Chapter 1

An Overview of Pervasive Computing Systems Juan Ye, Simon Dobson, and Paddy Nixon

Abstract Pervasive computing aims to create services that respond directly to their user and environment, with greatly reduced explicit human guidance. The possibility of integrating IT services directly into users’ lives and activities is very attractive, opening-up new application areas. But how has the field developed? What have been the most influential ideas and projects? What research questions remain open? What are the barriers to real-world deployment? In this chapter we briefly survey the development of pervasive computing and outline the significant challenges still being addressed. Keywords Pervasive computing, Ubiquitous computing, Location, Adaptation, Behaviour, Context, Situation

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Introduction of Pervasive Computing

This history of computing is peppered with paradigm shifts on how the relationship between humans and computers is perceived. After mainframe computing, minicomputing and personal computing, a fourth wave is now taking place – pervasive (or ubiquitous) computing, proposed by Mark Weiser in his seminal 1991 paper. Weiser describes a vision of pervasive computing that still inspires more than 15 years later: The most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they are indistinguishable from it. [1]

The essence of Weiser’s vision was the creation of environments saturated with computing capability and wireless communications, whose services were gracefully integrated with human user action [2]. Computing thus becomes pervasive; available always and everywhere.

Systems Research Group, School of Computer Science and Informatics, UCD Dublin, Belfield, Dublin 4, Ireland

K. Delaney, Ambient Intelligence with Microsystems, © Springer 2008

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We could distinguish (as some authors do) between ubiquitous computing that is provided by a continuous networked infrastructure of accessible devices, and pervasive computing that focuses on providing seamless and cognitively integrated services to users – however, this distinction is becoming increasingly unnecessary in the era of WiFi and Bluetooth networks and we shall focus almost exclusively on service provision. In pervasive systems, people rely on the electronic creation, storage, and transmission of personal, financial, and other confidential information. This in turn demands the highest security for these transactions, and requires access to time-sensitive data – all regardless of the physical location. Devices like personal digital assistants (PDAs), “smart” mobile phones, ultra-mobile laptops and office PCs, and even home entertainment systems are expected to work together in one seamlessly-integrated system. In addition, a pervasive computing environment assumes a number of invisible sensing/computational entities that collect information about the users and the environment. With the help of these entities, devices can deliver customised services to users in a contextual manner when they are interacting and exchanging information with the environment [3]. Simply put, pervasive computing is a post-desktop model of human-computer interaction where computation is embedded in everyday objects that gather information from users and their surrounding environments and accordingly provides customised services [4, 5]. Pervasive computing aims to empower people to accomplish an increasing number of personal and professional transactions using new classes of intelligent and portable appliances, devices, or artifacts with embedded microprocessors that allow them to employ intelligent networks and gain direct, simple, and secure access to both the relevant information and services. It gives people access to information stored on powerful networks, allowing them to easily take action anywhere and at any time. In principle, to be effective pervasive computing must simplify life by combining open standards-based applications with everyday activities. It must remove the complexity of new technologies, enable us to be more efficient in our work and leave us more leisure time; delivered thus, pervasive computing will become part of everyday life. Achieving this in practice will prove to be a challenge.

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Representative Examples of Pervasive Computing Applications

Pervasive computing is maturing from its origins as an academic research topic to a commercial reality. It has many potential applications, from the intelligent office and the smart home to healthcare, gaming and leisure systems and public transportation. Three specific application domains are outlined here: healthcare, public transportation, and the smart home. Pervasive healthcare is an emerging research discipline, focusing on the development and application of pervasive/ubiquitous computing technology for healthcare

