Malmö högskolaLärarutbildningenSkolutveckling och ledarskapVårterminen 2006 Filimon Fasola, Magdalena (2006) Könssegregerat eller könsintegrerat grupparbete? (Gender segregated or gender integrated workgroups?) Malmö: LärarutbildnnigenSyftet med examensarbetet var att undersöka vilken inställning till arbetet i könsintegrerade respektive könssegregerade grupper eleverna har i de klasser jag undervisade i under min vft. Undersökningen gjordes i två klasser, en sjua och en åtta. Arbetet avgränsades av tre frågeställningar:•Vilken inställning har eleverna till arbetet i könsintegrerade respektive könssegregerade grupper?•Har vanan att umgås med det motsatta könet utanför skolan någon betydelse för hur eleverna ser på arbetet i könsintegrerade respektive könssegregerade grupper?•Har gruppsammansättningen någon betydelse för hur eleverna arbetar?Arbetet grundar sig på litteraturstudier och en kvantitativ undersökning som består av en enkät.Min slutsats är att både pojkarna och flickorna i undersökningen föredrar könsintegrerade grupper.Nyckelord: Genus, grupparbete, könsintegration, könssegregationFörfattare: Magdalena Filimon FasolaHandledare: Elna Johansson / AbstractFilimon Fasola, Magdalena (2006) Gender segregated or gender integrated workgroups? The aim with the essay was to investigate what is the attitude that pupils in the classes I was teacher to during my practice have regarding gender integrated respective gender segregated workgroups. The investigation was made in two classes, a seventh and an eight. The study was limited by three questions:•What is the attitude that pupils have regarding gender integrated respective gender segregated workgroups?•Does the habit to spend time with the opposite sex outside the school have any significance for how the pupils perceive gender integrated respective gender segregated workgroups?•Does the group’s assembly have any significance for how pupils work? The work is based on studies of literature and a quantitative investigation which consists of a questionnaire.My conclusion is that both boys and girls who took part in the investigation prefer gender integrated workgroups. Key words: gender, gender integration, gender segregation, workgroups. Author: Magdalena Filimon FasolaSupervisor: Elna Johansson

Interest in flexible work arrangements has proliferated in the last years, fuelled by technological advancements that allow people to work from anywhere at any time. This systematic literature review explores the impact of flexibility in time and place of work on intra-group relations and subsequent effects on group-level outcomes. Findings suggest that flexibility in place of work has positive effects on employee-supervisor relationships but negative effects on co-worker relationships. Although teleworkers remain well connected to their co-workers and overall workgroup communication does not appear to change, informal socialisation processes are affected. Knowledge sharing and creation is subsequently challenged. Furthermore, managers of mixed workgroups face issues of fairness and justice as well as challenges of creating and maintaining group cohesion when some of their employees are not always present. However, little is known of how flexibility in time of work impacts intra-group relations and group outcomes from groups containing flexible workers have not been explored. This paper therefore identifies significant gaps in the literature and presents opportunities for further research.

The purpose of this study is to investigate the effectiveness of virtual peer-to-peer (P2P) workgroups as a platform for long-term collaboration in healthcare. Virtual peer-to-peer workgroups were developed and piloted by the Michigan Value Collaborative to increase knowledge and collaboration between providers across Michigan. The workgroups were designed to address barriers to change and long-term collaboration by allowing participants to share their improvement journey and provide feedback and ideas for improvement in a highly accessible platform. The pilot workgroups focused on heart failure readmission reduction initiatives as it is a much scrutinized metric and is penalized by public and private payers. Data on the workgroups were collected using pre and post-workgroup surveys filled out by participants. The results reveal that virtual peer-to- peer workgroups are effective in increasing knowledge and collaboration in the short term, but more study is required to judge their long term effectiveness in improving care at participating providers. Virtual peer-to-peer workgroups can serve as a foundation for increasing regional collaboration in healthcare as it is a very simple platform that does not require major financial or resource commitments.

