iotmeetupguildford#9: sociotal – face-to-face enabler for detecting real social interactions -...
TRANSCRIPT
Face-to-face enabler for detecting real social
MeetUp 2015, March 17th, Guildford
Mr Niklas Palaghias
PhD Researcher, University of Surrey
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Creating a socially aware citizen-centric Internet of Things
SOCIOTAL MeetUp
Project Fact Sheet
2
ES
RS
UK
FR
IT
NL
Project Coordinator:Prof. Klaus Moessner,University of SurreyTechnical Coordinator:Dr. Michele Nati
Title: SOCIOTAL
Contract Number: CNECT-ICT-609112
Duration: September 2013-August 2016
http://SOCIOTAL.eu
SOCIOTAL MeetUp
Main idea
Enable a citizen-inclusive and pro-citizen second wave of the IoT
‐ Encourage citizen participation in the IoT to boost (societal) innovation
‐ Provide direct benefits to citizens, help addressing true needs of neighbourhood, communities and society at large scale
‐ Put control into the hand of the citizen on who can access information
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Approach
Design and provide key enablers for a reliable, secure and trusted IoT environment by encouraging people to contribute their IoT devices and information flows
Provide a citizen-centric IoT approach towards creation of large-scale IoT solutions of interest to the society in order to exploit billions of new IoT information streams
By equipping communities with secure and trusted tools that increase user confidence in IoT environment, enable their transition to smart neighbourhoods, communities and cities
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Key Enablers
Detection of face-to-face (F2F) interactions to understand social relations
Radiolocation enablers – Location based ID
Indoor localisation based on magnetic field
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Social interactions
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Personal zone
Social zone
Public zone
Facing direction
Interaction zone
Intimate zone
(A)
(B)
(C)
(D)
Figure 1: Interpersonal distance and spatial arrangement.
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Enabler
Face-to-face (F2F) interaction detection‐ Proximity detection based
on machine-learning through
Bluetooth RSSI
‐ Relative orientation estimation
based on an improvement of
uDirect*
‐ Collaborative Sensing for
real-time and enhanced
F2F inference
*Patent: Hoseinitabatabaei SA, Gluhak A, Tafazolli R. Information determination in a portable device.
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Personal zone
Social zone
Public zone
Facing direction
Interaction zone
Intimate zone
(A)
(B)
(C)
(D)
Initial Evaluation
9 participants
Overall accuracy 81.4%
A
CE
B
D
Personal
Social
Social
Public
Social
Social
Social
Public
Social
Figure 2: Office environment including the points where participants
were standing.
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Developers
SOCIOTAL platform
‐ Context Broker based on FI-WARE Orion Broker
‐ Social Interaction detection app/lib
‐ Etc..
Query information about a particular entity
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SocIoTal
SERVER
193.144.201.50:3500
GET /SocIoTal_Context_UC_REST/NGSI10_API/queryContext/Entity_ID
HEADERS
Content-
type
application/json
Accept application/json
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Developers
Advanced query
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SocIoTal
SERVER
193.144.201.50:3500
POST /SocIoTal_Context_UC_REST/NGSI10_API/queryContext?limit=1
HEADERS
Content-
type
application/json
Accept application/json
Payload:
{"entities":[
{"type":"",
"isPattern":"false",
"id":"SocIoTal:SAN:SmartphoneContext:Dev_001"
}
],
"attributes":["SocialRelation"]
}
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Feedback Information
Date
App type
App utility‐ Good
‐ Bad
‐ Missing
What would you do differently?
What adjunctive function would you like to have?
Perceive privacy
Notes
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ContactNiklas Palaghias, PhD [email protected]
Institute for Communication Systems (ICS)University of Surrey
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