22nd International Conference on Advanced Information Networking and Applications - Workshops
Research on Context-Aware Mobile Computing Li Han, Salomaa Jyri, Jian Ma, Kuifei Yu Nokia Research Center, Beijing, China, 100013
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[email protected] [6]. Dey gave the definition: context is any information that can be used to characterize the situation of an entity. An entity is a person, place or object that is considered relevant to the interaction between a user and an application, including the user and applications themselves [7]. It is also a common method to research context through dividing context into different categories. Hofer et al. thought that context can be separated into physical context representing the level of environment sensors and logical context representing more abstract information about the environment [8]. Prekop and Burnett proposed definition of external and internal context, where the external refers to context that can be measured by hardware sensors, i.e., location, light, or air pressure, whereas the internal is mostly specified by the users or captured by monitoring user interactions, i.e., the user’s goals, tasks, business processed, the user’s emotional state [9]. It is a fact that context has no uniform or standard definition, so everyone can give his understanding about context. However in mobile computing area, the target of using context is to enable the device to better serve for people, either human computer interaction or context-aware mobile application/service. Classifying of context should embody human-centric essence. Here our understanding was given:
Abstract Context-aware mobile computing belongs to the field of ubiquitous computing. It aims to enable device to provide better service for people through applying available context information. In this paper, contextaware mobile computing was elaborated. Firstly, the history of definition of context was summarized, and then the new classification of human-centric context was proposed. The acquiring and application of context information was generalized in the following part. On the basis, the issues in context-aware mobile computing were pointed out and finally the architecture of context-aware Q&A service was presented. Although the context-aware Q&A service can not solve all the issues introduced in this paper, it can be considered as an attempt to provide mobile service through mining of previously gathered context information sensed.
1. Definition of context information So far, the research of context-aware computing has almost thirteen years of history. Mark Weiser pioneered the context-aware computing area under the vision of ubiquitous computing. It is also called pervasive computing and ambient intelligence, which was the method of enhancing computer use by making multiple computers available throughout the physical environment and making them effectively invisible to the user [1, 2]. The term “context-awareness” in ubiquitous computing was firstly introduced by Schilit in 1994 [3, 4]. Schilit divided context into three categories: computing context, user context and physical context. Since then, there have been numerous endeavors to define context. Schmidt defined context as knowledge about the user’s and IT device’s state, including surroundings, situation, and to a less extent, location [5]. Chen and Kotz thought that context was the set of environmental states and settings that either determined an application’s behavior or in which an application event occurred and is interesting to the user
978-0-7695-3096-3/08 $25.00 © 2008 IEEE DOI 10.1109/WAINA.2008.115
Context should be divided into physical, internal and social context. Physical context refers to real world nearby user, making up of physical things, such as computer, print, fax, building and so on. Internal context is composed by abstract things inside people, such as feeling, thought, task, action, interest and so on, which is very related to people. Social context means user’s social surrounding, that is to say, social relationship of user. It consists of persons related to user.
The three kinds of context possess the feature of timeliness. For every kind of context information, it can belong to past context, current context or future context. Figures 1 describes the classification about context.
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differentiated floors of a building with a high degree of precision [14].Place Lab [15] will provide coarsegrained accuracy position for commodity laptops, PDA and cell phones by listening for radio beacons such as 802.11 access points, GSM cell phone towers and fixed Bluetooth devices, and referencing the beacon’s positions in a cached database. Veljo Otsason et al. [16] present the first accurate GSM-based indoor localization system through the use of wide signalstrength fingerprints. Besides, there are numerous indoor location systems which adopted infrared [17, 18] and ultrasonic technology [19, 20], but these systems required extra hardware infrastructure installed.
