Supporting L3 femtocell mobility using the MOBIKE protocol

Proceeding of ACCESS 2011, The Second International Conference on Access Networks, Luxembourg City, Luxembourg, June 19-24, 2011Femtocells can be used to improve the indoor coverage and bandwidth of 3G cellular networks in homes and buildings. They are designed to be placed in a fixed location. However, their use would also be interesting in mobile environments such as public transportation systems. This paper studies the mobility limitations at the layer 3 and suggests an approach to support mobility on femtocell networks. This solution employs the protocols already defined in the femtocell architecture, minimizing thus the impact on it.This work has been supported by the Spanish Ministry of Science and Innovation, CONSEQUENCE project (TEC2010- 20572-C02-01) and partially supported by the Madrid regional community project CCG10-UC3M/TIC-4992

Supporting l3 femtocell mobility using the mobike protocol

Abstract-Femtocells can be used to improve the indoor coverage and bandwidth of 3G cellular networks in homes and buildings. They are designed to be placed in a fixed location. However, their use would also be interesting in mobile environments such as public transportation systems. This paper studies the mobility limitations at the layer 3 and suggests an approach to support mobility on femtocell networks. This solution employs the protocols already defined in the femtocell architecture, minimizing thus the impact on it

Analysis of location prediction performance of LZ algorithms using GSM Cell-based location data

Proceedings of the 5th International Symposium of Ubiquitous Computing and Ambient Intelligence (UCAMI 2011), December 5-8th, 2011, Riviera Maya, MexicoPredictions about users' next locations allow bringing forward their future context, thus having additional time to react. To make such predictions, algorithms capable of learning mobility patterns and estimating the next location are needed. This work is focused on making the predictions on mobile terminals, thus resource consumption being an important constraint. Among the predictors with low resource consumption, the family of LZ algorithms has been chosen to study their performance, analyzing the results drawn from processing location records of 95 users. The main contribution is to divide the algorithms into two phases, thus being possible to use the best combination to obtain better prediction accuracy or lower resource consumption.Proyecto CCG10-UC3M/TIC-4992 de la Comunidad Autónoma de Madrid y la Universidad Carlos III de Madri

A Bandwidth-Efficient Service for Local Information Dissemination in Sparse to Dense Roadways

Thanks to the research on Vehicular Ad Hoc Networks (VANETs), we will be able to deploy applications on roadways that will contribute to energy efficiency through a better planning of long trips. With this goal in mind, we have designed a gas/charging station advertising system, which takes advantage of the broadcast nature of the network. We have found that reducing the number of total sent packets is important, as it allows for a better use of the available bandwidth. We have designed improvements for a distance-based flooding scheme, so that it can support the advertising application with good results in sparse to dense roadway scenarios

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