Adaptive Pervasive

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International Journal of Perva

Computing and Communicat

Vol. 6 No. 3, 2

pp. 333

# Emerald Group Publishing Lim

1742-7

DOI 10.1108/17427371011084

Adaptive pervasiveadvertisement: scenarios and

strategiesAlberto Rosi, Alessandro Codeluppi and Franco Zambonelli

Dipartimento di Scienze e Metodi per lingegneria,University of Modena and Reggio Emilia, Reggio Emilia, Italy

Abstract

Purpose Starting from the premise that digital screens are pervading our everyday urban andsocial environments to serve a variety of purposes, the purpose of this paper is to show how screenscan be made aware of what is happening around them and based on specific strategies adaptaccordingly the advertisement flow to supply to users more engaging contents.Design/methodology/approach The paper presents an overview of future pervasive advertisement

scenarios, and sketches the architecture and implementation of a system for adaptive context-awarepervasive advertisement. Subsequently, with the help of a simulation environment, the paper evaluatesthe performances of several adaptive context-aware advertisement strategies, and compares themagainst non-adaptive ones.Findings The paper demonstrates that, in a wide range of conditions, an advertisement system basedon adaptive context-aware strategies leads to a gain in terms of commercial value with respect totraditional non-adaptive strategies for advertisement broadcasting.Practical implications A system for pervasive advertisement could be easily brought to life,leading advertisement companies to a much more targeted exploitation of the screen resource and,eventually, to higher revenues.Originality/value Adaptive advertisement systems can offer notable commercial advantages overtraditional advertisement systems even when visitors demonstrate poor collaboration towards the system.

Keywords Digital communication systems, Advertising media, Marketing strategy

Paper type Research paper

1. IntroductionAdvertising companies invest huge amounts of their earnings in the attempt of attractmore and more customers to their services and products. Marketing studies havealready outlined that traditional and very expensive advertisement strategies basedon massive ads bombarding over a generic audience are not very effective and, in somecases, are at risk of being counterproductive (Fritz, 1979). Thus, advertising companiesare always highly interested in newer communication media and advertisementstrategies that the technological development could provide.

Web ads, by exploiting browser cookies, information related to servicessubscriptions and self-provided public profiles, have represented a first try of

adaptively approaching user needs by fetching user preferences, and havedemonstrated to be very effective (Kumar et al., 2000; Langheinrich et al., 1999). Behindthe process there is the sense that companies products and services are to be deliveredto users potentially interested in, limiting the risky negative impact of annoying orirritating people. Clearly, this leads to a change in paradigm: while traditionaladvertisement systems lay on contents pushed by companies to users in the hope thatcould meet a latent need, adaptive advertisement allows to drastically reduce thedistance between the parties, targeting contents to the segment of populationpotentially interested in them, making every single product exposure more incisive andable to affect customers conception.

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/1742-7371.htm


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What can be reached in a fully pervasive environment (Castelli et al., 2007) in whichseveral and different pervasive devices (Eagle and Pentland, 2006; Estrin et al., 2002)(as PDA, Smartphone, GPS, RFID Tag, etc.) concur in defining a complete profile ofusers attending? Pervasive computing devices like PDAs and smartphones are

becoming mates of life and business of an ever larger share of people. However, as oftoday, such advertising screens display generic information in a simple cyclic way

independent of the situation (i.e. independent of who is actually close to that screen).The next evolutionary step, and in a way our contribution, is to conjugate the huge

capability offered from the above instruments to overcome the challenges arising in atraditional advertisement context: sending the right ads to the right people through theright media and the right time (Ranganathan and Campbell, 2002) A smart service

devoted to decide what information to display could exploit the availability ofcontextual information to adaptively decide what information to show on the basis

of the people around and of their activities and interests. This would increase the valueof the displayed advertisement both for users and for advertising companies.

In this context, the specific contributions of this paper are:. to discuss the main characteristics, features, and issues of a system for pervasive

advertisement, based on the smart usage of user profiles and short-range RFID

tags, to enable screens to sense and understand the characteristics of itspotential audience, and adapt its advertisements accordingly; an adaptive

pervasive advertising systems we implemented is also presented to demonstratehow easy to realize and deploy such a system could be; and

. to evaluate different strategies for the advertisement selection through simulations.

A non-sensitive strategy based on a prioristatic selection of advertisement, calledround robin, is compared to strategies based on a real-time adaptive mapping of

advertisements with potential audience in non-competitive and competitive

contexts. Simulations demonstrate that the above context-aware strategies,whatever the conditions of the simulation environment may vary and even in thecase users demonstrate poor collaboration, they lead to a conspicuous gain with

respect to a traditional strategy.

