Active GSM cell-id tracking

Dufková, Kateřina; Ficek, Michal; Kencl, Lukáš; Novák, Jakub; Kouba, J.; Gregor, Ivan; Danihelka, Jiri

doi:10.1145/1410012.1410015

Cited by 19 publications

(7 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To approximate the real distribution of users in the network, we base the distribution of user equivalents to cells on traces obtained by active tracking of selected users' cell associations, using the platform from [21]. The platform allows to periodically poll and store cell association of a set of users in a real-time manner and without user cooperation.…”

Section: B User Equivalentsmentioning

confidence: 99%

Energy savings for cellular network with evaluation of impact on data traffic performance

Dufková

Bjelica

Moon

et al. 2010

2010 European Wireless Conference (EW)

View full text Add to dashboard Cite

Abstract-We present a concrete methodology for saving energy in future and contemporary cellular networks. It is based on re-arranging the user-cell association so as to allow shutting down under-utilized parts of the network. We consider a hypothetical static case where we have complete knowledge of stationary user locations and thus the results represent an upper bound of potential energy savings. We formulate the problem as a binary integer programming problem, thus it is NP-hard, and we present a heuristic approximation method. We simulate the methodology on an example real cellular network topology with trafficand user distribution generated according to recently measured patterns. Further, we evaluate the energy savings, using realistic energy profiles, and the impact on the user-perceived network performance, represented by delay and throughput, at various times of day. The general findings conclude that up to 50% energy may be saved in less busy periods, while the performance effects remain limited. We conclude that practical, real-time usercell re-allocation methodology, taking into account user mobility predictions, may thus be feasible and bring significant energy savings at acceptable performance impact.

show abstract

Section: B User Equivalentsmentioning

confidence: 99%

Energy savings for cellular network with evaluation of impact on data traffic performance

Dufková

Bjelica

Moon

et al. 2010

2010 European Wireless Conference (EW)

View full text Add to dashboard Cite

show abstract

“…This interval was chosen to obtain the upper bound of energy consumption -the optimal interval is obviously dependent on the application, but as shown in Figure 8 (originally from [20]), a 5 minute interval ensures more than 70% probability that a device will not change its cell. For the application intended, a 2 minute tracking interval is sufficient [21].…”

Section: Impact On Battery Of Tracked Devicementioning

confidence: 99%

“…The accuracy of this method is relatively coarse-grained because cell coverage areas vary from hundreds of meters to few kilometers. However Cell-ID localization has already proved useful for certain applications [55,21].…”

Section: Introductionmentioning

confidence: 99%

Performance study of active tracking in a cellular network using a modular signaling platform

Ficek

Pop

Vláčil

et al. 2010

Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services

Self Cite

View full text Add to dashboard Cite

We present the SS7Box modular signaling platform, a tool for rapid application prototyping in a cellular mobile network, and examine in detail its performance limits for the application of active network-based tracking, called SS7Trac-ker. This application is a highly configurable, non-intrusive and cost-effective solution for large-scale data collection on user mobility in the network, uniquely enabling tracking of both active and passive mobile clients. We present performance studies of real deployment in an existing cellular network and document the measured as well as simulated performance limits such as platform interconnection utilization. Other factors, such as impact on battery consumption of the tracked device, are studied as well. Platform modularity and variability is discussed and demonstrated by further deployed use cases. We conclude by observing promising applicability for future cellular networks.

show abstract

“…Data used in this work were obtained by active tracking of a group of mobile phone users (unlike in [9]), using the platform from [8]. The platform allows to periodically poll and store cell association of a set of users in a real-time manner and without user cooperation.…”

Section: Experimental Datamentioning

confidence: 99%

“…The time scale is 2min, motivated by typical deployment times for near real time network management. Our approach is based on mining the UserCell association records obtained by active tracking [8]. We evaluate several prediction methods, such as Prediction by Partial Match (PPM), which was successfully used in [1] for location prediction of single users and LAST, which takes as prediction the last visited location.…”

Section: Introductionmentioning

confidence: 99%

Predicting User-Cell Association in Cellular Networks from Tracked Data

Dufková

Boudec

Kencl

et al. 2009

Mobile Entity Localization and Tracking in GPS-less Environnments

Self Cite

View full text Add to dashboard Cite

Abstract. We consider the problem of predicting user location in the form of user-cell association in a cellular wireless network. This is motivated by resource optimization, for example switching base transceiver stations on or off to save on network energy consumption. We use GSM traces obtained from an operator, and compare several prediction methods. First, we find that, on our trace data, user cell sector association can be correctly predicted in ca. 80% of the cases. Second, we propose a new method, called "MARPL", which uses Market Basket Analysis to separate patterns where prediction by partial match (PPM) works well from those where repetition of the last known location (LAST) is best. Third, we propose that for network resource optimization, predicting the aggregate location of a user ensemble may be of more interest than separate predictions for all users; this motivates us to develop soft prediction methods, where the prediction is a spatial probability distribution rather than the most likely location. Last, we compare soft predictions methods to a classical time and space analysis (ISTAR). In terms of relative mean square error, MARPL with soft prediction and ISTAR perform better than all other methods, with a slight advantage to MARPL (but the numerical complexity of MARPL is much less than ISTAR).

show abstract

Active GSM cell-id tracking

Cited by 19 publications

References 16 publications

Energy savings for cellular network with evaluation of impact on data traffic performance

Energy savings for cellular network with evaluation of impact on data traffic performance

Performance study of active tracking in a cellular network using a modular signaling platform

Predicting User-Cell Association in Cellular Networks from Tracked Data

Contact Info

Product

Resources

About