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and life wellness [6, 7]. Pervasive computing technologies introduce new diagnostic and monitoring methods that directly contribute to improvements in therapy and medical treatment [8]. These examples involve sensors and monitoring devices, such as blood pressure cuffs and glucose meters, which can collect and disseminate information to healthcare providers. They can support better understanding of facets of a patient’s daily lives and then appropriately modify therapies to the individual. One of the scenarios would be a hospital where a patient is constantly monitored, and the findings are linked to a diagnostic process. Thus, it could be possible to advise the hospital canteen to prepare special food for this particular patient and to adapt the patient’s specific medication according to his current health condition. Pervasive computing technologies can also improve the procedure of medical treatment (an example is given in Fig. 1.1). In emergency care, they can accelerate access to medical records at the emergency site or seek urgent help from multiple experts virtually. In the surgical field, they can collect and process an ever-increasing range of telemetric data from instruments used in an operating room and augment human ability to detect patterns that could require immediate action [9]. Pervasive computing technologies are also entering our everyday life as embedded systems in transportation [11, 12]. A number of applications have emerged. In tourist guides, a pervasive computing system can provide personalised services (like locating a specific type of restaurant or planning a daytrip) for visitors based on their location and preferences. In traffic control, a system can be immediately informed of incidences of congestion or the occurrence of accidents and notify all approaching drivers. In route planning, a system can suggest the most convenient routes for users based on the current traffic conditions and the transportation modes being used. At a public transportation hub, a system can provide high value-added services to improve customer convenience [13] (Fig. 1.2).

Fig. 1.1 An example scenario of pervasive computing technologies in Healthcare [10]

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Fig. 1.2 A “smart station vision” scenario of providing on-demand information services for customers from departure place to destination [13]

The introduction of pervasive computing into transportation is facilitated by a range of technologies, particularly networks and positioning systems. Pervasive computing technologies are also becoming essential components in the home environment. A house can be set up to act as an intelligent agent; perceiving the state of the home environment through installed sensors and acting through device controllers. The goal is to maximise the comfort and security of its inhabitants and minimise operation cost. For example, applications in a smart home can improve energy efficiency by automatically adjusting heating, cooling or lighting levels according to the condition of the inhabitants (for example, location or body temperature). They can also provide reminders of shopping orders according to the usage of groceries and schedule the entertainment system (for example, playing music or movie, or switching on a TV) according to the inhabitant’s hobbies and habits. In these cases, pervasive computing technologies are applied to identify, automate and predict the activity patterns of inhabitants from synthetic and real collected data [14, 15].

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The History and Issues of Pervasive Computing

Pervasive computing represents a major evolutionary step in a line of work dating back to the mid-1970s. Two distinct earlier steps are distributed systems and mobile

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computing [16]. Fig. 1.3 shows how research problems in pervasive computing relate to those in distributed systems and mobile computing. The advent of networking enabled independent personal computers to evolve into distributed systems. The mechanisms for linking remote resources provided a means of integrating distributed information into a single structure and distributing computing capabilities. The network has pioneered the creation of a ubiquitous information and communication infrastructure, and thus it is a potential starting-point for pervasive computing [17]. A similar evolution is driving distributed systems to become pervasive by introducing seamless access to remote information resources and communication with fault tolerance, high availability, and security. Mobile computing emerged from the integration of cellular technology and the network. Short-range wireless and wide-area wireless (or wired communication) then boosted the development of mobile computing. Both the size and price of mobile devices (for example, laptop or mobile phones) are falling everyday and could eventually support pervasive computing with inch-scale computing devices readily available to users for use in any human environment. In mobile computing, the research problems overlapping with pervasive computing include mobile networking, mobile information access, adaptive applications, energy-aware systems and location sensitivity. While it is possible to get caught up in the “pervasive-ness” part of this new technology, it is also important to realise how much such systems rely on existing information bases and infrastructures. In transportation, for example, services such as Google Maps provide much of the raw information needed to create the valueadded, location-based service. Pervasive systems are therefore only part of a larger information infrastructure. It is necessary to appreciate both how small a part of the overall system may need to be pervasive, but equally how large is the impact of providing seamless integration of services in everyday life [16]. This brings us to several research issues. The first issue is the effective use of smart spaces. A smart space is a work or living space with embedded computers, information appliances, and multi-modal sensors that allow people to work and live efficiently (together or individually) with an unprecedented access to information and support from local computers [18]. Examples of suitable sites for smart spaces include a business meeting room, a medical consultation meeting room, a training and education facility, a house, a classroom, and a crisis management command center. A smart space should adapt to the changes in an environment, recognising different users, and providing personalised services for them. The second research issue is invisibility, which was described by Weiser as follows: “there is more information available at our fingertips during a walk in the woods than in any computer system, yet people find a walk among trees relaxing and computers frustrating. Machines that fit the human environment instead of forcing humans to enter theirs will make using a computer as refreshing as taking a walk in the woods”. Streitz and Nixon summarised two forms of invisibility [2]. Physical invisibility refers to the miniaturisation of computing devices and their embedding within and throughout the individual and the environment; for example in clothes, glasses, pens, cups, or even the human body itself. Cognitive invisibility refers