The remainder of this paper is organized as follows. In Section 2, we introduce thegeneral scenario of adaptive pervasive advertisement and discuss the related works in

the area. We afterwards trace the design and present the general architecture andimplementation of our system for pervasive advertisement. In Section 3, we describe the

simulation environment and, before describing the strategies for the selection of theadvertisement to be shown, we introduce the concept of value associated to each relation

advertisement-user. In Section 4, we discuss and evaluate results coming fromsimulations varying strategies for advertisement selection. Section 5 concludes and

identifies areas for future research.

2. Adaptive pervasive advertisementIn this section, we introduce the general scenarios in which we consider pervasive

advertisement can soon be applied and take off, the related technological and researchworks having recently addressed similar issues. Also, by means of a prototype, we

show how pervasive advertisement systems can be indeed easily implemented withcommercial technologies.


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2.1 Concepts and scenariosThe scenario considers confined environments crowded by people. These could bemodern exhibition centers, malls, big museums, railway stations, or stadiums. There, it isrealistic to assume the presence (at least in the near future) of a pervasive infrastructure

of embedded devices such as sensors of various types, Wi-Fi and bluetooth connections,and user location systems (Sjodin, 2001) in fact, the owner of such locations can definitelyafford the costs of deploying such pervasive infrastructure providing good services topeople around and, in the end, increasing revenues or commercial competitiveness.

As another assumption, we assume visitors to carry portable devices, such as PDAs orsmart phones, which can be used to purchase electronic tickets, and/or be provided withRFID-based tickets. Indeed, exhibition centers, railways companies, etc. can again wellafford the cost of releasing RFID-based tickets or identifying tags. Via these sorts ofelectronic tickets, either on PDAs/smart phones or on RFID tags, one can conceive ofstoring and making accessible some kind of information about users. For instance, suchelectronic tickets could inherently contain information on the nature of the visit, i.e. whattype of entrance the user has paid, or if he/she has paid other possible fees, which implicitly

says something about the user him/herself. Similar considerations of course apply to e.g.RFID-based receipts in a mall and to many other documents that the user carries with him/her for the very fact of being in a specific environment. In any case, one can also think suchtickets to contain more explicit information about users, as resulting from, e.g. having theuser compiled a short questionnaire before obtaining the ticket. In general, it is notunrealistic to assume users agree to unveil some information about them, if this enablesthem to receive useful information and/or to receive some discounts on products or services.

Finally, we consider the presence, in the environment, of a number of digital screensthat display information about the location itself, the events thereafter hosted andorganized, as well as third-party commercial advertisements.

From all the above, it is possible to think that by means of an infrastructure thatcollects the personal information of users based on their current location theinformation to be displayed on a digital screen can be dynamically adapted to the typeof audience, i.e. to the profile of the users currently close to the screen. This can be usedto provide better and more focused information to users, and it can also be used toadaptively decide what advertisement to display depending on the specific interests ofthe current audience. Needless to say, this can be advantageous both to the owner of thescreen (which can better sell its advertising space) and to the advertising companies (towhich is ensured a more targeted advertisement).

The quantification of the commercial advantages of pervasive advertisement will beanalyzed later on in this paper. Independently of them, most observers agree that theabove scenario will become increasingly common in a few years, because of the ever-growing diffusion of portable devices and of the incredible success of PDAs and smart

phones, of RFID tags and sensor network technologies, and because digital screensincreasingly pervade our urban environments. Thus, pervasive advertisement has thepotential to exhibit a quick and astonishing commercial diffusion.

2.2 Related workIn recent years, several studies have been performed over general service provision inpervasive environments and our work starts mainly from the contribution of otherresearch groups (Ghorbel et al., 2006; Ranganathan and Campbell, 2002; Rashid et al.,2005; Rogers et al., 2007) in the field. However, it is worth reporting that a limitednumber of studies have specifically focused on pervasive advertisement.


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The work of the University of Illinois (Ranganathan and Campbell, 2002) on 2002outlines a new horizon for the advertising, tracing the path for reaching customers in anew and revolutionary way, delivering the right ads to the right people. Although thiswork stresses why pervasive advertisement will be a highly effective tool for both

advertisement companies and visitors, it does not show how to realize it, neither froman architectural point of view. This task has been taken in charge by the Handicom Lab(Ghorbel et al., 2006) which proposes assistive provision architecture in pervasiveenvironments. Although its modular architecture based on a distributed approach canmake it suitable for a variety of services, there included pervasive advertisementservices, the issue of pervasive advertisement has not been explicitly analyzed.

