Seaport Data Space for Improving Logistic Maritime Operations

[EN] The maritime industry expects several improvements to efficiently manage the operation processes by introducing Industry 4.0 enabling technologies. Seaports are the most critical point in the maritime logistics chain because of its multimodal and complex nature. Consequently, coordinated communication among any seaport stakeholders is vital to improving their operations. Currently, Electronic Data Interchange (EDI) and Port Community Systems (PCS), as primary enablers of digital seaports, have demonstrated their limitations to interchange information on time, accurately, efficiently, and securely, causing high operation costs, low resource management, and low performance. For these reasons, this contribution presents the Seaport Data Space (SDS) based on the Industrial Data Space (IDS) reference architecture model to enable a secure data sharing space and promote an intelligent transport multimodal terminal. Each seaport stakeholders implements the IDS connector to take part in the SDS and share their data. On top of SDS, a Big Data architecture is integrated to manage the massive data shared in the SDS and extract useful information to improve the decision-making. The architecture has been evaluated by enabling a port authority and a container terminal to share its data with a shipping company. As a result, several Key Performance Indicators (KPIs) have been developed by using the Big Data architecture functionalities. The KPIs have been shown in a dashboard to allow easy interpretability of results for planning vessel operations. The SDS environment may improve the communication between stakeholders by reducing the transaction costs, enhancing the quality of information, and exhibiting effectivenessThis work was supported in part by the European Union's Horizon 2020 Research and Innovation Programme through the PIXEL Port Project under Grant 769355, and in part by the Secretaria Nacional de Educacion Superior, Ciencia, Tecnologia e Innovacion (SENESCYT), EcuadorSarabia-Jácome, D.; Palau Salvador, CE.; Esteve Domingo, M.; Boronat, F. (2019). Seaport Data Space for Improving Logistic Maritime Operations. IEEE Access. 8:4372-4382. https://doi.org/10.1109/ACCESS.2019.2963283S43724382

AAL open source system for the monitoring and intelligent control of nursing homes

[EN] SAFE-ECH is an innovative intelligent AAL open source system for monitoring nursing homes, that creates an Ambient Intelligent environment in a residence by collecting and storing sensor monitoring data, performing intelligent data analysis and specific actions to enhance the safety, comfort and efficient care of aged people. Our system implements open standards of the Open Geospatial Consortium complying with Observations & Measurements Schema (O&M), SensorML and Sensor Web Enablement (SWE) specifications. Our system adapts to the specific needs of each nursing home, integrating the required sensors, actuators, rules and services. It is scalable and allows the management of several residences simultaneously.This research was partially funded by the European Union's Horizon 2020 research and innovation programme as part of the INTERIoT project under Grant Agreement 687283, and by SAFE-ECH funded by the Spanish Ministerio de Industria, Economía y Competitividad (MINECO) under Grant Agreement RTC-2015-4502-1González-Usach, R.; Collado, V.; Esteve Domingo, M.; Palau Salvador, CE. (2017). AAL open source system for the monitoring and intelligent control of nursing homes. IEEE Systems, Man, and Cybernetics Society. 1-6. https://doi.org/10.1109/ICNSC.2017.8000072S1

Federation of AAL & AHA systems through semantically interoperable framework

[EN] Ambient Assisted Living (AAL) and Active and Healthy Ageing (AHA) immensely benefit from IoT application. The federation of IoT platforms can multiply the benefits obtained by the operation of those systems in an isolated way, as it enables important synergies (e.g., intelligent information sharing, system cooperation, service enhancement). This federation requires the enablement of interoperability between the IoT systems, which represents a major challenge, as systems typically follow very different standards, data formats, semantic models and manners of representing the information. We have provided a technical solution in the frame of ACTIVAGE, a project that aims to federate multiple heterogeneous IoT platforms and systems associated to clusters of AHA Smart Homes in 12 regions across Europe, with the goal to improve the AHA service provided and create the first European AHA ecosystem. Our technical solution allows the enablement of full semantic interoperability across heterogeneous platforms and it has been validated in a test scenario. It enables significant AHA service enhancement within the ACTIVAGE ecosystem, as native applications from one platform could be used indistinctly by all federated platforms. Our solution allows good scalability federating new platforms, with linear and relatively low effort.This research work has been partially funded by LSP H2020 ACTIVAGE project under Grant Agreement Nº 732679.González-Usach, R.; Julián, M.; Esteve Domingo, M.; Palau Salvador, CE. (2021). Federation of AAL & AHA systems through semantically interoperable framework. 1-6. https://doi.org/10.1109/ICCWorkshops50388.2021.94735031