Results obtained from a literature review of scientific articles related to job satisfaction published between the years 2000 and 2010, are presented. Findings of research studies analyzed indicate the relationship between leadership, some aspects of the supervision, groups, teams and its influence on job satisfaction. The revised bibliography noted mainly the positive relationship between transformational leadership and the SWA model of supervision on job satisfaction. Also the importance of the influence of certain aspects such as the efficacy group, group cohesion and the need to share values for group work be more satisfying than individual work. / Se presenta los resultados obtenidos de una revisión bibliográfica de artículos científicos relacionados con la satisfacción laboral publicados entre los años 2000 y 2010. En el mismose presentan las conclusiones de distintas investigaciones que señalan la relación entre elliderazgo, algunos aspectos de la supervisión, los grupos y equipos de trabajo, y su influenciasobre la Satisfacción Laboral. La literatura revisada señala principalmente una relación positivaentre el liderazgo transformacional y el modelo de supervisión SWA con la satisfacciónlaboral. También la importancia de la influencia de ciertos aspectos grupales como la eficacia,la cohesión grupal y la necesidad de compartir valores para que el trabajo grupal sea mássatisfactorio que el trabajo individual.

The Australian business environment has been changing at an ever-increasing pace since the mid-1980s. Technological, economic and social changes have altered the working environment. There have been constant technological advances with information technology influencing most categories of work. Organisations in public and private sectors have ongoing expectations of increased productivity, increased quality of processes and swifter responsiveness to clients. Team roles have changed. Team members are multi-skilled and work is designed to emphasise the whole task. The Karpin Industry Task Force described a vision for an Australian business environment that would, by 2014, be one with a flexible, skilled and motivated workforce, world class managers, a customer comes first mentality, and an internationally competitive perspective. These characteristics are congruent with those of learning organisations. The aim of this study is to evaluate the suitability of groupware as the supporting infrastructure for a learning organisation. To do this, the study assesses the use of technology to support personal and team learning in a learning organisation, studies the impact of groupware on learning within workgroups, determines the extent to which communication and learning styles influence its effectiveness, and identifies ways in which groupware can be used to capture the information used to support knowledge management in an organisation. Two case studies are used to undertake this assessment. Three distinct yet related frameworks underpin this study. The first is that of Groupware and the related research frameworks of Computer Supported Cooperative Work (CSCW) and Computer Supported Collaborative Learning (CSCL). The second is the Learning Organisation and its supporting disciplines. The final framework is that of learning and the action-oriented learning processes. Each is examined and the interrelatedness of the frameworks is explored. The journey to produce this written material has been one of twists and turns, blind alleys and blinding revelations, observation and reflection. My choice of techniques has been eclectic, reflecting the breadth of theoretical material covered.

Este trabalho propõe um modelo probabilístico de conhecimento e raciocínio para um agente, denominado Agente Social, cujo principal objetivo é analisar o perfil dos alunos, usuários de um Sistema Tutor Inteligente chamado AMPLIA, e compor grupos de trabalho. Para formar estes grupos, o Agente Social considera aspectos individuais do aluno e estratégias de formação de grupos. A aprendizagem colaborativa envolve relações sociais cujos processos são complexos e apresentam dificuldade para sua modelagem computacional. A fim de representar alguns elementos deste processo e de seus participantes, devem ser considerados aspectos individuais, tais como estado afetivo, questões psicológicas e cognição. Também devem ser considerados aspectos sociais, tais como a habilidade social, a aceitação e a forma em que as pessoas se relacionam e compõem seus grupos de trabalho ou estudo. Sistemas Tutores Inteligentes, Sistemas Multiagente e Computação Afetiva são áreas de pesquisa que vem sendo investigadas de forma a oferecer alternativas para representar e tratar computacionalmente alguns destes aspectos multidisciplinares que acompanham a aprendizagem individual e colaborativa. O Agente Social está inserido na sociedade de agentes do portal PortEdu que, por sua vez, fornece serviços ao ambiente de aprendizagem AMPLIA O PortEdu é um portal que provê serviços para os ambientes educacionais integrados a ele. Este portal foi modelado em uma abordagem multiagente e cada serviço oferecido