systems

Localized scalability

Invisibility

Uneven conditioning

Fig. 1.3 Taxonomy of computer systems research problems in pervasive computing [16]

Location sensitivity GPS, WaveLan triangulation, context-awareness...

Energy-aware systems goal-directed adaptation, disk spin-down...

Adaptive applications proxies, transcoding, agility...

Mobile information access disconnected operation, weak consistency... Smart spaces

Mobile computing

Distributed

Mobile networking Mobile IP, ad hoc networks, wireless TCP fixes...

Distributed security encryption, mutual authentication..

Remote information access dist. file systems, dist. databases, caching...

High availability replication, rollback recovery...

Fault tolerance ACID, two-phase commit, nested transactions...

Remote communication protocol layering, RPC, end-to-end args...

Pervasive computing

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to the ability to use the system’s services in a manner that is free from distraction. A pervasive computing environment should interact with users at almost a subconscious level if it is to continuously meet the expectations of users; it should rarely present them with surprises. (This is also approximated by the minimal user distraction as described by Satyanarayanan [16].) The third research issue is localised scalability. Scalability is a critical problem in pervasive computing, since the intensity of interactions between devices will increase in these environments where more and more users are involved. The density of these interactions must be decreased by reducing distant interactions that are of little relevance to current applications. The fourth research issue is masking heterogeneity. The rate of penetration of pervasive computing technology into the infrastructure will vary considerably. To make pervasive computing technology invisible requires reductions in the amount of variation in different technologies, infrastructures, and environments.

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Significant Projects

Pervasive computing projects have been advanced both in academia and industry. Some of the most influential projects include Aura in Carnegie Mellon University [19], Oxygen at MIT [20], Gaia in UIUC [21], Sentient Computing at AT&T Laboratories in Cambridge [22], the Disappearing Computer initiative from the EU Fifth Framework Programme [23], the TRIL Center [6], GUIDE at Lancaster University [24], Cooltown in Hewlett-Packard [25], and EasyLiving in Microsoft [26]. Some of these projects will be described here to provide a sense of the breadth of research taking place in this topic. The Aura project in CMU aimed to design, implement, deploy, and evaluate a large-scaled computing system that demonstrates the concept of a “personal information aura”, which spans wearable, handheld, desktop and infrastructural computers [19]. In Aura, each mobile user was provided with an invisible halo of computing and information services that persisted regardless of the location. The goal was to maximise available user resources and to minimise distraction and drains on user attention. To meet the goal, many individual research issues evolved within the Aura project, from the work on hardware and network layers through the operating system and middleware to the user interface and applications. The Oxygen project depicted computation as human-centered and freely available everywhere, like the oxygen in the air we breathe [20]. Oxygen enabled pervasive, human-centered computing through a combination of specific user and system technologies. The project focused on the following technologies: device, network, software, perceptual, and user technologies. The Disappearing Computer initiative sought to design information artefacts based on new software and hardware architectures that were integrated into everyday objects, to coordinate these information artefacts to act together and to investigate new approaches that ensure user experience is consistent and engaging in an environment