Other proposals focus on gathering information about the location of users so as tomake it possible to enforce adaptive location-based strategies to push information andadvertisement to users on specific locations. For instance, the work of the LancasterUniversity (Rashid et al., 2005) without directly assuming access to users profiles, tries toinfer users needs and desires exploiting location-based information over bluetooth andWi-Fi connections, and to push targeted advertisements to users personal screens. Beside

the fact that we are more interested in public displays rather than private ones, that systemcan be considered a valid context-based user profile generator for pervasive advertisementsystem, which can well complement personal profile information provided, e.g. on anRFID-based ticket. Without the latter, the quality of inferred user profiles would be toostrictly related to the environment in which the application takes place and too unreliable.

Without any doubt, the proposal that more closely shares our vision is the one of theUniversity of Southampton (Rogers et al., 2007). Their system, called Bluescreen, isin fact an agent-based environment in which bidding agents with access to (a sort of)user profiles compete for showing their ads to visitors. Although our approach sharesseveral goals with this one, there are fundamental differences. First, while our idea is toselect commercial messages that maximize their commercial values (see also section 3.1),Bluescreen has the goal of minimizing the number of transmissions that are needed to

show the totality of commercials to the totality of people that are expected to transitthrough the system. Second, the very concept of user profile adopted is different andrather more similar to that already discussed for Rashid et al. (2005). In fact, unlike ourapproach, Bluescreen is not interested in what users are, or are not, interested in. Userprofiles are replaced by a probabilistic model, based upon independent Poissonprocess, that describe the number of users who are likely to be exposed to any advert inthe future considering occurrences in the past.

2.3 A general architecture for pervasive advertisementStarting from the broad scenario depicted in section 2.1, and relying on the experienceof past works in the area as discussed in section 2.2, it is rather simple to identify thefew key characteristics that a general system for adaptive pervasive advertisement hasto exhibit:

. For each screen in an environment, the system should be able to sense whichusers are around a given screen at a given time, and must be able to sense andanalyze their profiles.

. The system must rely on some strategies to identify, based on the sensed profilesof the people around a screen, what advertisement is worth to be displayed.

With regard to the first point, there is no need to look for sophisticated solutions. Byexploiting the fact that users profiles will be stored on some devices (either PDAs/smart


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phones or RFID tags) capable of short-range communication, one can immediatelyachieve the two-fold goal of locating users and collecting profiles. Simply, if either abluetooth receiver (for PDAs/smart phones) or an RFID reader (for RFID tags) isco-located with each screen, the very fact that the screen receives a profile implies that

the associated user is close to the screen. Thus, one screen can be easily made availableof user profiles in the proximity, determining its target audience, for later analyses.

With regard to the second point, there are a variety of possible strategies that thescreen can adopt to decide which one is the correct advertisement to show for a givenaudience. Indeed, the analysis of the pros and cons of such strategies is one of the veryimportant goals of the paper.

Overall, for each screen, the simple architecture of Figure 1 can be defined to act as ageneral reference architecture for pervasive advertisement. At each screen, some short-range wireless receivers must be present to collect profiles of close users. A computationalprocess, the profile analyzer, co-located with each screen can then analyze/aggregate/inferinformation about the users profiles. As a simple example, the profile analyzer canaggregate profiles to compactly represent in the form of class of interest percentage

tuples (e.g. sport 50 percent; literature 50 percent). The information produced bythe profile analyzer is then passed to another process, the strategy engine, in charge ofselecting, on the basis of the analyzed profile and of a database of advertisement info, theadvert to be properly displayed on the screen during the next time slot. We emphasizethat such architecture is totally technology-independent and very modular (e.g. the profileanalyzer is independent of the specific wireless technology adopted to collect profile, thesame as the strategy that can change without necessarily having to change the othercomponents). Also, such architecture does not exclude the possibility for the strategyengine to implement its decision by interacting with other screens, i.e. of collectinginformation from the profile analyzers of close screen and/or to adopt a distributedcooperative strategy by interacting with other strategy engines. These possibilities,however, are not further investigated in this paper.

