Discussion of context in the IoT Cloud

1. Panel Discussion
Deep Context-Awareness:
Context Coupling and New Types of Context
Information in the IoT Cloud World
Hong-Linh Truong
Faculty of Informatics, TU Wien, Austria
hong-linh.truong@tuwien.ac.at
http://rdsea.github.io
Acknowledgment: with discussions with Quang Minh Tran & Tran-Vu Pham
MobiQuitous 2017 Panel Discussion, 8 Nov 2017,
Melbourne, (c) Hong-Linh Truong
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2. Urban flooding cases in HoChiMinh
City
Considering context-aware in a large-scale complex interdependent services (not
just between a mobile application and its back-end services in a single stop)
MobiQuitous 2017 Panel Discussion, 8 Nov 2017,
Melbourne, (c) Hong-Linh Truong
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Multi-stakeholders with different (fast & slow changing) contexts
http://english.thesaigontimes.vnhttp://www.thanhniennews.com

3. Example of contexts in IoT &
Analytics
 How would you deal with the lack of data based
on context?
 E.g., depending on location, time and society: human
sensing + flooding data crowdsourcing + IoT
monitoring + human (realtime) data marketplace
 Can we provide quality of analytics based on
context?
 Quality of analytics: time, cost, data accuracy, etc.
MobiQuitous 2017 Panel Discussion, 8 Nov 2017,
Melbourne, (c) Hong-Linh Truong
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4. Deep context and context coupling
 Context-awareness in one-to-one interactions (e.g.,
location, network bandwidth, ) works in most applications
 Context-awareness impacting data and analytics across
multiple systems do not work (well):
 Data provisioning and collection context
incentives and infrastructures
 Quality of data context data provisioning and data
collection methods
 Quality of analytics context quality of data, social
behavior and business constraints
MobiQuitous 2017 Panel Discussion, 8 Nov 2017,
Melbourne, (c) Hong-Linh Truong
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5. Realizing deep context-awareness
MobiQuitous 2017 Panel Discussion, 8 Nov 2017,
Melbourne, (c) Hong-Linh Truong
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Person
Pctx
IoT Cloud
Infrastructure
IInfctx
Business
Bctx
Context-aware IoT
Data Analytics
Context-aware IoT
Data Collection
Transportation
Infrastructure
TInfctx
Data
Qctx
Environment
Ectx
2. Beyond typical context types: quality of data, quality of
analytics, incentives  new context models and reasoning
1. Protocols and middleware for coupling contexts through
and from many different systems

6. Thanks for your
attention!
Hong-Linh Truong
Faculty of Informatics
TU Wien
rdsea.github.io
MobiQuitous 2017 Panel Discussion, 8 Nov 2017,
Melbourne, (c) Hong-Linh Truong
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