Interpersonal context-aware communication services

IEEE Communications Magazine • January 201168 0163-6804/11/$25.00 © 2011 IEEE

NEW CONVERGED TELECOMMUNICATIONAPPLICATIONS FOR THE END USER

François Toutain, Orange Lab Lannion

Ahmed Bouabdallah, Telecom Bretagne

Radim Zemek, Orange Lab Tokyo

Claude Daloz, Orange Lab Lannion

Interpersonal Context-AwareCommunication Services

INTRODUCTION

Voice and data services involving interpersonalcommunication represent a substantial part of thebusinesses of telecom operators as well as ofmany new emerging players. Interpersonal com-munication characterizes written and verbalexchange of information, which can take place inboth one-to-one and group settings. However, theway people contact each other, the media, andthe communicated information often depend ontheir own situation and what they know or assumeabout their contact’s situation. While current com-munication services provide basic means to set upand manage conversations, they do not integrateknowledge about the parties’ environments, capa-bilities, and availabilities. There is considerableroom for improvement in this matter.

A possible answer lies in the contextualizationof communication services, which takes intoaccount the situation of the parties to adapt thedifferent steps of the communication (from initi-ation to termination) to users’ needs. Given thisability, these new services can be better suited tousers, providing more comfort, more relevantcontent, and more accurate, natural, or intuitivebehaviors. They can also provide real-time infor-mation about their contacts; in short, empowerusers.

Several context-aware communication ser-vices are already deployed or have been proto-typed in laboratories. However, the domain isstill in its infancy, and we expect numerous inno-vations to emerge in the coming years. In thisarticle we present some of the most significantwork done so far and expose possible directionsfor future research efforts.

This article is organized as follows. The firsttwo sections provide definitions and examples ofsuch context-aware services. We then presentseveral significant achievements in the domain,from current industrial offers to research proto-types, and review the associated challengesposed by the various approaches. We describethe gap between research advances and large-scale industrial deployment, and provide a tenta-tive timeline for the introduction of futurecontext-aware services. The final section con-cludes the article.

SOME BASIC DEFINITIONS

CONTEXT DATA HANDLINGContext represents any information that can beused to characterize the situation of an entity,where an entity is a person, a place, or anobject that is considered relevant [1]. Funda-mental context data, called the primary/primi-tive types, encompass identity , location ,activity/status, and time. Beyond that, a greatdiversity of data sources can be used as contex-tual information. Focusing on communicationapplications, we identify five different contextdimensions:• User (identity, activity, status, agenda, loca-

tion, movement, mood, age, etc.)• Network (access networks, identifier [e.g.,

cell ID], call records, received signalstrength indicator [RSSI], round-trip time,etc.)

• Social (people around the user, relation-ships with them, intimacy level, is the userattending a public event, subject of a con-versation, etc.)

ABSTRACT

Context awareness is a very large researchdomain, encompassing many issues ranging fromphysical measurement of a given situation to thequestion of social acceptance. It appears as one ofthe most promising technologies to evolve currentcommunication services into fluid, flexible,automagic, intuitive communication means. Wereview in this article the various applications of con-text awareness to convergent interpersonal commu-nication services, making clear the evolutionarypotential introduced by these techniques, from theclassical digital phone network to truly smart ser-vices, many of which remain to be invented.

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IEEE Communications Magazine • January 2011 69

• Physical (illumination level, ambient noiselevel, temperature, weather, etc.)

• Device (devices’ capabilities, energy used,remaining energy level, installed applica-tions, nearby devices, current volume set-ting, alert mode, etc.)Different sources can provide the same data;

however, the accuracy and credibility levels maybe different. For example, location informationabout a user’s whereabouts using the user’sschedule information may be different fromlocation information using GPS or cell-ID loca-tion. This discrepancy can indicate several differ-ent situations. For example, it may indicate thatthe user has forgotten about the appointment,that the appointment has been cancelled, or thatthe mobile phone has been stolen. Therefore, itis crucial for the success of context-aware ser-vices that context data accuracy and confidencebe properly managed.

