PERVASIVE COMPUTING Fully Modified_watermarked

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PERVASIVE COMPUTING

Abstract

This paper discusses the challenges in computer

systems research posed by the emerging field of

Pervasive computing or Ubiquitous computing. It first

examines the relationship of this new field to its

predecessors: distributed systems and mobile

computing. It then identifies four new research thrusts:

effective use of smart spaces, invisibility, localized

scalability, and masking uneven conditioning. Next, it

sketches a couple of hypothetical pervasive computing

scenarios, and uses them to identify key capabilities

missing from todays systems. The paper closes with a

discussion of the research necessary to develop these

capabilities.


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Ubiquitous computing (ubicomp) is a post-desktop

model ofhuman-computer interaction in which information processing

has been thoroughly integrated into everyday objects and activities.

In the course of ordinary activities, someone "using" ubiquitous

computing engages many computational devices and systems

simultaneously, and may not necessarily even be aware that they are

doing so. This model is usually considered advancement from thedesktop paradigm.

This paradigm is also described as pervasive computing,

ambient intelligence. When primarily concerning the objects involved,

it is also physical computing, the Internet of Things, haptic

computing, and things that think. Rather than propose a single

definition for ubiquitous computing and for these related terms,

taxonomy of properties for ubiquitous computing has been proposed,

from which different kinds or flavours of ubiquitous systems and

applications can be described

HOW IT HAPPENS?

At their core, all models of ubiquitous computing (also

called pervasive computing) share a vision of small, inexpensive,

robust networked processing devices, distributed at all scales

throughout everyday life and generally turned to distinctly common-

place ends. For example, a domestic ubiquitous computing

environment might interconnect lighting and environmental controls

with personal biometric monitors woven into clothing so that

illumination and heating conditions in a room might be modulated,

continuously and imperceptibly. Another common scenario posits

refrigerators "aware" of their suitably-tagged contents, able to both

plan a variety of menus from the food actually on hand, and warn

users of stale or spoiled food.
http://en.wikipedia.org/wiki/Human-computer_interactionhttp://en.wikipedia.org/wiki/Desktop_environmenthttp://en.wikipedia.org/wiki/Ambient_intelligencehttp://en.wikipedia.org/wiki/Internet_of_Thingshttp://en.wikipedia.org/wiki/Human-computer_interactionhttp://en.wikipedia.org/wiki/Desktop_environmenthttp://en.wikipedia.org/wiki/Ambient_intelligencehttp://en.wikipedia.org/wiki/Internet_of_Things


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Ubiquitous computing presents challenges across computer

science: in systems design and engineering, in systems modelling,

and in user interface design. Contemporary human-computer

interaction models, whether command-line, menu-driven, or GUI-

based, are inappropriate and inadequate to the ubiquitous case. Thissuggests that the "natural" interaction paradigm appropriate to a fully

robust ubiquitous computing has yet to emerge - although there is

also recognition in the field that in many ways we are already living in

an ubicomp world. Contemporary devices that lend some support to

this latter idea include mobile phones, digital audio players, radio-

frequency identification tags, GPS, and interactive whiteboards.

(An actuator is a mechanical device for moving or controlling a mechanism or system. An actuator

typically is a mechanical device that takes energy, usually transported by air, electric current, or liquid,

and converts that into some kind of motion)

Mark Weiser proposed three basic forms for ubiquitous systemdevices, see also Smart device: tabs, pads and boards.

Tabs: wearable centimetre sized devices Pads: hand-held decimetre-sized devices Boards: meter sized interactive display devices.

These three forms proposed by Weiser are characterised by being

macro-sized, having a planar form and on incorporating visual output

displays. If we relax each of these three characteristics we can

expand this range into a much more diverse and potentially more

useful range of Ubiquitous Computing devices. Hence, three

additional forms for ubiquitous systems have been proposed:
http://en.wikipedia.org/wiki/Command-linehttp://en.wikipedia.org/wiki/GUIhttp://en.wikipedia.org/wiki/Mobile_phonehttp://en.wikipedia.org/wiki/Digital_audio_playerhttp://en.wikipedia.org/wiki/Radio-frequency_identificationhttp://en.wikipedia.org/wiki/Radio-frequency_identificationhttp://en.wikipedia.org/wiki/GPShttp://en.wikipedia.org/wiki/Interactive_whiteboardhttp://en.wikipedia.org/wiki/Mark_Weiserhttp://en.wikipedia.org/wiki/Smart_devicehttp://en.wikipedia.org/wiki/Command-linehttp://en.wikipedia.org/wiki/GUIhttp://en.wikipedia.org/wiki/Mobile_phonehttp://en.wikipedia.org/wiki/Digital_audio_playerhttp://en.wikipedia.org/wiki/Radio-frequency_identificationhttp://en.wikipedia.org/wiki/Radio-frequency_identificationhttp://en.wikipedia.org/wiki/GPShttp://en.wikipedia.org/wiki/Interactive_whiteboardhttp://en.wikipedia.org/wiki/Mark_Weiserhttp://en.wikipedia.org/wiki/Smart_device


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Dust: miniaturised devices can be without visual output

displays, e.g., Micro Electro-Mechanical Systems (MEMS),

ranging from nanometres through micrometers to millimetres.

