Automatic user daily geographic tracking

El análisis de nuestra actividad diaria puede ser una fuente de información muy valiosa para conocer características de nuestros hábitos cotidianos que pueden pasar desapercibidas. En el presente Trabajo Fin de Grado se pretende detectar de forma automática la rutina diaria del usuario mediante el registro de las localizaciones en las que se detiene. Mediante el uso de un dispositivo móvil Android podemos detectar estas paradas utilizando los sensores disponibles en la mayoría de los dispositivos móviles inteligentes (smartphones). Estas paradas se almacenarán de forma totalmente automática y de manera local en el dispositivo móvil del usuario, pudiendo ser consultadas en cualquier momento. Adicionalmente, el usuario puede solicitar registrar una parada en su posición de forma manual si lo desea. También se podrá obtener información sobre la fecha y hora de las paradas, además de disponer de información sobre las redes inalámbricas detectadas en cada parada, tanto fijas (routers) como otros dispositivos móviles cercanos. También se podrán consultar las paradas de forma gráfica en un mapa de la zona mediante el uso de la API GoogleMaps. La aplicación, además, se puede configurar para adaptarla a distintos usos, gracias a los parámetros de distancia mínima a la que se considera una parada como nueva y tiempo mínimo de detección para que se determine si se ha producido una parada. El objetivo final es hacer consciente al usuario de su propio comportamiento, para permitir mejoras en su vida

Procesamiento de Eventos Complejos para la determinación de rutas en ciudades inteligentes

Una de las principales características de una ciudad inteligente es la publicación de datos abiertos que monitorizan diferentes aspectos de la misma, desde sistemas de movilidad a niveles de polen. Sin embargo, esta información se ofrece a los ciudadanos de forma genérica y carente de contexto. Sin el concurso de las personas en la generación y recogida de información, no es posible tener en cuenta sus preferencias y necesidades, ni el uso que hacen de los servicios. Para reducir este fenómeno y contextualizar los datos abiertos con información acerca de los ciudadanos, diseñamos anteriormente la arquitectura de referencia People as a Service, que permite recopilar información de los usuarios a partir de sus smartphones. En este trabajo vamos un paso más allá en la integración de un motor de inferencia para esta arquitectura usando la tecnología de Procesamiento de Eventos Complejos. En particular, recopilamos información de los sensores del smartphone para analizarla, transformándola en conocimiento sobre los hábitos del usuario. La posterior combinación de este conocimiento con los datos abiertos de la ciudad, permitirá que sus servicios puedan adaptarse a cada persona.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

A programming framework for people as a service

The number of devices connected to the internet is constantly growing, which implies an increased complexity when interacting with so many heterogeneous devices. Automating this process is key to keep up with this growth. This People as a Service model works towards developing virtual profiles for every user in their own mobile devices and under their full control. These profiles allow to establish user preferences and predefined parameters, which are then applied by the devices they connect to. By integrating both the information in the virtual profiles and these devices, we can create a context in which to make smart decisions and apply them automatically, all of this in a decentralised way. In order to show our proposal in action, we have developed a treasure hunting game as a proof of concept to bring to the spotlight the utility of an environment with programmatically adapted devices.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

One step towards dynamically programmable things: an implementation using Beacons

The number of devices connected to the internet is constantly increasing. This large amount of devices can be used to improve people’s interaction with their environment. However, this also implies an increase in the complexity when interacting with so many heterogeneous devices. Automating this process is key to keep up with this growth. This is the purpose of the People as a Service model (PeaaS), which works towards developing virtual profiles for every user in their own mobile devices under their full control. These profiles allow to establish preferences and predefined parameters, which are then used by the devices they connect to. By integrating both the information in the users’ virtual profiles and the information given by the devices they connect to, we can create a context in which to make smart decisions and apply them to the devices, all of this in a decentralised way. This allows for a programmatically configuration of the devices without the need of the user’s intervention. In order to put the proposal in action, we have developed a treasure hunting game as a proof of concept to bring to the spotlight the utility of an environment with programmatically adapted devices.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Vision-based techniques for automatic marine plankton classification

