Resultados de búsqueda para Internet of Things

El Internet de las Cosas (IoT) ha crecido rápidamente durante los últimos años transformando varios sectores industriales. La minera es una de las industrias que busca aprovechar el IoT implementando sistemas para optimizar los procesos de extracción de minerales. Sin embargo, existen retos de diseño, despliegue y operación de estos sistemas debido a la complejidad de las arquitecturas multicapa implementadas y a los cambios inesperados del ambiente dinámico que pueden afectar el funcionamiento. Por lo tanto, proponemos un lenguaje de dominio específico (DSL) para definir la estructura de las minas subterráneas de carbón, el sistema IoT multicapa y sus reglas de adaptación. El DSL fue implementado usando la plataforma MPS junto con un generador de código de los manifiestos para el despliegue con Kubernetes.

Un problema actual en el Internet de las Cosas (IoT) es la heterogeneidad de los dispositivos que, aún realizando la misma tarea, funcionan y se comunican de forma distinta. Esto supone que dispositivos con arquitecturas diferentes y con una alta variabilidad dependiente de información de contexto, espacio-temporal y de interacción con el entorno, tengan que coexistir para dar respuesta al usuario ante una necesidad. Por lo tanto, esto requiere de nuevos servicios de descubrimiento modernos que permitan buscar, registrar e interactuar con dispositivos que respetan arquitecturas diferentes y con una alta variabilidad. En este trabajo, se presenta un servicio de descubrimiento proactivo, capaz de adaptar los dispositivos IoT a una arquitectura común y de localizar los dispositivos desplegados en la misma red.

El incremento de las capacidades de computación de distintos dispositivos (elementos de la red, dispositivos finales, etc.) finales ha dado lugar a paradigmas como Fog, Edge, Mist o Crowd computing que tienen como objetivo explotar dichas capacidades para almacenar y procesar información, proporcionándola al entorno mediante APIs y servicios. Esta distribución de la computación permite mejorar la calidad de servicio, sobre todo en entornos con requisitos estrictos. Sin embargo, el uso de APIs y servicios desplegados de forma distribuida conlleva un esfuerzo extra al desarrollador, por la necesidad de controlar y coordinar la invocación a las distintas APIs y los resultados que proporcionan. En este artículo presentamos un compositor de APIs distribuidas (DAC), un sistema el cual permite recopilar y agregar la información de las APIs desplegadas en distintos dispositivos. Con el objetivo de, reducir el esfuerzo de su implementación, se ha definido una extensión de la especificación OpenAPI para facilitar su desarrollo y despliegue.

The development of the Internet of Things (IoT) came with the manufacturing of a huge amount of smart things equipped with sensors for making them aware of their environment, and with network connection for allowing remote interaction with them. However, most smart things still lack enough autonomy and context-awareness, hindering them from being people-friendly and actually useful for their users’ everyday tasks. IoT devices should take advantage of their sensors and smartness to react automatically to the needs of their users and to provide seamless interactions with them. Within this field, the authors work on the design of Digital Avatars, a mobile computing framework for dynamically programming interactions among smart devices. The framework is based on the virtual profile of the user, which is inferred, stored, and shared by their smartphone. The profile provides a personalized context for running scripts for the interaction with IoT devices. This way, smartphones become a digital avatar of the user, capable of acting as a personal and seamless interface with their IoT environment. In this work, we present a formalization of Digital Avatars by means of a Linda-based approach with multiple shared tuple spaces. By means of a case study, we show how properties of the systems can be proved, and we briefly describe an implementation of both the Digital Avatars framework and the case study.

The rate of world population aging is increasing. This situation directly affects all countries socially and economically and, increasing their compromise and effort to improve the living conditions of this sector of society. In environments with large influxes of elderly people, such as nursing homes, the use of technology has shown promise in improving their quality of life. The use of smart devices allows people to automate everyday tasks and learn from them to predict future actions. Additionally, smartphones capture a wealth of information that allows to adapt to nearby actuators according to people’s preferences and even detect anomalies in their behaviour. Current works are proposing new frameworks to detect these behaviours and act accordingly. However, these works are not focused on managing multi-device environments where sensors and smartphones data are considered to automate environments with elderly people or to learn from them. Also, the most of these works require a permanent Internet connection, so the full benefit of smart devices is not completely achieved. In this work, we present an architecture that takes the data from sensors and smartphones in order to adapt the behaviour of the actuators of the environment. In addition, it uses this data to learn from the environment to predict actions or to extrapolate the actions that should be executed according to similar behaviours. The architecture is implemented through a use-case based on a nursing home located in a rural area. Thanks to this work, the quality of life of the elderly is improved in a simple, affordable and transparent way for them.

