Presentation for RUSMART 2013 This paper describes an algorithm for discovery of convoys in database with proximity log. Traditionally, discovery of convoys covers trajectories databases. This paper presents a model for context-aware browsing application based on the network proximity. Our model uses mobile phone as proximity sensor and proximity data replaces location information. As per our concept, any existing or even especially created wireless network node could be used as presence sensor that can discover access to some dynamic or user-generated content. Content revelation in this model depends on rules based on the proximity. Discovery of convoys in historical user’s logs provides a new class of rules for delivering local content to mobile subscribers

1. Discovery of Convoys in
Network Proximity Log
Dmitry Namiot Lomonosov Moscow State University
dnamiot@gmail.com
Manfred Sneps-Sneppe ZNIIS, M2M Competence Center
manfreds.sneps@gmail.com
RUSMART 2013

2. • Discovery of convoys in database with proximity
log.
• Traditionally, discovery of convoys covers
trajectories databases. This paper presents a model
for context-aware browsing application based on the
network proximity.
• Can we restore trajectories by proximity time
series?
• How can we use proximity based trajectories in
mobile services?
About

3. Contents
Introduction
Context and location awareness
Trajectories
Discovery process
Conclusion

4. Introduction
Our model uses mobile phone as proximity sensor
and proximity data replaces location information
As per our concept, any existing or even especially
created wireless network node could be used as
presence sensor that can discover access to some
dynamic or user-generated content.
Content revelation in this model depends on rules
based on the proximity.

5. 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 is some
metric lets us compare network environments
• It may be more accurate than location-based
computing (especially for indoor)
• We can use dynamic nodes as our base (e.g.
hotspot right on the mobile phone)

6. Proximity <> Location
• Open Wi-Fi Access
Point right in the
mobile
• Our data could be
linked to this mobile
hotspot
• In general, we do not
need location for
presenting data
associated with
proximity info

7. Spot Expert (SpotEx)
• We can the traditional indoor positioning
schema on the first stage: detection of Wi-Fi
networks?
This detection already provides some
information about the location – just due to local
nature of Wi-Fi network.
• As the second step we can add the ability to
describe some rules (if-then operators, or
productions) related to the Wi-Fi access points.

8. 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.

9. 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

10. 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

11. 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

12. 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

13. 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
Conditional part contains predicates with proximity
data. For example, rank of hot spots, etc.

14. Discovery of convoys
• Discovery of convoys in historical user’s logs
provides a new class of rules for delivering local
content to mobile subscribers
• Simply, we should be able to provide a new set
of predicates for our rules:
If IN_GROUP_OF (N, t) Then …
N – describes a size of group
t – observed time interval
• A new set of use cases for proximity marketing

15. Convoys
• Convoy is a group of
moving object where
included objects are in
density connection the
consecutive time points
• Objects are density-
connected if a sequence
of objects exists that
connects the two objects
and the distance between
consecutive objects does
not exceed the given
value.

16. More about convoys
• A group of objects form a traveling
company, if members of the group are
density-connected for themselves during
some given time and the size of the group
is not less than the given threshold.
• The moving cluster - a shared set of
objects exists across some finite time, but
objects may leave and join a cluster during
the cluster’s life time

17. More about convoys
• Dynamic convoys allows dynamic members
under constraints imposed by some parameters
(actually, by user-defined parameters).
• An evolving convoy captures the relationship
between different stages of convoys, so that
convoys in some stage has more (fewer)
members than its previous stage.
• Flock is a set of objects that travel within a range
while keeping the same motion.

18. Group behavior
• Anyway, all patterns covering capturingAnyway, all patterns covering capturing
“collaborative” or “group” behavior between moving“collaborative” or “group” behavior between moving
objects.objects.
• The difference between all the above mentionedThe difference between all the above mentioned
patterns is the way they define the relationshippatterns is the way they define the relationship
between the moving objects.between the moving objects.
• In our paper we avoid restrictions on the sizes andIn our paper we avoid restrictions on the sizes and
shapes of the discovered trajectory patterns.shapes of the discovered trajectory patterns.
• It is due to nature our data and the way they areIt is due to nature our data and the way they are
collectedcollected

19. Proximity ring
• Wi-Fi access point with
omni-directional antenna
• Having proximity info only
we cannot distinguish two
groups that actually
reached our access point
from the opposite
directions
• Convoy is a group of
objects (mobile phones in
this particular case) with
the similar proximity track
within the given time
interval.

20. Proximity convoys
• It is consistent movement where the key metric
is the relative proximity of an access point.
• Two proximity tracks (sequences of proximity
records) are similar on some time interval if for
the each sequential measurement in the first
track we can get a sequential measurement
from the second track for approximately the
same timestamp where two networks snapshots
have at least one pair of comparable Wi-Fi
measurements.

21. About us
International team: Russia - LatviaInternational team: Russia - Latvia ((Moscow –Moscow –
Riga – VentspilsRiga – Ventspils).). Big history of developingBig history of developing
innovative telecom and software services,innovative telecom and software services,
international contests awardsinternational contests awards
Research areas are:Research areas are:
open API for telecom,open API for telecom,
web access for telecom data,web access for telecom data,
Smart Cities,Smart Cities,
M2M applications, context-aware computingM2M applications, context-aware computing..