Context awareness

Context awareness is a property of mobile devices that is defined complementarily to location awareness. Whereas location may determine how certain processes in a device operate, context may be applied more flexibly with mobile users, especially with users of smart phones. Context awareness originated as a term from ubiquitous computing or as so-called pervasive computing which sought to deal with linking changes in the environment with computer systems, which are otherwise static. The term has also been applied to business theory in relation to Contextual application design and business process management issues.[1]

Qualities of context

Various categorizations of context have been proposed in the past. Dey and Abowd (1999)[2] distinguish between the context types location, identity, activity and time. Kaltz et al. (2005)[3] identified the categories user&role, process&task, location, time and device to cover a broad variety of mobile and web scenarios. They emphasize yet for these classical modalities that any optimal categorization depends very much on the application domain and use case. Beyond more advanced modalities may apply when not only single entities are addressed, but also clusters of entities that work in a coherence of context, as e.g. teams at work or also single bearers with a multiplicity of appliances.

Some classical understanding of context in business processes is derived from the definition of AAA applications[4] with the following three categories:

these three terms including additionally location and time as stated.

Computer science

In computer science context awareness refers to the idea that computers can both sense, and react based on their environment. Devices may have information about the circumstances under which they are able to operate and based on rules, or an intelligent stimulus, react accordingly. The term context-awareness in ubiquitous computing was introduced by Schilit (1994).[5][6] Context aware devices may also try to make assumptions about the user's current situation. Dey (2001) define context as "any information that can be used to characterize the situation of an entity." [7]

While the computer science community initially perceived the context as a matter of user location, as Dey discuss,[7] in the last few years this notion has been considered not simply as a state, but part of a process in which users are involved; thus, sophisticated and general context models have been proposed (see survey[8]), to support context-aware applications which use them to (a) adapt interfaces, (b) tailor the set of application-relevant data, (c) increase the precision of information retrieval, (d) discover services, (e) make the user interaction implicit, or (f) build smart environments. For example: a context aware mobile phone may know that it is currently in the meeting room, and that the user has sat down. The phone may conclude that the user is currently in a meeting and reject any unimportant calls.[9]

Context aware systems are concerned with the acquisition of context (e.g. using sensors to perceive a situation), the abstraction and understanding of context (e.g. matching a perceived sensory stimulus to a context), and application behaviour based on the recognized context (e.g. triggering actions based on context).[10] As the user's activity and location are crucial for many applications, context awareness has been focused more deeply in the research fields of location awareness and activity recognition.

Context awareness is regarded as an enabling technology for ubiquitous computing systems. Context awareness is used to design innovative user interfaces, and is often used as a part of ubiquitous and wearable computing. It is also beginning to be felt in the internet with the advent of hybrid search engines. Schmidt, Beigl & Gellersen[11] define human factors and physical environment as two important aspects relating to computer science. More recently, much work has also been done to ease the distribution of context information; Bellavista, Corradi, Fanelli & Foschini survey[12] the several middleware solutions that have been designed to transparently implement context management and provisioning in the mobile system. Perera, Zaslavsky, Christen, & Georgakopoulos[13] have performed a comprehensive survey on context-aware computing from Internet of Things perspective by reviewing over 50 leading projects in the field. Further, Perera has also surveyed a large number of industrial products in the existing IoT marketplace from context-aware computing perspective.[14] Their survey is intended to serve as a guideline and a conceptual framework for context-aware product development and research in the IoT paradigm. The evaluation has been done using the theoretical framework developed by Dey and Abowd (1999)[2] more than a decade ago.

Human factors related context is structured into three categories: information on the user (knowledge of habits, emotional state, biophysiological conditions), the user’s social environment (co-location of others, social interaction, group dynamics), and the user’s tasks (spontaneous activity, engaged tasks, general goals). Likewise, context related to physical environment is structured into three categories: location (absolute position, relative position, co-location), infrastructure (surrounding resources for computation, communication, task performance), and physical conditions (noise, light, pressure, air quality).

