QoC-based Optimization of End-to-EndM-Health Data Delivery Services

Ing Widya (UoT),

Bert-Jan van Beijnum (UoT),

Alfons Salden (TI)

Outline:– introduction

• mobile-healthcare case

– context flow graph

– computational model

• freshness, availability, costs QoC

– computational example

– conclusions

• resource configuration & alternatives

Introduction• M-Health Application

PNO

InternetBackbone

mHP

ISPADSL

BlueTooth

Zigbee

UMTS

WiFi

Zigbee

GPRS

m-Health PortalBody Area Network (BAN) Internet Access

Front-End

Front-End

MBUMBU

modem

Front-End

Front-End

sisi

sisi

Back-End

Back-End

Context Flow Graph (CFG)

Zigbee1

NFE12

BTooth1

= pre-select node

S1

S2

S3

S5

= context generator node

S4

wired link

wired links

NMBU3

NFE11

NFE21

= aggregating node

NMBU11

ADSL

BTooth2

WiFi

NGW1

NBE1 NBE2BE-

processingWiFi

GPRSUMTS

FE1-processing

NFE22FE2-

processing Zigbee2

NMBU22

NGW2

GW-processing

• optimal path to bring health-data to professional ?

CFG, QoC and QoS

• QoC based selection– Quality of Context (information)

– QoC freshness(/up-to-dateness), availability, “costs”;

node A node Bresource

(processing/communication)

Context Information

Context Information

QoCat_BQoCat_AQoS

– QoC impeded by QoS

• max-plus algebra:– arithmetic maximum (instead minimum)

• properties: commutative, associative, …–

Computational Model

• min-plus algebra– additive operation:

– multiplicative operation:

b} {a, minimum b a

b a b a

cbcacbaScb,a,

arithmetic domain

Computational Model (..)

• aggregation & concatenation elements– algebraic expression

).Fr (d )Fr (d

Fr

Fr

d0

0d II Fr

S22maxS11

S2

S1

2

1maxFE

}Fr {d maximum Fr Sii 2 1, i

FE

– QoC arithmetic expression

S1

S2

NFE11

d1

d2

Computational Model (..)

• concatenation & pre-select element– algebraic expression

FE12zb

FE12z

bMBU1

Fr ) d d (

Fr d

d II Fr

FE12iz} {b, i

MBU1 Fr } {d minimum Fr

– QoC arithmetic expression

Zigbee1

NFE12

BTooth1NMBU1

1

Computational Model (..)

• End-to-End Freshness algebraic expression

I

I

I

I

......)FrdFr(dd

......)FrdFr(dd

......)FrdFr(dd

......)FrdFr(dd

IIIIFr

FE21z2maxFE11z1wh

FE21z2maxFE11z1w

FE21z2maxFE11z1u

FE21z2maxFE111zg

BE2

,Frd FrdFrdFr S4wl4maxS2wl2maxS1wl1FE11

S5wl5FE21 FrdFr

Computational Model (..)

wired link

wired links

Zigbee1

NMBU11

NMBU3

NGW1

NBE1 NBE2BE-

processing

GPRS

NFE11

NFE12

S1

S2

S3

FE1-processing

NFE21

NFE22S5FE2-

processing Zigbee2

NMBU22

S4

NGW2

• (1,1) element

• Availability & costs: independently & similarly

Computational Example

• QoS values (illustration)

• QoC results– three independently calculated QoC matrices

– 4x4 matrices of ranked alternative E2E paths

• Weighing Metrics– weighted quadratic norms

GPRS UMTS WiFi WFADSL ZB1 BT1 ZB2 BT2

Delay 5 4 3 1 7 10 4 8

Avail. 0,990 0,985 0,98 0,999/0 0,96 0,97 0,96 0,97

Costs 418 0 260 0 0 0 0 0

Computational Example (..)

• QoC for a rehabilitation training scenario– weights: wFr = 1, w1-Av = 150, wCo = 0.02 (not normalized)

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indoors outdoors

– indoors: path via ZigBee1, BTooth2 & WiFi + ADSL

– outdoors: path via ZigBee1, BTooth2 & UMTS

Conclusions

• QoC based selection of an optimal E2E transfer pathfor M-Health scenarios;

• Min-max-plus algebra for several QoC dimensions;• Future work:

– dynamic case

– use of (colored) Petri-Nets

– other workflow operations