Context Information vs. Sensor Information: A Model forCategorizing Context in Context-Aware Mobile Computing

Louise BarkhuusDepartment of Design and Use of Information Technology

The IT University of CopenhagenGlentevej 67, DK-2400 Copenhagen, Denmark

barkhuus@it.edu

Keywords: Context-aware computing, context modelling, ubiq-uitous computing, mobile computing, mobile collaborative work.

AbstractContext-aware computing proposes technology that adapts ac-cording to the user’s situation and environment. Within mobilecollaborative work the situation is viewed as an important fac-tor, here context-awareness attempts to facilitate coordinationin several ways. Although much research has been conducted,context-awareness is still defined very differently. Besides dif-fering in definitions of context, most research fails to separatebetween complex context information and precise sensor in-formation. Knowing that collaborative work can benefit fromsome degrees of context-awareness and that context-awarenessis essential to a ubiquitous computing environment, this paperfocuses on initial design of context-aware applications. Afterreviewing past categorization models of context, The ContextInformation Model is proposed. Categorizing from the user’sview point, the model separates strictly between context infor-mation and sensor information. It is then exemplified how themodel can be applied to an actual consumer device. The paperconcludes that if context-aware computing applications are tobe developed for real collaborative and consumer settings, sen-sor measures should be gradually incorporated to comprise asimplistic categorization of context rather than define context indetail.

1. INTRODUCTIONIn collaborative work, mobility of people as well as artifacts isoften relied upon as a facility to improve coordination. The mo-bility constitutes the flexibility needed for collaborative workand it is critical to collaborative work [6]. It is important tocommunicate and interact in order to collaborate and mobilecomputing offers a great range of possibilities for this. To makemobile devices more suited for the demands of the participantsof collaboration, the issue of context-awareness is relevant. Itdescribes mobile applications that change according to work sit-uation and collaboration context and thereby provide the userswith a more flexible work sphere.

Context-aware computing has been an active research area forover a decade without leaving any real-life applications on theconsumer market. From the PARCTab experiment of the early

nineties [7] to the more sophisticated tour-guides utilizing GPS[1, 4], the field has yet to climb outside the research laboratoriesand provide consumers with active autonomous applications fortheir mobile devices. The research of context-awareness is notlacking in amount and several different approaches have beentaken when dealing with the subject. From creating a widgetmodel [5] and describing a blackboard model [13], to charac-terizing context into several classes [3, 11], many researchershave attempted to structure the complex nature of context sens-ing into categorizations and models. Still, none of these mod-els are able to grasp the actual human understanding of the di-versity of context information as distinguished from the tech-nical understanding of sensor information. Most of the modelstake a technical approach, arguing for ways to develop context-aware computing that relies on sensor information as the pri-mary source of context, regardless that context categories of-ten consist of several pieces of sensor information. The fieldis in need of a common understanding of the relation betweencontext as a broad concept and actual sensor information. Thedevelopment of context-aware applications will benefit from amore strict distinction between the two kinds of information,which will support the initial design of application as well asthe analysis of existing context-aware technology. By draw-ing on past research and present context categorizations, thispaper suggests a model, The Context Information Model, sepa-rating the context information from the sensor information, andthereby facilitating the design process of context-aware appli-cations. As a future perspective and to support further under-standing of context within context-aware mobile computing, themodel should eventually provide insight into the analysis of ex-isting context-aware technology.

2. CONTEXT-AWARE COMPUTINGThe concept of context-aware computing describes infrastruc-tures that include sensor information from its surrounding en-vironment, physical as well as electronic, in the computing ap-plications. The collected information creates part of thecontextthat comprises the interaction between humans and computingunits; the applications change according to their present context.In collaborative work, context is an important issue, as coordi-nation seems to depend significantly upon the participants’ con-text. An example of a piece of relevant context information isthe most common question posed when communicating on mo-

bile phones: “where are you?”. Also the increased specializa-tion of computing devices make independent context variablesessential to mobile collaboration technology, which also sup-ports the vision of ubiquitous computing. Context-awareness isthe attempt to relate the technology to the actual collaborationsituation and environment it is part of.