Figure 1. Classification sketch map of context
2. Acquiring of context information To be able to utilize context information, there must be a mechanism to acquire context information and deliver it to the application software. Context information can be initiatively proposed by user, for example, user will propose his user name and other related information to form his profile when registering in some websites. Additionally, context information can be also sensed by mobile handheld device, such as mobile phone, or through specific sensor device installed in the specific location. For example, the room can be equipped with cameras and the context information related to people in this room can be easily obtained through cameras. This method needs to deploy many sensors so it is more suitable for specific places, such as, meeting rooms or hospitals. Recently the functionality of mobile handheld device has expanded with unprecedented speed and it has been empowered with stronger features than before. It is an indisputable fact that mobile handheld devices have become the most suitable candidates to sense context information without extra device deployment. The following part will pay more attention on the sensing context information through mobile handheld device.
2.2 Context information based on location Further context information can be inferred from absolute location information. Marmasse and Schmandt [21] identify a region through the use of GPS signal disappearing and then reappearing. Ashbrook and Starner [22] can infer and extract significant places by clustering GPS data based on a variant of the k-means clustering algorithm. Patterson et al. [23] use GPS data to infer and predict user transportation mode through fusing user historic sensor data with general commonsense knowledge of real world constraints. Liao et al. [24] use mode-changes such as GPS signal loss and acceleration peaks to identify frequented location in a totally unsupervised manner. Later Liao continued to research how to obtain further context information based on location and proposed a relational approach to identify significant places and discriminate between the activities performed at these locations by using GPS data [25]. However, the use of GPS data to infer context information has limitations for the scope of GPS application. Anderson and Muller conducted a controlled and preliminary study with GSM mobile phones to detect motion of a device [26, 27]. LOCADIO used a Hidden Markov Model to infer motion of a device using 802.11 radio signals [28]. Jong Hee Kang et al. [29] describe a clustering algorithm for extracting significant places from a trace of coordinates. Timothy Sohn et al. [30] use statistical classification and boosting techniques to distinguish if a person is walking, driving or remaining at one place with 85% accuracy, which shows that using GSM is feasible for using outside the laboratory and works well throughout people’s daily lives.
2.1 Location context information Location is the most important kind of information in the context. It belongs to physical context according to the understanding proposed in this paper. Global Positioning System (GPS) is perhaps the most widely used location-sensing system. Now there are many mobile phone equipped with GPS functionality, such as Nokia N95 and Samsung i550. However GPS signal strength is often too low to penetrate inside buildings and therefore GPS does not work well indoors. It becomes necessary to get location context information through other methods. RADAR system [10] can localize a laptop in the hallways of a small office building within 2-3 meters of its true location using fingerprints from four 802.11 access points. There have been works to improve accuracy of RADAR’s fingerprint matching algorithm [11, 12, 13] and
2.3 Other context information
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Although the above acquiring methods of context information are not comprehensive or detailed, it can delegate the current status and tendency of sensing context. For the physical context, location information is one of the most important parts and also easily obtained. The mining of location information attracts the attention of many researchers. Besides inferring and identification significant location and motion of users, a portion of social context can be mined based on location information and proximity [31]. Among the three kinds of context information proposed by the paper, the most difficult to sense is the internal context. Currently the commonly used method of obtaining internal context is that user actively submit internal context, for example, for example MSN users use nicknames to describe their feelings, emotions or other internal context information. Additionally, the emotional internal context information can be sensed by image recognition technology [32], however, it is immature to be applied in the context-aware mobile computing.
centered computing through a combination of specific user and system technologies. Standford’s Interactive workspaces project [36] is exploring new possibilities for people to work together in technology-rich spaces with computing and interaction devices on many different scales. NIST’s smart space [37] researched work environments with embedded computers, information appliances, and multi-modal sensors allowing people to perform tasks efficiently by offering unprecedented levels of access to information and assistance from computers. The Aware Home Research Project [38] is an interdisciplinary research endeavor at Georgia Tech whose aim was to create home environment that is aware of its occupants whereabouts and activities, providing services for its residents to enhance their quality of life or help them maintaining independence as they age. The above projects are typical examples in the field of ubiquitous computing, and they aim to provide services for users in specific scene, such as office, meeting room and home environment. Additionally, hospital was also a good case for applying contextaware computing [39] [40]. These projects involve many technical challenges, of which how to make application or service context-aware is one of the most important problem.