Figure A general architecture f

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Beside the careful study and comparison of various strategies for pervasiveadvertisement that we have performed in a simulation environment (and whichrepresent the core contribution of this paper as from sections 3 and 4), we have alsoimplemented a proof-of-concept prototype of a pervasive advertisement system. The

system architecture, as from Figure 1, employs a laptop for the implementation of themodel, some RFID tags embedded in our department badges, an RFID reader forcatching users profiles, and finally a 17 in. display (see Figures 2 and 3) asadvertisement sign. Such implementation confirmed us about the possibility of simplyimplementing and setting up the system with low-cost commercial technologies. Thedeployed of the system in the cafeteria of our department (see Figure 3) also gave us thefeeling that users can positively accept such kind of systems.

3. StrategiesIn this section, we introduce a number of adaptive strategies for advertisementselection, to dynamically decide which advertisement to show depending on the usersprofiles. Before introducing such strategies, and since strategies will be based on their

decisions on the value of displaying a given advertisement at a given time, we need toclarify the concept of value.

3.1 The concept of advertising valueDepending on the personal interests of people viewing, i.e. on their sensitiveness andreceptiveness to advertisement topics, an advertisement slot may have different valuesfor an advertisement company.

Let Nc be the number of advertisement companies, each with its own specificproducts/good to advertise; letNv the number of different visitor categories (each of whichexpressing a different degree of interest to different classes of products/goods). Eachadvertisement company, for each class of users, can determine the value of showing its

advertisement to a person of a class. In other words, one could determine the set of values:

vi;j i 1: :Nc; j 1: :Nv

Figure 2.The equipment


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determining the value for the advertisement company iof showing an advertisement to avisitor of class j. In our studies, to ensure an unbiased assignment of vi,j values fordifferent companies, we bound the total amount of values that companies can spreadacross users classes, i.e.:

XNvj1

vij 1;000 for all i

Hence the overall value of an advertising slot for a company iof showing an advertisement

corresponds to:

XNvj1

njvij

where njis the number of visitors from the categoryj expected to view the advertisement.Such conception of the value leads to a trivial consequence: the more advertisement

companies recognize a higher value to a class of visitors, the more money they wouldspend to transmit their ads.

3.2 Advertisement strategies

In order to trace the main features and peculiarities of our pervasive advertisementapproach, we have experienced three different strategies of transmission, representingthree possible ways by which advertisement system composes the sequence ofadvertisements that will be displayed.

3.2.1 Non-adaptive strategy: round robin. The non-adaptive round robin strategy isthe one typically adopted in the great majority of advertisement displays. Simply, the roundrobin strategy builds the stream of advertisements around a priori decided and staticsequence; advertisement system will be restricted to play this sequence in a cyclic way.

In our studies, the round robin strategy acts as the baseline upon which to evaluatethe advantages of adaptive pervasive advertisement strategies.

Figure A proof of conce

deployme


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3.2.2 Adaptive strategy: non-competitive (NCAS). The second transmission strategywe consider is simple as well, but it introduces adaptiveness (in the form of userawareness) because it builds the advertisement sequence following the preferences ofpeople in the proximity of an advertisement screen.

The strategy works as follows:. the advertisement screen senses the profiles of people around it (at least of those

for which a profile is available and is transmitted);

. it computes the value associated by company to people attending (based onpeople group of interests); and

. it broadcasts the ad corresponding to the company that maximizes the value,i.e. it shows at any time the best possible advertisement for the audience. Thatmeans finding the icompany that maximizes the following function:

XNv

j1

njvij

From a more commercial viewpoint, such non-competitive strategy puts the power ofdeciding what advertisement to show in the hands of the display owner. The displayowner, taking advantage from the above depicted rule that companies the morerecognize a value to a class of visitors, the more they would spend to transmit themtheir ads, simply tries to maximize the overall value of displayed advertisements and(consequently) its own incomes.

3.2.3 Adaptive strategy: competitive (CAS). The second adaptive strategy we haveexperienced considers the possibility for advertisement companies to compete fortransmitting commercials, which can more closely reflect a future scenario in which

display owners can dynamically decide where to spend their own advertising budgets.To clarify the potentials of a competitive strategy, consider the following scenario:

given a composition of visitors, the company A broadcasting its advertisement wouldobtain a value of 1,324 while the company B would achieve a value of 1,319. In accordwith the non-competitive strategy, the advertisement system would simply reward thecompany A despite the fact company B would be greatly interested in transmitting itscommercial as well, and would even able to pay more for it.

Overall, the competitive strategy works as follows:

. An advertisement screen senses the profiles of people around it.

