Un marco para la definición y transformación de modelos en los sistemas multiagentes

El Desarrollo del Software Dirigido por Modelos (DSDM) es un paradigma de desarrollo en el que los modelos son el producto principal, y a partir de ellos se generan los sistemas de forma automática, total o parcialmente. Del tratamiento de los modelos, surge la necesidad de denirlos y transformarlos, que se aborda respectivamente con la denición de metamodelos y transformaciones. Uno de los principales beneciarios del DSDM es la Ingeniería del Software Orientada a Agentes (ISOA). En ésta, se construyen Sistemas Multi-agente (SMAs), que son sistemas distribuidos compuestos por agentes autónomos que interactúan dando lugar a comportamientos complejos. Si bien algunas caracter ísticas del DSDM se han incorporado plenamente en la ISOA como prácticas habituales, todavía dos factores dicultan su implantación completa. En primer lugar, la denición de metamodelos depende de la experiencia del diseñador y no existen guías que faciliten esta labor. Por otro lado, las herramientas y lenguajes existentes no permiten denir transformaciones de modelos con un esfuerzo razonable. Por ello, esta tesis propone una guía para denir metamodelos y un procedimiento para generar Transformaciones. La guía incluye un armazón para estructurar los metamodelos, recomendaciones para las decisiones principales, y una secuencia de pasos para denir los metamodelos. El armazón se estructura en tres capas que contienen respectivamente la información del lenguaje de modelado, los aspectos de presentación de los modelos y la información especíca de las herramientas. En la capa del lenguaje de modelado, se proponen ciertas representaciones de los elementos y se dan las pautas para asociar cada elemento con la representación más apropiada. Para esto se debe elegir entre una representación heterogénea, que minimiza el número de elementos de meta-modelado necesarios para representar los elementos del modelo, o una representación homogénea, con más elementos de meta-modelado pero más fácil de procesar automáticamente. También se debe elegir entre una representación redundante o no redundante de las referencias entre los elementos, dependiendo del nivel de navegabilidad que se desee, ya que dichas referencias son dirigidas. En los aspectos de presentación, se propone usar vistas que hagan referencia a diccionarios globales, facilitando el procesamiento de los modelos y evitando inconsistencias. En la tercera capa, se considera la información especíca de las herramientas, que no se había considerado en aproximaciones anteriores. Como marco de experimentación, con esta guía se ha denido el metamodelo de la herramienta INGENIAS Development Kit (IDK) con el lenguaje ECore, permitiendo incorporar las facilidades tecnológicas de la comunidad Eclipse en dicha herramienta. Además, se ha denido un metamodelo para la generación de un editor de procesos de ISOA, basado en el Software Process Engineering Metamodel (SPEM) del Object Management Group (OMG). En esta tesis, se considera la aproximación conocida como Generación de Transformaciones Basadas en Ejemplo (GTBE), donde se generan transformaciones a partir de parejas de modelos prototipo origen y destino. De esta forma, se evita que el diseñador tenga que conocer los lenguajes de transformación y tratar con detalles de bajo nivel de la especi�cación de los modelos, tales como las primitivas de meta-modelado involucradas en cada elemento del modelo. Las transformaciones generadas transforman los modelos que encajan en los modelos prototipo origen en los nuevos modelos que encajen en los modelos prototipo destino. Es habitual que exista un mecanismo de asociación de atributos para referenciar los modelos prototipo origen desde los modelos prototipo destino, indicando así la forma de transferir la información en las transformaciones generadas. En esta línea, esta tesis presenta un nuevo algoritmo y herramienta para la GTBE, que mejora a los trabajos existentes en varios aspectos. En primer lugar, se permite realizar el mecanismo de asociación de atributos desde varios elementos de cada modelo prototipo origen, mientras que las aproximaciones existentes sólo permiten esta asociación desde un elemento de cada modelo prototipo. Este avance permite combinar información de los atributos de diferentes elementos. Además, el algoritmo permite trabajar con grafos no conexos en los modelos prototipo origen y destino, característica no presente en los trabajos anteriores. Como experimentación, se han generado transformaciones con utilidad práctica en ciertos desarrollos de SMAs. En los ejemplos propuestos, se observa como ciertas transformaciones no podrían haber