3. Context-aware application Existing context-awareness applications can be divided into three groups: intelligent space, providing information based on context and mobile contextsharing application. Intelligent space gives us an ideal vision of future life: device would be disappearing but service would be omnipresent, in which context-aware is one of the most key technologies. Providing information based on context embodies intelligence of service or application, which will change the using way of Internet from people looking for information to information looking for people. Mobile context sharing application can enrich and facilitate the communication between people.
3.2 Providing Information based on context Tourist guide can be considered as a typical example of providing information based on context. Cyberguide project [41] aimed to develop a series of prototypes of a mobile hand-held context-aware tour guide. The initial prototypes of Cyberguide were designed to assist a very specific kind of tourist to the GVU Center Lab and the long-term goal is an application that knows where the tourist is, what she is looking at, application can predict and answer questions she might pose, and can provide the ability to interact with other people and the environment. Since then, a series of such mobile tourist guides have recently been proposed, offering a wide range of functionalities with respect to context-awareness and adaptation [42] [43] [44] [45] [46] [47] [48] [49] [50]. A comprehensive comparison about them was given in [51], which also pointed out the major issues of these existing prototypes, such as contextual information not fully exploited, proprietary representation of context data and so on. Social context information, i.e. the existing relationships among a group of tourists, is seldom taken into account; and the social dimension of tourism is very important because tourist often enjoy tourist activities in groups [52].
3.1 Intelligent space The Active Badge system [33] from the Olivetti Research Lab at the beginning of the 90’s is generally considered to be one of the first context-aware systems. With the system people could be located in an office and calls forwarded to the closest phone through socalled active badges. The ParcTab system [34] was developed at the Xerox Palo Alto Research Center in the beginning of the 90’s. The system aimed to provide ubiquitous computing and context-awareness in an office environment. MIT’s Intelligent Room [35] is a highly interactive environment that uses embedded computation to observe and participate in normal everyday events, such as collaboration. It is part of Oxygen project, which enables pervasive, human-
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mind is important for mobile application/service but it is unfeasible to let people express that explicitly. That kind of context information should be inferred from passed and current context information. Privacy should be taken into consideration when acquiring and utilizing context information. The target to get context information is to make technology serve people better. However the better service is provided only after knowing as much as possible user’s information and thus privacy concerns must be involved. How to balance the relationship between obtaining context and protection privacy is also important problem in the context-aware mobile computing.
3.3 Mobile context sharing applications FriendZone application [53] consists of the following Location-Based Community services: Instance Messenger and Locator, Anonymous IM and Location-based chat. FriendZone can be said as a commercial attempt to provide mobile context-aware service, especially location context sharing between communication people. Nakanishi et al. [54] proposed a Java-based solution called iCAMS2 where the phonebook of a handheld phone shows the social surroundings and location of friends. Awarephone [55] aims to minimizing unwanted interruptions by enabling a social awareness through the use of contextawareness systems among hospital clinicians. The context-aware IM system [56] focuses on communication based on context-aware message in hospital. Contextcontacts [57], was built on top of the ContextPhone platform, and presents several meaningful, automatically communicated situation cues of trusted others in a format integrated to the standard contact book of Nokia S60 Smartphones. The familiar stranger is a social phenomenon first addressed by the psychologist Stanley Milgram in his 1972 essay on the subject [58]. Jabberwocky [59] and the Telelogs application [60] are targeted to provide service for the familiar stranger relationships through mobile communication technology. Jabberwocky application scans other Bluetooth devices in its ambient environment and provides graphical information of familiar strangers nearby for its user. While Telelogs application can make familiar strangers interact through sharing auditory blog and related comments. [61] proposes an application to assist users in building common ground by means of identifying shared context-user’s address books. BlueAware [62, 63] tries to match profile of users nearby through Bluetooth technology in order to push their face-toface interaction between users who do not know each other but probably should.