Many data can be provided by way of auto-mated processes, working on raw data acquiredby some sensors:• Physical sensors (e.g., accelerometers), per-

ceiving a physical property and providing acontinuous, discrete, or symbolic measure.

• Logical sensors, a device or computer pro-cess able to sense some logical state of agiven process and provide a measure (e.g.,computer activity).

• Aggregated sensors, a logical constructionproviding data, which is derived from anoth-er process, typically by fusion from severalsensors. An example is a virtual humanpresence sensor which can be built with amovement detector + noise sensor + chairpressure sensor + computer activity sensor+ ….In addition to sensors, a great variety of con-

text data can be acquired by operators fromtheir logs, regarding call activity, mobile phonelocation, service configuration, and so on.

Context data are generally aggregated into amodel. According to [2], a well designed modelis a key accessor to the context in any context-aware system. The current research aims atdelivering generic context models, which allowsophisticated querying as well as reasoning algo-rithms that facilitate context sharing and inter-operability of applications. The most promisingapproach uses semantic models based on ontolo-gies [2].

Implementation of context data models is thesubject of various storage and distribution strate-gies [3], giving rise to architectural alternativesbetween centralized vs. distributed systems, andbetween classical vs. innovative approaches (e.g.,a micro-blogging platform used for context pub-lishing).

CONTEXT-AWARE SERVICESContext-aware processing generally involvesadapting to context conditions or using contextinformation. From a low-level operational pointof view, there are two fundamental modes ofoperations for a process to be context-aware.

The first mode is context-triggered action asdescribed in [4], whereby the transition to agiven context state triggers an action. In thismode a change in the context situation is thus

the cause for performing an action, which mayimpact the current context or not. An exampleapplication is a contextual reminder service,which is loaded with an IF-THEN rule describ-ing the context conditions giving rise to a notifi-cation message. As soon as the context situationis similar (or close enough, depending on therule characterization) to the one described, thecontext reminder service fires and delivers theassociated message.

The second mode of operation can bedescribed as context-dependent reaction. In thismode the process adapts its response to stimula-tion according to the user’s context data (e.g., anevent or a user action). More precisely, theapplication response depends on the event andthe current context. An example of a context-dependent reaction could be a phone service,which, upon receiving the event of an incomingcall, would use its context data telling it the useris at the movies to adapt its response and chooseto deflect the call to a messaging service. In real-world situations, the response also depends onuser preferences and service parametering(which may be perceived as part of the user’scontext). Also, the event can be multifaceted(e.g., an incoming call from a given caller, with agiven call priority, subject, etc.)

Context-aware services, using the aforemen-tioned operation modes, will allow operators toprovide truly personalized services, taking intoaccount the users’ context as it evolves overtime.

HOW CONTEXT AWARENESS COULDENRICH INTERPERSONAL

COMMUNICATION SERVICES

Context-aware services range from classicalapplications augmented with some degree ofcontext awareness to a whole new class of inno-vative services specifically built around context.In the rest of this section we illustrate the poten-tial of context-aware services with several scenar-ios, some features of which are inspired byknown studies.

ADAPTIVE MOBILE PHONE SCENARIOMy mobile phone changes its operation modedepending on my situation. It recognizes situa-tions such as when I am working, when I am driv-ing, when I am socializing, and when I needprivacy. For each mode the address book can pre-sent different contacts, sorted differently. Theapplication menu also adapts so that, for instance,professional applications are in front only when Iam working. The call handling system knows howto prioritize incoming calls and whether to pre-sent them or not in order to minimize disruption,depending on my current activity, my capabilities,and the caller’s situation. In addition, my mobilephone evolves with me, by suggesting new func-tionalities when I may need them.

