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Application adaptation in M-Health. 1/8. Application adaptation in M-Health. -- Smart distribution of vital sign processing. PhD: Hailiang Mei Supervisor: Ing Widya. Application adaptation in M-Health. 2/8. Context-aware. Adaptation. Context-Aware vs. Adaptation.

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slide1

Application adaptation in M-Health

1/8

Application adaptation in M-Health

-- Smart distribution of vital sign processing

PhD: Hailiang Mei

Supervisor: Ing Widya

slide2

Application adaptation in M-Health

2/8

Context-aware

Adaptation

Context-Aware vs. Adaptation

  • Taxonomy of context-aware(Dey, Salber et al. 2001)
    • presenting information and services to users
    • automatically trigger a command or reconfigure the system
    • attaching context information for later retrieval
  • Adaptation is “modification of an organism or its parts that makes it more fit for existence under the conditions of its environment” (Webster)
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Application adaptation in M-Health

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“Link degradation”

“Node degradation”

Vital Sign Processing Unit

Why need adaptation?

  • Problem
    • In an operational environment, a mismatch may occur between node resource and associated process demanddue to the context changes.
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Application adaptation in M-Health

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Home

G-way

FE

MBU

BE

PL

.

ADSL

.

WiFi

ZB

micro-adaptation

meso-adaptation

macro-adaptation

Taxonomy

patient

specialist

WiFi

BT

GPRS

  • policy-based control
  • hand-over mechanisms (incl. discovery,.. )
  • assignment computing (intelligence)
  • process distribution (infrastructure)
  • vital sign representation framework (data format)
  • scenario adaptation (multi-disciplinary)
  • stakeholders model
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Application adaptation in M-Health

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Research Status

  • RC1: Vital sign representation framework
    • Awareness D4.9 “Vital sign model” (in Docushare)
    • Automatic tooling supports will be furtherinvestigated
  • RC2: Process distribution (infrastructure support)
    • Awareness D4.14 “An architecture for smart distribution of health signal processing” (in Docushare)
    • D4.14+ is under consideration (more in-depth discussion on dynamic reconfiguration)
    • OSGI based implementation is planned
  • RC3: Assignment computing (intelligence)
    • Awareness D4.25 “Decision making mechanism of VSPU distribution”
    • So far, seems the most challenging and interesting stuff 
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Application adaptation in M-Health

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Assignment computing (approach)

  • Available resources(Processing Node)and demanded processes (VSPU) can be modeled as labeled graphs (e.g. DAG)
  • To find a good “Matching”
  • It is generally a NP-hard problem
    • Simpler solution exist for some constrained cases (2 nodes, chain2chain, etc)
    • Otherwise, rely on heuristic approaches (Lo 1988)
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Application adaptation in M-Health

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Max-flow/Min-cut

A simpler case: Two nodes assignment (Stone 1977)

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Application adaptation in M-Health

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Main references

  • Dey, A. K., D. Salber, et al. (2001). "A Conceptual Framework and a Toolkit for Supporting the Rapid Prototyping of Context-Aware Applications." Human-Computer Interaction (HCI) Journal 16: 97-166.
  • Lo, V. M. (1988). "Heuristic algorithms for task assignment in distributed systems." Computers, IEEE Transactions on 37(11): 1384.
  • Stone, H. S. (1977). "Multiprocessor scheduling with the aid of network flow algorithms." IEEE Transactions on Software Engineering 3: 85-93.
  • Cormen, T. H., C. E. Leiserson, et al. (2001). Introduction to Algorithms, MIT Press and McGraw-Hill.
  • Broens, T., A. v. Halteren, et al. (2005). Freeband AWARENESS D4.14 "An architecture for smart distribution of health signal processing".
  • Mei, H., I. Widya, et al. (2005). Freeband AWARENESS D4.9 "Vital sign model“.
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Backup slides
  • In the following pages
vital sign model system environment view

1

5

6

2

3

4

7

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MBU = Mobile Base Unit

HG = Home Gateway

CDB = Central DataBase

Vital Sign Model(system/environment view)
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