Resonating Experiences of Self and Others enabled by a Tangible Somaesthetic Design

Digitalization is penetrating every aspect of everyday life including a human's heart beating, which can easily be sensed by wearable sensors and displayed for others to see, feel, and potentially "bodily resonate" with. Previous work in studying human interactions and interaction designs with physiological data, such as a heart's pulse rate, have argued that feeding it back to the users may, for example support users' mindfulness and self-awareness during various everyday activities and ultimately support their wellbeing. Inspired by Somaesthetics as a discipline, which focuses on an appreciation of the living body's role in all our experiences, we designed and explored mobile tangible heart beat displays, which enable rich forms of bodily experiencing oneself and others in social proximity. In this paper, we first report on the design process of tangible heart displays and then present results of a field study with 30 pairs of participants. Participants were asked to use the tangible heart displays during watching movies together and report their experience in three different heart display conditions (i.e., displaying their own heart beat, their partner's heart beat, and watching a movie without a heart display). We found, for example that participants reported significant effects in experiencing sensory immersion when they felt their own heart beats compared to the condition without any heart beat display, and that feeling their partner's heart beats resulted in significant effects on social experience. We refer to resonance theory to discuss the results, highlighting the potential of how ubiquitous technology could utilize physiological data to provide resonance in a modern society facing social acceleration.Comment: 18 page

Seamless connectivity:investigating implementation challenges of multibroker MQTT platform for smart environmental monitoring

Abstract. This thesis explores the performance and efficiency of MQTT-based infrastructure Internet of Things (IoT) sensor networks for smart environment. The study focuses on the impact of network latency and broker switching in distributed multi-broker MQTT platforms. The research involves three case studies: a cloud-based multi-broker deployment, a Local Area Network (LAN)-based multi-broker deployment, and a multi-layer LAN network-based multi-broker deployment. The research is guided by three objectives: quantifying and analyzing the latency of multi-broker MQTT platforms; investigating the benefits of distributed brokers for edge users; and assessing the impact of switching latency at applications. This thesis ultimately seeks to answer three key questions related to network and switching latency, the merits of distributed brokers, and the influence of switching latency on the reliability of end-user applications

Assessing the effectiveness of a longitudinal knowledge dissemination intervention: Sharing research findings in rural South Africa

Knowledge dissemination interventions (KDIs) are integral to knowledge brokerage activities in research as part of the ethics of practice, but are seldom evaluated. In this case study, we critically reflect on an annual KDI as part of knowledge brokerage activities in the MRC/Wits-Agincourt Unit health and demographic surveillance system (HDSS) in rural South Africa from 2001 to 2015. The HDSS findings on births, deaths and migrations, as well as nested research project results, were shared with villagers, village leaders and service providers. The data used for this case study comprised secondary analysis of 13 reports and 762 evaluation forms of annual village-based meetings; records of requests for data from stakeholders; and qualitative analysis of 15 individual and five focus group interviews with local leaders and service providers involving 60 people. Over time, the KDI evolved from taking place over one week a year to being extended over six months, and to include briefings with service providers and local leaders. Attendance at village-level meetings remained low at an average of 3 per cent of the total adult population. Since 2011, the KDI village-based meetings have developed into an embedded community forum for discussion of topical village issues. There has been a decrease in requests for health-care and other services from the research unit, with a concurrent increase in research-related questions and requests for data from service providers, village leaders and political representatives. We conclude that, in this setting, the dissemination of research findings is not a linear exchange of information from the researchers to village residents and their leadership, but is increasingly multi-directional. KDIs are a key component of knowledge brokerage activities and involve, influence and are influenced by other aspects of knowledge brokerage, such as identifying, engaging and connecting with stakeholders and supporting sustainability