The definition of context differs depending on theoretical ap-proach. The concept is often described according to sensor in-formation or as a broader concept regarding the overall environ-ment. Although the concept is used widely in the area of ubiqui-tous computing, it seems that no standard definition of it exists.When reviewing the literature within the area, the abstractionof the concept is evident; from using context-awareness to de-scribe a simple location-aware system to requiring utilization ofseveral environmental measures, the concept is in dire need forat least a common understanding. In order to grasp the com-plexity of context in relation to computing technology, the re-search within context-awareness is reviewed and summed up ina definition that should work as the foundation for the proposedmodel.

2.1 Defining Context-AwarenessThe first to actually use the word context-awareness were Schilitand Theimer. In 1993 they introduced the termdynamic cus-tomization, which describes a new kind of application capableof adapting during a session and, although not being the firstcontext-aware application, the notion of such attribute probablyinspired the termcontext-aware computingintroduced a yearlater in 1994 [10, 9]. Schilit and Theimer defined it as “theability of a mobile user’s applications to discover and react tochanges in the environment they are situated in” [9]. While thisis a very broad definition they narrow it by defining context in-formation as location, identities of nearby people and objectsand changes to those objects. However, the actual context infor-mation that Schilit and Theimer use in their Active Map Serviceis limited to location, perhaps because of technical limitations.They suggest that future context-aware applications should in-clude much more than location information, a suggestion thatnumerous researchers have attempted to build upon.

Brown et al. define context-awareness simply as applicationsthat change behavior according to the user’s context. They de-scribe how laboratory context-aware applications can be usedin real settings, by developingstick-e notes, a generalized wayof representing context written in a standard notation (SGML),as part of their context paradigm [2]. Acknowledging that mostcontext-aware applications only utilize location as context, thestick-e note should facilitate a more broad use of context. Theirdescription of context information is thus not limited but ex-amples that are given include temperature, season of the yearas well as the information defined by Schilit and Theimer. Thecontext information is relevant for the understanding of context-awareness, therefore the description of context measures is acommon way of defining it. Some researchers solely provideexamples of context as a definition of context-awareness (eg. [5,11]). The lack of a comprehensive definition is also a source ofconflicting definitions, meaning that one application is consid-ered context-aware by some researchers but not by others.

Striving towards a useful definition, one of the most simple andample definitions of context-awareness is still the one providedby Schilit and Theimer. Limited to thediscovery and reac-tion to changes in environment, it captures the essence of areactional system that takes environmental measures into ac-count [9]. Because it includes ‘discovery’, it also limits theapplications in question to the ones that change independentlyof direct user input contrasting the applications that change ac-cording to predefined modes and personalization such as pro-files, which in some cases are also referred to as context-aware(eg. continuous context-aware application as defined by Brownet al. [2]). Schilit and Theimer’s definition of context-awarenessis closely related to the definition proposed in this paper. Theproposed definition here, claims that context-awareness isanapplications ability to detect and react to environment vari-ables autonomously. However, as environment and situationare the most commonly used synonyms for context within theresearch area [5], the concepts only define the overall notion ofcontext-awareness, context is not defined in a more thoroughmatter. Therefore context is often defined by categorization ofcontext and sensor information, which is the essence of context-awareness and also this paper’s further approach.

3. CATEGORIZATIONS OF CONTEXTThe categorization of context information is common for a largepart of research within the area and aims to supply the definitionof context-awareness with a more clear understanding. How-ever context is not a static construct and therefore leaves manyresearchers with a categorization scheme that is not necessarilyuseful for the development of real applications. It seems rea-sonable to categorize context theoretically to get further insightinto the concept, but it means that the classification schemes areforemost used to clarify the diversity of context information ingeneral. One example is the distinguishing between comput-ing factors (networks connection, user interface size etc.) andhuman factors (identity of user, social situation etc.). Some cat-egorizations within context-aware computing attempt to clas-sify the actual applications [2, 8] as for exampleactiveor pas-siveapplications [3]. The active application is one that auto-matically adapts to context measurements and thereby changesthe application’s behavior, where the passive application onlypresents new or updated information to the user. These cat-egorizations strive to define criteria for determining if an ap-plication is actually context-aware and how different levels ofcontext-awareness can be applied to applications.