5. Context-aware Q&A Context-aware technology can enable many Internet services get better via enhancing intelligent feature. The following section will use a Q&A service as an example to describe how to provide context-aware service. For the current Q&A web site, people always look for the questions that they can answer, such as Ask Yahoo!. In fact, people can only answer a part of questions depending of his background knowledge. If questions can look for people who can answer the question, people can save much energy and time to answer questions, not looking for questions which they can answer. This paper proposes the concept of context-aware Q&A for mobile phone. This concept has the following differences from the previous similar work: ① the context of every question will be fully taken into consideration; ② all the content of every user, including question or answer, and related context information will be stored and analyzed to know more about the user; ③ the service will actively look for people closely related question thinking that these people can answer the question according to their history. Thus the context-aware Q&A will provide answers quickly for people who propose questions and increase the efficiency of people who answer questions by utilizing context information. When a client proposes question to the server, he will also submit related context information besides question. When the server processes the question, it will take context information of the question and history of the submitter into consideration. The history of every user includes questions he has proposed, related context information corresponding every question and further meaning based on this information. When the server receives a question, firstly it will search the existing Q&A database to determine whether the question already exists. If yes, the server will return the existing answer to the
4. Lesson learned In this section, we will briefly summarize the major issues of context-aware mobile computing. Not all of the context information can be easily obtained, especially information about internal context. Some related technologies exist in laboratories and only for specific occasions, such as people emotion context information. The context information obtained is not fully utilized. The further meaning based on context information obtained can be mined to acquire highlevel context information. The knowledge in people
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submitter. If no, the server will intelligently analyze the question to determine who can answer the question from the user database. Then, the server will actively send the question to related users or wait related users to use the service and then show the question. Or then send the contact of related users to the submitter, which depends on the requirements given by the submitter. The final result of every question in contextaware Q&A is answer itself or the contact of people who can know answer.
[4] Bill Schilit and Marvin Theimer, “Disseminating Active Map Information to Mobile Hosts”, IEEE Network, 8(5), 1994, pp.22-32. [5] Albrecht Schmidt, Kofi Asante Aidoo, Antti Takaluoma, Urpo Tuomela, Kristof Van Laerhoven and Walter Van de Velde, “Advanced interaction in context”, In proceedings of First International Symposium on Handheld and Ubiquitous Computing, Karlsruhe, Germany, September 1999,pp.89101. [6] Guanling Chen and David Kotz, “A survey of contextaware mobile computing research”, Technical Report TR2000-381, Computer Science Department, Dartmouth College, Hanover, New Hampshire, November 2000. [7] Anind K Dey, “Understanding and using context”, Personal and Ubiquitous Computing, 5(1), February 2001, pp.4-7. [8] Hofer T., Schwinger W., Pichler M., Leonhartsberger G. and Altmann J. “Context-awareness on mobile devices – the hydrogen approach”, In Proceedings of the 36th Annual Hawaii