Application adaptation in M-Health

1. Application adaptation in M-Health 1/8 Application adaptation in M-Health -- Smart distribution of vital sign processing PhD: Hailiang Mei Supervisor: Ing Widya

2. 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)

3. Application adaptation in M-Health 3/8 “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.

4. Application adaptation in M-Health 4/8 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

5. Application adaptation in M-Health 5/8 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 

6. Application adaptation in M-Health 6/8 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)

7. Application adaptation in M-Health 7/8 Max-flow/Min-cut A simpler case: Two nodes assignment (Stone 1977)

8. Application adaptation in M-Health 8/8 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“.

9. Backup slides • In the following pages

10. Decision making A snapshot of the VSPU redistribution mechanism Task Resource D4.14 VSPU Distribution

11. 1 5 6 2 3 4 7 8 MBU = Mobile Base Unit HG = Home Gateway CDB = Central DataBase Vital Sign Model(system/environment view)