A multimodal Fingerprint-based Indoor Positioning System for airports

[EN] Indoor Localization techniques are becoming popular in order to provide a seamless indoor positioning system enhancing the traditional GPS service that is only suitable for outdoor environments. Though there are proprietary and costly approaches targeting high accuracy positioning, Wi-Fi and BLE networks are widely deployed in many public and private buildings (e.g. shopping malls, airports, universities, etc.). These networks are accessible through mobile phones resulting in an effective commercial off-the-self basic infrastructure for an indoor service. The obtained positioning accuracy is still being improved and there is on-going research on algorithms adapted for Wi-Fi and BLE and also for the particularities of indoor environments. This paper focuses not only on indoor positioning techniques, but also on a multimodal approach. Traditional proposals employ only one network technology whereas this paper integrates two different technologies in order to provide improved accuracy. It also sets the basis for combining (merging) additional technologies, if available. The initial results show that the positioning service performs better with a multimodal approach compared to individual (monomodal) approaches and even compared with Google¿s geolocation service in public spaces such as airports.This work was supported in part by the European Commission through the Door to Door Information for Airports and Airlines Project under Grant GA 635885 and in part by the European Commission through the Interoperability of Heterogeneous IoT Platforms Project under Grant 687283.Molina Moreno, B.; Olivares-Gorriti, E.; Palau Salvador, CE.; Esteve Domingo, M. (2018). A multimodal Fingerprint-based Indoor Positioning System for airports. IEEE Access. 6:10092-10106. https://doi.org/10.1109/ACCESS.2018.2798918S1009210106

Hybrid Delay-Based Congestion Control for Multipath TCP

[EN] Current algorithms for MPTCP (as LIA, OLIA, BALIA, or wVegas) present loss-based congestion control on the exception of wVegas. Delay-based congestion control allows a preventive action against congestion, capable to avoid loss up to some extent, unlike loss-based congestion control. Additionally delay-based congestion control induces lower delay and presents higher fairness, but poor performance interoperating with loss-based flows, as get a poor share of the available bandwidth. We propose DAIMD, a hybrid congestion control for Multipath TCP, based on the delay-based AIMD scheme, which benefits from better, preventive detection of congestion, a more responsive use of queues and consequently low induced delay, as well as the capability to coexist in fair conditions with loss-based flows in shared links. Our system presents its own analysis criteria for detecting incipient congestion that differs from other delay-based schemes on which it is based, such as CDG, delay-based AIMD and Vegas.This research has received funding from the European Union's Horizon 2020 research and innovation programme as part of the 'Interoperability of Heterogeneous IoT Platforms' (INTER-IoT) project under Grant Agreement nº730; 687283.González-Usach, R.; Pradilla-Cerón, JV.; Esteve Domingo, M.; Palau Salvador, CE. (2016). Hybrid Delay-Based Congestion Control for Multipath TCP. 1-6. https://doi.org/10.1109/MELCON.2016.7495389S1

A Novel Method of Spatiotemporal Dynamic Geo-Visualization of Criminal Data, Applied to Command and Control Centers for Public Safety