3.1 Environmental CategorizationThe most common way of categorizing context is to classify theinformation into separate environmental clusters. Basing theircategorization on Schilit et al. for example [8], Dey et al. dividethe environment into three levels: Computing context (eg. net-work connectivity and nearby resources), user context (eg. userprofile, location and people nearby) and physical context (eg.lighting and noise level) [5]. Schmidt and Forbess, on the otherhand, only divide context environment into two categories, hu-man factors and physical environment, where the physical en-vironment consists of Dey et al.’s computing context as well astheir physical context [12]. These categories have a set of fea-

tures and these features each have a range of values. The modelis created to be exhaustive in nature; the sensor information andits values are the last step in the categories. This way the modelcan be adapted according to a specific application and its pur-pose.

Most of the categorizations do not consider non-environmentalfactors that often make up the larger context, such as the user’sinternal state or the task that is to be carried out. The fact that asituation is fluid and ever-changing in nature and that it cannotalways be predicted from small pieces of sensor information isonly sparingly mentioned in this relation. It is an important real-ization when attempting to develop context-aware applications,but even though the non-environmental measures are not eas-ily categorized, the modelling of physical factors can be usefulfor a greater understanding of the limited possibilities of usingcontext information.

3.2 The Utilization of Categorization in Ac-tual Applications

Although Schmidt et al. classify context in two categories (hu-man factors and physical environment) the categorization is notapplied to their sensor fusion that is proposed as a tool for ac-quiring context information [11, 12]. Instead the sensor fusionproposes a four layered architecture, which provides the levelsof abstraction necessary to implement context-awareness (sen-sors, cues, contexts and scripting). The approach is detailed butactual end-use of the applications is not considered in contrastto the Context Information Model. Apart from Schmidt et al.,the reviewed categorizations of context do not separate contextinformation from sensor information. Chen and Kotz mentionthe difficulty of acquiringhigh-level contexts(eg. social situa-tion) and relate it to low-level sensors by suggesting to combineseveral pieces of information to create a high-level context [3].The concept is interesting but not illustrated by examples of ac-tual implemented applications.

One categorization that is utilized to develop an actual applica-tion is one used in the GUIDE project presented by Cheverstet al. [4]. Although mainly using location information, the au-thors categorize information into object types:navigation pointobjectsandlocation objects. Location objects are the actual lo-cated physical objects such as a specific building and navigationpoint object are the way-points between location objects that as-sist in navigation.

The categorizing of context information aims to provide a moreclear understanding of the broad concept and of which con-textual measures could be utilized in context-aware comput-ing. Most categorizations are derived from earlier ones (eg. [5]and [3]) and classify the context not in relation to sensor infor-mation but in relation to a notion of what the application shouldknow. Dey et al.’s categorization of context, for example, is notvery clear [5]: Thenetwork capacityinformation that is partof the computing environment is sensor specific, meaning it ismeasurable, where thesocial situationthat is part of the user en-vironment is situation specific, meaning that it needs a humandefinition (perhaps comprised of numerous pieces of sensor in-formation) before it can be measured.

3.3 Limiting the Sensor InformationAlthough the categorization of context information enables amore structured thinking of context-awareness, only in rare caseswill more than a handful of separate context measures be used inactual applications. When designing an application it is there-fore only relevant to define context for the specific purpose ofthe application. Research shows that even though much ef-fort has been done to use as much information as possible, thecumbersomeness of the implementations increases dramaticallywith every new piece of sensor information included. Relatingto the unpredictability of situations, it is likely that more con-text measures do not necessarily provide an application with amore precise definition of a specific context. Since overall con-text is not very predictable in nature, the information that com-prises the situation should be limited and the possible actionsthat the application take limited as well. Many of the reviewedcategorizations of context lack the consideration that sensor in-formation should be limited in relation to the end-use of theapplication. Categorizations of context would therefore benefitfrom a more application specific perspective, one that the modelof Context Information attempts to provide.