International Conference on System Sciences, 2002, pp.292-302. [9] Prekop P. and Burnett M. Activities, “context and ubiquitous computing”, Special Issue on Ubiquitous Computing Computer Communications, Vol.26, No.11, 2003, pp.1168-1176. [10] P.Bahl and V.N. Padmanabhan, “RADAR: An inbuilding RF-based user location and tracking system”, In Infocom, 2000, pp.775-784. [11] P.Bahl, A.Balachandran and V.Padmanabhan, “Enhancements to the RADAR User Location and Tracking System”, Microsoft Reasearch, Technical Report, Feb.2000. [12] A.Ladd, K.Bekris, G.Marceau, A.Ruddys, L.Kavraki and D.Wallach, “Robotics-based location sensing using wireless Ethernet”, In proceedings of the Tenth ACM International Conference on Mobile Computing and Networking, 2002. [13] M.Youssef, A.Agrawala and U.Shankar, “WLAN Location Determination via Clustering and Probalility Distributions”, In IEEE Percom 2003, March 2003. [14] A.Haeberlen, E.Flannery, A.M. Ladd, A.Rudys, D.S.Wallach, and L.E.Kavraki, “Practical robust localization over large-scale 802.11 wireless networks”, In proceedings of the Tenth ACM International Conference on Mobile Computing and Networking, Philadelphia, PA,Sept.2004. [15] A.LaMarca, Y.Chawathe, S.Consolvo, J.Hightower, I.Smith, J.Scott, T.Sohn, J.Howard, J.Hughes, F.Potter, J.Tabert, P.Powledge, G.Borriello and B.Schilit, “PlaceLab: Device Positioning Using Radio Beacons in the Wild”, In the Proceed of 3rd Annual Conference on Pervasive Computing, 2005. [16] Veljo Otsason, Alex Varshavsky, Anthony LaMarca and Eyal de Lara, “Accurate GSM Indoor Localization”. [17] Roy Want, Andy Hopper, Veronica Falcao and Jonathan Gibbons, “The Active Badge location system”, ACM Transactions on Information Systems, 10(1), January, 1992, pp.91-102. [18] Gregory D. Abowd, Christopher G. Atkeson, Jason Hong, Sue Long, Rob Kooper and Mike Pinkerton, “Cyberguide: A mobile context-aware tour guide”, Wireless Networks, 3(5), October 1997, pp.421-433.
Figure 2 Architecture of context-aware Q&A
6. Conclusion and further work In this paper, we elaborate over context-aware mobile computing. Although context-aware mobile computing has a long research history, it is not applied widely in our daily life. In the area of research, there are still many technological challenges, such as specific occasion optimized image recognition. The other two kinds of context-aware applications will soon become more and more popular. It is fact that some novel applications in context-aware mobile computing are emerging in high speed. Further meanings obtained from coarse context data will be helpful for all kinds of context-aware mobile applications. We will concentrate on mining context information in the future and develop the prototype of context-aware Q&A based on the research results.
7. References [1] Mark Weiser, “The computer for the 21st century”, Scientific American, September 1991, pp.94-104. [2] Mark Weiser, “Some computer science issues in ubiquitous computing”, Communications of the ACM, 36(7), July 1993, pp.75-84. [3] Bill Schilit, Norman Adams and Roy Want, “Contextaware computing applications”, In Proceedings of IEEE Workshop on Mobile Computing Systems and Applications, Santa Cruz, California, December 1994, pp85-90.
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[19] Andy Harter, Andy Hopper, Pete Steggles, Andy Ward and Paul Webster, “The anatomy of a context-aware application”, In proceedings of Fifth Annual ACM/IEEE International Conference on Mobile Computing and Networking, Seattle, WA, August 1999,pp.59-68. [20] Nissanka B. Priyantha, Anti Charkraborty and Hari Balakrishnan, “The Cricket location-support system”, In proceedings of the sixth Annual ACM International Conference on Mobile Computing and Networking Boston, MA, August 2000. [21] Matalia Marmasse and Chris Schmandt, “Locationaware information delivery with commotion”, In proceedings of the 2nd International