Análisis de las aplicaciones móviles en el sector turístico

El trabajo está orientado a mostrar una aproximación al estado de la cuestión sobre las aplicaciones móviles, tema que goza de una historiografía reciente aunque muy abundante. Es por eso que se ha considerado hacer un recorrido a lo largo de la historia del turismo y a su consecuente evolución debido a la aparición de las nuevas tecnologías y a su adaptación en el sector. Además, no sólo se habla sobre la innovación tecnología en sí, sino que se analiza el cambio sociológico que ha habido en el turista y la importancia que tiene este tipo de servicio en el marketing turístico y en la economía. Además, ya que se analiza a lo largo de este apartado una selección de aplicaciones móviles españolas, se ha recopilado información procedente de informes que muestran cuál es la realidad de las apps en España y qué factores son los que se recomiendan aplicar a la hora de su creación. Una vez analizados estos conceptos teóricos se pasará a una fase más práctica donde se realiza una selección de 20 aplicaciones turísticas de las que se plasman sus principales características en unas fichas realizadas expresamente para este trabajo. A continuación, se extraerán los datos considerados como inputs y outputs y se determinarán los niveles de eficiencia de las aplicaciones a través de DEA (Análisis Envolvente de Datos). Además, mediante el Benchmarking se localizarán aquellas susceptibles de poder ser imitadas por otras con peores resultados, ya que esta técnica persigue aumentar los niveles de eficiencia y eficacia. Consecuentemente, se extraerán unas conclusiones y un listado de buenas prácticas y factores de éxito. Por último, se hablará sobre el interés de este trabajo, futuras líneas de investigación y futuros proyectos donde se encuentra uno concretamente que está en proceso de desarrollo consistente en una aplicación móvil enfocada a familias dentro de la ciudad de Zaragoza, denominada, en su primera fase: "Happy Aventura"

An adaptive handover prediction scheme for seamless mobility based wireless networks

We propose an adaptive handover prediction (AHP) scheme for seamless mobility based wireless networks. That is, the AHP scheme incorporates fuzzy logic with AP prediction process in order to lend cognitive capability to handover decision making. Selection metrics, including received signal strength, mobile node relative direction towards the access points in the vicinity, and access point load, are collected and considered inputs of the fuzzy decision making system in order to select the best preferable AP around WLANs. The obtained handover decision which is based on the calculated quality cost using fuzzy inference system is also based on adaptable coefficients instead of fixed coefficients. In other words, the mean and the standard deviation of the normalized network prediction metrics of fuzzy inference system, which are collected from available WLANs are obtained adaptively. Accordingly, they are applied as statistical information to adjust or adapt the coefficients of membership functions. In addition, we propose an adjustable weight vector concept for input metrics in order to cope with the continuous, unpredictable variation in their membership degrees. Furthermore, handover decisions are performed in each MN independently after knowing RSS, direction toward APs, and AP load. Finally, performance evaluation of the proposed scheme shows its superiority compared with representatives of the prediction approaches.Sadiq, AS.; Fisal, NB.; Ghafoor, KZ.; Lloret, J. (2014). An adaptive handover prediction scheme for seamless mobility based wireless networks. Scientific World Journal. 2014. doi:10.1155/2014/610652S2014You, I., Han, Y.-H., Chen, Y.-S., & Chao, H.-C. (2011). Next generation mobility management. Wireless Communications and Mobile Computing, 11(4), 443-445. doi:10.1002/wcm.1136Sepúlveda, R., Montiel-Ross, O., Quiñones-Rivera, J., & Quiroz, E. E. (2012). WLAN Cell Handoff Latency Abatement Using an FPGA Fuzzy Logic Algorithm Implementation. Advances in Fuzzy Systems, 2012, 1-10. doi:10.1155/2012/219602Song, W. (2012). Resource reservation for mobile hotspots in vehicular environments with cellular/WLAN interworking. EURASIP Journal on Wireless Communications and Networking, 2012(1). doi:10.1186/1687-1499-2012-18Sadiq, A. S., Bakar, K. A., Ghafoor, K. Z., Lloret, J., & Khokhar, R. (2013). An Intelligent Vertical Handover Scheme for Audio and Video Streaming in Heterogeneous Vehicular Networks. Mobile Networks and Applications, 18(6), 879-895. doi:10.1007/s11036-013-0465-8Nahrstedt, K. (2011). Quality of Service in Wireless Networks Over Unlicensed Spectrum. Synthesis Lectures on Mobile and Pervasive Computing, 6(1), 1-176. doi:10.2200/s00383ed1v01y201109mpc008Magagula, L. A., Chan, H. A., & Falowo, O. E. (2011). Handover approaches for seamless mobility management in next generation wireless networks. Wireless Communications and Mobile Computing, 12(16), 1414-1428. doi:10.1002/wcm.1074Sadiq, A. S., Bakar, K. A., Ghafoor, K. Z., Lloret, J., & Mirjalili, S. (2012). A smart handover prediction system based on curve fitting model for Fast Mobile IPv6 in wireless networks. International Journal of Communication Systems, 27(7), 969-990. doi:10.1002/dac.2386Çeken, C., Yarkan, S., & Arslan, H. (2010). Interference aware vertical handoff decision algorithm for quality of service support in wireless heterogeneous networks. Computer Networks, 54(5), 726-740. doi:10.1016/j.comnet.2009.09.018Dutta, A., Das, S., Famolari, D., Ohba, Y., Taniuchi, K., Fajardo, V., … Schulzrinne, H. (2007). Seamless proactive handover across heterogeneous access networks. Wireless Personal Communications, 43(3), 837-855. doi:10.1007/s11277-007-9266-3Xu, C., Teng, J., & Jia, W. (2010). Enabling faster and smoother handoffs in AP-dense 802.11 wireless networks. Computer Communications, 33(15), 1795-1803. doi:10.1016/j.comcom.2010.04.044Holis, J., & Pechac, P. (2008). Elevation Dependent Shadowing Model for Mobile Communications via High Altitude Platforms in Built-Up Areas. IEEE Transactions on Antennas and Propagation, 56(4), 1078-1084. doi:10.1109/tap.2008.91920

Voice Capacity and Data Response Time in Cognitive Radio Networks

The growing interest towards wireless communication services over the recent years has increased the demand for radio spectrum. Inefficient spectrum management together with the scarcity of the radio spectrum is a limiting factor for the development of modern wireless networks. As a solution, the idea of cognitive radio networks (CRNs) is introduced to use licensed spectrum for the benefit of the unlicensed secondary users. However, the preemptive priority of the licensed users results in random resource availabilities at the secondary networks, which makes the quality-of-service (QoS) support challenging. With the increasing demand for elastic/interactive data services (internet based services) and wireless multimedia services, QoS support becomes essential for CRNs. This research investigates the voice and elastic/interactive data service support over CRNs, in terms of their delay requirements. The packet level requirements of the voice service and session level delay requirements of the elastic/interactive data services are studied. In particular, constant-rate and on-off voice traffic capacities are analyzed over CRNs with centralized and distributed network coordination. Some generic channel access schemes are considered as the coordination mechanism, and call admission control algorithms are developed for non-fully-connected CRNs. Advantage of supporting voice traffic flows with different delay requirements in the same network is also discussed. The mean response time of the elastic data traffic over a centralized CRN is studied, considering the shortest processor time with and without preemption and shortest remaining processor time service disciplines, in comparison with the processor sharing service discipline. Effects of the traffic load at the base station and file length (service time requirement) distribution on the mean response time are discussed. Finally, the relationship between the mean response times of interactive and elastic data traffic is studied