4. THE CONTEXT INFORMATIONMODEL

The Context Information Model is developed to support the ini-tial design of context-aware applications as opposed to othercategorizations that facilitate a better understanding of what con-text is. Eventually it can be expanded to provide a frameworkfor analyzing context-awareness in present technology but theprimary purpose is the implementation of context measures inmobile devices. When developing mobile computing to supportcollaboration, the context information is essential to the successand the quality of the work. However, in order not to confuse orannoy the user, the context information and the sensor informa-tion should be limited and clearly separated so that the actualsituation is not compromised by overly detailed or irrelevantcontext categories. Where other researchers categorize contextin broad classes, such as human, physical or computational fac-tors, the Context Information Model categorizes the actualuseof the device or application before drawing out the context in-formation needed in the specific use. This approach enables thevague measures of environment and situation to be modelledaccording to the specific task that the application is developedfor.

The model emphasizes a strict separation between context infor-mation and specific sensor information; the separation enablesthe development of context measures that are limited and clearlydefined. The distinction is often overlooked by research and thetwo kinds of information are treated similarly in categorizationmodels. When discussing location for example, it is necessaryto differ between relative location (defined as ‘in that specificoffice’ or ‘in that user’s car’) and actual position (the sensorinformation retrieved by for example the GPS system, such asx degrees north by y degrees west). The Context InformationModel introduces three levels of contextual information:userlevel, context information levelandsensor information level.

Figure 1: The Context Information Model.

User LevelThe user level is the actual task that the device or applicationis developed for such ashuman communicationand human-computer communication. Human communication includes voicecommunication, data communication (eg. text-messages and e-mail) and visual communication (eg. video conferences). Human-computer communication includes search for information, overa network (eg. the Internet) and within the device (eg. search fora phone number in a cell-phone’s address book), as well as in-formation input to either network services or internal use withinthe application (eg. a personal agenda). Note that the generalmodel is not created to be exhaustive in nature and more userlevels could be added in order to expand it. As will be shownin the application of the model, most tasks fall within these twogroups, but it is likely that more user levels exist. However, theoverall purpose of the model is to describe context in a morespecific manner, therefore only a limited scope of tasks are in-cluded in the general model.

Context Information LevelThe context information level consists of the context informa-tion that is relevant for the user level. The context informationis defined in a complex manner as it is general context, whichis to be described before the applicable sensor information canbe found. The context information is limited to the informa-tion relevant for the use since excess context information wouldmake the design of applications increasingly complex withoutthe real benefit of context-awareness. An irrelevant piece ofcontext information, for example, for a human communicationsituation is the user’s identity. Because mobile devices are per-sonal (at least the vast majority), the context is centered aroundthe specific user and the information is something that does not

change1. Each level is a number of predefined classificationsfor each user level where most context information will be de-scribed in words rather than exact measures. The predefinedclassifications of social situation could for example be ‘meet-ing’, ‘outside’, ‘general day’ or ‘night’. All actual situationsshould then fall within these predefined but fairly general cate-gories, a factor often proposed within context-aware computing;most early research dealt with a very limited number of distinctsituations.

Sensor Information LevelThe sensor information level consists of the specific sensor in-formation necessary to determine the elements of the contextlevel. Depending on both user level and context informationlevel, the sensor information is what the device or applicationneeds in order to become context-aware. At this level it is notrelevant, for example, to describe information that can be mea-sured in an ambiguous way; location should be described asexact position where the relative location would belong to thecontext information level. A piece of context information canconsist of several pieces of sensor information. The complexexample of social situation is comprised by noise level, time ofday compared to predefined agenda, temperature and actual po-sition. These pieces of sensor information are in the exampleof human communication enough to distinguish (however notextensively) between the predefined social situations.