Sistema de comunicação sem fios de suporte à monitorização ambiental

Poor indoor air quality in classrooms can lead to decreased students’ performance, and affect the health and comfort of the occupants. The purpose of this dissertation is to deploy a system for environmental monitoring support through wireless communications technologies and long range networks. The prototype developed allows to collect continuous measurement of temperature, relative humidity, Volatile Organic Compounds (VOC), air pressure, oxygen and carbon dioxide. Evaluations were done using LoRaWAN protocol in selected classrooms during the winter semester at University of Aveiro. It demonstrates how to collect, integrate, analyse, and visualize real-time air quality data collected.A má qualidade do ar no interior das salas de aula pode levar à diminuição do desempenho dos alunos, uma vez que a qualidade do ar é um factor fundamental a ser controlado para garantir a saúde e o conforto dos ocupantes. Esta dissertação tem como objectivo desenvolver um sistema de suporte à monitorização ambiental através de tecnologias de comunicação sem fios e de redes de longo alcance. O protótipo desenvolvido permite recolher medições contínuas de temperatura, humidade relativa, Compostos Orgânicos Voláteis (VOC), pressão do ar, oxigénio e dióxido de carbono. Foram realizados testes em salas de aulas selecionadas durante o semestre de inverno na Universidade de Aveiro usando o protocolo LoRaWAN. É demonstrado como recolher, integrar, analisar e visualizar em tempo real os dados obtidos.Mestrado em Engenharia de Computadores e Telemátic

Improving Laboratory Learning Outcomes: An Investigation Into the Effect of Contextualising Laboratories Using Virtual Worlds and Remote Laboratories.

This thesis presents research into improving learning outcomes in laboratories. It was hypothesised that domain specific context can aid students in understanding the relationship between a laboratory (as a proxy for reality), the theoretical model being investigated within the laboratory activity and the real world. Specifically, the research addressed whether adding domain context to a laboratory activity could improve students' ability to identify the strengths and limitations of models as predictors of real-world behaviour. The domain context was included in a laboratory activity with the use of a remote radiation lab set within a context-rich virtual world. The empirical investigation used a pretest-posttest control group design to assess whether there was a statistically significant difference in the learning outcome between a treatment group who completed the lab in a contextualised virtual world, and the control group who conducted the activity in an empty virtual world. The results showed that there were no statistically significant differences between the groups and therefore there are cases where contextualising a laboratory activity will not have an effect on students' ability to identify the strengths and limitations of models as predictors of real-world behaviour. This research postulates that previous exposure to the model, the level of awareness students had of the context and the lack time available for reflection may have masked or attenuated the effect of the context. This research has contributed a framework for the analysis and design of domain context in laboratory activities, and an interface for integrating iLabs laboratories into the Open Wonderland virtual world. It has explicitly clarified the relationship between context, labs, models and the real world. Most significantly, this research has contributed knowledge to the field of laboratory learning outcomes and the understanding of how domain context affects laboratory activities