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filled with such information artefacts [23]. This initiative included GLOSS [27], e-Gadgets [28], Smart-its [29], and other projects. A typical example was the GLOSS project (GLObal Smart Spaces), which aimed to provide information technology that respected social norms – allowing established ways of interaction to be generated or saved as required [27]. The project provided a theoretical framework and a technological infrastructure to support emerging functionality paradigms for user interactions. The goal was to make computing cognitively and physically disappear. The TRIL Center is a coordinated group of research projects addressing the physical, cognitive and social consequences of aging, recognising the increase in the aging population globally. The center’s objective is to assist older people around the world to live longer from wherever they call home, while minimising their dependence on others and improving routine interactions with healthcare systems. It entails multi-disciplinary research on pervasive technologies to support older people living independently [6]. The Cooltown project in HP aimed to provide an infrastructure for nomadic computing; that is, nomadic users are provided with particular services that are integrated within the entities in the everyday physical world through which users go about their everyday lives [25]. This project focused on extending web technology, wireless networks and portable devices to bridge the virtual link between mobile users, physical entities, and electronic services. The Microsoft EasyLiving project developed prototype architecture and technologies for building intelligent environments [26]. This project supported research addressing middleware, geometric world modeling, perception, and service description. The key features included computer vision of person-tracking and visual user interaction, the combination of multiple sensor modalities, the use of a geometric model of the world to provide context, the automatic or semi-automatic calibration of sensors and model building. Fine-grained events, adaptation of user interfaces, as well as deviceindependent communication and data protocols and extensibility were also addressed.

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Open Research Issues

Pervasive computing offers a framework for new and exciting research across the spectrum of computer science. New research themes cover basic technology and infrastructure issues, interactions where computers are invisible and pressing issues of privacy and security [3, 30].

5.1

Hardware Components

Hardware devices are expected to be cheaper, smaller, lighter, and have longer battery life without compromising their computing and communications capabilities. Their cost and size should make it possible to augment everyday objects with built-in computing devices (for example the prototype in Fig. 1.4). These everyday

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Fig. 1.4 The Mediacup is an ordinary coffee cup with sensors, processing and communication embedded in the base [31].

objects can then potentially gather information (including light, temperature, audio, humidity, and location) from its environment, then transmit it, and take actions based upon it. These devices should generally be low-power in order to free them from the constraints of existing or dedicated wired power supplies. Specialised circuit designs may permit operation over a much wider range of voltages or enable power savings using other optimisation techniques. Chalmers suggested that it may be possible to use solar cells, fuel cells, heat converters, or motion converters to harvest energy [30]. Other resource constraints can also be overcome. Satyanarayanan described Cyber-foraging as a potentially effective way to dynamically augment the computing resources of a wireless mobile computer by exploiting local wired hardware infrastructure [16].

5.2

Software Engineering

In pervasive computing systems, the number of users and devices will greatly increase, as will the degrees of interaction between them. A tremendous number of applications are distributed and installed separately for each device class, processor family, and operating system. As the number of devices grows, these applications will become unmanageable. Pervasive computing must find ways to mask heterogeneity since, in the implementation of pervasive computing environments, it is hard

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to achieve uniformity and compatibility. The challenges encompass a new level of component interoperability and extensibility and new dependability guarantees, including adaptation to changing environments, tolerance of routine failures, and security despite a shrunken basis of trust [32]. From a systematic perspective, infrastructures deployed in a pervasive computing system should be long-lived and robust. These infrastructures include sensors and devices, hardware for input and output interaction, software for manipulating and controlling interaction devices, and communication structures from a small to large scale. These infrastructures will be able to perform in situ upgrades and updates and the interactions within this infrastructure should be fluent. This can be enabled by developing an appropriate programming primitive. This new programming model will deal with sensor communication, the semantics of the system (for example, knowledge, data, and software for applications), the corresponding implementations, and so forth [3].

5.3

Context-awareness

Perception or context-awareness is an intrinsic characteristic of intelligent environments. Context can be any information about a user, including environmental parameters such as location, physiological states (like body temperature and heart rate), an emotional state, personal history, daily activity patterns, or even intentions and desires. All of this context is acquired from various kinds of sensors, which are distributed in a pervasive computing environment. Compared to traditional data in a database, context has much richer and more flexible structures and, thus, it is much more dynamic and error-prone. This requires a new data management model to represent context in a sharable and reusable manner and to resolve uncertainty by merging multiple conflicting sensor data streams. It is also required to deal with a huge amount of real-time data and contain a storage mechanism for fresh and out-dated context. The research in modeling context has developed from the simplest key-value pattern [33] to object-oriented models [34], logical models [35], graphical models [36], and ontology models [37]. After analysing the typical context models in pervasive computing, Strang [38] and Ye [39] regarded ontologies as the most promising technique to model and reason about context. In terms of software, the error-prone nature of context and contextual reasoning alter the ways in which we must think about decision and action. Any decision may be made incorrectly due to errors in input data, and we cannot blame poor performance on poor input data quality: we must instead construct models that accommodate uncertainty and error across the software system, and allow low-impact recovery.