Symposium in Handheld and Ubiquitous Computing, Bristol, UK, September 2000. [22] Ashbrook, D., Starner, T. “Learning significant locations and predicting user movement with GPS”, In International Symposium on Wearable Computing, Seattle, WA, 2002. [23] Donald J.Patterson, Lin Liao, Dieter Fox and Henry Kautz, “Inferring High-Level Behavior from Low-Level Sensors”, In Proceedings of the 5th International Conference on Ubiqutious Computing, Seattle, WA, October 2003 [24] Liao L., Fox D. and Kautz H, “Learning and Inferring Transportation Routine”, In Proceedings of AAAI04, 2004. [25] Liao L.,Fox D.,Kautz H, “Location-based Activity Recognition using Relational Markov Networks”, In Proceedings of the International Conference on Artificial Intelligence (IJCAI 2005). [26] Anderson, I. and Muller, H, “Context Awareness via GSM signal Strength Fluctuation”, In Pervasive 2006, Late Breaking Results, pp.27-31. [27] I.Anderson and H.Muller. “Practical activity recognition using GSM data”, Technical Report CSTR-06-016, Department of Computer Science, University of Bristol, July 2006. [28] Krumm, J. and Horvitz, E, “LOCADIO: Inferring Motion and Location from Wi-Fi Signal Strengths”, In Mobiquitous 2004, pp.4-13. [29] J.H.Kang, W.Welbourne, B.Stewart and G.Borriello. “Extracting places from traces of locations”, In Proceedings of the 2nd ACM International Workshop on Wireless Mobile Applications and Services on WLAN Hotspots, Philadelphia, PA,USA, 2004,pp.110-118. [30] Timothy Sohn, Alex Varshavsky, Anthony LaMarca, et al, “Mobility Detection using Everyday GSM Traces”, In Proceedings of the 8th International Conference on Ubiquitous Computing (Ubicomp 2006), p212-224. Sep.2006. [31] Eagle N. and Pentland A, “Reality Mining: Sensing Complex Social Systems”, Personal and Ubiquitous Computing, Jan.2006. [32] R.Cowie, E.Douglas-Cowie, N.Tsapatsoulis, Y.Votsis, S.Kolias, W.Fellenz and J.Taylor, “Emotion Recognition and Human Computer Interaction”, IEEE Sign. Proc. Magazine, No.1,p32-80,2001. [33] Want R., Hopper A., Falcao V. and Gibbons J. “The Active Badge Location System”, ACM Transactions on Information System, 10(1),1992, pp.91-102. [34] Want R., et al. “An Overview of the Parctab Ubiquitous Computing Eiroment”, IEEE Personal Communications, Vol 2, No 6, December 1995, pp.28-43.
[35] MIT Oxygen Project, http://oxygen.csail.mit.edu/index.html [36] Standford iRoom http://iwork.stanford.edu/ [37] Rosenthal L. and Standford V., “NIST Smart Space: pervasive computing initiative”, In Proceedings of IEEE 9th International workshops on Enabling Technologies Infrastructure for Collaborative Enterprises, 2000, pp.6-11. [38] The Aware Home: http://www.awarehome.gatech.edu/ [39] J.E.Bardram, C.Bossen, A.Lykke-Olesen, K.H.Madsen and R.Nielsen, “Virtual video prototyping of pervasive healthcare systems”, In Conference proceedings on Designing Interactive Systems: processes, practices, methods, and techniques, ACM Press, 2002,pp.167-177. [40] Bardram J.E., “Applications of Context Aware Computing in Hospital Work – Examples and Design Principles”, In Proceedings of the 2004 ACM Symposium on Applied Computing, ACM press, 2004, pp. 1574-1579. [41] Long S., et al, “Rapid Prototyping of Mobile Contextaware Applications: The Cyberguide Case Study”, The 2nd ACM International Conference on Mobile Computing and Networking, November, 1996, pp.10-12. [42] Van Setten M., Pokraev S., Koolwaaij J, “ContextAware Recommendations in Mobile Tourist Application COMPASS”, In Nejdl, W. & De Bra, P. (Eds.). AH 2004, LNCS 3137, Springer-Verlag, 2004. [43] Poslad S., Laamanen H., Malaka R., Nick A., Buckle P., Zipf A, “CRUMPET: Creation of User-Friendly Mobile Services Personalised for Tourism”, In Proceedings of 3G 2001 – Second Int.Conf. on 3G Mobile Communication Technologies,London, UK, 2001,pp.26-29. [44] Cheverst K.,Davies N., Mitchell K., Friday A. “Experiences of developing and deploying