1This lack of user identity information in the general model should not be con-fused with the application’s knowledge ofotherusers’ or devices’ identity.

Figure 2: An example of The Context Information Model applied to a static function.

4.1 The Applied Context Information ModelThe model attempts to illustrate how the complexity of contextcan be limited in scope and implementation by considering onlyrelevant context-information. Actual context as a setting forcollaboration differs highly according to non-measurable fac-tors such as the user’s internal state. The model does there-fore not attempt to solve greater problems of what actually con-stitutes more complex contexts, such as social environment orsurrounding people and objects’ identity. It merely suggests amethod of defining sensor information measures that could addto an understanding of context in real life context-aware appli-cations. The Context Information Model is developed to designstatic functions as well as dynamic functions, which the two ex-amples should illustrate. The static functions are those that runcontinuously on the device, where dynamic ones are specificallyapplied to specific tasks that need to be fulfilled.

Example of an Applied Static FunctionTo exemplify the utilization of The Context Information Modelfor actual consumer applications, a cell phone with context-aware features is proposed. The cell phone is developed with thecapability to measure sensor information according to the con-text and thereby act according to the social situation. Since theprimary use of a cell phone is human communication, context-aware features could include the device’s ability to, on the basisof social situation, adjust the volume of the speaker as well asringing tone. Figure 2 illustrates the applied model for this ap-plication. The predefined categories of the context level in thisexample are limited to three relying on noise level, temperatureand time of day as sensor information. The social situation con-text categories would in this example consist of ‘high’, ‘regular’and ‘discreet’. A high noise level during the day will result inthe ‘high’ context category where a medium noise level resultsin a ‘regular’ category. However if the measured temperature islower than 15 degrees Celsius (60 degrees Fahrenheit), the con-text category would always be set to ‘high’ because the devicevery likely would be outside. The third context category is the‘discreet’ category that is also applied during the night. As thisis a simplified example, the context categories are not utilizedto the furthest extent but only attempts to indicate a possible sit-uation based on few pieces of sensor information. Note that the

user level of the applied as well as the general model does notimply the actual action that the device/application should per-form; this factor is not part of the model. Instead the task is ageneral task that the user needs to perform. The context infor-mation level solely consists of context information that wouldbe relevant for the specific task. Therefore, the criteria in thecontext information level can be limited in reality as all infor-mation might not be necessary to make the application performthe desired action.

Example of an Applied Dynamic FunctionThe example of a dynamic function could be applied to any mo-bile device. Dynamic functions could be specifically related tothe device’s use and application, as in this example of an appli-cations ability to ‘find the nearest of predefined friends’, seenin figure 3. The function of ‘finding the nearest friend’ is partof Human-computer communicationand would rely on the con-text information ofrelative location. The context informationof relative location is determined by sensor information mea-suring the user’s actual position, the predefined friends’ identityand the actual position of these friends. In the example of adynamic function such as this, thecriteria of the function con-sists of the context and sensor information, contrasting the staticfunctions where the context level is divided into several contextcategories.

Limitations of the Model’s ApplicabilityThese examples of the model’s application are very simplifiedand the the model still only offers limited opportunity to relatespecific functions of the application to the user tasks. The tasksare termed in a general manner but when applying the model tothe design of a real consumer application, the function should bespecified within the boundary of the user level. Functions thatlie within the scope of control systems (systems that control fea-tures within the device without the user involvement), such asmobile phones’ automatic change between cell base stations, arenot part of the proposed model. The important distinction thatthe model emphasizes however, is the separation of the generalnotion of context information from the actual sensor informa-tion. It is not claiming that general context is found from fewpieces of sensor information, it is merely suggesting a method

Figure 3: An example of The Context Information Model applied to a dynamic function.

where sensor information can be utilized to support dynamic aswell as static functions of mobile applications. Eventually, themodel could be applied to more complex functions of deviceapplications, which should be subject for future research.