[EN] This article shows a novel geo-visualization method of dynamic spatiotemporal data that allows mobility and concentration of criminal activity to be study. The method was developed using, only and significantly, real data of Santiago de Cali (Colombia), collected by the Colombian National Police (PONAL). This method constitutes a tool that allows criminal influx to be analyzed by concentration, zone, time slot and date. In addition to the field experience of police commanders, it allows patterns of criminal activity to be detected, thereby enabling a better distribution and management of police resources allocated to crime deterrence, prevention and control. Additionally, it may be applied to the concepts of safe city and smart city of the PONAL within the architecture of Command and Control System (C2S) of Command and Control Centers for Public Safety. Furthermore, it contributes to a better situational awareness and improves the future projection, agility, efficiency and decision-making processes of police officers, which are all essential for fulfillment of police missions against crime. Finally, this was developed using an open source software, it can be adapted to any other city, be used with real-time data and be implemented, if necessary, with the geographic software of any other C2S.This work was co-funded by the European Commission as part of H2020 call SEC-12-FCT-2016-thrtopic3 under the project VICTORIA (No. 740754). This publication reflects the views only of the authors, and the Commission cannot be held responsible for any use which may be made of the information contained therein. The authors would like to thank Colombian National Police and its Office of Telematics for their support on development of this project.Salcedo-González, ML.; Suarez-Paez, JE.; Esteve Domingo, M.; Gomez, J.; Palau Salvador, CE. (2020). A Novel Method of Spatiotemporal Dynamic Geo-Visualization of Criminal Data, Applied to Command and Control Centers for Public Safety. ISPRS International Journal of Geo-Information. 9(3):1-17. https://doi.org/10.3390/ijgi9030160S11793Lacinák, M., & Ristvej, J. (2017). Smart City, Safety and Security. Procedia Engineering, 192, 522-527. doi:10.1016/j.proeng.2017.06.090Neumann, M., & Elsenbroich, C. (2016). Introduction: the societal dimensions of organized crime. Trends in Organized Crime, 20(1-2), 1-15. doi:10.1007/s12117-016-9294-zPhillips, P., & Lee, I. (2012). Mining co-distribution patterns for large crime datasets. Expert Systems with Applications, 39(14), 11556-11563. doi:10.1016/j.eswa.2012.03.071Linning, S. J. (2015). Crime seasonality and the micro-spatial patterns of property crime in Vancouver, BC and Ottawa, ON. Journal of Criminal Justice, 43(6), 544-555. doi:10.1016/j.jcrimjus.2015.05.007Spicer, V., & Song, J. (2017). The impact of transit growth on the perception of crime. Journal of Environmental Psychology, 54, 151-159. doi:10.1016/j.jenvp.2017.09.002Beland, L.