The Internet of Things (IoT) paradigm offers applications the potential of automating real-world processes. Applying IoT to intensive domains comes with strict quality of service (QoS) requirements, such as very short response times. To achieve these goals, the first option is to distribute the computational workload throughout the infrastructure (edge, fog, cloud). In addition, integration of the infrastructure with enablers such as software-defined networks (SDNs) can further improve the QoS experience, thanks to the global network view of the SDN controller and the execution of optimization algorithms. Therefore, the best placement for both the computation elements and the SDN controllers must be identified to achieve the best QoS. While it is possible to optimize the computing and networking dimensions separately, this results in a suboptimal solution. Thus, it is crucial to solve the problem in a single effort. In this work, the influence of both dimensions on the response time is analyzed in fog computing environments powered by SDNs. DADO, a framework to identify the optimal deployment for distributed applications is proposed and implemented through the application of mixed integer linear programming. An evaluation of an IIoT case study shows that our proposed framework achieves scalable deployments over topologies of different sizes and growing user bases. In fact, the achieved response times are up to 37.89% lower than those of alternative solutions and up to 15.42% shorter than those of state-of-the-art benchmarks.

The Internet of Things (IoT) is growing globally at a fast pace. However, the increase in IoT devices has brought with it the challenge of promptly detecting and combating the cybersecurity threats that target them. To deal with this problem, we propose an intelligent architecture that integrates Complex Event Processing (CEP) technology and the Machine Learning (ML) paradigm in order to detect different types of IoT security attacks in real time. In particular, such an architecture is capable of easily managing event patterns whose conditions depend on values obtained by ML algorithms. Additionally, a model-driven graphical tool for security attack pattern definition and automatic code generation is provided, hiding all the complexity derived from implementation details from domain experts. The proposed architecture has been applied in the case of a healthcare IoT network to validate its ability to detect attacks made by malicious devices. The results obtained demonstrate that this architecture satisfactorily fulfils its objectives.

Fostering sustainability is paramount for Smart Cities development. Lately, Smart Cities are benefiting from the rising of Big Data coming from IoT devices, leading to improvements on monitoring and prevention. However, monitoring and prevention processes require visualization techniques as a key component. Indeed, in order to prevent possible hazards (such as fires, leaks, etc.) and optimize their resources, Smart Cities require adequate visualizations that provide insights to decision makers. Nevertheless, visualization of Big Data has always been a challenging issue, especially when such data are originated in real-time. This problem becomes even bigger in Smart City environments since we have to deal with many different groups of users and multiple heterogeneous data sources. Without a proper visualization methodology, complex dashboards including data from different nature are difficult to understand. In order to tackle this issue, we propose a methodology based on visualization techniques for Big Data, aimed at improving the evidence-gathering process by assisting users in the decision making in the context of Smart Cities. Moreover, in order to assess the impact of our proposal, a case study based on service calls for a fire department is presented. In this sense, our findings will be applied to data coming from citizen calls. Thus, the results of this work will contribute to the optimization of resources, namely fire extinguishing battalions, helping to improve their effectiveness and, as a result, the sustainability of a Smart City, operating better with less resources. Finally, in order to evaluate the impact of our proposal, we have performed an experiment, with non-expert users in data visualization.

Mutation testing is a well-known fault-based technique that has been applied to different domains as new technologies have appeared. Evolutionary Mutation Testing (EMT) finds mutants that are useful to produce new test cases. It uses evolutionary algorithms to reduce the number of mutants that are generated, keeping as many difficult to kill and stubborn mutants (strong mutants) as possible in the reduced set. Given the popularity of real-time systems, the MuEPL mutation system was developed for the Esper Event Processing Language (EPL), a query language aimed at the Internet of Things (IoT). In past work, EMT was integrated into MuEPL, and it reduced the cost of finding strong mutants in some EPL queries but not in others. This study takes a step forward by proposing and evaluating two metaheuristics for EMT that combine EMT and random selection: one which bootstraps the hall of fame with a random subset (Bootstrapped EMT), and one which falls back to random selection after a certain point (Inverse EMT). While BEMT is shown to outperform IEMT in most cases, BEMT has not managed to outperform EMT. An additional experiment studies the impact of low-quality mutation operators in the relative performance of BEMT, IEMT and plain EMT. Results suggest that the MuEPL RRO operator was the reason for the poor performance of EMT in some scenarios.