Applications in Situational or Social Awareness

Context awareness has been applied to the area of Computer-Supported Cooperative Work (CSCW) to help individuals work and collaborate more efficiently with each other. Since the early 1990s, researchers have developed a large number of software and hardware systems that can collect contextual information (e.g., location, video feeds, away status messages) from users. This information is then openly shared with other users, thereby improving their situational awareness, and allowing them to identify natural opportunities to interact with each other. In the early days of context-aware computing, many of the systems developed for this purpose were specifically designed to assist businesses or geographically separated work teams collaborate on shared documents or work artifacts. More recently, however, there has been a growing body of work that demonstrates how this technique can also be applied to groups of friends or family members to help keep them apprised of each other's activities.

To date, systems that use context awareness to improve situational awareness can be characterised by:

The most common context to obtain and share for the purposes of improving situational awareness is the user's location. In an early prototype, the Active Badge system,[15] for example, each user had a uniquely identifying badge that could be tracked via a series of overhead infrared sensors. As users walked throughout a building, their location was constantly monitored by a centralized server. Other users could then view this information (either in text form, or on a map, as was done in later work[16]) to determine if a user is in her office, thereby allowing them to determine the best time to stop by for an unplanned conversation. Location was also shared in the PeopleTones,[17] Serendipity,[18] and the Group Interaction Support Systems[19] to help users determine when they are near friends, users with shared personal interests, and teammates, respectively. In comparison with Active Badge, which only displays location information, these systems are more proactive, and will alert the users when they are in proximity of each other. This lets the user know when a potential interaction opportunity is available, thereby increasing his/her chances of taking advantage of it.

Another popular context to share is a user's work activity, often by sharing video. In the Community Bar system,[20] researchers developed a desktop application that periodically took screenshots of the user's display. This information was then shared with the user's co-workers so that they could know what documents/artifacts their teammates was working on, and provided a common frame of reference so that users could n talk about these artifacts as if they were collocated. In Montage,[21] users are given the ability to remotely activate the webcam on another user's computer for a brief amount of time. This capability to "glance" at another user lets users see if they are busy or preoccupied, which in turn helps them better determine the most opportune time to initiate a conversation.

A third type of context to share to improve or enhance situational awareness is the user's audio. In the Thunderwire system,[22] researchers developed an audio-only media space that allowed friends to share raw audio from their mobile devices' microphones. This system, which in essence was a perpetual conference-call, allowed users to listen to other users' audio in order to determine if and when they were participating in a conversation. The WatchMe[23] and ListenIn [24] systems also rely heavily on audio in order to determine if and when a user was potentially interruptable. Unlike Thunderwire, however, these systems rely on machine learning algorithms in order to analyze the user's audio and determine if the user is talking. This allows the system to provide other users with the same context (i.e., whether or not the user is in a conversation) without having to share the actual audio, thereby making it more privacy centric.

A fourth type of context that is commonly shared is the user's overall activity. In the Hubbub[25] and Conchat[26] systems, researchers developed a series of instant messenger applications that monitored when the user was active or idle using either software, or environmental sensors, respectively. This information could then be provided to other users to let them know if and when their friends were available to respond to their messages. In the Digital Family Portrait system,[27] researchers developed a digital picture frame that provides qualitative visualizations of a user's (i.e., an elderly parent/grandparent) daily activities. This visualization is then provided to the user's adult children so that they could "check up" on their parents without having to explicitly bother them, thereby allowing them to "age in place."

Although these system demonstrate how context awareness can be used to support situational and social awareness, the long term effectiveness of these systems are not yet fully understood. Many of the systems described above have only been evaluated at a conceptual level, or to demonstrate that the system is technically feasible. Consequently, while initial results from these studies suggest that context awareness can support situational awareness tasks, more longitudinal data is required.

Application in health care

Context-aware mobile agents[28] are a best suited host implementing any context-aware applications. Modern integrated voice and data communications equips the hospital staff with smart phones to communicate vocally with each other, but preferably to look up the next task to be executed and to capture the next report to be noted.