sido generadas por otras herramientas de GTBE. [ABSTRACT] The Model Driven Development (MDD) is a paradigm of development in which models are the main product and systems are totally or partially generated from these models. When processing models, it is necessary to de�ne and transform them, which is accomplished with the de�nition of metamodels and transformations respectively. One of the �elds that is most bene�tted from MDD is the Agent Oriented Software Engineering (AOSE). AOSE is concerned with the development of Multi-agent Systems (MASs), which are distributed systems made of autonomous agents that interact with each other producing emergent behaviors. Although some aspects of MDD are considered in AOSE, there are still two facts that prevent AOSE from fully incorporating MDD. First, the deinition of metamodels depends on the expertise of designers, and there are not guidelines that facilitate this task. Second, existent tools and languages do not allow practitioners to de�ne model transformations with a moderate effort. Thus, this thesis proposes a guideline for defining metamodels and a mechanism for generating transformations. The guideline includes a framework for structuring metamodels, recommendations for taking the main decisions, and a sequence of activities for defining metamodels. The framework is structured in three layers that respectively contain the information of a modeling language, the aspects for presenting its models, and the tool-specific information. In the modeling language layer, several representations of elements are proposed, and the guideline indicates the manner in which each element can be associated with the most appropriate representations. In this guideline, practitioners decide between a heterogenous representation, which minimizes the number of meta-modeling elements required to represent the modeling elements, and a homogenous representation, which uses more meta-modeling elements but is easier to be automatically processed. The guideline also includes the choice between a redundant and a non-redundant representation, regarding the degree of navigability that is desired. It is worth noticing that references between elements are directed, so the e�ective processing of certain operations over relationships requires redundant inverse references. Regarding the aspects for presenting models, the guideline proposes to use views that make references to global dictionaries, facilitating the processing of models and avoiding inconsistencies. The third layer considers the tool-speci�c information, which is not considered in the existent approaches. For experimentation, the metamodel of the INGENIAS Development Kit (IDK) has been de�ned following the guideline using the ECore language, allowing the incorporation of the technological facilities of the Eclipse community in the IDK. Furthermore, a metamodel is de�ned for the generation of an editor of processes in AOSE based on the Software Process Engineering Metamodel (SPEM) of the Object Management Group (OMG). The second issue considered in this thesis is the di�culties for a cost-efective de�nition of model transformations. For this problem, this research considers the approach known as Model Transformation By-Example (MTBE), which generates transformations from pairs of source and target model prototypes, avoiding that designers have to learn transformation languages and deal with low-level details of the metamodels of the involved modeling languages. The generated transformations transform models that match source prototype models into the new models that �t the target prototype models. These approaches usually include a mechanism for the mapping of attributes that refer to source prototype models from elements in the target prototype models, indicating the way of transferring information in the generated transformations. In this line of research, this thesis presents a new algorithm and tool for MTBE, which overcome some limitations of the existent approaches. First, the presented approach provides a mechanism for mapping attributes from several elements of each source prototype model, while existent approaches only provide this mapping from one element of each source prototype model. This improvement allows one to combine information from the attributes of diferent elements. Moreover, the presented algorithm can process non-connected graphs in the source and target model prototypes, which is not possible in other approaches. For experimentation, the tool has generated transformations with practical utility in certain MAS developments. In the proposed examples, one can observe that certain transformations cannot be generated by other MTBE tools

