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Towards Quality of Service-Awareness of Mobile Healthcare Services

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Reference/Citation for a latest scientific paper: Katarzyna Wac, Maddalena Fiordelli, Mattia Gustarini, Homero Rivas, Quality of Life Technologies: Experiences from the Field and Key Research Challenges, IEEE Internet Computing, Special Issue: Personalized Digital Health, July/August 2015.

Reference/Citation: Katarzyna Wac, Towards Quality of Service-Awareness of Mobile Healthcare Services, ISfTeH Student's Videoconference Session - MedeTel, May 2009

Reference/Citation to a scientific paper: Katarzyna Wac, Mortaza Bargh, Bert-Jan van Beijnum, Richard Bults, Pravin Pawar, Arjan Peddemors, Power- and Delay-Awareness of Health Telemonitoring Services: the MobiHealth System Case Study, IEEE JSAC, Special Issue on Wireless and Pervasive Communications in Healthcare, 27(4): 525-536, IEEE Press, May 2009.

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Towards Quality of Service-Awareness of Mobile Healthcare Services

  1. 1. Katarzyna WAC (PhD candidate & Ambassadress) Towards Quality of Service AwarenessTowards Quality of Service-Awareness of Mobile Healthcare Services ISfTeH Student’s Videoconference Session - MedeTel 2009
  2. 2. MobiHealth System Infrastructure overviewInfrastructure overview m-health SP [ i i f ][service infra] Mobile Network Operator [2.5/3G] hybrid network [Internet] Care professional (specialist) [wired computer]Patient(s) [wired computer] [Mobile BAN] Care professional (GP) [mobile computer] page 2 - 6/3/2009 - Copyright 2007-2009 MobiHealth BV
  3. 3. Quality of Service ‘best-effort’ networksbest-effort networks BEsys 2 5 3 3 5G/WLAN/ Mobile Operator Enterprise Network Internet2.5-3-3.5G/WLAN/… Patient BAN Network Network host • emerging mobile applications • users have QoS-requirements and QoE-expectations (ITU, 2003; ITU, 2005; ITU, 2006; GSMWorld, 2008) – QoE expectations for mobile: comparable to the QoE provided by the existing Internet-services (Afuah & Tucci, 2000) – healthcare: user can be in a critical state • success of delivery depends on QoS-provided by underlying heterogeneous networking environment – current solutions: traditional QoS-management or user ‘lock-in’ (Buschken, 2004) page 3 - 6/3/2009 - Copyright 2007-2009 MobiHealth BV (Chalmers, 1999; Seitz, 2003; Bless, 2004; Saldatos, 2005; Gomez, 2005; ITU-T, 2006)
  4. 4. Proposed Solution Eff ti bil ti• Effective mobile computing – QoS-management via Mobile Web 2.0: collaborative sharing of QoS- information • QoS-management: get ‘best of best-effort’ by: – QoS-predictions for QoS provided by networks (history-based) – QoS-control • adapt mobile application to this QoS • change network provider to obtain better QoSg p – QoS-monitoring - measure the QoS to see if it matched predictions and to acquire new history – Facilitate fulfillment of QoS requirements and QoE-expectations, bring userq p , g satisfaction page 4 - 6/3/2009 - Copyright 2007-2009 MobiHealth BV (Wac, et al. 2005a; Wac, et al. 2006a; Wac, et al. 2007; Wac, et al. 2008a)
  5. 5. QoSIS: Quality-of-Service Information System • Functional requirements – QoS-monitoring and information • Non-functional requirements – performance: speed accuracy dependabilityQoS monitoring and information storage – QoS-information processing – QoS-predictions derivation and performance: speed, accuracy, dependability – low comm./process./storage overhead – low power consumption – low cost high data security/privacyQoS predictions derivation and dissemination – low cost, high data security/privacy – high scalability – fault tolerance, traceability pervasiveness to end user page 5 - 6/3/2009 - Copyright 2007-2009 MobiHealth BV – pervasiveness to end-user
  6. 6. QoSIS: Technical Feasibility ll d h f Qtek 9090 • collected one month of QoS-information – months: Nov-Dec 2007 and Qtek 9090 Apr-May 2008 – Geneva city (Switzerland) • 4 most frequent locations Geneva 4 most frequent locations – One COPD patient, 2 BANs – health telemonitoring service Q S d t d l• QoS measure: data delay • different networks – GPRS-Sunrise, WLAN-UniGeGPRS Sunrise, WLAN UniGe page 6 - 6/3/2009 - Copyright 2007-2009 MobiHealth BV
  7. 7. Health Telemonitoring Delay - Predictions • Predictions inputp – location, time, RSSI, battery, network technology and provider, observed data-rate • Different targets: delay category• Different targets: delay category – L/H category for threshold of e.g. 750, 1500, 2500,… ms – 4 or 5 user-defined categories accurate real-time Q S di i f ibl• Different scenarios – Days: 1-1, 5-1, 7-1, 14-7, 13-13, … Location network technology provider QoS-predictions are feasible – Location – network technology - provider – Intermediate locations – From 2007 to 2008 • Different machine learning techniques – Bayesian, trees, rules, non-linear functions, ‘lazy’ (48 in total) page 7 - 6/3/2009 - Copyright 2007-2009 MobiHealth BV
  8. 8. QoSIS.net: Business Viability • Based on MCM-business model framework* Product– Product • service: QoS-prediction service (mobile) • service medium: wireless • supporting services: QoSIS net: service maintenance AAA customer: handovers• supporting services: QoSIS.net: service maintenance, AAA, customer: handovers – Customers and Value chains (SLA, service delivery, payment) • mobile service providers like MobiHealth and their users, MNOs as SPs – Costs business enterprise– Costs • QoSIS.net a) for services setup/maintenance and b) marketing costs for new customers’ acquisition • Customer a) mobile device with location-determination b) device’s resources the providing QoS-predictions service i i bl ) ) QoS-prediction service usage e.g. battery – Revenues • QoSIS.net: customer pay for service usage (e.g. monthly, per transaction) is viable – Social environment • Informed consent: privacy sensitive location-time is acquired from users • critical success factor: reach a critical mass of users – content ‘prosumers’ page 8 - 6/3/2009 - Copyright 2007-2009 MobiHealth BV * By Institute of Media and Communications, Uni of St. Gallen, CH (Hoegg & Stanoevska-Slabeva, 2005)
  9. 9. Conclusion effective mobile computing: QoS/QoE-management via Mobile Web 2.0 • QoSIS: proactive QoS-management anywhere-anytime-anyhow• QoSIS: proactive QoS-management anywhere-anytime-anyhow – networks NOT designed for inverted producer-consumer paradigm applications – QoS-measurement: network delays/effective data-rates not knownQoS measurement: network delays/effective data rates not known until measured – QoS-predictions: feasible technically and viable business-wise • Novel - empowering mobile service providers & their users – Beyond current QoS-management frameworks; builds on ‘best-effort’ Beyond current user ‘lock in’ in network– Beyond current user lock-in in network – No need for changes in the existing network infrastructures – Builds upon a collaborative sharing of QoS-information page 9 - 6/3/2009 - Copyright 2007-2009 MobiHealth BV

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