However, all attempts to support staff with such approaches are hampered till failure of acceptance with the need to look up upon a new event for patient identities, order lists and work schedules. Hence a well suited solution has to get rid of such manual interaction with a tiny screen and therefore serves the user with

Basically such contextually well formed approach requires scheduled workflows, as all necessary preparation must refer to given orders and set schedules. Working free hand or ex tempore does not provide such qualities.

Additionally, none of the RFID, WLAN or RTLS locating solutions advertising for most precise locating serve the required quality, as determining a location in conventional attitude looking for absolute coordinates fails either technically or economically.

However, the coincidence of personnel with patient is easily detected and delivers all options of reliable automating at reasonable effort. An escape is fairly available with the method of unilateration or fuzzy locating, where no absolute coordinates are required, but just the radial distance between personnel smart phone and patient wrist band.[29] Such distance estimating is entirely sufficient to detect the context of servicing the patient, as no service is ever applied to patients only at larger distance. Even in nuclear medicine or with radiology diagnosis, the haptic contact of staff with patient comes before staff retreats while the patient gets exposed to isotopes or X-rays.

Applications in Pervasive Games

Pervasive gaming, a new genre in the field of entertainment, is leveraging the sensed human contexts to adapt game system behaviors. By blending of real and virtual elements and enabling users to physically interact with their surroundings during the play, people can become fully involved in pervasive games and attain better gaming experience. For example, a pervasive game that uses the contexts of human activity and location in smart homes is reported by [30]