-P., & Brent, D. A. (2018). Traffic and crime. Journal of Public Economics, 160, 96-116. doi:10.1016/j.jpubeco.2018.03.002Newspaper of National Circulation in Colombia, E.T. Robos en Trancones en El Tintal—Bogotá—.ELTIEMPO.COM https://www.eltiempo.com/bogota/robos-en-trancones-en-el-tintal-168226Nueva Modalidad de Atraco a Conductores en Los Trancones de Bogotá|ELESPECTADOR.COM http://www.elespectador.com/noticias/bogota/nueva-modalidad-de-atraco-conductores-en-los-trancones-de-bogota-articulo-697716Carrillo, P. E., Lopez-Luzuriaga, A., & Malik, A. S. (2018). Pollution or crime: The effect of driving restrictions on criminal activity. Journal of Public Economics, 164, 50-69. doi:10.1016/j.jpubeco.2018.05.007Twinam, T. (2017). Danger zone: Land use and the geography of neighborhood crime. Journal of Urban Economics, 100, 104-119. doi:10.1016/j.jue.2017.05.006Sadler, R. C., Pizarro, J., Turchan, B., Gasteyer, S. P., & McGarrell, E. F. (2017). Exploring the spatial-temporal relationships between a community greening program and neighborhood rates of crime. Applied Geography, 83, 13-26. doi:10.1016/j.apgeog.2017.03.017Roth, R. E., Ross, K. S., Finch, B. G., Luo, W., & MacEachren, A. M. (2013). Spatiotemporal crime analysis in U.S. law enforcement agencies: Current practices and unmet needs. Government Information Quarterly, 30(3), 226-240. doi:10.1016/j.giq.2013.02.001Sustainable Development Goals|UNDP https://www.undp.org/content/undp/en/home/sustainable-development-goals.htmlGiménez-Santana, A., Caplan, J. M., & Drawve, G. (2018). Risk Terrain Modeling and Socio-Economic Stratification: Identifying Risky Places for Violent Crime Victimization in Bogotá, Colombia. European Journal on Criminal Policy and Research, 24(4), 417-431. doi:10.1007/s10610-018-9374-5Kim, S., Jeong, S., Woo, I., Jang, Y., Maciejewski, R., & Ebert, D. S. (2018). Data Flow Analysis and Visualization for Spatiotemporal Statistical Data without Trajectory Information. IEEE Transactions on Visualization and Computer Graphics, 24(3), 1287-1300. doi:10.1109/tvcg.2017.2666146Kounadi, O., & Leitner, M. (2014). Spatial Information Divergence: Using Global and Local Indices to Compare Geographical Masks Applied to Crime Data. Transactions in GIS, 19(5), 737-757. doi:10.1111/tgis.12125Khalid, S., Shoaib, F., Qian, T., Rui, Y., Bari, A. I., Sajjad, M., … Wang, J. (2017). Network Constrained Spatio-Temporal Hotspot Mapping of Crimes in Faisalabad. Applied Spatial Analysis and Policy, 11(3), 599-622. doi:10.1007/s12061-017-9230-xLopez-Cuevas, A., Medina-Perez, M. A., Monroy, R., Ramirez-Marquez, J. E., & Trejo, L. A. (2018). FiToViz: A Visualisation Approach for Real-Time Risk Situation Awareness. IEEE Transactions on Affective Computing, 9(3), 372-382. doi:10.1109/taffc.2017.2741478Xue, Y., & Brown, D. E. (2006). Spatial analysis with preference specification of latent decision makers for criminal event prediction. Decision Support Systems, 41(3), 560-573. doi:10.1016/j.dss.2004.06.007Nakaya, T., & Yano, K. (2010). Visualising Crime Clusters in a Space-time Cube: An Exploratory Data-analysis Approach Using Space-time Kernel Density Estimation and Scan Statistics. Transactions in GIS, 14(3), 223-239. doi:10.1111/j.1467-9671.2010.01194.xAnuar, N. B., & Yap, B. W. (2018). Data Visualization of Violent Crime Hotspots in Malaysia. Soft Computing in Data Science, 350-363. doi:10.1007/978-981-13-3441-2_27Malik, A., Maciejewski, R., Towers, S., McCullough, S., & Ebert, D. S. (2014). Proactive Spatiotemporal Resource Allocation and Predictive Visual Analytics for Community