ADAPTIVE MESSAGING SERVICEWhen I am watching TV, an incoming messageis displayed on the TV screen rather than on mymobile. As soon as someone enters the living

Context-aware pro-

cessing generally

involves adapting to

context conditions or

using context infor-

mation. From a low-

level, operational

point of view, there

are two fundamental

modes of operations

for a process to be

context-aware: con-

text-triggered action

and context-depen-

dent reaction.


IEEE Communications Magazine • January 201170

room, the display of the message is obfuscatedor switched to my mobile phone in order to pre-serve my correspondence privacy. The serviceknows which messages are relevant and whichcan be safely displayed in the presence of otherpeople. The same messaging application is ableto adapt its interface to the abilities of my five-year-old daughter. When I join her in front ofthe TV set, additional tools are displayed for mybenefit in case I wish to help my daughter withsome difficult operation.

The two scenarios above describe dynamicadaptation to changing conditions. Adaptationcan concern interfaces, service behavior, dynami-cally changing priorities, content adaptation, andalso call and message routing on various devices.The general benefits for users are improved rele-vance and comfort.

CONTEXT-AWARE ADDRESS BOOKOn my devices, I can consult the real-time situa-tion of my contacts. Every contact is displayedwith animations and data informing me clearlyand without effort. At a glance, I can see that agiven contact is unreachable/busy/currently atwork/inside a movie theatre/ commuting/withother people/currently standing on line and soon. I can sort the contacts according to a givencontext dimension, using display strategies thatgive the best possible rendering (e.g., a map forlocation-based data, or a list sorted by decreasingavailability, mood, etc.). I can see which contactsare in front of a TV set and what they are watch-ing, people currently listening to music, browsingthe web on which site, and so on. I can alsoreview historical data and get some predictionsabout the future situation of any given contact. Ican infer who will be the first available for a chat,who is nearby for a coffee break, at what time ofthe day I can reach someone, and so forth.

The above scenario describes an alternativeway to make use of context knowledge by pro-viding it to the user, thereby empowering her,rather than imposing on her some predefinedadaptation, be it well received or not.

Presenting the user with complex contextdata is an important issue. For instance, thereare several ways by which a caller could beinformed that the callee is at the movie beforethe call goes through. One is the status mecha-nism, which may be automatically filled for bet-ter user experience and service performance.Another is for the network to build up someaggregate context state, using the various contextdata it can gather from the callee’s current situa-tion. Yet another way is for the authorized callerto access the callee’s device and get a taste of thecurrent situation of the callee.

CONTEXT-SENSITIVE DIALERMy device provides me with call suggestions. Byanalyzing availability, mutual interests, and thehistory of common calls and their subjects, itdetermines when I can best call a friend, and caneven initiate the call if my configuration allowsit. For instance, the device can suggest calling acontact in order to set up a coffee break, givenour current location and agendas, and the factthat we usually take the opportunity to share abreak when possible. When I face a plumbing

issue at home, the device suggests a givenplumber based on location and ability to fix theissue quickly. My 15-year-old niece uses thesame service with her best friend, so audio com-munication is automatically set up whenever oneof them is engaged in some interesting activity.Thus, they share a strong, close relationship.

CONTEXT-SENSITIVE NOTIFIERWith this application, I can be notified wheneversome specified event occurs automatically, con-cerning me or my contacts, with no manualupdate from either me or my contacts. Forinstance, I can set up a notification as soon as Icome back home, or as soon as Alice is back ather office and available for a chat, or whenever Imeet Bob out of the workplace.

HABIT-BASED AUTOMATIONMy telecommunication services usage habits aremonitored and analyzed in order to provide rele-vant automated enhancements. After detectingthat I never answer a call when driving, the ser-vice offers to automate call transfer to voicemail, while providing me with a means to cancelit easily on my mobile phone. After detectingthat I often call my wife when I visit a supermar-ket, the service asks my authorization, thenoffers to notify her whenever I go shopping, pro-viding her with the opportunity to brief meabout the grocery list before I leave the store.