é implementado por um agente específico. Os ambientes educacionais que utilizam os serviços do portal também são sociedades de agentes e, em geral, Sistemas Tutores Inteligentes. O ambiente AMPLIA (Ambiente Multiagente Probabilístico Inteligente de Aprendizagem) foi projetado para suportar o treinamento do raciocínio diagnóstico e modelagem de domínios de conhecimento incerto e complexo, como a área médica. Este ambiente usa a abordagem de Redes Bayesianas onde os alunos constróem suas próprias redes para um problema apresentado pelo sistema através de um editor gráfico de Redes Bayesianas. Neste trabalho, o editor do AMPLIA foi adaptado para uma versão colaborativa, que permite a construção das redes por vários alunos remotos conectados ao sistema. É através deste editor que o Agente Social observa e interage com os alunos sugerindo a composição dos grupos. Foram realizados experimentos práticos acompanhados por instrumentos de avaliação, com o objetivo de analisar a composição de grupos sugerida pelo Agente Social e relacioná-la com os grupos formados espontaneamente pelos alunos no ambiente de sala de aula. O resultado do trabalho individual e dos grupos também foi analisado e discutido nesta pesquisa. / This research proposes a probabilistic knowledge and reasoning model for an agent, named Social Agent, whose main goal is to analyze students' profiles and to organize them in workgroups. These students are users of an Intelligent Tutoring System named AMPLIA. In order to suggest those groups, the Social Agent considers individual aspects of the students and also strategies for group formation. Collaborative learning involves social relationships with complex processes which are difficult to model computationally. In order to represent these relationships, we should consider several aspects of the student, such as affective state, psychological issues, and cognition. We should also consider social aspects such as social ability, social acceptance and how people relate to each other, and how they compose their workgroups. Intelligent Tutoring Systems, Multiagent Systems and Affective Computing are research areas which our research group have been investigating, in order to represent and to deal computationally with multidisciplinary issues involving individual and collaborative learning. The Social Agent is part of an agent society of the PortEdu Portal, which provides services to AMPLIA. PortEdu is an educational portal which provides facilities to educational environments integrated to it. This portal has been modeled using a multiagent approach and each of its services is represented by a specific agent. The educational environments that make use of the portal's services are also agent societies and, in general, Intelligent Tutoring Systems. AMPLIA (Probabilistic Multiagent Learning Environment) has been designed in order to support diagnostic reasoning and the modeling of diagnostic hypotheses in domains with complex and uncertain knowledge, such as the medical domain. This environment uses a Bayesian Networks approach in which students build their own networks for a clinical case through a Bayesian Network graphical editor. Here, the AMPLIA editor has been adapted and extended to a collaborative version, which enables the network construction for remote students connected to the system. Through this editor, the Social Agent observes and interacts with students, suggesting the composition of workgroups. Practical experiments using assessment tools have been carried out, in order to analyze the workgroups suggested by the Social Agent and to compare them with groups naturally composed by students in the classroom. The results of the work done by individual students and by workgroups were also analyzed and discussed in this research.

Este trabalho propõe um modelo probabilístico de conhecimento e raciocínio para um agente, denominado Agente Social, cujo principal objetivo é analisar o perfil dos alunos, usuários de um Sistema Tutor Inteligente chamado AMPLIA, e compor grupos de trabalho. Para formar estes grupos, o Agente Social considera aspectos individuais do aluno e estratégias de formação de grupos. A aprendizagem colaborativa envolve relações sociais cujos processos são complexos e apresentam dificuldade para sua modelagem computacional. A fim de representar alguns elementos deste processo e de seus participantes, devem ser considerados aspectos individuais, tais como estado afetivo, questões psicológicas e cognição. Também devem ser considerados aspectos sociais, tais como a habilidade social, a aceitação e a forma em que as pessoas se relacionam e compõem seus grupos de trabalho ou estudo. Sistemas Tutores Inteligentes, Sistemas Multiagente e Computação Afetiva são áreas de pesquisa que vem sendo investigadas de forma a oferecer alternativas para representar e tratar computacionalmente alguns destes aspectos multidisciplinares que acompanham a aprendizagem individual e colaborativa. O Agente Social está inserido na sociedade de agentes do portal PortEdu que, por sua vez, fornece serviços ao ambiente de aprendizagem AMPLIA O