Policing and Law Enforcement. IEEE Transactions on Visualization and Computer Graphics, 20(12), 1863-1872. doi:10.1109/tvcg.2014.2346926Arietta, S. M., Efros, A. A., Ramamoorthi, R., & Agrawala, M. (2014). City Forensics: Using Visual Elements to Predict Non-Visual City Attributes. IEEE Transactions on Visualization and Computer Graphics, 20(12), 2624-2633. doi:10.1109/tvcg.2014.2346446Hu, Y., Wang, F., Guin, C., & Zhu, H. (2018). A spatio-temporal kernel density estimation framework for predictive crime hotspot mapping and evaluation. Applied Geography, 99, 89-97. doi:10.1016/j.apgeog.2018.08.001Yang, D., Heaney, T., Tonon, A., Wang, L., & Cudré-Mauroux, P. (2017). CrimeTelescope: crime hotspot prediction based on urban and social media data fusion. World Wide Web, 21(5), 1323-1347. doi:10.1007/s11280-017-0515-4ToppiReddy, H. K. R., Saini, B., & Mahajan, G. (2018). Crime Prediction & Monitoring Framework Based on Spatial Analysis. Procedia Computer Science, 132, 696-705. doi:10.1016/j.procs.2018.05.075Devia, N., & Weber, R. (2013). Generating crime data using agent-based simulation. Computers, Environment and Urban Systems, 42, 26-41. doi:10.1016/j.compenvurbsys.2013.09.001Kuo, P.-F., Lord, D., & Walden, T. D. (2013). Using geographical information systems to organize police patrol routes effectively by grouping hotspots of crash and crime data. Journal of Transport Geography, 30, 138-148. doi:10.1016/j.jtrangeo.2013.04.006Camacho-Collados, M., & Liberatore, F. (2015). A Decision Support System for predictive police patrolling. Decision Support Systems, 75, 25-37. doi:10.1016/j.dss.2015.04.012Kagawa, T., Saiki, S., & Nakamura, M. (2019). Analyzing street crimes in Kobe city using PRISM. International Journal of Web Information Systems, 15(2), 183-200. doi:10.1108/ijwis-04-2018-0032Jentner, W., Sacha, D., Stoffel, F., Ellis, G., Zhang, L., & Keim, D. A. (2018). Making machine intelligence less scary for criminal analysts: reflections on designing a visual comparative case analysis tool. The Visual Computer, 34(9), 1225-1241. doi:10.1007/s00371-018-1483-0Suarez-Paez, J., Salcedo-Gonzalez, M., Esteve, M., Gómez, J. A., Palau, C., & Pérez-Llopis, I. (2018). Reduced computational cost prototype for street theft detection based on depth decrement in Convolutional Neural Network. Application to Command and Control Information Systems (C2IS) in the National Police of Colombia. International Journal of Computational Intelligence Systems, 12(1), 123. doi:10.2991/ijcis.2018.25905186Suarez-Paez, J., Salcedo-Gonzalez, M., Climente, A., Esteve, M., Gómez, J. A., Palau, C. E., & Pérez-Llopis, I. (2019). A Novel Low Processing Time System for Criminal Activities Detection Applied to Command and Control Citizen Security Centers. Information, 10(12), 365. doi:10.3390/info10120365Esteve, M., Perez-Llopis, I., & Palau, C. E. (2013). Friendly Force Tracking COTS solution. IEEE Aerospace and Electronic Systems Magazine, 28(1), 14-21. doi:10.1109/maes.2013.6470440Esteve, M., Perez-Llopis, I., Hernandez-Blanco, L. E., Palau, C. E., & Carvajal, F. (2007). SIMACOP: Small Units Management C4ISR System. Multimedia and Expo, 2007 IEEE International Conference on. doi:10.1109/icme.2007.4284862OpenStreetMap http://www.openstreetmap.or

