer he 65-h second of simulaed ime, he disseminaion process resars due o he presence of ravelers inside he communiy as depiced by he second phase of he process in Figure 3c. Ineresingly, afer some ime boh phases show a decrease in he number

8 Hi rae Hi rae Figure 4. (a) Hi rae rend of (black curve) and (gray curve) afer a channel injecion a 3000s (b) Iems Iems (a) (b) Figure 5. Mean number of iems exchanged in a nework of 200 nodes. Channel injecion a 3000s. of exchanged iems ha is an indicaor of he convergence of he diffusion and, even more imporan, i demonsraes ha does no wase resources o reransmi useless conens. By conras, in order o maximize he convergence velociy in, he daa exchange never sops even when all he iems are deemed as recognized (In ha case, according o nodes exchange daa iems seleced according o a uniform sampling process). Thus, i becomes clear he advanage coming from he probabilisic approach when compared o he nework load induced by, as shown in Figure 3d. Now we wan o sudy how behaves in a more challenging scenario. A a cerain ime during he simulaion, a se of new iems belonging o a new channel are injeced in he environmen. A randomly assigned populariy is assigned o he new channel, a random se of nodes (of equal cardinaliy for each group) is chosen o change heir curren subscripion in favor of he new channel injeced. Due o his change, hese nodes mus clean heir SC jus afer having run he MT 2 B algorihm o load in OC possible useful iems. A his poin he usual probabilisic recogniion approach sars o be applied also o he new channel. From Figure 4a we can noice ha, in a scenario of 200 nodes, afer he channel injecion a 3000s boh and reac o he new simulus, hough wih differen inensiy. seems o be more more responsive, bu le us remember ha i has been fine uned o obain his resul. By conras auonomically responds o he channel injecion resoring he hi rae rend jus afer 1000s. This proves ha well approximae he behaviour of ha, due o is fine uning, represens an he upper bound for his scenario. Figure 4b shows in more deail his behaviour. Moreover, in Figure 5 we can see wha happens o he nework load when he disseminaion process resars due o he injecion of a new channel boh for and. Finally, as anicipaed before, we presen he resuls of a sensiiveness analysis o evaluae he robusness of our approach in presence of an even less reliable diffusion informaion abou channels. Thus we devised a series of experimens where he IH size was reduced up o 40% of is iniial size, ha, in normal condiions is se o he number of iems presen in he scenario (200). Resuls can be found in Table III where we repored boh he maximum coverage obained and he corresponding convergence ime. These experimen show ha finely dimensioning he size of IH is no of primary imporance. Even when IH is drasically under dimensioned, sill archives almos 100% hi rae (even hough hrough a slower disseminaion process). B.F. size reducion 100% 80% 60% 40% Hi rae 99% 97% 98% 98% Conv. ime 2100s 4000s 10400s 16300s Table III SENSITIVITY ANALYSIS WITH REDUCED BLOOM FILTER SIZE.

9 VI. CONCLUSION This paper explois he very recen idea of using funcional models of he human brain s cogniive processes o drive daa disseminaion in opporunisic neworks. Iniial work in his area [6] has exploied he recogniion heurisic (a very well esablished model in he cogniive psychology field) o design an algorihm whereby nodes, upon conacs, recognise (i.e., quickly deermine) wha daa iems available on he encounered node hey should fech o help heir disseminaion. In [6] he main focus was on demonsraing he general viabiliy of his idea, bu he proposed algorihm suffers from significan scalabiliy problems, and mus be fine uned o obain opimal resuls. In his paper we solve he above problems, by proposing for he firs ime a soluion suiable for concree implemenaion in opporunisic neworks. Firsly, in his paper he decisions aken by nodes are based on aggregae informaion abou daa iems, and do no require ha hey keep sae informaion for each and every single daa iem available in he nework. Using aggregae informaion drasically reduces he sae mainained by nodes, makes he sysem much more scalable, and suiable for adopion in large scale environmens. In paricular, he sae mainained wih he algorihm proposed in his paper is consan wih respec o he number of daa iems available in he environmen, while he approach in [6] he sae mainained by each node grows linearly wih he number of daa iems. Imporanly, such an improvemen in scalabiliy is no paid wih a significan reducion of he performance of he daa disseminaion process, as nodes are sill able o receive wha hey are ineresed ino wihin a similar amoun of ime. Second, in he proposed algorihm nodes use a probabilisic approach o deermine he relevance of daa iems and he usefulness of furher replicaing hem. This provides wo key advanages. On he one hand, he proposed algorihm does no need a priori uning of is parameers o mach he characerisics of he environmen where i operaes, bu i is able o dynamically learn he correc behaviour and adap i where he environmen changes (e.g., new ypes of daa are injeced). Second, even in saic condiions i explois he probabilisic characerisics o change - once in a while - he behaviour learn by monioring he environmen condiions, and i is hus able o explore new, and possibly beer, configuraions. ACKNOWLEDGMENT This work is funded parially by he EC under he FET-AWARENESS RECOGNITION Projec, gran , FIRE EINS (FP ) and parially by he Ialian Minisry of Educaion, Universiy and Research under he IN PEOPLENET (2009BZM837) Projec. REFERENCES [1] C. Boldrini, M. Coni, A. Passarella, Design and performance evaluaion of conenplace, a social-aware daa disseminaion sysem for opporunisic neworks, Compu. New. 54, , [2] C. Boldrini and A. Passarella. 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