PortEdu é um portal que provê serviços para os ambientes educacionais integrados a ele. Este portal foi modelado em uma abordagem multiagente e cada serviço oferecido é implementado por um agente específico. Os ambientes educacionais que utilizam os serviços do portal também são sociedades de agentes e, em geral, Sistemas Tutores Inteligentes. O ambiente AMPLIA (Ambiente Multiagente Probabilístico Inteligente de Aprendizagem) foi projetado para suportar o treinamento do raciocínio diagnóstico e modelagem de domínios de conhecimento incerto e complexo, como a área médica. Este ambiente usa a abordagem de Redes Bayesianas onde os alunos constróem suas próprias redes para um problema apresentado pelo sistema através de um editor gráfico de Redes Bayesianas. Neste trabalho, o editor do AMPLIA foi adaptado para uma versão colaborativa, que permite a construção das redes por vários alunos remotos conectados ao sistema. É através deste editor que o Agente Social observa e interage com os alunos sugerindo a composição dos grupos. Foram realizados experimentos práticos acompanhados por instrumentos de avaliação, com o objetivo de analisar a composição de grupos sugerida pelo Agente Social e relacioná-la com os grupos formados espontaneamente pelos alunos no ambiente de sala de aula. O resultado do trabalho individual e dos grupos também foi analisado e discutido nesta pesquisa. / This research proposes a probabilistic knowledge and reasoning model for an agent, named Social Agent, whose main goal is to analyze students' profiles and to organize them in workgroups. These students are users of an Intelligent Tutoring System named AMPLIA. In order to suggest those groups, the Social Agent considers individual aspects of the students and also strategies for group formation. Collaborative learning involves social relationships with complex processes which are difficult to model computationally. In order to represent these relationships, we should consider several aspects of the student, such as affective state, psychological issues, and cognition. We should also consider social aspects such as social ability, social acceptance and how people relate to each other, and how they compose their workgroups. Intelligent Tutoring Systems, Multiagent Systems and Affective Computing are research areas which our research group have been investigating, in order to represent and to deal computationally with multidisciplinary issues involving individual and collaborative learning. The Social Agent is part of an agent society of the PortEdu Portal, which provides services to AMPLIA. PortEdu is an educational portal which provides facilities to educational environments integrated to it. This portal has been modeled using a multiagent approach and each of its services is represented by a specific agent. The educational environments that make use of the portal's services are also agent societies and, in general, Intelligent Tutoring Systems. AMPLIA (Probabilistic Multiagent Learning Environment) has been designed in order to support diagnostic reasoning and the modeling of diagnostic hypotheses in domains with complex and uncertain knowledge, such as the medical domain. This environment uses a Bayesian Networks approach in which students build their own networks for a clinical case through a Bayesian Network graphical editor. Here, the AMPLIA editor has been adapted and extended to a collaborative version, which enables the network construction for remote students connected to the system. Through this editor, the Social Agent observes and interacts with students, suggesting the composition of workgroups. Practical experiments using assessment tools have been carried out, in order to analyze the workgroups suggested by the Social Agent and to compare them with groups naturally composed by students in the classroom. The results of the work done by individual students and by workgroups were also analyzed and discussed in this research.

Este trabalho propõe um modelo probabilístico de conhecimento e raciocínio para um agente, denominado Agente Social, cujo principal objetivo é analisar o perfil dos alunos, usuários de um Sistema Tutor Inteligente chamado AMPLIA, e compor grupos de trabalho. Para formar estes grupos, o Agente Social considera aspectos individuais do aluno e estratégias de formação de grupos. A aprendizagem colaborativa envolve relações sociais cujos processos são complexos e apresentam dificuldade para sua modelagem computacional. A fim de representar alguns elementos deste processo e de seus participantes, devem ser considerados aspectos individuais, tais como estado afetivo, questões psicológicas e cognição. Também devem ser considerados aspectos sociais, tais como a habilidade social, a aceitação e a forma em que as pessoas se relacionam e compõem seus grupos de trabalho ou estudo. Sistemas Tutores Inteligentes, Sistemas Multiagente e Computação Afetiva são áreas de pesquisa que vem sendo investigadas de forma a oferecer alternativas para representar e tratar computacionalmente alguns destes aspectos multidisciplinares que acompanham a aprendizagem individual e