TABSAOND: A technique for developing agent-based simulation apps and online tools with nondeterministic decisions

Agent-based simulators (ABSs) have successfully allowed practitioners to estimate the outcomes of certain input circumstances in several domains. Although some techniques and processes provide hints about the construction of these systems, some aspects have not been discussed yet in the literature. In this context, the current approach presents a technique for developing ABSs. Its focus is to guide practitioners in designing and implementing the decision-making processes of agents in nondeterministic scenarios. As an additional technological innovation, the ABSs are deployed as both mobile apps and online tools. This work illustrates the current approach with two case studies in the fields of (a) health and welfare and (b) tourism. These case studies have also been developed with the most similar technique from the literature for comparing both techniques. The presented technique improved the simulated outcomes in terms of their similarity with the real ones. The obtained ABSs were more efficient and reliable for large amounts of agents (e.g. 10,000 – 400,000 agents). The development time was lower. Both the framework and the implementation of a case study are freely distributed as open-source to facilitate the reproducibility of the experiments and to assist practitioners in applying the current approach

Human-Centric AI for Trustworthy IoT Systems With Explainable Multilayer Perceptrons

[EN] Internet of Things (IoT) widely use analysis of data with artificial intelligence (AI) techniques in order to learn from user actions, support decisions, track relevant aspects of the user, and notify certain events when appropriate. However, most AI techniques are based on mathematical models that are difficult to understand by the general public, so most people use AI-based technology as a black box that they eventually start to trust based on their personal experience. This article proposes to go a step forward in the use of AI in IoT, and proposes a novel approach within the Human-centric AI field for generating explanations about the knowledge learned by a neural network (in particular a multilayer perceptron) from IoT environments. More concretely, this work proposes two techniques based on the analysis of artificial neuron weights, and another technique aimed at explaining each estimation based on the analysis of training cases. This approach has been illustrated in the context of a smart IoT kitchen that detects the user depression based on the food used for each meal, using a simulator for this purpose. The results revealed that most auto-generated explanations made sense in this context (i.e. 97.0%), and the execution times were low (i.e. 1.5 ms or lower) even considering the common configurations varying independently the number of neurons per hidden layer (up to 20), the number of hidden layers (up to 20) and the number of training cases (up to 4,000).This work was supported in part by the U.K. Engineering and Physical Sciences Research under Grant EP/N028155/1, in part by the Programa Iberoamericano de Ciencia y Tecnologia para el Desarrollo (CYTED) through the CITIES: Ciudades inteligentes totalmente integrales, eficientes y sotenibles under Grant 518RT0558, and in part by the Spanish council of Science, Innovation and Universities from the Spanish Government through the Diseno colaborativo para la promocion del bienestar en ciudades inteligentes inclusivas under Grant TIN2017-88327-R.García-Magariño, I.; Muttukrishnan, R.; Lloret, J. (2019). Human-Centric AI for Trustworthy IoT Systems With Explainable Multilayer Perceptrons. IEEE Access. 7:125562-125574. https://doi.org/10.1109/ACCESS.2019.2937521S125562125574

System to Recommend the Best Place to Live Based on Wellness State of the User Employing the Heart Rate Variability

[EN] The conditions of the environment where a person lives have a great impact on his wellness state. When buying a new house, it is important to select a place that aids in improving the wellness state of the buyer or, at least, keeps it at the same level. A deficient wellness state implies an increase of stress and the appearance of some effects associated with it. Heart rate variability (HRV) allows measuring the stress or wellness levels of a person by measuring the difference in time between heartbeats. A low HRV is related to high stress levels whereas a high HRV is associated with a high wellness state. In this paper, we present a system that measures the wellness and stress levels of home buyers by employing sensors that measure the HRV. Our system is able to process the data and recommend the best neighborhood to live in considering the wellness state of the buyer. Several tests were performed utilizing different locations. In order to determine the best neighborhood, we have developed an algorithm that assigns different values to the area in accordance with the HRV measures. Results show that the system is effective in providing the recommendation of the place that would allow the person to live with the highest wellness state.Lacuesta Gilabert, R.; García-García, L.; García-Magariño, I.; Lloret, J. (2017). System to Recommend the Best Place to Live Based on Wellness State of the User Employing the Heart Rate Variability. IEEE Access. 5:10594-10604. doi:10.1109/ACCESS.2017.2702107S1059410604

A technique for designing glossary activities with facial authentication

Nowadays, one of the key challenges for distance education is to be able to verify the students’ identity in order to check if they are actually who they claim to be when they are doing their online tasks and to avoid identity thief. This can be achieved through facial authentication software. In e-learning, thanks to this technology there is a way to confirm that the students are not committing fraud in their studies and besides to improve this kind of education by equaling its validity and prestige to traditional face-to-face education. The goal of this research is to avoid fake users that perform educational tasks on behalf of others in the Learning Management Systems (LMSs), and more specifically to develop a new technique to design activities with glossaries that properly allow control of the student learning process through facial authentication software. The presented technique is composed of several steps that guide instructors in the elaboration of this kind of activities. This work has used Moodle platform for the experimentation, and analyzes the experience of 67 students with the activities designed with the presented technique

A Repository of Method Fragments for Agent-Oriented Development of Learning-Based Edge Computing Systems