5.4

Interaction

By interaction, we mean the way that a user interacts with an environment, with other people and with computers. As pervasive computing environments become

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increasingly part of our everyday lives, people will start interacting with these environments more intensively. The way that people interact with each other is enriched with a hybrid mix of communication technologies and interaction devices, including multi-media and multi-modal technologies [30]. Interactive elements in an environment will range from small-scale embedded or wearable devices that focus on the individual to large-scaled installations that focus on the general public. Each interactive element may bring about significant overhead and complexity in the users’ interaction, particularly if it has a different mode of interaction from other devices or it is a poor fits with users’ everyday activities. It has long been the objective of interface design to remove physical interfaces as a barrier between the user and the work s/he wishes to accomplish via the computer. Input devices like the keyboard, mouse and display monitor have been commercial standards for nearly fifteen years [40]. This type of physical interface is anything but transparent to the user and it violates the vision of pervasive-ness without intrusion. As the vision becomes fulfilled and computational services are spread throughout an environment, advances are needed to provide alternative interaction techniques. Put another way, the essential quality of pervasive interfaces is that they be scrutable, in that they support the construction of predictive and explanatory mental models by users [41]. Proactivity and transparency should be balanced during the interaction. A user’s need for, and tolerance of proactivity is likely to be closely related to his/her level of expertise during a task and to his/her familiarity with the environment. To strike the balance between proactivity and transparency, a system should be able to infer these factors by observing user behaviour and context. We have to explore a range of new technologies that support interaction with, and through, diverse new devices and sensing technologies. These include gesture-based approaches that exploit movement relative to surfaces and artifacts, haptic approaches that exploit the physical manipulation of artifacts, as well as speech-based interfaces. We should also treat pervasive computing as part of the language and culture and open up powerful associations with other disciplines that handle activity, space and structure [30].

5.5

Security, Privacy and Trust

With the growth of the internet, security has become an important research topic, including the issues of authority, reliability, confidentiality, trustworthiness, and so on. More specifically, the security issue involves the cryptographic techniques used to secure the communication channels and required data, the assessment of the risk of bad things happening in an environment or specific situation, and the development of safeguards and countermeasures to militate against these risks [5]. Security is a much more severe issue in pervasive computing, since pervasive computing is hosted in a much larger network that involves a huge number of different types of computing devices. These devices can be “invisible” or anonymous (that is, with unknown origin). They can also join or leave any network in an ad hoc manner. These factors intensely complicate security in pervasive systems.

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Privacy is the claim of individuals, groups, or institutions to determine for themselves when, how and to what extent information is communicated to others [42]. Privacy is about determining how to control and manage users’ privacy, which is an existing problem in distributed and mobile computing. To provide personalised behaviour for users, a pervasive computing system needs to perceive all kinds of user context, including tracking user movement, monitoring user activities and exploring user profiles (like habits or interests) from browsed web pages. This massive amount of user information is collected in an invisible way and can potentially be inappropriately presented or misused. In this context, privacy control is not only about setting rules and enforcing them, but also about managing and controlling privacy adaptively according to changes in the degree of disclosure of personal information or user mobility [5]. In a pervasive computing environment, mobile entities benefit from the ability to interact and collaborate in an ad-hoc manner with other entities and services within the environment. The ad-hoc interaction means entities will face unforeseen circumstances ranging from unexpected interactions to disconnected operations, often with incomplete information about other entities and the environment [5]. The mechanism of trust is required to control the amount of information or resources that can be revealed in an interaction. Risk analyses evaluate the expected benefit that would motivate users to participate in these interactions. Trust management is needed to reason about the trustworthiness of potential users and to make autonomous decisions on who can be trusted and to what degree.