The last three scenarios are modest attemptsat describing applications specially designed tomake use of context data about users. Indeed,the increased availability of context data is envis-aged to foster the emergence of a whole class ofinnovative applications and services, which arespecifically built around context. These new ser-vices are nowadays largely unheard of.

FROM EXISTING SERVICES TORESEARCH QUESTIONS

The domain of context-based services is an activearea, integrating preliminary industrial achieve-ments as well as academic studies focusing onremaining tough questions. In this section weillustrate this problematic area by first looking atsome significant work, from which we infer theresearch issues.

KNOWING ABOUTHIGH-LEVEL CONTEXT IS COMPLICATED

CenceMe [5] classifies the user’s current loca-tion, physical activity (e.g., walking, sitting, run-ning, mobile), whether the user is in aconversation, and other aspects of the user’scontext like whether the user is at a party.CenceMe demonstrates how mobile sensors ofmultiple types (e.g., accelerometer, GPS, sound,camera) can process raw sensor data to producea richer description of the user’s situated con-text. All recognitions are eventually uploaded toa server and shared with the user’s Facebooksocial network (Fig. 1).

SenSay (Sensing and Saying) [6] is a context-aware mobile phone that adapts to dynamicallychanging environmental and physiological states.

When I am watching

TV, an incoming

message is displayed

on the TV screen

rather than on my

mobile. As soon as

someone enters the

living-room, the dis-

play of the message

is obfuscated, or

switched to my

mobile phone, in

order to preserve my

correspondence

privacy.



In addition to manipulating ringer volume, vibra-tion, and phone alerts, SenSay can provideremote callers with the ability to communicatethe urgency of their calls, make call suggestionswhen they are idle, and provide the caller withfeedback on the current status of the SenSayuser. A number of sensors including accelerome-ters, light, and microphones provide data aboutthe user’s context. A decision module uses a setof rules to analyze the sensor data and manage astate machine composed of uninterruptible(busy), idle, active, and normal states. The phonealleviates cognitive load on users by variousmethods including detecting when the user isuninterruptible and automatically setting theringing mode to manner mode.

Although some efforts have already showngreat results as presented before, the generalproblem of knowing about high-level context is avery difficult issue. This problem is bestdescribed by considering the gap between low-level raw data provided by the known contextsensors, and the high-level knowledge that canbe required by smart applications involved ininteractions with human users. Let us illustratethis with an application pondering whether tointerrupt a user with an incoming event, consid-ering that the user is sitting at her desk with norecent activity on the devices (mouse, keyboard,etc.). Is the user deep in thought, about to makea mathematical breakthrough (better not inter-rupt her in these cases), daydreaming, or maybewaiting for a call for lunch?

LEARNING FROM CONTEXTOrange Lab Tokyo has been working on a con-text awareness enabler and prediction engine,tailored to enable user habit detection withrespect to communication services. Thisapproach allows the user to easily configure astandard profile with respect to two services:incoming call management and notification. Thissimple profile is then enriched by detecting somehabits related to the user context. Contextualdata is analyzed by the prediction engine tounderstand user preferences and communicationhabits.

In the same way, the management of incom-ing calls has been addressed by MicrosoftResearch in their Bayesphone service. Thisclient-server system predicts whether the user

would like to be interrupted by a phone call dur-ing a meeting or have the system take a messageinstead [7].

The two previous approaches rely on single-user context-based learning techniques. Theinherent limitations of such an approach arelinked to the learning duration necessary beforethe system becomes effective, and their inabilityto adapt to a user’s habit change. To addressthese shortcomings, a natural way consists oftaking into account the collective behaviors of alarge user population in order to determinesome common behavior actions. For instance, arule obtained through this approach could statethat users who are at a movie theatre usuallyturn off ringing. Applying such a technique (e.g.,from ethnography) to context awareness can bea creative way to extract knowledge from a largeset of users, which can prove very effective forsome applications. The subject is described insome detail in [8], where the authors introduce acontext-aware system aimed at predicting auser’s preference using past experiences of like-minded users. It remains to be assessed how andto what extent this principle can be applied tocommunication services.