colaborativa. O Agente Social está inserido na sociedade de agentes do portal PortEdu que, por sua vez, fornece serviços ao ambiente de aprendizagem AMPLIA O PortEdu é um portal que provê serviços para os ambientes educacionais integrados a ele. Este portal foi modelado em uma abordagem multiagente e cada serviço oferecido é implementado por um agente específico. Os ambientes educacionais que utilizam os serviços do portal também são sociedades de agentes e, em geral, Sistemas Tutores Inteligentes. O ambiente AMPLIA (Ambiente Multiagente Probabilístico Inteligente de Aprendizagem) foi projetado para suportar o treinamento do raciocínio diagnóstico e modelagem de domínios de conhecimento incerto e complexo, como a área médica. Este ambiente usa a abordagem de Redes Bayesianas onde os alunos constróem suas próprias redes para um problema apresentado pelo sistema através de um editor gráfico de Redes Bayesianas. Neste trabalho, o editor do AMPLIA foi adaptado para uma versão colaborativa, que permite a construção das redes por vários alunos remotos conectados ao sistema. É através deste editor que o Agente Social observa e interage com os alunos sugerindo a composição dos grupos. Foram realizados experimentos práticos acompanhados por instrumentos de avaliação, com o objetivo de analisar a composição de grupos sugerida pelo Agente Social e relacioná-la com os grupos formados espontaneamente pelos alunos no ambiente de sala de aula. O resultado do trabalho individual e dos grupos também foi analisado e discutido nesta pesquisa. / This research proposes a probabilistic knowledge and reasoning model for an agent, named Social Agent, whose main goal is to analyze students' profiles and to organize them in workgroups. These students are users of an Intelligent Tutoring System named AMPLIA. In order to suggest those groups, the Social Agent considers individual aspects of the students and also strategies for group formation. Collaborative learning involves social relationships with complex processes which are difficult to model computationally. In order to represent these relationships, we should consider several aspects of the student, such as affective state, psychological issues, and cognition. We should also consider social aspects such as social ability, social acceptance and how people relate to each other, and how they compose their workgroups. Intelligent Tutoring Systems, Multiagent Systems and Affective Computing are research areas which our research group have been investigating, in order to represent and to deal computationally with multidisciplinary issues involving individual and collaborative learning. The Social Agent is part of an agent society of the PortEdu Portal, which provides services to AMPLIA. PortEdu is an educational portal which provides facilities to educational environments integrated to it. This portal has been modeled using a multiagent approach and each of its services is represented by a specific agent. The educational environments that make use of the portal's services are also agent societies and, in general, Intelligent Tutoring Systems. AMPLIA (Probabilistic Multiagent Learning Environment) has been designed in order to support diagnostic reasoning and the modeling of diagnostic hypotheses in domains with complex and uncertain knowledge, such as the medical domain. This environment uses a Bayesian Networks approach in which students build their own networks for a clinical case through a Bayesian Network graphical editor. Here, the AMPLIA editor has been adapted and extended to a collaborative version, which enables the network construction for remote students connected to the system. Through this editor, the Social Agent observes and interacts with students, suggesting the composition of workgroups. Practical experiments using assessment tools have been carried out, in order to analyze the workgroups suggested by the Social Agent and to compare them with groups naturally composed by students in the classroom. The results of the work done by individual students and by workgroups were also analyzed and discussed in this research.

leadership styles homogeneous versus heterogeneous manufacturing leaders contractor workforce

[ES] La interoperabilidad entre los objetos comunicados es el objetivo principal del internet de las cosas (IoT). Algunos esfuerzos para lograrlo han generado diversas propuestas de arquitecturas, sin embargo, aún no se ha llegado a un conceso. Estas arquitecturas difieren en el tipo de estructura, grado de centralización, algoritmo de enrutamiento, métricas de enrutamiento, técnicas de descubrimiento, algoritmos de búsqueda, segmentación, calidad de servicio y seguridad, entre otros. Algunas son mejores que otras, dependiendo del entorno en el que se desempeñan y del tipo de parámetro que se use. Las más populares son las orientadas a eventos o acciones basadas en reglas, las cuales han permitido que IoT ingrese en el mercado y logre una rápida masificación. Sin embargo, su interoperabilidad se basa en alianzas entre fabricantes para lograr su compatibilidad. Esta solución se logra en la nube con