1. CENTRE CEllular Network Trajectories Reconstruction Environment F. Giannotti, A. Mazzoni, S. Puntoni, C. Renso KDDLab, Pisa

2. CENTRE An environment to generate synthetic spatio- temporal data representing mobile telephones localizations inside cellular network

3. The GeoPKDD process Mobile devices positioning data

4. Cellular Architecture GSM/UMTS protocol: area covered by a number of antennas (BTS). Each antenna emits a signal that cover a circular area whose diameter varies from few hundred meters (urban area) to kilometers (rural area). ● Overlap Areas ● Movements of users through the network leave tracks in databases called VLR/HLR registers

5. The need of data generation ● Data availability: GSM/UMTS location registers are critical for telephony companies, mainly due to privacy issues ● Analysis algorithm need to be tested in extreme situations (which rarely occur in real life scenario) ● the need to test analysis algorithms on different datasets with predicable characteristics in order to validate results

6. Data Generation ● Input raw data of the Geopkdd process are positioning data (referred in space and in time) from telephone companies ● Mobile device position data in GSM/UMTS network are entries in VLR/HLR registers coming from Antennas representing the presence of mobile telephone inside the antenna signal area, called Log Log: (obj_id, ant_id, t, d) ● Device obj_id at time t has been detected by antenna ant_id at distance d.

7. Log Generation In order to generate Log data, people movements through the network has to be simulated Generation of people movements in time and space across an area covered by GSM/UMTS network Moving object evolution generation

8. Moving object data generation Moving object - objects that evolve in space and time Mobile device can be abstracted as a Moving point. Generate synthetic moving objects trajectories There are some Spatio-Temporal data generators available in the literature. ● Most famous and general-purpose are GSTD and G-TERD. ● Other are more application-oriented (Oporto)

9. Moving Object Data Generation IDEA: Generate people movement data exploiting existing spatio-temporal data generator We choose GSTD: – General – provide general-purpose functionalities – focus on moving points (GTERD focuses on moving regions) – extendible (written in java, source code available) Extend/modify GSTD to generate different scenario of people (carrying mobile phones) moving across the network

10. GSTD – Main Idea ● Iteratively generate Instances of moving object (obj_id, timestamp, spacestamp) ● Each Instance represent a specific localization of moving point ● Temporal consecutives Instances of the same object represent the evolution of the object (trajectory) ● Spatial and temporal timestamp are computed exploiting random distributions (defined by the user) ● objects evolution controlled by user defining input parameters

11. GSTD Instances Y=y1 X=x1 (obj_1, t1,X1,Y1) Evolution of object obj_1

12. GSTD – objects behavoir GSTD has been extended to add semantics to objects evolution ● Agility - how the object changes spatial movements ● Obstacles infrastructure that limit the object movements ● Group movements

13. CENTRE – overall architecture

14. GSTD* GSTD data generator has been extended to better represent people movements and to generate more “realistic” scenario in a flexible way. Features improved: ● manipulation of input parameters ● groups management ● obstacles infrastructure, now defined by the user

15. Groups Management ● GSTD* provides an improved group behavoir simulation function ● Groups can be defined by input parameters giving different moving behavoir for groups of objects ● Moreover, the group changing function has been defined in order to model objects that change behavoir (group) over time (fickleness). ● Example: people travelling by car (moving fast following roads), then walking (moving slower everywhere) etc.

16. Log Generation ● Logs are computed during object evolution generation taking as input the Antennas cover map ● At each Instance, a Log is generated detecting which antennas cover the spatial coordinates of the object, at time t. (obj_id, ant_id, t) Three logs are generated: (obj_1, t, ant1) (obj_1, t, ant2) (obj_1, t, ant3)

17. Trajectories Reconstruction Given (generated) Logs, a first step towards analysis algorithms is the trajectories reconstruction ● Trajectories are reconstructed from Logs in an approximated way (Logs introduce granularity error in positioning) ● Trajectories reconstruction module can be exploited on real data ● However, having original synthetic trajectories allows us to validate and test different reconstruction methods.

18. Ongoing and Future Work ● Better simulation of real life situations (modelling other antenna shapes, cellular disconnections, temporal cyclic groups behavoir etc). ● Study of trajectories reconstruction methods (heuristics definition) ● Uncertainty representation ● Trajectories warehouse