. It computes the values associated by companies to people attending (based on peoplegroup of interests). These values are ordered in a decreasing way and open for

inspection by advertising companies (i.e. by software agents acting on their behalf).. A call for tender (i.e. a first-price sealed bid auction) is open among all the

advertisement companies for which the value of the advertisement exceeds acompetition threshold calculated as a percentage of the maximum obtainablevalue. The adoption of a competition threshold comes from the practical need tolimit the participants to the call for tender to those companies that have somereal interest in transmitting.

. By placing bids, advertising companies will compete for the right of transmittingads.


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. The company that wins (i.e. that places the highest bid) will transmit thecommercial. In the case of non-unique highest bids, a winner is selected randomly.

Let us now explain, in details, the way by which advertising companies can evaluatewhat bid to place. Advertisement companies submitting bids would have to consider:

. The value they accord to the tender call itself, or in a more appropriate way, thedegree of attractiveness it shows. It could be calculated as the ratio betweenmessage potential value Vm (expressed by the product between nj and vij) andthe mean instantaneous value V of messages transmitted until that moment(a message of potential value equal to 1,000 assumes different attractiveness ifthe mean value obtained for former messages with the same composition ofvisitors has been 500 or 1,500).

. Companys economic budget: at the beginning of every simulation companies detainan initial budget b of 1,000 unities, which is decreased (by the amount correspondingto the placed bid) each time a company wins a bid. In our experiments, to make thesystem closed, and to avoid that a company could run out its funds, the amount paidfor the winning bid is afterwards split between other competitors.

. Their need for winning the race. Companies have a personal degree of satisfactiongs, which varies in a range between one (the lowest degree of satisfaction) and ten(full satisfaction). Initial satisfaction is equal to five for all companies, it is increasedby one for a company each time it wins a bid and decreased by one each time itloses. The more a company is satisfied the less it will offer for transmitting itscommercials. Considering the mathematical function described some rows below,the degree of satisfaction makes companies offering different amounts even at thepresence of the same group of visitors. This prevents the emergence of static andlong lasting links between a company and a particular group of visitors.

Based on the above considerations, the bid that a company could have advantage toplace can be calculated following this mathematical function:

xVm; b; Gs b Vm

V

1

gs

In other words, the bid amount results to be proportional to the remaining budget and tothe degree of attractiveness the attending visitors induce, while it is reversely related totheir degree of satisfaction (which depends from the number of auctions awarded so far).

Clearly, since companies cannot offer lump sum higher than their budget, the actualbid will be:

bid minfxVmess;

b;

Gs;

bg

As an additional consideration, we are aware that a vast number of different possiblecompetition strategies (i.e. auction models) upon which to rely (Gelenbe, 2008) could beconsidered. However, the analysis of the detailed impacts of different kinds ofcompetitive strategies is beyond the scope of this paper.

3.3 Simulation environmentAs depicted before, we consider a scenario in which a crowd of people is visiting an area(e.g. an exhibition center) populated by several advertisement screens. For reproducing


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such a scenario we have built a simulation environment based on RePast (recursiveporous agent simulation toolkit), a Java toolkit for the simulation of agent-basedmodels.

The model we define for our simulations foresees three classes of main actors (agents):

visitors, advertisement screens (which we call towers), and advertising companies. Suchagents are spread in a simulated landscape (shed) in which activities and interactionstake place.

Visitors move into the shed and they are represented by colored spots (as in Figure 4);different colors represent the different classes of interests. At simulation startup, visitorsare spread in a random way over the shed. Visitors are programmed to be independentrespect each others and to be free moving over the shed, representing a crowd wanderingin a closed space. Visitor movements are continuous; during every unit of time agentscould choose to remain still or eventually to change their position. For a unit of time, onlymovements inside a circle of radius equal to one are allowed. Moreover, visitor agentskeep in mind latest movements, from this comes that agents preserve their directions forseveral steps avoiding unnatural behaviors characterized by continuous sheers.

Towers, which simulate advertisement screens, embed the logic for collecting profilesof nearby visitors, computing values of advertisements, selecting advertisements (or, forcompetitive strategies, issuing call for tenders and selecting winners), and (virtually)displaying advertisements. It is assumed that towers have a circular sensing range(Field) for visitors profiles of specific size (FieldSize). This represents the range withinusers profiles can be collected, and the range within advertisements can be effectivelyseen by visitors (e.g. a bluetooth connection can be established at a maximum distanceof 10 m that could represent the maximum distance at which visitors comfortably attendto an LCD display transmitting advertisements).

At simulation startup, towers are placed over the shed and programmed with:

. a list of companies with relative information;

. a list of visitor categories with relative information; and

. the value associated to every relation companyvisitor category.