A Novel Low Processing Time System for Criminal Activities Detection Applied to Command and Control Citizen Security Centers

[EN] This paper shows a Novel Low Processing Time System focused on criminal activities detection based on real-time video analysis applied to Command and Control Citizen Security Centers. This system was applied to the detection and classification of criminal events in a real-time video surveillance subsystem in the Command and Control Citizen Security Center of the Colombian National Police. It was developed using a novel application of Deep Learning, specifically a Faster Region-Based Convolutional Network (R-CNN) for the detection of criminal activities treated as "objects" to be detected in real-time video. In order to maximize the system efficiency and reduce the processing time of each video frame, the pretrained CNN (Convolutional Neural Network) model AlexNet was used and the fine training was carried out with a dataset built for this project, formed by objects commonly used in criminal activities such as short firearms and bladed weapons. In addition, the system was trained for street theft detection. The system can generate alarms when detecting street theft, short firearms and bladed weapons, improving situational awareness and facilitating strategic decision making in the Command and Control Citizen Security Center of the Colombian National Police.This work was co-funded by the European Commission as part of H2020 call SEC-12-FCT-2016-Subtopic3 under the project VICTORIA (No. 740754). This publication reflects the views only of the authors and the Commission cannot be held responsible for any use which may be made of the information contained therein.Suarez-Paez, J.; Salcedo-Gonzalez, M.; Climente, A.; Esteve Domingo, M.; Gomez, J.; Palau Salvador, CE.; Pérez Llopis, I. (2019). A Novel Low Processing Time System for Criminal Activities Detection Applied to Command and Control Citizen Security Centers. Information. 10(12):1-19. https://doi.org/10.3390/info10120365S1191012Wang, L., Rodriguez, R. M., & Wang, Y.-M. (2018). A dynamic multi-attribute group emergency decision making method considering expertsr hesitation. International Journal of Computational Intelligence Systems, 11(1), 163. doi:10.2991/ijcis.11.1.13Esteve, M., Perez-Llopis, I., & Palau, C. E. (2013). Friendly Force Tracking COTS solution. IEEE Aerospace and Electronic Systems Magazine, 28(1), 14-21. doi:10.1109/maes.2013.6470440Senst, T., Eiselein, V., Kuhn, A., & Sikora, T. (2017). Crowd Violence Detection Using Global Motion-Compensated Lagrangian Features and Scale-Sensitive Video-Level Representation. IEEE Transactions on Information Forensics and Security, 12(12), 2945-2956. doi:10.1109/tifs.2017.2725820Shi, Y., Tian, Y., Wang, Y., & Huang, T. (2017). Sequential Deep Trajectory Descriptor for Action Recognition With Three-Stream CNN. IEEE Transactions on Multimedia, 19(7), 1510-1520. doi:10.1109/tmm.2017.2666540Arunnehru, J., Chamundeeswari, G., & Bharathi, S. P. (2018). Human Action Recognition using 3D Convolutional Neural Networks with 3D Motion Cuboids in Surveillance Videos. Procedia Computer Science, 133, 471-477. doi:10.1016/j.procs.2018.07.059Kamel, A., Sheng, B., Yang, P., Li, P., Shen, R., & Feng, D. D. (2019). Deep Convolutional Neural Networks for Human Action Recognition Using Depth Maps and Postures. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 49(9), 1806-1819. doi:10.1109/tsmc.2018.2850149Zhang, B., Wang, L., Wang, Z., Qiao, Y., & Wang, H. (2018). Real-Time Action Recognition With Deeply Transferred Motion Vector CNNs. IEEE Transactions on Image Processing, 27(5), 2326-2339. doi:10.1109/tip.2018.2791180Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2016). Region-Based Convolutional Networks for Accurate Object Detection and Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(1), 142-158. doi:10.1109/tpami.2015.2437384Suarez-Paez, J., Salcedo-Gonzalez, M., Esteve, M., Gómez, J. A., Palau, C., & Pérez-Llopis, I. (2018). Reduced computational cost prototype for street theft detection based on depth decrement in Convolutional Neural Network. Application to Command and Control Information Systems (C2IS) in the National Police of Colombia. International Journal of Computational Intelligence Systems, 12(1), 123. doi:10.2991/ijcis.2018.25905186Ren, S., He, K., Girshick, R., & Sun, J. (2017). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6), 1137-1149. doi:10.1109/tpami.2016.2577031Hao, S., Wang, P., & Hu, Y. (2019). Haze Image Recognition Based on Brightness Optimization Feedback and Color Correction. Information, 10(2), 81. doi:10.3390/info10020081Peng, M., Wang, C., Chen, T., & Liu, G. (2016). NIRFaceNet: A Convolutional Neural Network for Near-Infrared Face Identification. Information, 7(4), 61. doi:10.3390/info7040061NVIDIA CUDA® Deep Neural Network library (cuDNN)https://developer.nvidia.com/cuda-downloadsWu, X., Lu, X., & Leung, H. (2018). A Video Based Fire Smoke Detection Using Robust AdaBoost. Sensors, 18(11), 3780. doi:10.3390/s18113780Park, J. H., Lee, S., Yun, S., Kim, H., & Kim, W.-T. (2019). Dependable Fire Detection System with Multifunctional Artificial Intelligence Framework. Sensors, 19(9), 2025. doi:10.3390/s19092025García-Retuerta, D., Bartolomé, Á., Chamoso, P., & Corchado, J. M. (2019). Counter-Terrorism Video Analysis Using Hash-Based Algorithms. Algorithms, 12(5), 110. doi:10.3390/a12050110Zhao, B., Zhao, B., Tang, L., Han, Y., & Wang, W. (2018). Deep Spatial-Temporal Joint Feature Representation for Video Object Detection. Sensors, 18(3), 774. doi:10.3390/s18030774He, Z., & He, H. (2018). Unsupervised Multi-Object Detection for Video Surveillance Using Memory-Based Recurrent Attention Networks. Symmetry, 10(9), 375. doi:10.3390/sym10090375Muhammad, K., Hamza, R., Ahmad, J., Lloret, J., Wang, H., & Baik, S. W. (2018). Secure Surveillance Framework for IoT Systems Using Probabilistic Image Encryption. IEEE Transactions on Industrial Informatics, 14(8), 3679-3689. doi:10.1109/tii.2018.2791944Barthélemy, J., Verstaevel, N., Forehead, H., & Perez, P. (2019). Edge-Computing Video Analytics for Real-Time Traffic Monitoring in a Smart City. Sensors, 19(9), 2048. doi:10.3390/s19092048Aqib, M., Mehmood, R., Alzahrani, A., Katib, I., Albeshri, A., & Altowaijri, S. M. (2019). Smarter Traffic Prediction Using Big Data, In-Memory Computing, Deep Learning and GPUs. Sensors, 19(9), 2206. doi:10.3390/s19092206Xu, S., Zou, S., Han, Y., & Qu, Y. (2018). Study on the Availability of 4T-APS as a Video Monitor and Radiation Detector in Nuclear Accidents. Sustainability, 10(7), 2172. doi:10.3390/su10072172Plageras, A. P., Psannis, K. E., Stergiou, C., Wang, H., & Gupta, B. B. (2018). Efficient IoT-based sensor BIG Data collection–processing and analysis in smart buildings. Future Generation Computer Systems, 82, 349-357. doi:10.1016/j.future.2017.09.082Jha, S., Dey, A., Kumar, R., & Kumar-Solanki, V. (2019). A Novel Approach on Visual Question Answering by Parameter Prediction using Faster Region Based Convolutional Neural Network. International Journal of Interactive Multimedia and Artificial Intelligence, 5(5), 30. doi:10.9781/ijimai.2018.08.004Cho, S., Baek, N., Kim, M., Koo, J., Kim, J., & Park, K. (2018). Face Detection in Nighttime Images Using Visible-Light Camera Sensors with Two-Step Faster Region-Based Convolutional Neural Network. Sensors, 18(9), 2995. doi:10.3390/s18092995Zhang, J., Xing, W., Xing, M., & Sun, G. (2018). Terahertz Image Detection with the Improved Faster Region-Based Convolutional Neural Network. Sensors, 18(7), 2327. doi:10.3390/s18072327Bakheet, S., & Al-Hamadi, A. (2016). A Discriminative Framework for Action Recognition Using f-HOL Features. Information, 7(4), 68. doi:10.3390/info7040068(2018). Robust Eye Blink Detection Based on Eye Landmarks and Savitzky–Golay Filtering. Information, 9(4), 93. doi:10.3390/info9040093Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90. doi:10.1145/3065386Jetson Embedded Development Kit|NVIDIAhttps://developer.nvidia.com/embedded-computingNVIDIA TensorRT|NVIDIA Developerhttps://developer.nvidia.com/tensorrtNVIDIA DeepStream SDK|NVIDIA Developerhttps://developer.nvidia.com/deepstream-sdkFraga-Lamas, P., Fernández-Caramés, T., Suárez-Albela, M., Castedo, L., & González-López, M. (2016). A Review on Internet of Things for Defense and Public Safety. Sensors, 16(10), 1644. doi:10.3390/s16101644Gomez, C., Shami, A., & Wang, X. (2018). Machine Learning Aided Scheme for Load Balancing in Dense IoT Networks. Sensors, 18(11), 3779. doi:10.3390/s18113779AMD Embedded RadeonTMhttps://www.amd.com/en/products/embedded-graphic