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Changing Perspective Through Augmented Materials

From the perspective of this book, of course, embedding hardware components into everyday artifacts, evolved from the approaches related to those shown in Fig. 1.4, is an exciting prospect This will have a significant impact on the design of hardware, since it must integrate into materials that would not usually be considered as substrates for integrated devices; furthermore, they must withstand treatment (such as going through a dishwasher cycle!) not normally inflicted on computing devices. From a software perspective, combining pervasive computing with truly embedded devices emphasizes many of the issues raised in this chapter. In particular, such systems have limited interface bandwidth, possibly coupled with a rich variety of sensors. They must therefore rely substantially both on local inference and on connections to the wider world to access non-local information. At a system’s level, perhaps the greatest challenge is in the deployment, selfmanagement, self-organisation, self-optimisation and self-healing of networks of embedded systems: the self-* properties identified within autonomic systems. Such properties apply to computing capabilities [43], but perhaps more significantly they also apply to communications capabilities [44] of systems that must manage themselves with minimal human direction in very dynamic environments. Such self-reliance exacerbates the need for end-to-end management of uncertainty and

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so magnifies the need for different programming approaches. Dobson and Nixon have argued [45] for models that embrace explicit modeling of context and the environment, which may then be used to derive communications behaviour and evolve it in a principled way over time. Other approaches, based on inherently self-stabilising algorithms, similarly promise to exploit, rather than conflict with, dynamic interactions and changing goals, although the realization of all these techniques remain elusive.

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Conclusions

Pervasive computing systems offer the potential to deploy computing power into new areas of live not necessarily addressed by traditional approaches. It is important to note that many of these areas simultaneously address issues of wellness, social inclusion, disability support and other facets of major significance to society. The challenges remain daunting, however, at hardware-, software- and systemslevel. Pervasive systems must offer seamlessly-integrated services in a dynamic environment, with little explicit direction, as well as uncertain sensing and reasoning and must do so over protracted periods without management intervention. Existing research has generated existence proofs that applications can be constructed in the face of these challenges, but it remains to be demonstrated as to whether more complex systems can be deployed. To address these problems, we need to broaden our discourse in certain areas and revisit long-standing design assumptions in others. Interfaces must be considered widely, and firmly from the perspective of user modelling and model formation. Traditional programming language structures and design methods do no obviously provide the correct abstractions within which to develop pervasive applications. Correct behaviour must be maintained even in the presence of known-to-be-faulty input data, where it may be more appropriate to refuse to act rather than act incorrectly – or it may not - depending entirely on the application. We are confident that the existing research strands will be broadened and deepened as these challenges are answered. Acknowledgements This work is partially supported by Science Foundation Ireland under the projects, “Towards a semantics of pervasive computing” [Grant No. 05/RFP/CMS0062], “Secure and predictable pervasive computing” [Grant No. 04/RPI/1544], and “LERO: the Irish Software Engineering Research Centre” [Grant No. 03/CE2/I303-1].

References 1. M. Weiser. “The Computer for the 21st Century”. Scientific American, pp. 94–104. September 1991. 2. N. Streitz and P. Nixon. “The Disappearing Computer”. The Communication of ACM, 48(3), pp. 33–35. March 2005. 3. P. Nixon and N. Streitz. EU-NSF joint advanced research workshop: “The Disappearing Computer. Workshop Report and Recommendation”. http://www.ercim.org/EU-NSF/index. html. April 2004.