ANY BUSINESS HERE?Due to the fact that context awareness is a recentdomain in the industry, such services are onlybeginning to emerge on the market.

For example, FourSquare may well be one ofthe first companies monetizing context aware-ness activities. Foursquare offers location ser-vices delivered by way of embedded softwareavailable for Android phones and the iPhone.The startup is very innovative in defining funand appealing functionalities, such as for anindividual to be recognized as the mayor of aplace where she regularly shows up. The serviceincites commercial locations to provide specialoffers to their mayors, thus setting up friendlycompetitions, and compelling users to attend theplace and buy food/drinks or other services. Anonline forum dedicated to the business is alsooffered, allowing buzz discussions around it, withmutual benefits. Actually, FourSquare decidedearly to provide context application program-ming interfaces (APIs) to third parties, and sev-eral services have already been built on top ofthese APIs (Fig. 2).

Figure 1. CenceMe user interface.

Orange Lab Tokyo

has been working on

a context awareness

enabler and predic-

tion engine, tailored

to enable user habit

detection with

respect to communi-

cation services. This

allows the user to

easily configure a

standard profile with

respect to two ser-

vices: incoming call

management and

notification.



Despite this interesting approach, severalexperts recall that users are often unwilling topay for mobile context-aware services [9].Prospective marketing work is still needed in thisarea. For instance, the various actors may inves-tigate fulfilling specific roles in the context man-agement ecosystem. A plausible evolution of theindustry is to implement a federation model,with some actors being context providers, con-text aggregators, or context brokers. Multipartyagreements may well be found between theseactors, allowing value creation and distribution.This emergence of a lucrative context informa-tion business is yet to be witnessed.

USER ACCEPTABILITY IS YET TO BE CONQUEREDWord of Mouth is an experimental communica-tion service developed by Orange Lab Lannion,aimed at web surfers, providing them with somecontext information about other people whohappen to visit the same website they are visit-ing, in real time. The service is implemented as aplug-in for the Firefox browser, and provides achat window for shared textual communication,along with access to the user’s address book. Byusing an authenticated identity, the user canactually use her Orange address book. Thisenables users to be informed about the websitestheir contacts are currently visiting, and jointhem in a chat session by visiting the same web-site, or even to place a web phone call thanks toan embedded flash phone application. Privacy isensured by way of a single-click setting for pri-vate surfing. This rather basic approach is a firststep toward allowing users to control their priva-cy (Fig. 3).

Concerns for privacy are a very current issue,with publicized incidents involving superstar webservices such as Google Mail or Facebook. Nev-ertheless, the current trend leads to a trade-offbetween the perceived benefits of the service orthe product, and the perceived (or assumed)consequences of allowing a third party to accesssome private data. Given this situation, privacymanagement applied to the context awarenessdomain is of paramount importance, and couldwell be the differentiating factor between future

service successes and failures. Some of the bigquestions to solve include:• Which context data are more sensitive to

privacy concerns?• Are some application classes better per-

ceived than others?• Is a telecom operator a trusted party with

respect to context data?• How can consumers’ trust be increased?

The more general issue is related to accept-ability of context-aware services, which includes,besides privacy issues, the questions of serviceaccuracy and credibility, and user understanding.

Users will accept context-aware services onlyif they keep control over the data and services.This issue includes the ability to disable services,data portability between actors, right of access,right of inspection, and right to oblivion.

Context-aware services would be rejected ifthe input data and inference processes are notaccurate, because they would lead to misunder-standing and inappropriate behaviors (missedcalls, lost messages, etc.)