See also

Literature

References

  1. ↑ Rosemann, M., & Recker, J. (2006). "Context-aware process design: Exploring the extrinsic drivers for process flexibility". In T. Latour & M. Petit. 18th international conference on advanced information systems engineering. proceedings of workshops and doctoral consortium. Luxembourg: Namur University Press. pp. 149–158.
  2. ↑ 2.0 2.1 Towards a Better Understanding of Context and Context-Awareness
  3. ↑ Kaltz, J.W., Ziegler, J., Lohmann, S. (2005). "Context-aware Web Engineering: Modeling and Applications" (PDF). Revue d'Intelligence Artificielle 19 (3): 439–458. doi:10.3166/ria.19.439-458.
  4. ↑ CISCO AAA Overview
  5. ↑ B. Schilit, N. Adams, and R. Want. (1994). "Context-aware computing applications". IEEE Workshop on Mobile Computing Systems and Applications (WMCSA'94), Santa Cruz, CA, US. pp. 89–101. CiteSeerX: 10.1.1.29.5833.
  6. ↑ Schilit, B.N. and Theimer, M.M. (1994). "Disseminating Active Map Information to Mobile Hosts". IEEE Network 8 (5): 22–32. doi:10.1109/65.313011.
  7. ↑ 7.0 7.1 Dey, Anind K. (2001). "Understanding and Using Context". Personal Ubiquitous Computing 5 (1): 4–7. doi:10.1007/s007790170019.
  8. ↑ Cristiana Bolchini and Carlo A. Curino and Elisa Quintarelli and Fabio A. Schreiber and Letizia Tanca (2007). "A data-oriented survey of context models" (PDF). SIGMOD Rec. (New York, NY, USA: ACM) 36 (4): 19–26. doi:10.1145/1361348.1361353. ISSN 0163-5808.
  9. ↑ Schmidt, A.; Aidoo, K.A.; Takaluoma, A.; Tuomela, U.; Van Laerhoven, K; Van de Velde W. (1999). "Advanced Interaction in Context" (PDF). 1st International Symposium on Handheld and Ubiquitous Computing (HUC99), Springer LNCS, Vol. 1707. pp. 89–101.
  10. ↑ Schmidt, Albrecht (2002). "Ubiquitous Computing - Computing in Context". PhD dissertation, Lancaster University.
  11. ↑ Albrecht Schmidt, Michael Beigl and Hans-W. Gellersen (December 1999). "There is more to Context than Location" (PDF). Computers & Graphics (Elsevier) 23 (6): 893–902. doi:10.1016/s0097-8493(99)00120-x.
  12. ↑ Paolo Bellavista, Antonio Corradi, Mario Fanelli and Luca Foschini (August 2012). "A Survey of Context Data Distribution for Mobile Ubiquitous Systems". Computing Surveys, ACM 44 (4): 1–45. doi:10.1145/2333112.2333119.
  13. ↑ Charith Perera, Arkady Zaslavsky, Peter Christen, and Dimitrios Georgakopoulos (2013). "Context Aware Computing for The Internet of Things: A Survey". Communications Surveys Tutorials, IEEE. Early Access Articles (n/a): 1–44. doi:10.1109/SURV.2013.042313.00197.
  14. ↑ Perera, Charith; Liu, Harold; Jayawardena, Srimal; Chen, Min. "A Survey on Internet of Things From Industrial Market Perspective". Access, IEEE 2: 1660–1679. doi:10.1109/ACCESS.2015.2389854. Retrieved 1 February 2015.
  15. ↑ Want, R., Hopper, A., Falcao, V., & Gibbons, J. (1992). "The Active Badge Location System". ACM Transactions on Information Systems 10 (1): 91–102.
  16. ↑ McCarthy, J. F., & Meidel, E. S. (1999). "ACTIVE MAP: A visualization tool for location awareness to support informal interactions". Handheld and Ubiquitous Computing: 158–170.
  17. ↑ Li, K. A., Sohn, T. Y., Huang, S., & Griswold, W. G. (2008). "Peopletones: a system for the detection and notification of buddy proximity on mobile phones.". MobiSys '08. pp. 160–173.
  18. ↑ Eagle, N. & Pentland, A. (2005). "Social Serendipity: Mobilizing Social Software". IEEE Pervasive Computing 4 (2): 28–34.
  19. ↑ Ferscha, A., Holzmann, C., & Oppl, S. (2004). "Context awareness for group interaction support". MobiWac '04. pp. 88–97.
  20. ↑ Tee, K., Greenberg, S., and Gutwin, C. (2006). "Providing Artifact Awareness to a Distributed Group Through Screen Sharing". CSCW '06. pp. 99–108.
  21. ↑ Tang, J. and Rua, M. (1994). "Montage: Providing Teleproximity for Distributed Groups". CHI '94. pp. 37–43.
  22. ↑ Ackerman, M., Hindus, D., Mainwaring, S., and Starr, B. (1997). "Hanging on the 'Wire: A Field Study of an Audio-Only Media Space". Transactions on Computer-Human Interaction 4 (1): 39–66.
  23. ↑ Marmasse, N., Schmandt, C., & Spectre, D. (2004). "WatchMe: communication and awareness between members of a closely-knit group". UbiComp '04. pp. 214–231.
  24. ↑ Rosas, G. M. V. (2003). "ListenIN: Ambient Auditory Awareness at Remote Places". Doctoral dissertation, MIT Media Lab.
  25. ↑ Isaacs, E., Walendowski, A., and Ranganthan, D (2002). "Hubbub: A Sound-Enhanced Mobile Instant Messenger that Supports Awareness and Opportunistic Interactions". CHI '02. pp. 333–340.
  26. ↑ Ranganathan, A., Campbell, R. H., Ravi, A., & Mahajan, A. (2002). "Conchat: A context-aware chat program". Pervasive Computing 1 (3): 51–57.
  27. ↑ Mynatt, E.D., Rowan, J., Craighill, S., and Jacobs, A (2001). "Digital family portraits: supporting peace of mind for extended family members". CHI '01. pp. 333–340.
  28. ↑ Burstein et.al. Context Aware Mobile Agents in Healthcare
  29. ↑ Wireless authenticating & authorising system
  30. ↑ B. Guo, R. Fujimura, D. Zhang, M. Imai.Design-in-Play: Improving the Variability of Indoor Pervasive Games. Multimedia Tools and Applications, 2011.

External links