una plataforma que unifica a las diferentes marcas aliadas. Esto permite la introducción de estas tecnologías a la vida común de los usuarios pero no resuelve problemas de autonomía ni de interoperabilidad. Además, no incluye a la nueva generación de redes inteligentes basadas en cosas inteligentes. La arquitectura propuesta en esta tesis toma los aspectos más relevantes de las cuatro arquitecturas IoT más aceptadas y las integra en una, separando la capa IoT (comúnmente presente en estas arquitecturas), en tres capas. Además, está pensada para abarcar redes de proximidad (integrando diferentes tecnologías de interconexión IoT) y basar su funcionamiento en inteligencia artificial (AI). Por lo tanto, esta propuesta aumenta la posibilidad de lograr la interoperabilidad esperada y aumenta la funcionalidad de cada objeto en la red enfocada en prestar un servicio al usuario. Aunque el sistema que se propone incluye el procesamiento de una inteligencia artificial, sigue los mismos aspectos técnicos que sus antecesoras, ya que su operación y comunicación continúan basándose en la capa de aplicación y trasporte de la pila de protocolo TCP/IP. Sin embargo, con el fin de aprovechar los protocolos IoT sin modificar su funcionamiento, se crea un protocolo adicional que se encapsula y adapta a su carga útil. Se trata de un protocolo que se encarga de descubrir las características de un objeto (DFSP) divididas en funciones, servicios, capacidades y recursos, y las extrae para centralizarla en el administrador de la red (IoT-Gateway). Con esta información el IoT-Gateway puede tomar decisiones como crear grupos de trabajo autónomos que presten un servicio al usuario y enrutar a los objetos de este grupo que prestan el servicio, además de medir la calidad de la experiencia (QoE) del servicio; también administra el acceso a internet e integra a otras redes IoT, utilizando inteligencia artificial en la nube. Al basarse esta propuesta en un nuevo sistema jerárquico para interconectar objetos de diferente tipo controlados por AI con una gestión centralizada, se reduce la tolerancia a fallos y seguridad, y se mejora el procesamiento de los datos. Los datos son preprocesados en tres niveles dependiendo del tipo de servicio y enviados a través de una interfaz. Sin embargo, si se trata de datos sobre sus características estos no requieren mucho procesamiento, por lo que cada objeto los preprocesa de forma independiente, los estructura y los envía a la administración central. La red IoT basada en esta arquitectura tiene la capacidad de clasificar un objeto nuevo que llegue a la red en un grupo de trabajo sin la intervención del usuario. Además de tener la capacidad de prestar un servicio que requiera un alto procesamiento (por ejemplo, multimedia), y un seguimiento del usuario en otras redes IoT a través de la nube. / [CA] La interoperabilitat entre els objectes comunicats és l'objectiu principal de la internet de les coses (IoT). Alguns esforços per aconseguir-ho han generat diverses propostes d'arquitectures, però, encara no s'arriba a un concens. Aquestes arquitectures difereixen en el tipus d'estructura, grau de centralització, algoritme d'encaminament, mètriques d'enrutament, tècniques de descobriment, algoritmes de cerca, segmentació, qualitat de servei i seguretat entre d'altres. Algunes són millors que altres depenent de l'entorn en què es desenvolupen i de el tipus de paràmetre que es faci servir. Les més populars són les orientades a esdeveniments o accions basades en regles. Les quals li han permès entrar al mercat i aconseguir una ràpida massificació. No obstant això, la seva interoperabilitat es basa en aliances entre fabricants per aconseguir la seva compatibilitat. Aquesta solució s'aconsegueix en el núvol amb una plataforma que unifica les diferents marques aliades. Això permet la introducció d'aquestes tecnologies a la vida comuna dels usuaris però no resol problemes d'autonomia ni d'interoperabilitat. A més, no inclou a la nova generació de xarxes intel·ligents basades en coses intel·ligents. L'arquitectura proposada en aquesta tesi, pren els aspectes més rellevants de les quatre arquitectures IoT mes acceptades i les integra en una, separant la capa IoT (comunament present en aquestes arquitectures), en tres capes. A més aquesta pensada en abastar xarxes de proximitat (integrant diferents tecnologies d'interconnexió IoT) i basar el seu funcionament en intel·ligència artificial. Per tant, aquesta proposta augmenta la possibilitat d'aconseguir la interoperabilitat esperada i augmenta la funcionalitat de cada objecte a la xarxa enfocada a prestar un servei a l'usuari. Tot i que el sistema que es proposa inclou el processament d'una intel·ligència artificial, segueix els mateixos aspectes tècnics que les seves antecessores, ja que, la seva operació i comunicació se segueix basant en la capa d'aplicació i transport de la pila de protocol TCP / IP. No obstant això, per tal d'aprofitar els protocols IoT sense modificar el seu funcionament es crea un protocol addicional que s'encapsula i s'adapta a la seva càrrega útil. Es tracta d'un protocol que s'encarrega