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4. M. Jonsson, (2002). “Context shadow: An infrastructure for context aware computing”. Proceedings of the Workshop on artificial intelligence in mobile systems (AIMS) in conjunction with ECAI 2002, Lyon, France. 5. P. Nixon, W. Wagealla, C. English, and S. Terzis, “Privacy, Security, and Trust Issues in Smart Environments”. Book Chapter of Smart Environments: Technology, Protocols and Applications, pp. 220–240. Wiley, October 2004. 6. The INTEL Technology Research Center for Independent Living. http://www.trilcentre.org. 7. L. Coyle, S. Neely, G. Stevenson, M. Sullivan, S. Dobson and P. Nixon. “Sensor fusion-based middleware for smart homes”. International Journal of Assistive Robotics and Mechatronics 8(2), pp. 53–60. 2007. 8. J. Bohn, F. Gartner and H. Vogt. “Dependability Issues of Pervasive Computing in a Healthcare Environment”. Proceedings of the first International Conference on Security in Pervasive Computing, in Boppard, Germany, pp.53–70. 2003. 9. G. Borriello, V. Stanford, C. Narayanaswami, and W. Menning. “Pervasive Computing in Healthcare”. Proceedings of the International Conference on Pervasive computing, pp. 17–19. 2007. 10. K. Adamer, D. Bannach, T. Klug, P. Lukowicz, M.L. Sbodio, M. Tresman, A. Zinnen, and T. Ziegert, “Developing a Wearable Assistant for Hospital Ward Rounds: An Experience Report”. Proceedings of the International Conference for Industry and Academia on Internet of Things 2008. 11. K. Farkas, J. Heidemann and L. Iftode. “Intelligent Transportation and Pervasive Computing”. IEEE Pervasive Computing 5(4), pp. 18–19. October 2006. 12. R. Cunningham and V. Cahill. “System support for smart cars: requirements and research directions”. Proceedings of the 9th workshop on ACM SIGOPS European workshop: beyond the PC: new challenges for the operating system, pp.159–164. 2000. 13. The JR-EAST Japan Railway Company Research & Development. The Smart Station Vision Project. http://www.jreast.co.jp/e/development/theme/station/station08.html 14. D. J. Cook, M. Youngblood, E. O. Heierman, III and K. Gopalratnam. “MavHome: An AgentBased Smart Home”. Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, pp. 521–524. 2003. 15. B. Logan, J. Healey, M. Philipose, E. M. Tapia, S. S. Intille, “A Long-Term Evaluation of Sensing Modalities for Activity Recognition”. Proc. 9th International Conference on Ubiquitous computing (Ubicomp 2007) pp. 483–500. 16. M. Satyanarayanan, “Pervasive Computing: Vision and Challenges”. IEEE Personal Communications, 8(4), pp.10–17. August 2001. 17. D. Saha, A. Mukherjee. “Pervasive Computing: A Paradigm for the 21st Century”, Computer, 36(3), pp. 25–33. March 2003. 18. V. Stanford, J. Garofolo, O. Galibert, M. Michel, C. Laprun. “The NIST Smart Space and Meeting Room Projects: Signals, Acquisition, Annotation and Metrics”. Proc. ICASSP 2003 in special session on smart meeting rooms, vol.4, pp. IV-736-9 April 6–10, 2003. 19. J. P. Sousa, and D. Garlan. “Aura: an Architectural Framework for User Mobility in Ubiquitous Computing Environments. Software Architecture: System Design, Development, and Maintenance”. Proc. 3rd Working IEEE/IFIP Conference on Software Architecture, Jan Bosch, Morven Gentleman, Christine Hofmeister, Juha Kuusela (Eds), Kluwer Academic Publishers, pp. 29–43. August 25–31, 2002. 20. R. Weisman, Oxygen burst. The Boston Globe, June 21, 2004. 21. M. Román, C. K. Hess, R. Cerqueira, A. Ranganathan, R. H. Campbell, and K. Nahrstedt. “Gaia: A Middleware Infrastructure to Enable Active Spaces”. In IEEE Pervasive Computing, pp. 74–83, Oct–Dec 2002. 22. Cambridge. Sentient computing. http://www.cl.cam.ac.uk/research/dtg/research/wiki/Sentient Computing. 23. The Disappearing Computer Initiative. http://www.disappearing-computer.net.

