This document discusses the challenges of client thickness and context awareness in pervasive computing environments. It addresses how the minimum thickness of a client is determined based on the worst conditions it must operate in. With a moderately thick client, simple tasks can be handled locally while more intensive tasks are completed remotely. The document also raises questions around quantifying client thickness, migrating applications between thin and thick clients, and using portable infrastructure to augment environments. It explores issues in obtaining and representing context, as well as balancing the transparency and visibility of proactive system actions.

1. 1
© 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 (42)
Drilling Down - The Difficult Problems (19)
 4.5. Client Thickness – cont.2
 The minimum acceptable thickness of a client for a given app:
Determined by the worst-case conditions under which the app must
run satisfactorily
 A very thin client suffices if it has assured high-bandwidth, low-
latency wireless communication to nearby computing
infrastructure, and if batteries recharged/replaced easily
 If in even a single location visited by a user these assumptions do
not hold, the client must be thick enough to compensate at that
location
 This is especially true for interactive applications where crisp
response is important
… continued...

2. 2
© 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 (43)
Drilling Down - The Difficult Problems (20)
 4.5. Client Thickness – cont.3
 With a client of modest thickness, it may be possible to preserve
responsiveness by:
 Handling simple cases locally & relying on remote infrastructure
only for more compute-intensive situations
OR:
 Execute part of the application locally & ship a much-reduced
intermediate state over a weak wireless link to a remote compute
server for completion
 The hybrid mode of speech recognition in Odyssey [24] is an
example of this approach.
OR:
 Client recognizes that a key assumption is not being met, and
alerts its user with a message
 Client could also suggest possible corrective actions
 E.g., moving to a nearby location known “help” the application
 An extremely thin-client will be unsatisfactory for pervasive
computing in the foreseeable future
 Due to uneven conditioning of environments
At the same time, considerable merit in not having to carry or wear a
client thicker than absolutely necessary

3. 3
© 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 (44)
Drilling Down - The Difficult Problems (21)
 4.5. Client Thickness – some research questions:
 Can the concepts of client thickness and environmental
conditioning be quantified? Are there ‘‘sweet spots’’ in the
design space where a modest increase in client thickness
yields considerable improvement in performance and
usability?
 Can a proactive system alert a user in a timely manner
before he leaves a benign environment for a less
hospitable one?
 In that context, can an application be transparently
migrated between a thinner and a thicker client
whenever needed
 What are the kinds of applications for which such migration is
feasible and useful? What is the impact on usability?

4. 4
© 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 (45)
Drilling Down - The Difficult Problems (22)
 4.5. Client Thickness – some research questions – cont.
 Is it possible to build cost-effective modular computers
that can be physically reconfigured to serve as the optimal
mobile clients under diverse environmental conditions?
 Can a proactive system advise a user to reconfigure
when appropriate?
 E.g., knowing her travel plans, can such a system guide her in
configuring her system so that it is of adequate thickness at all
times?
 Can semi-portable infrastructure be carried with a user to
augment less hospitable environments?
 E.g., in a poorly conditioned environment, can a thin
body-worn computer extend its capabilities by wireless
access to a full-function laptop brought by the user?
 This is analogous to carrying both a briefcase and a wallet when you
travel; the briefcase is not physically on your person at all times, but
it is close enough to provide easy access to things too large to fit in
your wallet
 Is this a usable and practical strategy to cope with
uneven conditioning?

5. 5
© 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 (46)
Drilling Down - The Difficult Problems (23)
 4.6) Context Awareness
 Context-awareness is necessary for PERV to be minimally intrusive
 Context aware = cognizant of its user’s state and surroundings, and
modifying its behavior based on this information
 A user’s context
 Can be quite rich
 Some user’s attributes in user’s context:
Physical location / Physiological state (e.g., body temperature and heart
rate) /
Emotional state (e.g., angry, distraught, or calm) / Personal history /
Daily behavioral patterns / …
 A human assistant given such context would make decisions proactively
 Anticipating user needs
 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?
 Key challenge: obtaining information needed to function in a context-
aware manner
 Desired information may be part of UPCS (user’s personal computing
space)
 E.g., UPCS has schedules, calendars, address books, to-do lists, …
 More dynamic information has to be sensed in real time from the user’s
environment
 E.g., position, orientation, the identities of people nearby, locally observable
objects and actions, emotional and physiological state, …

6. 6
© 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 (47)
Drilling Down - The Difficult Problems (24)
 4.6. Context Awareness – cont.
 Context Awareness – some research issues:
 How is context represented internally? How is this information
combined with system and application state? Where is context
stored?
 Does it reside locally, in the network, or both?
What are the relevant data structures and algorithms?
 How frequently does context information have to be consulted?
What is the overhead of using context? How to keep this overhead
low?
 What are the minimal services from an environment to provide to
make context-awareness feasible? What are reasonable fallback
positions if an environment does not provide such services? Is
historical context useful?
 What are the relative merits of different location-sensing
technologies?
 Under what circumstances should one be used in preference to
another?
 Should location information be treated just like any other context
information, or should it be handled differently?
 Is historical context useful?

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 (48)
Drilling Down - The Difficult Problems (25)
 4.7) [LTL:] Proper Level of Transparency in
Proactivity [Paper: Balancing Proactivity and Transparency]
 Transparency = invisibility
 [LTL:] Proactivity can be visible or transparent (invisible )
 Visible when system asks user for specific behavior to
perform a proactive task
 E.g., Aura asking Jane to go to Gate 15 or warning Fred not to
show slides with sensitive data (in Scenario 1)
 Transparent when there is no system interaction with
user when system performs a proactive task
 E.g., Aura warms up projector without asking Fred (in
Scenario 2)

8. 8
© 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 (49)
Drilling Down - The Difficult Problems (26)
 4.7) [LTL:] Proactivity and Its Transparency [Paper: Balancing Proactivity and Transparency] – cont.1
 [LTL:] Visible system proactivity can annoy a user …
 … if system gives a bad advice
 Or the user just thinks that the advice is bad
 … if system interrupts a user too often with advice
 Annoyed even with the best advice
 And prone to ignore it
 [LTL:] Transparent system proactivity can annoy a user too
 E.g., if Aura warms up a projector in a wrong room,
and Fred has to wait for warm up in the right room
 If Aura told Fred in which room it watms up the projectoir
(visible), Fred would notice mistake &correct Aura’s action