Skin: fabrics based upon light emitting and conductive

polymers, organic computer devices, can be formed into moreflexible non-planar display surfaces and products such as clothes

and curtains, see OLED display. MEMS device can also be

painted onto various surfaces so that a variety of physical world

structures can act as networked surfaces of MEMS.

Clay: ensembles of MEMS can be formed into arbitrary three

dimensional shapes as artefacts resembling many different

kinds of physical object.

UNDERSTANDING THE MODULES IN PERVASIVE

COMPUTING:

Three major issues which make pervasive computing to stand out and

be on of its kind are:

1. Distributed systems

2. Mobile computing

3. Pervasive computing

4. Internal network which governs all other devices (either hand

held or static)

Distributed Systems

The field of distributed systems arose at the intersection of

personal computers and local area networks. The research that

followed from the mid-1970s through the early 1990s created a

conceptual framework and algorithmic base that has proven to be of

enduring value in all work involving two or more computers connected

by a network whether mobile or static, wired or wireless, sparse or

pervasive.

The factors governing distributed systems are:
http://en.wikipedia.org/wiki/Microelectromechanical_systemshttp://en.wikipedia.org/wiki/OLED_displayhttp://en.wikipedia.org/wiki/Microelectromechanical_systemshttp://en.wikipedia.org/wiki/OLED_display


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remote communication, including protocol layering, remoteprocedure call, the use of timeouts, and the use of end-to-endarguments in placement of functionality.

fault tolerance, including atomic transactions, distributed andnested transactions, and two-phase commit.

high availability, including optimistic and pessimistic replicacontrol, mirrored execution, and optimistic recovery.

remote information access, including caching, function shipping,distributed file systems, and distributed databases.

security, including encryption-based mutual authentication and

privacy.

Mobile Computing

The appearance of full-function laptop computers and

wireless LANs in the early 1990s led researchers to confront the

problems that arise in building a distributed system with mobile

clients. The field of mobile computing was thus born. Although many

basic principles of distributed system design continued to apply, four

key constraints of mobility forced the development of specialized

techniques. These constraints are: unpredictable variation in network

quality, lowered trust and robustness of mobile elements, limitations

on local resources imposed by weight and size constraints, and

concern for battery power consumption. Mobile computing is still a

very active and evolving field of research, whose body of knowledge

awaits codification in textbooks. The results achieved so far can be

grouped into the following broad areas:

mobile networking, including Mobile IP, ad hoc protocols, andtechniques for improving TCP performance in wireless networks.

mobile information access, including disconnected operation,bandwidth-adaptive file access, and selective control of dataconsistency.

support for adaptative applications, including trans-coding by

proxies and adaptive resource management.


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system-level energy saving techniques, such as energy awareadaptation, variable-speed processor scheduling, and energy-sensitive memory management.

location sensitivity, including location sensing and location-awaresystem behavior

Pervasive Computing

We characterized a pervasive computing environment asone saturated with computing and communication capability, yet sogracefully integrated with users that it becomes a technology thatdisappears. Since motion is an integral part of everyday life, such atechnology must support mobility; otherwise, a user will be acutely

aware of the technology by its absence when he moves. Hence, theresearch agenda of pervasive computing subsumes that of mobilecomputing, but goes much further.

Drilling Down

Practical realization of pervasive computing will require us to

solve many difficult design and implementation problems. Building on

the discussion in earlier sections, we now look at some of these

problems at the next level of detail. Our goal is only to convey an

impressionistic picture of the road ahead. We make no claim of


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completeness or exclusiveness this specific set of topics is merely a

sampling of the problem space, presented in no particular order. In

this discussion, we assume that each user is immersed in a personal

computing space that accompanies him everywhere and mediates all

interactions with the pervasive computing elements in hissurroundings. This personal computing space is likely to implement on

a body-worn or handheld computer (or a collection of these acting as

a single entity). We refer to this entity as a client of its pervasive

computing environment, even though many of its interactions may be

peer-to-peer rather than strictly client-server. As indicated by the

discussion below, the client needs to be quite sophisticated and,

hence, complex.

User Intent

For proactivity to be effective, it is crucial that a pervasive

computing system track user intent. Otherwise, it will be almost

impossible to determine which system actions will help rather than

hinder the user. For example, suppose a user is viewing video over a

network connection whose bandwidth suddenly drops. Should the

system (a) reduce the fidelity of the video, (b) pause briefly to find

another higher-bandwidth connection, or (c) advise the user that thetask can no longer be accomplished? The correct choice will depend

on what the user is trying to accomplish. Todays systems are poor at

capturing and exploiting user intent. On the one hand are generic

applications that have no idea what the user is attempting to do, and

can therefore offer little support for adaptation and proactivity. On the

other hand are applications that try to anticipate user intent but do so

very badly gimmicks like the Microsoft paperclip are often more

annoying than helpful.