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24. K. Cheverst, N. Davies, K. Mitchell, A. Friday and C. Efstratiou. “Developing a Contextaware Electronic Tourist Guide: Some Issues and Experiences”. Proceedings of CHI 2000, pp. 17–24, in Netherlands. April 2000. 25. J. Barton, T. Kindberg. “The challenges and opportunities of integrating the physical world and networked systems”. Technical report TR HPL-2001-18 by HP Labs. 2001. 26. The Easy Living Project. http://research.microsoft.com/easyliving/. 27. J. Coutaz, J. Crowley, S. Dobson and D. Garlan. “Context is key”. Communications of the ACM 48(3), pp. 49–53. March 2005. 28. The e-Gadgets Project. http://www.extrovert-gadgets.net/. 29. The Smart-Its Project. http://www.smart-its.org/. 30. D. Chalmers, M. Chalmers, J. Crowcroft, M. Kwiatkowska, R. Milner, E. O’Neill, T. Rodden, V. Sassone, and M. Slomen. “Ubiquitous Computing: Experience, Design and Science”. Technical report by the UK Grand Challenges Exercise. February 2006. 31. H. Gellersen, A. Schmidt, M. Beigl. “Multi-Sensor Context-Awareness in Mobile Devices and Smart Artefacts”. Mobile Networks and Applications, 7(5), pp. 341–351. 2002. 32. T. Kindberg and A. Fox. “System Software for Ubiquitous Computing”. IEEE Pervasive Computing 1(1), pp.70–81. January, 2002. 33. A. K. Dey. “Understanding and using context”. Personal Ubiquitous Computing, 5(1):4–7. 2001. 34. A. Schmidt, M. Beigl, and H. W. Gellersen. “There is more to Context than Location”. Computers and Graphics, 23(6), pp. 893–901, 1999. 35. C. Ghidini and F. Giunchiglia. “Local Models Semantics, or Contextual Reasoning = Locality + Compatibility”. Artificial Intelligence, 127(2):221–259, 2001. ISSN 0004-3702. 36. K. Henricksen, J. Indulska, and A. Rakotonirainy. “Modeling context information in pervasive computing systems”. Proceedings of the First International Conference on Pervasive Computing, pp.167–180, London, UK, 2002. Springer-Verlag. 37. H. Chen, T. Finin, and A. Joshi. “An Ontology for Context-Aware Pervasive Computing Environments”. Special Issue on Ontologies for Distributed Systems, Knowledge Engineering Review, 18(3):197–207. May 2004. 38. T. Strang and C. Linnhoff-Popien. “A context modeling survey”. In Proceedings of the Workshop on Advanced Context Modelling, Reasoning and Management, Nottingham/ England. Sepember 2004. 39. J. Ye, L. Coyle, S. Dobson and P. Nixon. “Ontology-based Models in Pervasive Computing Systems”. Knowledge Engineering Rev. 22(04), pp. 513–347. 2007. 40. G. D. Abowd. “Software Engineering Issues for Ubiquitous Computing”. Proc. 21st International Conference on Software Engineering, pp.75–84. 1999. 41. M. Czarkowski and J. Kay. “Challenges of scrutable adaptation”. Proc. 11th International Conf on Artificial Intelligence in Education, pp. 404–407. IOS Press. 2003. 42. A. F. Westin. “Privacy and Freedom”. Publisher: Bodley Head.1970. 43. J. Kephart and D. Chess. “The vision of autonomic computing”. IEEE Computer 36(1), pp.41–52. January 2003. 44. S. Dobson, S. Denazis, A. Fernández, D. Gaïti, E. Gelenbe, F. Massacci, P. Nixon, F. Saffre, M. Schmidt and F. Zambonelli. “A survey of autonomic communications”. ACM Transactions on Autonomous and Adaptive Systems 1(2), pp. 223–259. December 2006. 45. S. Dobson and P. Nixon. “More principled design of pervasive computing systems”. In Rémi Bastide and Jörg Roth, eds, Human computer interaction and interactive systems. LNCS 3425. Springer Verlag. 2004.

An Overview of Pervasive Computing Systems

digital assistants (PDAs), “smart” mobile phones, ultra-mobile laptops and office. PCs, and even home .... for pervasive computing [17]. A similar evolution is ... could eventually support pervasive computing with inch-scale computing devices.

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