Discovery of Convoys in Network Proximity Log

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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

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Discovery of Convoys in Network Proximity Log

  1. 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. 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. 3. Contents Introduction Context and location awareness Trajectories Discovery process Conclusion
  4. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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..

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