Thank You for referencing this work, if you find it useful! 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.

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. 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]
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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)
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(Chalmers, 1999; Seitz, 2003; Bless, 2004; Saldatos, 2005; Gomez, 2005; ITU-T, 2006)

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
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(Wac, et al. 2005a; Wac, et al. 2006a; Wac, et al. 2007; Wac, et al. 2008a)

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
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– pervasiveness to end-user

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
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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)
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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’
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* By Institute of Media and Communications, Uni of St. Gallen, CH (Hoegg & Stanoevska-Slabeva, 2005)

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
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