4.2 The Model’s Relation to Other ModelsMost of the previously reviewed categorizations and modelsfor developing context-aware applications take a technical ap-proach such as Dey et al.’s three categories of context (comput-ing, user and physical context) [5] or present technical specifi-cations such as Cheverst et al.’s GUIDE architecture [4]. TheContext Information Model takes context-awareness one stepback and views context from the user’s perspective in order tooffer a suggestion for a limited variety of context information.Many of the context-aware applications developed in researchlabs also use a limited set of context and sensor informationbut their reasons for this often seem to be due to technical con-straints rather than a conscious limitation made regarding theend-use of the product. A model such as The Context Informa-tion Model is therefore not directly comparable to the previousreviewed categorizations. The model is not attempting to coverthe more technically oriented issues of context and sensor mea-suring or the more wide definition of context. It is merely onesuggestion to how context can be applied in actual consumerapplications. In contrast to other categorizations the model isapplication- and task specific, leaving it vulnerable to future ap-plications that could possibly make certain parts irrelevant. Thenon-exhaustive and flexible nature however, enables it to adaptand hopefully support new applications.

5. CONCLUSIONSCollaborative work often requires a sense of situation, whichcontext-awareness strives to supply. While context is not a staticmeasure, the sensor information that is available can to a certainextent aid mobile applications used for coordination. This paperhas reviewed the modelling and categorization of context withincontext-aware computing in order to find if a common under-standing of context-awareness exists. Finding the categoriza-tions of context to be lacking in differentiation between contextinformation and sensor information, The Context Information

Model is proposed as a means of designing context-aware ap-plications from a limited amount of context measures. Lastly,examples of the model’s application to consumer devices arepresented, leaving more complex user functions for later expan-sions of the model.

Although context is a highly complex matter that is difficult totrace or determine, by considering individual measures it shouldbe possible to create usable interactive applications, which aidcollaborative work. While the Context Information Model isnot an attempt to trace the general context or situation of theusers, it should support real-life applications that take a limitednumber of sensor measures into account. The presented modelalso offers potential for further development in that comprehen-siveness is not strived for in its present state. The analysis ofpresent context-aware applications is for example one area thatshould be considered in future revision and development of themodel. Further expansion also includes the actual design of acontext-aware application on basis of the model, which wouldenable insight to its usefulness beyond initial context design anddefinition. The model consists of features that should allow itto scale up or down to other potential applications; the potentialexploration of context-awareness should prove the employmentof such applications to be of great benefit to the future users ofmobile devices.

Context-awareness is one of the more complex areas of mo-bile computing and the differences in definition of context issource of great differences of approach. Although much re-search is based on previous researchers’ results, the variety ofapproaches is impairing future research within context-awaremobile computing. Context-awareness is defined in differentterms depending on the overall area focused on such as for ex-ample ubiquitous computing as opposed to collaborative stud-ies. The challenge lies within a common understanding of nothow to define context but in the treatment and definition ofsensor information aspart of context information. However,the diversity of research also results from the early state thatcontext-aware computing occupies; evidently not many applica-tions, research based as well as consumer oriented, take advan-tage of more than a limited number of context measures (oftenonly one: location). The increasing complexity of sensor tech-nology and the possibility of tracing relevant context measures,make context-awareness a challenging and large scale research

area, especially in relation to mobile collaborative work. If thegoal is to create a transparent and ubiquitous computing envi-ronment, context-awareness is a key factor for new consumertargeted applications. The area possesses great potential but inorder to provide the consumer market with actual applications,future research should focus on gradually incorporating contextmeasures into applications as well as finding a common catego-rization of context and sensor information.

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BiographyLouise Barkhuus is a Ph.D. student at The IT University ofCopenhagen, where she also obtained her M. Sc. in ComputerScience in 2001. She is currently researching context-awareness,and in particular the design of context-aware applications formobile devices.