Adaptation Strategy

Adaptation is necessary when there is a significant mismatch

between the supply and demand of a resource. The resource in

question may be wireless network bandwidth, energy, computing

cycles, memory, and so on. There are three alternative strategies for

adaptation in pervasive computing. First, a client can guide

applications in changing their behaviour so that they use less of a

scarce resource. This change usually reduces the user-perceived


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quality, or fidelity, of an application. Odyssey is an example of a

system that uses this strategy. Second, a client can ask the

environment to guarantee a certain level of a resource. This is the

approach typically used by reservation-based QoS systems. From the

viewpoint of the client, this effectively increases the supply of ascarce resource to meet the clients demand. Third, a client can

suggest a corrective action to the user. If the user acts on this

suggestion, it is likely (but not certain) that resource supply will

become adequate to meet demand. All three strategies are important

in pervasive computing. The existence of smart spaces suggests that

some of the environments encountered by a user may be capable of

accepting resource.

Context Awareness

A pervasive computing system that strives to be minimally

intrusive has to be context-aware. In other words, it must be

cognizant of its users state and surroundings, and must modify its

behavior based on this information. A users context can be quite rich,

consisting of attributes such as physical location, physiological state

(such as body temperature and heart rate), emotional state (such as

angry, distraught, or calm), personal history, daily behavioralpatterns, and so on. If a human assistant were given such context, he

or she would make decisions in a proactive fashion, anticipating user

needs. In making these decisions, the assistant would typically not

disturb the user at inopportune moments except in an emergency.

Can a Pervasive Computing system emulate such a human

assistant?

A key challenge is obtaining the information needed tofunction in a context-aware manner. In some cases, the desired

information may already be part of a users personal computing

space. For example, that space may include schedules, personal

calendars, address books, contact lists, and to-do lists. More dynamic

information has to be sensed in real time from the users

environment. Examples of such information include position,

orientation, the identities of people nearby, locally observable objects

and actions, and emotional and physiological state. Implementing a

context-aware system requires many issues to be addressed.


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4.7. Balancing Proactivity and Transparency

Proactivity is a double-edged sword. Unless carefully

designed, a proactive system can annoy a user and thus defeat the

goal of invisibility. How does one design a system that strikes the

proper balance at all times? Self-tuning can be an important tool in

this effort. A mobile users need and tolerance for proactively are

likely to be closely related to his level of expertise on a task and his

familiarity with his environment. A system that can infer these factors

by observing user behaviour and context is better positioned to strike

the right balance. Historically, the ideal in system design has beentransparency. For example, caching is attractive in distributed file

systems because it is completely transparent. Unfortunately, servicing

a cache miss on a large file over a low-bandwidth wireless network

takes so long that most users would rather be asked first whether

they really need the file. However, a flurry of such interactions can

overwhelm the user.

4.8. Privacy and Trust

Privacy, already a thorny problem in distributed systems

and mobile computing, is greatly complicated by pervasive

computing. Mechanisms such as location tracking, smart spaces, and

use of a user becomes more dependent on a pervasive computing

system, it becomes more knowledgeable about that users

movements, behaviour patterns and habits. Exploiting this

information is critical to successful proactivity and self-tuning. At the

same time, unless use of this information is strictly controlled, it can

be put to a variety of unsavoury uses ranging from targeted

spam to blackmail. Indeed, the potential for serious loss of privacy

may deter knowledgeable users from using a pervasive computing

system Greater reliance on infrastructure means that a user must

trust that infrastructure to a considerable extent. Conversely, the

infrastructure needs to be confident of the users identity and

authorization level before responding to his requests. It is a difficultchallenge to establish this mutual trust in a manner that is minimally


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intrusive and thus preserves invisibility. Privacy and trust are likely to

be enduring problems in pervasive computing.

Summary

This paper began by exposing some of the limitations

behind the way mobile computing devices are used today. As the

scenario illustrated, today's applications do not enable people to

perform many of the tasks they need to do, do not provide satisfying


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user experiences, and fall far short of the potential for pervasive

computing. For pervasive computing to meet the expectations of

mobile users, fundamental changes need to occur in the way people

perceive the roles of devices, applications and the environment.

Again, devices need to be perceived as portals into theapplication/data space supported by the environment, rather than

repositories of custom software. Applications need to be seen as tasks

performed on behalf of a user, not as programs written to exploit the

resources of a specific computer. And, the computing environment

needs to be recognized as an extension of the user's surroundings,

not a virtual space for hosting and running programs.

To realize this vision of devices, applications and

environments, we believe a new application model is needed. The

model is characterized by a device-independent application

development process, which includes abstract specification of the

application front-end and the application's resource and service

requirements. The model includes a highly dynamic load-time system

supporting application discovery, resource and capability negotiation,

and application apportioning. The run-time system allows the

resources to be dynamically shared among client devices and servers.

It also includes monitoring and check pointing, and enables a runningapplication to migrate from device to device or to simultaneously

utilize the interface capabilities of multiple devices.

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