QoC-based Optimization of End-to-End M-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 Internet Backbone mHP ISP ADSL BlueTooth Zigbee UMTS WiFi Zigbee GPRS m-Health Portal Body Area Network (BAN)Internet Access Front- End MBU modem Front- End sisi sisi Back- End

Context Flow Graph (CFG) Zigbee1 N FE12 BTooth1 = pre-select node S1 S2 S3 S5 = context generator node S4 wired link wired links N MBU3 N FE1 1 N FE2 1 = aggregating node N MBU 11 ADSL BTooth2 WiFi N GW1 N BE1 N BE2 BE- processing WiFi GPRS UMTS FE1- processing N FE22 FE2- processing Zigbee2 N MBU 22 N GW2 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 Anode B resource (processing/communication) Context Information QoC at_B QoC at_A QoS –QoC impeded by QoS

max-plus algebra: –arithmetic maximum (instead minimum) properties: commutative, associative, … – Computational Model min-plus algebra –additive operation: –multiplicative operation: arithmetic domain

Computational Model (..) aggregation & concatenation elements –algebraic expression –QoC arithmetic expression S1 S2 N FE1 1 d1d1 d2d2

Computational Model (..) concatenation & pre-select element –algebraic expression –QoC arithmetic expression Zigbee1 N FE12 BTooth1 N MBU 11

Computational Model (..) End-to-End Freshness algebraic expression

Computational Model (..) wired link wired links Zigbee1 N MBU 11 N MBU3 N GW1 N BE1 N BE2 BE- processing GPRS N FE1 1 N FE12 S1 S2 S3 FE1- processing N FE2 1 N FE22 S5 FE2- processing Zigbee2 N MBU 22 S4 N GW2 (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 GPRSUMTSWiFiWFADSLZB1BT1ZB2BT2 Delay Avail.0,9900,9850,980,999/00,960,970,960,97 Costs

Computational Example (..) QoC for a rehabilitation training scenario –weights: w Fr = 1, w 1-Av = 150, w Co = 0.02 (not normalized) indoors outdoors –indoors: path via ZigBee1, BTooth2 & WiFi + ADSL –outdoors: path via ZigBee1, BTooth2 & UMTS

Conclusions QoC based selection of an optimal E2E transfer path for M-Health scenarios; Min-max-plus algebra for several QoC dimensions; Future work: –dynamic case –use of (colored) Petri-Nets –other workflow operations