Figure 4.The simulationenvironment


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Therefore towers will periodically act as follows:

. sense visitors within the field;

. compute the value of messages;

. select commercials to be broadcast according to the selected strategy; and

. store information related to broadcasted ads (company and value) for the sake oflater analysis and evaluation.

For the case of the competitive strategy, an advertising company agent for each ofthe advertising companies is associated to each of the towers, with the goal ofparticipating to the call for tenders issued by the associated tower.

4. ResultsTo evaluate the effectiveness of adaptive pervasive advertisement we have tested, inthe above described simulation environment, the non-adaptive round robin strategyand the adaptive non-competitive strategy and finally compared them with eachothers. Following, we evaluate the impact of competitive strategies.

The simulation environment enabled us to flexibly test the effect of adaptivepervasive advertisement on a variety of different situations, by varying:

. the number of towers;

. the number of visitors;

. the radius in pixels (FieldSize) of towers field; and

. the percentage of visitors enabled or willing to communicate their personalprofiles to towers.

4.1 Main hypothesis

Simulations performed were built over the following hypothesis:

(1) Sheds dimensions are fixed at 1,000 750 cells.

(2) Shed does not contain hurdles neither fixed routes, visitor movements areabsolutely not constrained.

(3) Towers fields do not cross each others: visitors can be reached by only onecommercial at time.

(4) During simulation setup visitors are spread over the shed in a random way, yetpreserving a rather homogeneous distribution.

(5) Visitors are branched in groups, one for each category of interest. Groups haveequal cardinality and their presence is fixed during the simulation.

(6) The value of every company-category couple remains unchanged across a setof simulations. In particular, companies act considering the table of valuesreported in Table I, characterized by a high standard deviation (about 30) forusers interests. The impact of different standard deviations will also be analyzedlater on.

(7) Every simulation lasts 4,000 ticks (units of time), or to be more precise, thenecessary time for towers to transmit a total of 200 commercials in eachsimulation. For each considered combination of parameters a total of 25simulations have been performed.


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(8) Basic simulation parameters (around which we played by varying individualparameters to evaluate their specific impacts) are:

. number of visitors (nv) 800;

.

fieldSize (in cells) (fs) 250; and. number of towers (nt) 5.

4.2 Round robin vs NCASLet us first compare the performances of the first two transmission strategies weintroduced, round robin and a NCAS. Performances will be measured altering, time bytime, basic simulation parameters: number of towers, the number of visitors andtowers field size.

4.2.1 Changing the number of visitors. Simulations have been performed employingdefault values for fs and nt. The number of visitors has varied from 400 to 1,600.Results are presented in Figure 5.

Figure 5.Single advertisementmean value over thechange in the numberof visitors

Table I.Companies-intereststable of values

Value classes of visitorsCompanies 1 2 3 4 5

1 88 1 1 3 72 5 75 10 8 23 10 5 5 75 54 5 10 75 5 55 10 5 5 5 75


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It is noticeable that an adaptive strategy enables companies to steadily reach a highervalue from each commercial with respect to traditional round robin transmissionstrategy. Figure 5, in particular, underlines that the influence between the number ofvisitors and commercial mean value is linear (as expected, in that the higher the number

of persons seeing a commercial, the higher the value of such commercial).However, the relative gain in exploiting an adaptive strategy over a non-adaptive

one is higher when the number of visitors is low. The reason for this can be found in theconsideration that a growth in the number of visitors leads them to be more denselydistributed over the shed and, consequently, also more uniformly distributed as far asthe profile is concerned.

This necessarily leads towers to sense inside respective fields of detection an increasingbalanced set of user profiles. This makes increasingly harder to select a single commercialable to fully fit users tastes. Consequently, the mean value of every single commercialdecreases.

4.2.2 Changing tower field size. We assume that the size of towers fields representsthe different ranges to which transmission devices (as RFID reader, bluetooth and

Wi-Fi access point) can broadcast messages. For this set of simulations we opt fordefault values of nv and nt, field sizes have assumed instead the following values(in terms of simulation cells): 125, 250, 500.

The results, represented in Figure 6, show that even if, in both cases, the gain growsfollowing the growth of the field size, NCAS demonstrates to perform permanentlybetter respect to round robin. In particular its gain reaches its peak, 66 percent, whentransmission conditions are at the worst (very short range of transmission or rathervery low probability of sensing the right users preferences) while decreases up to the12 percent when the field size is maximum.

