This document discusses the evolution of mobile computing and pervasive computing. It describes how mobile computing involved distributed systems with mobile clients, and key constraints of mobility like unpredictable networks and limited resources. Pervasive computing builds on this by integrating computing capability throughout the environment so it is seamlessly used in everyday life. Research in pervasive computing also considers effective use of smart spaces, invisibility of computing technology, localized scalability, and masking uneven conditions.

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© 2007 by Leszek T. Lilien
Based on: M. Satyanarayanan, “Pervasive Computing: Vision and Challenges,” IEEE Personal Communications, 2001
Pervasive Computing vs. Distributed Systems & Mobile Computing (5)
2.2. Mobile Computing
 Mobile Computing
 The early 1990s - full-function laptops + wireless LANs
 Early mobile computing systems:
A distributed system with mobile clients
 [LL:] Current mobile computing systems:
Any nodes can be mobile
 Solutions from MOBI
 Many basic principles of DIST design continued to apply to MOBI
BUT
 Four key constraints of mobility required specialized techniques [31] for
MOBI:
 Unpredictable variation in network quality
 Lowered trust and robustness of mobile elements
 Limitations on local resources imposed by weight and size constraints
 Concern for battery power consumption
 Mobile computing - still a very active and evolving field of research
 Awaits codification in textbooks (as of 2000)

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© 2007 by Leszek T. Lilien
Based on: M. Satyanarayanan, “Pervasive Computing: Vision and Challenges,” IEEE Personal Communications, 2001
Pervasive Computing vs. Distributed Systems & Mobile Computing (6)
Mobile Computing (2)
 Specific subarea solutions for MOBI
 Mobile networking
 Incl. mobile IP [2], ad hoc protocols [27], techniques for
improving TCP performance in wireless networks [1, 5]
 Mobile information access
 Incl. disconnected operation [17], bandwidth-adaptive file
access [21], selective control of data consistency [38, 39]
 Support for adaptive applications
 Incl. transcoding by proxies [12], adaptive resource
management [24]
 System-level energy saving techniques
 Incl. energy-aware adaptation [11], variable-speed processor
scheduling [45], energy-sensitive (=adaptive) memory
management [18].
 Location sensitivity
 Incl. location sensing [42, 43], location-aware (=adaptive) system
behavior [32, 35, 41]

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© 2007 by Leszek T. Lilien
Based on: M. Satyanarayanan, “Pervasive Computing: Vision and Challenges,” IEEE Personal Communications, 2001
Pervasive Computing vs. Distributed Systems & Mobile Computing (7)
2.3. Pervasive Computing
 Pervasive Computing
 Recall:
Pervasive computing environment - one saturated with
computing & communication capability, yet so gracefully
integrated with users that it becomes a ‘‘technology that
disappears’’
 Since motion is an integral part of everyday life, PERV
must support mobility
 Otherwise, a user will be acutely aware of the technology by its
absence when she moves
 Hence, research in pervasive computing subsumes that
of mobile computing
BUT …

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© 2007 by Leszek T. Lilien
Based on: M. Satyanarayanan, “Pervasive Computing: Vision and Challenges,” IEEE Personal Communications, 2001
Pervasive Computing vs. Distributed Systems & Mobile Computing (8)
Pervasive Computing (2)
…
 Research in pervasive computing goes much
further
 PERV includes four additional research thrusts
 Effective Use of Smart Spaces
 Invisibility
 Localized Scalability
 Masking Uneven Conditioning
 See Figure 1 (next slide)

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© 2007 by Leszek T. Lilien
Based on: M. Satyanarayanan, “Pervasive Computing: Vision and Challenges,” IEEE Personal Communications, 2001
Pervasive Computing vs. Distributed Systems & Mobile Computing (9)
Pervasive Computing (3)
 Figure 1: Taxonomy of Computer Systems Research Problems in PERV
1) New problems are
encountered as one moves from
left to right in this figure.
2) Solution of many previously-
encountered problems becomes
more complex.
As the modulation symbols
suggest, this increase in
complexity is multiplicative rather
than additive
it is very much more difficult to
design and implement a pervasive
computing system than a simple
distributed system of comparable
robustness and maturity.
Note:This figure describes
logical relationships, not
temporal ones.
The evolution of research effort over
time has loosely followed this picture
BUT
There have been cases where research
effort on some aspect of pervasive
computing began relatively early. E.g.,
work on smart spaces began in the early
1990’s and proceeded relatively
independently of work in MOBI

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© 2007 by Leszek T. Lilien
Based on: M. Satyanarayanan, “Pervasive Computing: Vision and Challenges,” IEEE Personal Communications, 2001
Pervasive Computing vs. Distributed Systems & Mobile Computing (10)
Pervasive Computing (4)
2.3.1) Effective Use of Smart Spaces
 A space:
 An enclosed area
 E.g., a meeting room or corridor
OR:
 A well-defined open area
 E.g., a courtyard or a city square
 Smart space = space with embedding computing
infrastructure
 E.g., smart space within buildings
 Created by embedding computing infrastructure within building
infrastructure
 Smart space brings together two worlds disjoint until
now [16]:
 Physical space (physical world)
 Cyberspace

7. 7
© 2007 by Leszek T. Lilien
Based on: M. Satyanarayanan, “Pervasive Computing: Vision and Challenges,” IEEE Personal Communications, 2001
Pervasive Computing vs. Distributed Systems & Mobile Computing (11)
Pervasive Computing (5)
1) Effective Use of Smart Spaces – cont.
 The fusion of physical world (PHYS) and cyber-world
(CYB) enables sensing and control of one world by the
other
 Example of sensing and control of PHYS by CYB
 Automatic adjustment of heating, cooling and lighting levels in a
room based on an occupant’s electronic profile (incl. presence
patterns)
 Example of sensing and control of CYB by PHYS (the other
direction)
 Software on a user’s computer may behave differently
depending on where the user is currently located
 Note:
Smartness may extend to individual objects, whether
located in a smart space or not

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© 2007 by Leszek T. Lilien
Based on: M. Satyanarayanan, “Pervasive Computing: Vision and Challenges,” IEEE Personal Communications, 2001
Pervasive Computing vs. Distributed Systems & Mobile Computing (12)
Pervasive Computing (6)
2.3.2) Invisibility
 The ideal expressed by Weiser: complete disappearance
of pervasive computing technology from a user’s
consciousness = invisible computing
 A reasonable approximation: minimal user distraction
([LTL:] = minimal visibility)
 If a PERV environment continuously meets user expectations and
rarely presents him with surprises, it allows him to interact almost
at a subconscious level [46]
BUT:
 Getting too close to the ideal might not be perfect:
A (small) degree of visibility might be needed
 ([LTL:] I hope that abobe I correctly interpret the following text:
“At the same time, a modicum of anticipation may be essential to
avoiding a large unpleasant surprise later — much as pain alerts a
person to a potentially serious future problem in a normally-
unnoticed body part.”)