Aprendizaje Colaborativo en Profesionales de Nuevas Tecnologías

En los cursos de Nuevas Tecnologías de la Información y las Comunicaciones orientados a alumnos con estudios universitarios, se debe considerar en el aprendizaje métodos que permitan formar al alumno utilizando grupos de trabajo y donde las prácticas desempeñadas sean casos reales. Muchos de estos alumnos van a formar parte, o están formando parte, de grupos de trabajo en empresas, grupos de investigación, etc. El método que se propone, pretende desarrollar el currículum de los alumnos desde un entorno colaborativo práctico donde cada alumno es una pieza importante del trabajo a realizar y donde el resultado final depende de todos. Para ello se realizan prácticas colaborativas, con escaso contenido teórico, donde se combinan factores de descubrimiento, análisis y consulta entre compañeros. Posteriormente se realiza una clase magistral y participativa, por parte del profesor, del contenido teórico correspondiente a dichas prácticas.Jiménez Herranz, JM.; Lloret, J.; Esteve Domingo, M.; Díaz Santos, JR. (2004). Aprendizaje Colaborativo en Profesionales de Nuevas Tecnologías. The International Institute of Informatics and Systemics (IIIS). http://hdl.handle.net/10251/671

Sistema Distribuido de Detección de Sismos Usando una Red de Sensores Inalámbrica para Alerta Temprana

El detectar eventos disruptivos usando sensores COTS como los utilizados en smartphones representa un gran desafío pero también una oportunidad interesante. En este artículo se presenta una arquitectura de sistema de tiempo real crítico, jerárquica y distribuida, que hace uso de smartphones que actúan como sensores a través de una aplicación de bajo consumo de energía que convierte a sus acelerómetros en acelerógrafos. Los smartphones desplegados forman una red de sensores que detecta, analiza y notifica un pico sísmico. El sistema optimiza cálculos distribuidos y capacidades de comunicación en smartphones para proveer tiempo extra para alertas tempranas en escenarios de desastre de tipo sísmico, aunque puede ser empleada como solución a otros desastres naturales. Se propone una solución innovadora de bajo coste que realiza análisis tanto espaciales como temporales, no presentes en otros trabajos, lo cual lo hace más preciso y personalizable permitiendo adaptarse a las características geográficas de la zona, de red, y recursos tanto humanos como monetarios. La arquitectura ha sido validada mediante una extensa evaluación, consiguiendo como resultado notificaciones tempranas que adelantan en decenas de segundos el pico máximo del sismo en la zona del epicentro y aún más para zonas más alejadas; y la considerable reducción de falsas alarmas. Adicionalmente la arquitectura propuesta incluye una administración post-evento que mejora la capacidad operativa, logística y de telecomunicaciones desde un solo nivel central, y al mismo tiempo, mantiene al usuario informado de centros de refugios cercanos, mejores rutas, rutas seguras para una mejor decisión.Zambrano Vizuete, AM.; Pérez Llopis, I.; Palau Salvador, CE.; Esteve Domingo, M. (2015). Sistema Distribuido de Detección de Sismos Usando una Red de Sensores Inalámbrica para Alerta Temprana. Revista Iberoamericana de Automática e Informática Industrial RIAI. 12(3):260-269. doi:10.1016/j.riai.2015.06.00226026912

A Smart System for Sleep Monitoring by Integrating IoT With Big Data Analytics

[EN] Obtrusive sleep apnea (OSA) is one of the most important sleep disorders because it has a direct adverse impact on the quality of life. Intellectual deterioration, decreased psychomotor performance, behavior, and personality disorders are some of the consequences of OSA. Therefore, a real-time monitoring of this disorder is a critical need in healthcare solutions. There are several systems for OSA detection. Nevertheless, despite their promising results, these systems not guiding their treatment. For these reasons, this research presents an innovative system for both to detect and support of treatment of OSA of elderly people by monitoring multiple factors such as sleep environment, sleep status, physical activities, and physiological parameters as well as the use of open data available in smart cities. Our system architecture performs two types of processing. On the one hand, a pre-processing based on rules that enables the sending of real-time notifications to responsible for the care of elderly, in the event of an emergency situation. This pre-processing is essentially based on a fog computing approach implemented in a smart device operating at the edge of the network that additionally offers advanced interoperability services: technical, syntactic, and semantic. On the other hand, a batch data processing that enables a descriptive analysis that statistically details the behavior of the data and a predictive analysis for the development of services, such as predicting the least polluted place to perform outdoor activities. This processing uses big data tools on cloud computing. The performed experiments show a 93.3% of effectivity in the air quality index prediction to guide the OSA treatment. The system's performance has been evaluated in terms of latency. The achieved results clearly demonstrate that the pre-processing of data at the edge of the network improves the efficiency of the system.This work was supported in part by the European Union's Horizon 2020 Research and Innovation Programme through the Interoperability of Heterogeneous IoT Platforms Project (INTER-IoT) under Grant 687283, in part by the Escuela Politecnica Nacional, Ecuador, and in part by the Secretaria Nacional de Educacion Superior, Ciencia, Tecnologia e Innovacion (SENESCYT), Ecuador.Yacchirema-Vargas, DC.; Sarabia-Jácome, DF.; Palau Salvador, CE.; Esteve Domingo, M. (2018). A Smart System for Sleep Monitoring by Integrating IoT With Big Data Analytics. IEEE Access. 6:35988-36001. https://doi.org/10.1109/ACCESS.2018.2849822S3598836001