Figure Single advertiseme

mean value over thechange in towers field s


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Such trend, as for the one depicted by the variation in the number of visitors, finds itsmotivation in the difficulty of selecting a single commercial able to fully fit userstastes. It is worth noticing that, even the gain decreases augmenting the field size, theadaptive strategy continues to perform the best.

4.2.3 Varying the percentage of visitors sending personal information. This set ofsimulations has been performed with the aim of testing advertisement system behaviorvarying the percentage of visitors sending personal information. The number of visitorsand towers field size were left fixed respectively to 800 units and 250 cells; the number oftowers was five. Clearly, simulations exploiting the round robin strategy of transmission arenot influenced by the variable under exam. Results in Figure 7 demonstrate to follow thetrend exhibited during previous simulations. In particular they show that even whenvisitors do demonstrate poor collaboration towards the pervasive advertisement system(e.g. not providing their personal information for lack of time, for practical or privacy-related reasons), NCAS will anyway perform better than round robin. That happens despitegain on single advertisement mean value falls from initial 26 percent (when the degree ofvisitors sending personal information is 100 percent) to an ending 8.54 percent in the worstcase. Even the recognition of limited number of user profiles leads anyhow to a gain.

This result is very encouraging because it shows that the advantages of adaptivepervasive advertisement can be preserved even for those scenarios in which it is notpossible to collect detailed information about all users, or in which users are unwillingto make their personal preferences public.

4.3 Round robin vs CASNext simulations will introduce a new element inside the mechanism of advertisementselection, the competition between advertiser companies. Our NCAS has been

Figure 7.Single advertisementmean value over thechange in the numberof visitors sendinginformation


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developed with the aim of experimenting adaptive elements in the context of thepervasive advertisement. However this leads to an ideal situation in which users areforced to attend only the ads of companies achieving the maximum mean value for thataudience. The outlined situation appears unrealistic for two main reasons:

. Visitors after watching several times the same ads could be bored of them (itmakes their values lower).

. A company, who totals up a mean value just a bit lower respect to the winnerone, has all the interest in transmitting its advertisement that would be howeverenjoyed by people attending.

Our competitive strategy is built over these considerations and is realized adding someconstrains (think at the degree of satisfaction and the budget) to the non-competitivestrategy. This obviously leads that, from a performance point of view, in the computationof advertisement mean value NCAS results anyhow an upper bound respect to CAS.

In this new competitive context, the performed simulations aim at investigating:

.

How the degree of competition between companies affects the value oftransmitted strategies.

. What happens if companies would base their considerations on different tablesof company-group of interest values.

4.3.1 The influence of competition on advertisement mean value. This set ofsimulation has been performed fixing the number of towers to five, the field size to 250cells, the number of visitors to 800, and the number of groups of interests to five. To testthe impact of competition, and accordingly to our CAS for pervasive advertisement, wehave varied the competition interval (i.e. the interval of values in which companiesexpress interest in bidding) from 0 to 55 percent of advertising value, implying an

increasing number of companies to compete for each advertisement slot as thispercentage increases (0 percent corresponds to no competition, in that only thecompany with the highest value would compete).

Figure 8 suggests the following considerations:

. Since competition threshold does not represent a variable for the round robintransmission strategy, commercial mean value is clearly not influenced by it.

. Similar considerations apply for the non-competitive strategy, which representsan upper bound to the competitive one (the message with the highest value isalways transmitted).

. In the presence of competition, the chance that a message with a value lower thanthe maximum one is selected increases as the competition increases.

Nevertheless, even in the presence of high-degrees of competition, a relevant gainover the non-adaptive round robin strategy is preserved.

These results show that adaptive pervasive advertisement can be effective even in thepresence of competitive mechanisms. Yet, we have not analyzed the impact of thepresented competitive strategies (or of other competitive strategies) that can beconceived for such a scenario on advertisement prices. That is, our goal here is to showthat the advantages of pervasive advertisement in better targeting the audience arerelevant both in the absence and in the presence of competition among advertisementcompanies. Understanding how these should translate in some actual pricing of


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advertising slot (tuning the price of a slot depending on the audience), and whatstrategies should advertising companies adopt in deciding how much to pay for a

given slot, is out of the scope of this paper.4.3.2 The influence on message mean value altering the table of values. Thelastsetof

simulations wants to investigate the variation in the mean value of broadcasted messageswhen the relation between companies and classes of interests changes. CAS and round

robin have been performed on simulations employing five towers, a field size of 250 cells,800 visitors, and a competition threshold of 5 percent. Four profiles of values associatedby companies to visitors classes of interests have been identified. The first profile

characterizes specialized companies (the interest is pointing at one only class of visitor) andexhibits the highest value of standard deviation (equal to 44), the forth profile represents anopposite situation where companies are equally interested in all the classes, standard

deviation is the lowest one and equal to 0. Profiles 2 and 3 are a middle ground between theprevious ones with values of deviation standard, respectively, equal to 38 and 14.