The user understanding issue is that context-aware computing does not typically give the userfeedback on which context information is beingused as input to control and decision making.Interface intelligibility of context-aware systemsis a big research opportunity as not muchprogress has been made here.

On a more general level, context-aware sys-tems can give rise to concerns related to stateagencies accessing private data, subject to specif-ic laws and regulations, which resort to legalissues, out of the scope of this article.

EN ROUTE TO FULL CONTEXT-BASED SERVICESFigure 4 depicts a timeline we envisage for themass market introduction of context-aware ser-vices. The first services to be deployed, includingsome applications already available, focus on thesupply of context data to users, and let themmake informed decisions regarding their commu-nication needs. These services, based on low-levelautomation, are expected to become availablewithin three to five years. Then a second wave ofofferings will focus on higher-level automation,and provide applications capable of automaticservice adaptation. From moderately complex toadvanced applications, these services are expect-ed to come into play within five to ten years.

FROM RESEARCH PROTOTYPES TO FULL-SCALECONTEXT MANAGEMENT SYSTEMS

A great amount of research effort related tocontext-aware systems has paved the way towardindustrial context-aware systems, to be deployedby telcos as a part of the new infrastructuresallowing converged services (e.g., IP MultimediaSubsystem [IMS] and Long Term Evolution[LTE]). Although no large-scale advanced con-text-aware system has been implemented yet, itstems from research results that the heart ofcontext-aware systems depends on the way con-text data is structured and managed. A consen-sual architecture is made of three layers:• A lower layer is concerned with managing

sensors and gathering context data fromthem.

Figure 2. FourSquare user interface.

The user understand-

ing issue is that con-

text-aware

computing does not

typically give the user

feedback on which

context information

is being used as

input to control and

decision making.

Interface intelligibility

of context-aware sys-

tems is a big

research opportunity

as not much

progress has been

made here.



• A middle layer embeds data models, han-dling data abstraction through a semanticapproach, which provides powerful toolssuch as inference and conflicting data han-dling.

• A higher layer exposes an API to client ser-vices, with generic primitives to harness thepower of context data (e.g., primitives con-cerned with notifying applications, e.g.,primitives based on the nature of contextdata, similar to geographical databases).Implementing such a system within a telco

infrastructure leads to a horizontal approach,introducing a new enabler dedicated to con-text data management. Much work is still nec-essary to define, in interoperable ways, thedetails of this enabler. Regarding standardiza-tion, such an enabler is yet to be described,for instance, by the Open Mobile Alliance(OMA), along with the necessary interfacesand protocols to interoperate with the existingenablers (e.g., user profile). The issues of pri-vacy and user control must be addressed, tak-ing into account the existing telco databasesand policies. The emergence of context-awarearchitectures is also tied to increasing capabil-ities at the device and sensor levels. Furthereffort is also required at the interpersonalcommunication service design stage, in orderto harness the potential of contextual infor-mation.

CONCLUSIONIn this article we have reviewed the variousapplications of context awareness, making clearthe evolution potential introduced by these tech-niques, from the classical digital phone networkto truly smart services, many of which remain tobe invented. After a dozen years of researchefforts, many interesting results have beenachieved, and many more questions have beenraised, indicating a very promising field forfuture innovation. We have presented four broadquestions, which have already been investigated

for some time by the research community, butwhich we believe can still yield considerableadvances:• How to combine raw context data into high-

level context knowledge• How to leverage large context datasets to

identify users’ behaviors, and how to designadaptive services

• How to generate business from context ser-vices

• How to gain users’ trust and acceptance inthese servicesWe have also summarized the next steps

toward industrial-grade context management sys-tems, which will become major assets of telcos’converged infrastructures. There is a growingawareness among IT specialists that contextawareness is one of the key evolution tracks forfuture business and personal IT services. Usingcontext-aware capabilities, communication ser-vices will adapt to human users, whereas beforeusers had to adapt to the technology of commu-nication services.

