Emulating home automation installations through component-based web technology
Autores: J.A. Asensio, J. Criado, N. Padilla, L. Iribarne.
Keywords: Home Automation, Emulation, KNX, Multimedia Web Components, IoT, COScore infrastructure
Abstract: The Internet of Things mechanisms enable the management of home environments since they can be developed as IoT based information systems. From standard smart homes to automated buildings, including other kind of domotics and inmotics solutions, every system must be tested and validated before its installation. The current tools offered by IoT and home automation vendors lack in emulation features close to the real behavior of the devices. In many cases, delaying the verification actions until the hardware is acquired and installed may cause some drawbacks, for example, from the economic point of view. This paper presents a solution for emulating home automation environments which are based on the KNX standard and can be represented by architectures of devices. The emulation consist of developing virtual implementations of real devices which operate and communicate through web technology. The technology implementing these virtual devices allows us to develop components which can provide different type of data related to the installation (audio, video, text, animations, images, etc.). The architectures can be managed using web services and their behavior can be tested through web user interfaces showing the mentioned data. Furthermore, virtual and physical devices are connected to validate the interoperability between the real installation and the emulation.
A Recommender System for Component-based Applications using Machine Learning Techniques
Autores: A.J. Fernández-García, L. Iribarne, A. Corral, J. Criado, J.Z. Wang.
Keywords: Machine learning, Recommender Systems, Feature engineering, Feature Selection, Component-based interfaces, interaction information acquisition.
Abstract: Software designers are striving to create software that adapts to their users’ requirements. To this end, the development of component-based interfaces that users can compound and customize according to their needs is increasing. However, the success of these applications is highly dependent on the users’ ability to locate the components useful for them, because there are often too many to choose from. We propose an approach to address the problem of suggesting the most suitable components for each user at each moment, by creating a recommender system using intelligent data analysis methods. Once we have gathered the interaction data and built a dataset, we address the problem of transforming an original dataset from a real component-based application to an optimized dataset to apply machine learning algorithms through the application of feature engineering techniques and feature selection methods. Moreover, many aspects, such as contextual information, the use of the application across several devices with many forms of interaction, or the passage of time (components are added or removed over time), are taken into consideration. Once the dataset is optimized, several machine learning algorithms are applied to create recommendation systems. A series of experiments that create recommendation models are conducted applying several machine learning algorithms to the optimized dataset (before and after applying feature selection methods) to determine which recommender model obtains a higher accuracy. Thus, through the deployment of the recommendation system that has better results, the likelihood of success of a component-based application is increased by allowing users to find the most suitable components for them, enhancing their user experience and the application engagement.
A Cross-Device Architecture for Modelling Authentication Features in IoT Applications
Autores: D. Alulema, J. Criado, L. Iribarne.
Keywords: Internet of Things, T-health, digital TV, model engineering, security
Abstract: The Internet of Things has presented a rapid development, due to the over-crowding of hardware and software platforms, greater deployment of communications networks, development of data analysis tools, among others. This development has led to a boom in applications focused on areas as varied as Smart Cities, Smart Agro, Smart Buildings, Smart Home, and Smart Health, in which people and things are interconnected. This is one of the reasons by which a review of the main technologies involved in the emergence of the Internet of Things must be carried out to determine those characteristics allowing that interconnection, but without neglecting security. This issue allows the user to feel con dent to use these new services. In this work, we propose a cross-device architecture that integrates technologies and implementations in homes, and uses basic authentication as a security scheme. To validate the cross-device proposal, a case study scenario has been designed, including and integrating digital-TV (DTV), Smart Phones and wearables devices for monitoring users physical activity.
Simulating rainfall, water evaporation and groundwater flow in three-dimensional satellite images with cellular automata
Autores: M. Espínola, J.A. Piedra, R. Ayala, L. Iribarne, S. Leguizamon, J.Z. Wang.
Keywords: Cellular automata, Remote sensing, DEM satellite images, Water simulation
Abstract: Remote sensing has been used in numerous environmental simulations with the aim of solving and improving many different kinds of problems, e.g., meteorology applications, soil quality studies, water resource exploration, and environmental protection. Besides, cellular automata have been widely used in the field of remote sensing for simulating natural phenomena over two-dimensional satellite images. However, simulations on Digital Elevation Models (DEM), or three-dimensional (3D) satellite images, are scarce. This paper presents a study of modeling and simulation of the weather phenomena of rainfall, water evaporation and groundwater flow in 3D satellite images through a new algorithm, developed by the authors, named RACA (RAinfall with Cellular Automata). The purpose of RACA is to obtain, from the simulation, numerical and 3D results related to the total cumulative flow and maximum level of water that allow us to make decisions on important issues such as analyzing how climate change will affect the water level in a particular area, estimating the future water supply of a population, establishing future construction projects and urban planning away from locations with high probability of flooding, or preventing the destruction of property and human life from future natural disasters in urban areas with probability of flooding.
D2R-TED: Data—Domain ReductionModel for Threshold-Based Event Detection in Sensor Networks
Autores: Fernando Leon-Garcia, Jose Manuel Palomares and Joaquin Olivares
Keywords: WSN; event detection; data compression
Abstract: The reduction of sensor network traffic has become a scientific challenge. Different compression techniques are applied for this purpose, offering general solutions which try to minimize the loss of information. Here, a new proposal for traffic reduction by redefining the domains of the sensor data is presented. A configurable data reduction model is proposed focused on periodic duty–cycled sensor networks with events triggered by threshold. The loss of information produced by the model is analyzed in this paper in the context of event detection, an unusual approach leading to a set of specific metrics that enable the evaluation of the model in terms of traffic savings, precision, and recall. Different model configurations are tested with two experimental cases, whose input data are extracted from an extensive set of real data. In particular, two new versions of Send–on–Delta (SoD) and Predictive Sampling (PS) have been designed and implemented in the proposed data–domain reduction for threshold–based event detection (D2R-TED) model. The obtained results illustrate the potential usefulness of analyzing different model configurations to obtain a cost–benefit curve, in terms of traffic savings and quality of the response. Experiments show an average reduction of 76% of network packages with an error of less than 1%. In addition, experiments show that the methods designed under the proposed D2R–TED model outperform the original event–triggered SoD and PS methods by 10% and 16% of the traffic savings, respectively. This model is useful to avoid network bottlenecks by applying the optimal configuration in each situation
Efficient pavement crack detection and classification
Autores: A. Cubero-Fernandez, Fco. J. Rodriguez-Lozano, Rafael Villatoro,
Joaquin Olivares and Jose M. Palomares
Keywords: Road safety, Road maintenance, Crack detection,
Pavement crack, Automatic detection, Heuristic classifier
Abstract: Each year, millions of dollars are invested on road maintenance and reparation all over the world. In order to minimize costs, one of the main aspects is the early detection of those flaws. Different types of cracks require different types of repairs; therefore, not only a crack detection is required but a crack type classification. Also, the earlier the crack is detected, the cheaper the reparation is. Once the images are captured, several processes are applied in order to extract the main characteristics for emphasizing the cracks (logarithmic transformation, bilateral filter, Canny algorithm, and a morphological filter). After image preprocessing, a decision tree heuristic algorithm is applied to finally classify the image. This work obtained an average of 88% of success detecting cracks and an 80% of success detecting the type of the crack. It could be implemented in a vehicle traveling as fast as 130 kmh or 81 mph.
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