The paper introduces a new approach for using wireless sensors on mobile phones for integrating data from social networks.

1. Wireless Networks Sensors
and Social Streams
Dmitry Namiot Lomonosov Moscow State University
dnamiot@gmail.com
Manfred Sneps-Sneppe Ventspils University College
manfreds.sneps@gmail.com
AINA 2013

2. About
•a new approach for using wireless sensors on
mobile phones for integrating data from social
networks
• Our model uses Wi-Fi proximity approach as a
service
• Any existing or even especially created Wi-Fi hot
spot could be used as presence sensor that can
trigger access for some content published in social
networks.
• An especially developed mobile application
(context-aware browser) can present that
information to mobile subscribers.

3. Contents
Introduction
Context and location awareness
Spot Expert (SpotEx)
Social check-ins
Conclusion

4. Context awareness
Modern applications adopt a context-aware
perspective to manage:
a)communication among users and among
systems, or between the system and the user,
b) situation-awareness, like modeling location and
environment aspects (physical situation) or the
current user activity (personal situation)
c) knowledge chunks: determining the set of
situation-relevant information, services or behaviors

5. Sensors and Social Networks
• A typical example: City Sense. Collects sensor
data extracted from GPS-enabled cell phones
and taxi cabs in order to determine where the
people are, and then deliver this information to
subscribers with mobile devices.
• In general, this application is designed to track
important trends in the behavior of people in the
city.
• But: it introduces own social network. Can we
merge sensors with existing networks?

6. Spatio-temporal streams
• Where-when-what scheme: location, time and topic.
• Social stream is a collection of different spatio-
temporal-thematic points. Each point has got the
following attributes: location, time stamp and topic.
Topic here is some content, extracted from the tweets
(e.g., hashtag).
• Point = { location, time stamp, topic }
• Stream = [ Point1, Point2 … ]

7. Proximity vs. Location
• Can we replace location with proximity?
• And proximity here is the network proximity.
• In other words: use mobile user’s position relatively
network nodes (e.g. Wi-Fi access points)
• It could be more precise (especially for indoor)
• We can use dynamic Wi-Fi access points
(e.g. Hotspots on mobile phones) too.

8. Spot Expert (SpotEx)
• It is a perfect example for network proximity as a
service
• What if we stop our traditional indoor positioning
schema on the first stage:
detection of Wi-Fi networks?
• This detection actually already provides some
information about the location – just due to local
nature of Wi-Fi network.
•And as the second step we add the ability to
describe some rules (if-then operators, or
productions) related to the Wi-Fi access points.

9. SpotEx
• Our rules will simply use the fact that the particularly
Wi-Fi network is detected. And based on this
conclusion we will open (read – make them visible)
some user-defined messages to mobile terminals.
• Actually it is a typical example for the context
aware computing. The visibility for user-defined
text (content) depends on the network context.
• This approach uses Wi-Fi proximity
• Any Wi-Fi hot spot works here just as presence sensor.

10. SpotEx
So, our service contains the following components:
• database (store) with productions (rules) associated
with Wi-Fi networks
• rule editor. Web application (including mobile web)
that lets users add (edit) rule-set, associated with
some Wi-Fi network
• mobile applications, that can detect Wi-Fi networks,
check the current conditions against the database
and execute productions

11. SpotEx – use cases
The most obvious use cases:
• Some shop can deliver deals/discount/coupons right
to mobile terminals as soon as the user is near some
predefined point of sale.
We can describe this feature as “automatic check-in”
for example. Rather than directly (manually or via
some API) set own presence at some place (e.g.
similar to Foursquare, Facebook Places etc.)
with SpotEx mobile users can pull data automatically
and anonymously

12. SpotEx – use cases
• Campus admin can deliver news and special
announces
• Hyper local news in Smart City projects could be tight
(linked) to the public available networks and delivered
information via that channel etc.
• The most interesting (by our opinion, of course) use
case: Wi-Fi hot spot being opened right on the mobile
phone

13. SpotEx – use cases
• Open Wi-Fi Access
Point right in the
mobile
• Add (link) proximity
rules for this access
point exactly
• Rules will define data
chunks (info snippets)
for users nearby this
phone

14. SpotEx productions
Each rule looks like a production (if-then operator).
The conditional part includes the following objects:
Wi-Fi network identity,
signal strength (optionally),
time of the day (optionally),
client ID (MAC-address)
History of visits

15. SpotEx productions
In other words it is a set of operators like:
IF network_SSID IS ‘mycafe’ AND
time is 1pm – 2pm THEN
{ present the coupon for lunch }
It is like expert system
We can use well known algorithm for the
processing: Rete

16. Check-in
• Just a special form (record, status) in the social
networks
• They are always a part of the social stream
• They contain location information. Either directly
(latitude, longitude) or as a link to some
predefined place
• Based on SpotEx idea about “automatic check-
in”: can we replace location with proximity here?

17. Social Check-in - 1
• Place is just a Wi-Fi fingerprint (Wi-Fi nodes,
RSSI)
• Places are dynamical. There is no “directory” for
places
• Social check-in is just a Wi-Fi fingerprint again,
confirmed (signed) by some social network ID
• Social check-ins are temporal
• Social check-ins could be saved outside of the
social stream (to keep privacy). E.g., they are
not a part of Facebook news feed, etc.

18. Social Check-in -2
• In-proximity checkin
example
• Saves Wi-Fi
fingerprint in the
external database
• Each record is signed
by the user (e.g.,
Facebook ID)

19. Applications for social check-ins
• We can compare Wi-Fi fingerprints and
get a list of social IDs (e.g. Facebook IDs)
around
• Using social network API we can collect
“local” news feed
• This system could be used as a social
discovery tool: “show me, who else is here
right now”. And it works not only for my
existing social circle. E.g., I can see new
Facebook’s members around.

20. Conclusion
• A new model for context-aware data discovery for
mobile users.
• This model based on the ideas of Wi-Fi and Bluetooth
proximity and introduces a new form of check-in
service for social networks.
• The proposed check-in service based on network
proximity rules instead of geo points and lets link
together wireless networks information and social
streams.
• Service can use existing as well as the especially
created networks nodes as presence triggers for
discovering the relevant content from social networks.

21. About us
International team: Russia - Latvia (Moscow –
Riga – Ventspils). Big history of developing
innovative telecom and software services,
international contests awards
Research areas are:
open API for telecom,
web access for telecom data,
Smart Cities,
M2M applications, context-aware computing.