Plankton are an important component of life on Earth. Since the 19th century, scientists have attempted to quantify species distributions using many techniques, such as direct counting, sizing, and classification with microscopes. Since then, extraordinary work has been performed regarding the development of plankton imaging systems, producing a massive backlog of images that await classification. Automatic image processing and classification approaches are opening new avenues for avoiding time-consuming manual procedures. While some algorithms have been adapted from many other applications for use with plankton, other exciting techniques have been developed exclusively for this issue. Achieving higher accuracy than that of human taxonomists is not yet possible, but an expeditious analysis is essential for discovering the world beyond plankton. Recent studies have shown the imminent development of real-time, in situ plankton image classification systems, which have only been slowed down by the complex implementations of algorithms on low-power processing hardware. This article compiles the techniques that have been proposed for classifying marine plankton, focusing on automatic methods that utilize image processing, from the beginnings of this field to the present day.Funding for open access charge: Universidad de Málaga / CBUA. Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. The authors wish to thank Alonso Hernández-Guerra for his frm support in the development of oceanographic technology. Special thanks to Laia Armengol for her help in the domain of plankton. This study has been funded by Feder of the UE through the RES-COAST Mac-Interreg pro ject (MAC2/3.5b/314). We also acknowledge the European Union projects SUMMER (Grant Agreement 817806) and TRIATLAS (Grant Agreement 817578) from the Horizon 2020 Research and Innovation Programme and the Ministry of Science from the Spanish Government through the Project DESAFÍO (PID2020-118118RB-I00)

Modelado de TCP en un entorno celular con dual connectivity

This paper proposes a TCP implementation in a system-level simulator. This LTE-Advanced simulator provides Dual Connectivity (DC), which allows user equipments (UEs) to receive data simultaneously from two evolved NodeBs (eNBs) in order to boost the performance in a heterogeneous network. In this work, a TCP abstraction is described to predict TCP version Reno performance in an accurate and computationally efficient way. The proposed model is used to show the impact of DC on the user throughput and dropped packets when UEs are downloading a file through TCP.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Active Learning Methodology for Expert-Assisted Anomaly Detection in Mobile Communications

Due to the great complexity, heterogeneity, and variety of services, anomaly detection is becoming an increasingly important challenge in the operation of new generations of mobile communications. In many cases, the underlying relationships between the multiplicity of parameters and factors that can cause anomalous behavior are only determined by human expert knowledge. On the other hand, although automatic algorithms have a great capacity to process multiple sources of information, they are not always able to correctly signal such abnormalities. In this sense, this paper proposes the integration of both components in a framework based on Active Learning that enables enhanced performance in anomaly detection tasks. A series of tests have been conducted using an online anomaly detection algorithm comparing the proposed solution with a method based on the algorithm output alone. The obtained results demonstrate that a hybrid anomaly detection model that automates part of the process and includes the knowledge of an expert following the described methodology yields increased performance.This project is partially funded by the Junta de Andalucía through the UMA-CEIATECH-11 (DAMA-5G) project. It is also framed in the PENTA Excellence Project (P18-FR-4647) by the Consejería de Transformación Económica, Industria, Conocimiento y Universidades (Regional Ministry of Economic Transformation, Industry, Knowledge and Universities), and in part by the European Union–Next Generation EU within the Framework of the Project “Massive AI for the Open RadIo b5G/6G Network (MAORI)”. Partial funding for open access charge: Universidad de Málag

Autonomous monitoring framework for cellular networks

The arrival of a new generation of mobile networks as 5G (5th Generation), brings with it greater complexity in the management of the network due to new services and scenarios. In this context, SON (Self-Organizing Networks) becomes a key factor, given its ability for automate tasks and reduce human workload. Monitoring the network turns out be a crucial task, as it acts as the basis for the other SON functions. This paper proposes a methodology for automate monitoring of mobile networks based on their KPI (Key Performance Indicator).This project is partially funded by the Junta de Andalucía through the UMA-CEIATECH-11 (DAMA-5G) project. It is also framed in the PENTA Excellence Project (P18-FR-4647) by the Consejería de Transformación Económica, Industria, Conocimiento y Universidades (Regional Ministry of Economic Transformation, Industry, Knowledge and Universities). Moreover, it is also partially funded by Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Optimización de la QoE de un servicio de vídeo streaming en un entorno celular

The rising traffic demand in mobile communications networks, as well as the increase in the number of available services and the expectations of users have led operators to seek new techniques to optimize mobile networks. In this way, traditional optimization techniques, based on improving the quality of service offered to users, have given way to new techniques based on improving the quality of experience (QoE) perceived by users. In this work, a study to improve the QoE perceived by the users in a real time video streaming service from the optimization of the transmission buffer size of the RLC (Radio Link Control) layer is presented. The optimization, which has been carried out in a simulated cellular environment, has been performed for two different system bandwidth values, thus proving the relationship between the quality perceived by the users, the optimal buffer size and the available bandwidth.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech