Location Embedding and Deep Convolutional Neural Networks for Next Location Prediction

International audienc

Geolocation Android Mobile Phones Using GSM/UMTS

The proliferation of cellular network enabled users through various positioning tools to track locations, location information is being continuously captured from mobile phones, created a prototype that enables detected location based on using the two invariant models for Global Systems for Mobile (GSM) and Universal Mobile Telecommunications System (UMTS). The smartphone application on an Android platform applies the location sensing run as a background process and the localization method is based on cell phones. The proposed application is associated with remote server and used to track a smartphone without permissions and internet. Mobile stored data location information in the database (SQLite), then transfer it into location API to obtain locations result implemented in Google Maps. Track a smartphone with fixed identifiers mostly SSN (SIM (Subscriber Identity Module) Serial Number) and IMEI (International Mobile Equipment Identity) derived from an identifying string unique to the user's device. The result located place is Moderate correct according to the (GSM) and (UMTS) cellular networks which is used for obtaining location information

Location Embedding and Deep Convolutional Neural Networks for Next Location Prediction

International audienc

Study of LZ-Based Location Prediction and Its Application to Transportation Recommender Systems

Predicting users’ next location allows to anticipate their future context, thus providing additional time to be ready for that context and react consequently. This work is focused on a set of LZ-based algorithms (LZ, LeZi Update and Active LeZi) capable of learning mobility patterns and estimating the next location with low resource needs, which makes it possible to execute them on mobile devices. The original algorithms have been divided into two phases, thus being possible to mix them and check which combination is the best one to obtain better prediction accuracy or lower resource consumption. To make such comparisons, a set of GSM-based mobility traces of 95 different users is considered. Finally, a prototype for mobile devices that integrates the predictors in a public transportation recommender system is described in order to show an example of how to take advantage of location prediction in an ubiquitous computing environment