Results in Figure 9 show that:

.

For profile 1, because of segmentation of interests and of the competitionthreshold at 5 percent companies do not compete, therefore CAS behaves verysimilar to NCAS (see in addiction Figure 8).

. Profile 2 produces the previous set of simulations (with competition threshold at

5 percent) we have performed. At the same way, CAS leads to a gain of about25 percent over round robin.

. For profiles 3 and 4, following the trend that comes out from section 4.3.1,competition between companies decreases the gain; the more the battle betweencompanies is harsh, the more every advertisement message looses its value.

Figure 8.Single advertisementmean value over thechange in the degreeof competition


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. In other words, for adaptive competitive strategies to be effective, there mustbe some differentiations in user profiles as well as in the specific ideal targets ofadvertising companies.

4.4 Summary of experiments

Results coming from simulations demonstrate that, in a wide range of differentconditions, an advertisement system based on sensitive strategies leads to a gain interms of commercial mean value with respect to any traditional strategy foradvertisement broadcasting. In particular, what the experiments have outlined is that:

. For adaptive strategies to be highly effective, each screen must target a limitednumber of visitors. By increasing the cardinality of the audience (i.e. in very crowdedenvironments or by having each screen a too wide size of action) the specificity ofthe target smooths, making it difficult to effectively select the right advertisement.

. Adaptive strategies preserve effectiveness even when a limited percentage of theaudience contributes to transmit its personal preferences (or, which is the same,when the available technology enables to capture only a limited percentage of the

audience profiles).. Adaptive strategies perform very well even at the presence of competition

among advertising companies, although both a too high degree of competition ora lack of specificity in the audience tend to reduce the value of advertisingmessages.

Finally, another important result has to be mentioned. A pervasive advertisementsystem exploiting an adaptive strategy demonstrates to be fully operative andadvantageous in term of value even if only a limited portion of visitors would agree toprovide their personal information.

Figure Advertisement valuwhen profiles chan


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5. Conclusions and future workThe increasing diffusion of digital screens in our everyday urban environments canserve a variety of purposes from informing us about social and cultural events toshowing commercial advertisements. A new pervasive paradigm applied to modern

computing technologies can help screens to understand who is close to them andadapt accordingly the content shown on the screen, supplying to users a better service.

The aim of this paper has been to demonstrate that the depicted system for pervasiveadvertisement could be easily brought to life, leading advertisement companies to a muchmore targeted exploitation of the screen resource and, eventually, to higher revenues. Afterhaving introduced the concept of adaptive pervasive advertisement and its potentials,along with the design and implementation of a simple yet effective advertisement system,we have presented and evaluated different strategies for composing the sequence ofadvertisements to be displayed. In a word, our study has demonstrated that adaptivecontext-aware strategies (i.e. those able to select advertisements based on the actualaudience) are very effective (i.e. lead to a conspicuous commercial gain), whatever theconditions of the simulation environment may vary, even in the case visitors demonstrate

poor collaboration, and even when advertisers have to compete with each others.Future works will investigate a more structured and autonomic distributed architecture

to support strategies based on cooperation among multiple screens. Also, further studieswill be performed on strategies. Despite depicted strategies demonstrate to perform well,they are very simple and represent only a first approach to this field of research. Morecomplex and distributed strategies will certainlylead to new and more exhaustive results.

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About the authorsAlberto Rosi, PhD, is Contract Researcher at the Agent and Pervasive Computing Group at theUniversity of Modena and Reggio, and has been since 2006. He obtained the Laurea degree inBusiness Management in 2006 from the University of Modena and Reggio Emilia. His currentresearch interests include distributed and pervasive computing, context-awareness, and sensornetworks. Alberto Rosi is the corresponding author and can be contacted at: [email protected]

Alessandro Codeluppi is an Engineer. He obtained the Laurea degree in BusinessManagement in 2006 from the University of Modena and Reggio Emilia.

Franco Zambonelli is a Professor of Computer Science at the University of Modena andReggio Emilia. His research interests include distributed and pervasive computing, multiagentsystems, and agent-oriented software engineering. He received his PhD in computer science from

the University of Bologna. He is a Member of the IEEE, the ACM, AIIA, and TABOO.

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