Figure 3. Orange Lab's Word of Mouth user interface, as a plug-in within Firefox.

Figure 4. Deployment projection of context-aware services and applications.

Tough questions: • Acceptability of context-aware services, largely related to the management of users’ privacy • Interface intelligibility, user control

Tough questions:• Acquiring and managing large datasets, requiredto develop the various service components. • Combining raw context data to determine consistent user context, involving modeling and machine learning techniques

Empower users: provide contact’s knowledge to

the user

Context-based services:automatic service adaptation

+

-

2010 2013 2016

Service automationand personalization,context data variety



REFERENCES[1] A.K. Dey and G. D. Abowd, “Toward a Better Under-

standing of Context and Context-Awareness,” Proc. 1stInt’l. Symp. Handheld Ubiquitous Comp., LNCS, vol.1707, 1999, pp. 304–7.

[2] T. Strang and C. Linnhoff-Popien, “A Context ModellingSurvey,” 6th Int’l. Conf. Ubiquitous Comp. ‘04, Wksp.Advanced Context Modeling, Reasoning, Mgmt., Sept.2004.

[3] M. Baldauf, S. Dustdar, and F. Rosenberg, “A Survey onContext-Aware Systems,” Int’l. J. Ad Hoc UbiquitousComp., vol. 2, no. 4, 2007, pp. 263–77.

[4] B. Schilit, D. Hilbert, and J. Trevor, “Context-AwareCommunication,” IEEE Wireless Commun., Oct. 2002,pp. 46–54.

[5] E. Miluzzo et al., “Sensing Meets Mobile Social Net-works: The Design, Implementation, and Evaluation ofthe CenceMe Application,” ACM SenSys, Raleigh, NC,2008, pp. 337–50.

[6] D. Siewiorek et al., “SenSay: A Context-Aware MobilePhone,” Proc. 7th IEEE Int’l. Symp. Wearable Comp.,White Plains, NY, Oct. 2005, pp. 248–49.

[7] E. Horvitz et al., “Bayesphone: Precomputation of Con-text-Sensitive Policies for Inquiry and Action in MobileDevices,” Proc. User Modeling, 2005.

[8] A. Chen, “Context-Aware Collaborative Filtering System:Predicting the User’s Preference in the Ubiquitous Com-puting Environment,” Proc. LoCA ‘05, LNCS, vol. 3479,2005, pp. 244–53.

[9] M. de Reuver and T. Haaker, “Designing Viable BusinessModels for Context-Aware Mobile Services,” TelematicsInformatics Rev., no. 26, 2009, pp. 240–48

BIOGRAPHIESFRANÇOIS TOUTAIN received his Ph.D. in computer sciencefrom the University of Rennes, France, in 1998. Prior tojoining Orange Labs in 2004, he worked as a CTO for astartup company dedicated to the web and telecom con-vergence. He is now a project manager within OrangeLabs, with research interests focusing on interpersonalcommunication and context-aware services.

AHMED BOUABDALLAH ([email protected]) joined Telecom Bretagne in 1994 as an assistantprofessor in the Network, Security, and Multimedia Depart-ment. Prior to joining Telecom Bretagne, he received hisPh.D. in computer science from the University of Franche-Comté in 1993.

RADIM ZEMEK received his M.Sc. degree in electronics andinformatics from Brno University of Technology, CzechRepublic, in 2003. From April 2004 to March 2005 he wasa research student at Osaka University, Japan. He thenentered Osaka University to obtain a Ph.D. degree in infor-mation and communications technology, which hereceived in 2008. He joined Orange Labs in Tokyo in 2008where he works on topics related to broadband accessand services.

CLAUDE DALOZ received his M.Sc. in computer science fromthe University of Besançon in 1997 and his telecommunica-tion engineering degree from the National Institute ofTelecommunication at Evry in 1999. He is responsible for aresearch program on interpersonal communication forOrange Labs.


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Interpersonal context-aware communication services