[EN] The upcoming avenue of IoT, with its massive generated data, makes it really hard to train centralized systems with machine learning in real time. This problem can be addressed with learning-based edge computing systems where the learning is performed in a distributed way on the nodes. In particular, this work focuses on developing multi-agent systems for implementing learning-based edge computing systems. The diversity of methodologies in agent-oriented software engineering reflects the complexity of developing multi-agent systems. The division of the development processes into method fragments facilitates the application of agent-oriented methodologies and their study. In this line of research, this work proposes a database for implementing a repository of method fragments considering the development of learning-based edge computing systems and the information recommended by the FIPA technical committee. This repository makes method fragments available from different methodologies, and computerizes certain metrics and queries over the existing method fragments. This work compares the performance of several combinations of dimensionality reduction methods and machine learning techniques (i.e., support vector regression, k-nearest neighbors, and multi-layer perceptron neural networks) in a simulator of a learning-based edge computing system for estimating profits and customers.The authors acknowledge PSU Smart Systems Engineering Lab, project "Utilisation of IoT and sensors in smart cities for improving quality of life of impaired people" (ref. 52-2020), CYTED (ref. 518RT0558), and the Spanish Council of Science, Innovation and Universities (TIN2017-88327-R).García-Magariño, I.; Nasralla, MM.; Lloret, J. (2021). A Repository of Method Fragments for Agent-Oriented Development of Learning-Based Edge Computing Systems. IEEE Network. 35(1):156-162. https://doi.org/10.1109/MNET.011.2000296S15616235

ABS-TrustSDN: An Agent-Based Simulator of Trust Strategies in Software-Defined Networks

Software-defined networks (SDNs) have become a mechanism to separate the control plane and the data plane in the communication in networks. SDNs involve several challenges around their security and their confidentiality. Ideally, SDNs should incorporate autonomous and adaptive systems for controlling the routing to be able to isolate network resources that may be malfunctioning or whose security has been compromised with malware. The current work introduces a novel agent-based framework that simulates SDN isolation protocols by means of trust and reputation models. This way, SDN programmers may estimate the repercussions of certain isolation protocols based on trust models before actually deploying the protocol into the network

PriorityNet App: A mobile application for establishing priorities in the context of 5G ultra-dense networks

The devices and implementations of 5G networks are continuously improving, and people will probably use them daily in the near future. 5G networks will support ultra-dense networks. In the literature, several works apply 5G networks in smart cities and smart houses. One of the most common features of these works is to use priorities in tasks, such as the management of electrical consumption at houses, waste collection in cities, or pathfinding in self-driving cars. The proper management of priorities facilitates that urgent service requests are rapidly attended. However, to the best of our knowledge, the literature lacks appropriate mechanisms for considering users’ priorities in the 5G ultra-dense networks. In this context, we propose a mobile application that allows citizens to request smart city services with different priority levels. The experiments showed the high performance of the app and its scalability when increasing priority list sizes. This app obtained 72.3% of usability in the system usability scale and 82.9% in the ease-of-use dimension of the usefulness, satisfaction, and ease of use questionnaire

Defenses Against Perception-Layer Attacks on IoT Smart Furniture for Impaired People

[EN] Internet of Things (IoT) is becoming highly supportive in innovative technological solutions for assisting impaired people. Some of these IoT solutions are still in a prototyping phase ignoring possible attacks and the corresponding security defenses. This article proposes a learning-based approach for defending against perception-layer attacks performed on specific sensor types in smart furniture for impaired people. This approach is based on the analysis of time series by means of dynamic time warping algorithm for calculating similarity and a novel detector for identifying anomalies. This approach has been illustrated by defending against simulated perception-layer magnetic attacks on a smart cupboard with door magnetic sensors. The results show the performance of the proposed approach for properly identifying these attacks. In particular, these results advocate an accuracy about 95.5% per day.This work was supported in part by the research project Utilisation of IoT and Sensors in Smart Cities for Improving Quality of Life of Impaired People under Grant 52-2020, in part by the Ciudades Inteligentes Totalmente Integrales, Eficientes Y Sotenibles (CITIES) funded by the Programa Iberoamericano de Ciencia y Tecnologia para el Desarrollo (CYTED) under Grant 518RT0558, in part by the Diseno Colaborativo Para La Promocion Del Bienestar En Ciudades Inteligentes Inclusivas under Grant TIN2017-88327-R funded by the Spanish Council of Science, Innovation and Universities from the Spanish Government, and in part by the Ministerio de Economia y Competitividad in the Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento under Grant TIN2017-84802-C2-1-P.Nasralla, MM.; García-Magariño, I.; Lloret, J. (2020). Defenses Against Perception-Layer Attacks on IoT Smart Furniture for Impaired People. IEEE Access. 8:119795-119805. https://doi.org/10.1109/ACCESS.2020.3004814S119795119805