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In the framework of massive sensing and smart sustainable cities, this work presents an urban distributed acoustic sensing testbed in the vicinity of the School of Technology and Telecommunication Engineering of the University of Granada, Spain. After positioning the sensing technology and the state of the art of similar existing approaches, the results of the monitoring experiment are described. Details of the sensing scenario, basic types of events automatically distinguishable, initial noise removal actions and frequency and signal complexity analysis are provided. The experiment, used as a proof-of-concept, shows the enormous potential of the sensing technology to generate data-driven urban mobility models. In order to support this fact, examples of preliminary density of traffic analysis and average speed calculation for buses, cars and pedestrians in the testbed's neighborhood are exposed, together with the accidental presence of a local earthquake. Challenges, benefits and future research directions of this sensing technology are pointed out. [ABSTRACT FROM AUTHOR]

Using RNN for automatic detection and classification of volcano seismic signals at Deception Island VolcanoManuel Titos (1), Angel Bueno (1), Luz García (1), Isaac Álvarez (1), Luciano Zuccarello (2), Jesus Ibanez (2), M. Carmen Benítez (1). (1) Departamento de Teoría de la Señal Telemática y Comunicaciones, Universidad de Granada, Spain(2) Departamento de Física Teórica y del Cosmos, Universidad de Granada, SpainSeismic monitoring of active volcanoes is a remote sensing technique used to determine seismic sources deep beneath volcanoes. As a result, the wide range of volcanic-seismic signals reflect the complex underlying physics of volcanic processes. Machine Learning and signal processing algorithms have emerged as a reliable approach to identify active volcanic sources. In this research, we propose a new framework for the detection and classification of volcano-seismic signals based on deep learning. Concretely, this work introduces recurrent neural networks (RNN), long short term memory (LSTM) and gated recurrent unit (GRU), to detect and classify continuous streams of volcanoseismic data. A representative dataset from Deception Island Volcano (Antarctica) containing volcano-tectonic earthquakes, long period events, volcanic tremors and hybrid events is used to train these models. Generalization capabilities are tested with data from the recent seismic survey performed at Deception Island Volcano in 2017 by the XXX Spanish Antarctic scientific expedition. Experimental results show that RNN, LSTM and GRU can exploit temporal and frequency information from continuous seismic data, attaining recognition accuracy above 90% of events correctly detected and classified. Further, despite the variations in geophysical properties of the seismic events within the volcano across eruptive periods, results provide good generalization accuracy. This result expands the possibilities of RNNs for real-time monitoring of volcanic activity, despite seismic sources changes over time.This work is supported by the following research projects: TEC2015-68752 (KNOWAVES, MINECO/FEDER); European Union's Horizon 2020 Research and Innovation Programme Under the Marie Sklodowska-Curie Grant Agreement no 798480 [ABSTRACT FROM AUTHOR]

Improving Volcanic Data Storage and Management by means of a SQL DatabaseAntonio Mora (1), Gustavo Romero (2), Carmen Benítez (1), Manuel Titos (1), Sonia Mota (1), Isaac Álvarez (1)(1) Departamento de Teoría de la Señal Telemática y Comunicaciones, Universidad de Granada, Spain(2) Departamento de Arquitectura y Tecnología de Computadores, Universidad de Granada, SpainIn geophysics, and more specifically in volcanology, seismic data are collected continuously through stations equipped with sensors that sample at low frequency. As a result of this data gathering, many millions of samples are obtained in a campaign (after some months). This could be considered as a Big Data issue due to the huge amount of different information obtained and different data storage formats. Nowadays this problem has not been addressed efficiently to date.This work presents a proposal for an optimal storage, maintenance and access to geophysical data. Thus, in order to deal with this huge amount of information, a specialized SQL database scheme has been designed. It has been later optimized in a number of ways, such as: selecting the best data management engine, using the optimal type of search index, or tuning the different configuration parameters.The database consist of three different 'logical' data blocks: catalogued data, collected data, and processed data. The first type refers to data about volcanic events previously identified by the corresponding geophysical entities and publicly available. The second type is formed by data gathered in different campaigns in volcanoes, considering the sensors deployed in stations, and, from them, the different physical magnitudes they can measure. The third type corresponds to the knowledge extracted from the raw data in previous types obtained through the application of manual and automatic (Data Mining/Machine Learning) techniques for the identification of volcanic events or the prediction of eruptions, for instance. The latter could be seen as a 'service' that the system would offer to end-users.As a proof of concept, the database has been feed with a part of the data collected in a campaign at Etna volcano (six sensors in different stations during one year). Several experiments and tests have been conducted by geophysicists and computer science researchers to validate its utility.This work has been supported by research project TEC2015-68752 (KNOWAVES, MINECO/FEDER). [ABSTRACT FROM AUTHOR]