a context-aware tourist guide: the GUIDE project”, In Proceedings of the 6th annual international conference on Mobile computing and networking, August, Boston, 2000. [45] O’Grady M.J., O’Hare G.M.P, Gulliver’s Genie: Agency, Mobility & Adaptivity. Computer & Graphics, Special Issue on Pervasive Computing and Ambient Intelligence Mobility, Ubiquity and Wearables Get Together , Vol 28, No 4, Elsevier, 2004. [46] Anegg H.,Kunczier H., Michlmayr E., Pospischil G., Umlauft M., “ LoL@: Designeing a Location Based UMTS Application”, OVE-Verbandszeitshrift e&I, Springer, 2002. [47] Krosche J., Baldzer J., Boll S., “MobiDENK-Mobile Multimedia in Monument Conservation”, IEEE MultiMedia, 11(2),2004. [48] Kamar A., “Mobile Tourist Guide (m-ToGuide)”, Deliverable 1.4, Project Final Report. IST-2001-36004, 2003. [49] Roth J., “Context-Aware Web Applications using the PinPoint”, IADIS International Conference WWW/Internet 2002, Lissabon, IADIS Press, 2002, pp.3-10. [50] Scherp A., Boll S., “mobileMM4U-Framework support for dynamic personalized multimedia content on mobile systems”, In Proceeds of Des Techniques and Applications for Mobile Commerce Track der Multi-Konferenz Wirtschaftsinformatik 2004. [51] Schwinger W., Grun Ch., Proll B., Retschitzegger W., Schauerhuber A., “Context-Awareness in Mobile Tourism Guide – A Comprehensive Survey”, Technical Report, Johannes Kepler University Linz, IFS/TK, July, 2005.
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[52] Luca Buriano, “Exploiting Social Context Information in Context-Aware Mobile Tourism Guides”, In Proceeding of Mobile Guide 2006, Turin,Italy, 2006. [53] Burak A. and Sharon T., “Analyzing usage of location based services”, In CHI’03 Extended Abstracts on Human factors on computing systems, ACM Press, Florida, USA, 2003, pp.970-971. [54] Nakanishi Y., Kumazawa S.,Tsuji T. and Hakozaki K., “ICAMS2: developing a mobile communication tool using location information and schedule information with J2ME”, In Proceeding of the 5th International Symposium on Human Computer Interaction with Mobile Devices and Services. (MobileHCI’03), Udine, Italy,September 2003, pp.400-404. [55] Bardram J.E. and Hansen T.R. “The AWARE architecture: supporting context-mediated social awareness in mobile cooperation”, In Proceeding of Computer supported cooperative work (CSCW’04), Chicago, USA, 2004, pp.192201. [56] M.A.Munoz, M.Rodriguez, J.F.Center, A.I.MartinezGarcia and V.M.Gonzalez. “Context-Aware Mobile Communication in Hospitals”, IEEE Computer, 36(9), September 2003, pp.38-46. [57] Antti Oulasvirta, Mika Raento and Sauli Tiitta. “ContextContacts: Re-Designing SmartPhone’s Contact Book to Support Mobile Awareness and Collaboration”. In
Proceeding of Mobile HCI 2005, New York, Salzburg, Austria, 2005, pp.167-174. [58] S.Milgram, J.Sabini and M.Silver, “The individual in a social world: essays and experiments”. Longman Higher Education, 1997. [59] Paulos E. and Goodman E., “The familiar stranger: anxiety, comfort and play in public spaces”, In Proceedings of CHI’04, Vienna, Austria, pp.223-230. [60] Davis B. and Karahalis K., “Telelogs: a social communication space for urban environments”. In Proceedings of the 7th International Conference on Human Computer interaction with Mobile Device & Services (MobileHCI’05), Salzburg, Austria, pp.231-234. [61] Kostakos V., O’Neill E. and Shahi A., “Building common ground for face to face interactions by sharing mobile device context”. In M.Hazas,J.Krumm, and T.Strang (Eds.) LOCA 2006, Springer LNCS 3987, pp.222-238. [62] Eagle N. and Pentland A. “Social serendipity: mobilizing social software”, IEEE Pervasive Computing, Special Issue: The Smart phone, April-June 2005, pp.28-34. [63] Eagle N. and Pentland A, “Reality mining: sensing complex social systems”. Journal of Personal and Ubiquitous Computing, December 2005, pp.1-14.
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