de descobrir les característiques d'un objecte (DFSP) dividides en funcions, serveis, capacitats i recursos, i les extreu per centralitzar-la en l'administrador de la xarxa (IoT-Gateway). Amb aquesta informació l'IoT-Gateway pot prendre decisions com crear grups de treball autònoms que prestin un servei a l'usuari i encaminar als objectes d'aquest grup que presten el servei. A més de mesurar la qualitat de l'experiència (QoE) de el servei. També administra l'accés a internet i integra a altres xarxes Iot, utilitzant intel·ligència artificial en el núvol. A l'basar-se aquesta proposta en un nou sistema jeràrquic per interconnectar objectes de diferent tipus controlats per AI amb una gestió centralitzada, es redueix la tolerància a fallades i seguretat, i es millora el processament de les dades. Les dades són processats en tres nivells depenent de el tipus de servei i enviats a través d'una interfície. No obstant això, si es tracta de dades sobre les seves característiques aquests no requereixen molt processament, de manera que cada objecte els processa de forma independent, els estructura i els envia a l'administració central. La xarxa IoT basada en aquesta arquitectura té la capacitat de classificar un objecte nou que arribi a la xarxa en un grup de treball sense la intervenció de l'usuari. A més de tenir la capacitat de prestar un servei que requereixi un alt processament (per exemple multimèdia), i un seguiment de l'usuari en altres xarxes IoT a través del núvol. / [EN] Interoperability between communicating objects is the main goal of the Internet of Things (IoT). Efforts to achieve this have generated several architectures' proposals; however, no consensus has yet been reached. These architectures differ in structure, degree of centralisation, routing algorithm, routing metrics, discovery techniques, search algorithms, segmentation, quality of service, and security. Some are better than others depending on the environment in which they perform, and the type of parameter used. The most popular are those oriented to events or actions based on rules, which has allowed them to enter the market and achieve rapid massification. However, their interoperability is based on alliances between manufacturers to achieve compatibility. This solution is achieved in the cloud with a dashboard that unifies the different allied brands, allowing the introduction of these technologies into users' everyday lives but does not solve problems of autonomy or interoperability. Moreover, it does not include the new generation of smart grids based on smart things. The architecture proposed in this thesis takes the most relevant aspects of the four most accepted IoT-Architectures and integrates them into one, separating the IoT layer (commonly present in these architectures) into three layers. It is also intended to cover proximity networks (integrating different IoT interconnection technologies) and base its operation on artificial intelligence (AI). Therefore, this proposal increases the possibility of achieving the expected interoperability and increases the functionality of each object in the network focused on providing a service to the user. Although the proposed system includes artificial intelligence processing, it follows the same technical aspects as its predecessors since its operation and communication is still based on the application and transport layer of the TCP/IP protocol stack. However, in order to take advantage of IoT-Protocols without modifying their operation, an additional protocol is created that encapsulates and adapts to its payload. This protocol discovers the features of an object (DFSP) divided into functions, services, capabilities, and resources, and extracts them to be centralised in the network manager (IoT-Gateway). With this information, the IoT-Gateway can make decisions such as creating autonomous workgroups that provide a service to the user and routing the objects in this group that provide the service. It also measures the quality of experience (QoE) of the service. Moreover, manages internet access and integrates with other IoT-Networks, using artificial intelligence in the cloud. This proposal is based on a new hierarchical system for interconnecting objects of different types controlled by AI with centralised management, reducing the fault tolerance and security, and improving data processing. Data is preprocessed on three levels depending on the type of service and sent through an interface. However, if it is data about its features, it does not require much processing, so each object preprocesses it independently, structures it and sends it to the central administration. The IoT-Network based on this architecture can classify a new object arriving on the network in a workgroup without user intervention. It also can provide a service that requires high processing (e.g., multimedia), and user tracking in other IoT-Networks through the cloud. / González Ramírez, PL. (2022). Interconnection Architecture of Proximity Smart IoE-Networks with Centralised Management [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/181892