Distributed collaborative knowledge management for optical network

Network automation has been long time envisioned. In fact, the Telecommunications Management Network (TMN), defined by the International Telecommunication Union (ITU), is a hierarchy of management layers (network element, network, service, and business management), where high-level operational goals propagate from upper to lower layers. The network management architecture has evolved with the development of the Software Defined Networking (SDN) concept that brings programmability to simplify configuration (it breaks down high-level service abstraction into lower-level device abstractions), orchestrates operation, and automatically reacts to changes or events. Besides, the development and deployment of solutions based on Artificial Intelligence (AI) and Machine Learning (ML) for making decisions (control loop) based on the collected monitoring data enables network automation, which targets at reducing operational costs. AI/ML approaches usually require large datasets for training purposes, which are difficult to obtain. The lack of data can be compensated with a collective self-learning approach. In this thesis, we go beyond the aforementioned traditional control loop to achieve an efficient knowledge management (KM) process that enhances network intelligence while bringing down complexity. In this PhD thesis, we propose a general architecture to support KM process based on four main pillars, which enable creating, sharing, assimilating and using knowledge. Next, two alternative strategies based on model inaccuracies and combining model are proposed. To highlight the capacity of KM to adapt to different applications, two use cases are considered to implement KM in a purely centralized and distributed optical network architecture. Along with them, various policies are considered for evaluating KM in data- and model- based strategies. The results target to minimize the amount of data that need to be shared and reduce the convergence error. We apply KM to multilayer networks and propose the PILOT methodology for modeling connectivity services in a sandbox domain. PILOT uses active probes deployed in Central Offices (COs) to obtain real measurements that are used to tune a simulation scenario reproducing the real deployment with high accuracy. A simulator is eventually used to generate large amounts of realistic synthetic data for ML training and validation. We apply KM process also to a more complex network system that consists of several domains, where intra-domain controllers assist a broker plane in estimating accurate inter-domain delay. In addition, the broker identifies and corrects intra-domain model inaccuracies, as well as it computes an accurate compound model. Such models can be used for quality of service (QoS) and accurate end-to-end delay estimations. Finally, we investigate the application on KM in the context of Intent-based Networking (IBN). Knowledge in terms of traffic model and/or traffic perturbation is transferred among agents in a hierarchical architecture. This architecture can support autonomous network operation, like capacity management.La automatización de la red se ha concebido desde hace mucho tiempo. De hecho, la red de gestión de telecomunicaciones (TMN), definida por la Unión Internacional de Telecomunicaciones (ITU), es una jerarquía de capas de gestión (elemento de red, red, servicio y gestión de negocio), donde los objetivos operativos de alto nivel se propagan desde las capas superiores a las inferiores. La arquitectura de gestión de red ha evolucionado con el desarrollo del concepto de redes definidas por software (SDN) que brinda capacidad de programación para simplificar la configuración (descompone la abstracción de servicios de alto nivel en abstracciones de dispositivos de nivel inferior), organiza la operación y reacciona automáticamente a los cambios o eventos. Además, el desarrollo y despliegue de soluciones basadas en inteligencia artificial (IA) y aprendizaje automático (ML) para la toma de decisiones (bucle de control) en base a los datos de monitorización recopilados permite la automatización de la red, que tiene como objetivo reducir costes operativos. AI/ML generalmente requieren un gran conjunto de datos para entrenamiento, los cuales son difíciles de obtener. La falta de datos se puede compensar con un enfoque de autoaprendizaje colectivo. En esta tesis, vamos más allá del bucle de control tradicional antes mencionado para lograr un proceso eficiente de gestión del conocimiento (KM) que mejora la inteligencia de la red al tiempo que reduce la complejidad. En esta tesis doctoral, proponemos una arquitectura general para apoyar el proceso de KM basada en cuatro pilares principales que permiten crear, compartir, asimilar y utilizar el conocimiento. A continuación, se proponen dos estrategias alternativas basadas en inexactitudes del modelo y modelo de combinación. Para resaltar la capacidad de KM para adaptarse a diferentes aplicaciones, se consideran dos casos de uso para implementar KM en una arquitectura de red óptica puramente centralizada y distribuida. Junto a ellos, se consideran diversas políticas para evaluar KM en estrategias basadas en datos y modelos. Los resultados apuntan a minimizar la cantidad de datos que deben compartirse y reducir el error de convergencia. Aplicamos KM a redes multicapa y proponemos la metodología PILOT para modelar servicios de conectividad en un entorno aislado. PILOT utiliza sondas activas desplegadas en centrales de telecomunicación (CO) para obtener medidas reales que se utilizan para ajustar un escenario de simulación que reproducen un despliegue real con alta precisión. Un simulador se utiliza finalmente para generar grandes cantidades de datos sintéticos realistas para el entrenamiento y la validación de ML. Aplicamos el proceso de KM también a un sistema de red más complejo que consta de varios dominios, donde los controladores intra-dominio ayudan a un plano de bróker a estimar el retardo entre dominios de forma precisa. Además, el bróker identifica y corrige las inexactitudes de los modelos intra-dominio, así como también calcula un modelo compuesto preciso. Estos modelos se pueden utilizar para estimar la calidad de servicio (QoS) y el retardo extremo a extremo de forma precisa. Finalmente, investigamos la aplicación en KM en el contexto de red basada en intención (IBN). El conocimiento en términos de modelo de tráfico y/o perturbación del tráfico se transfiere entre agentes en una arquitectura jerárquica. Esta arquitectura puede soportar el funcionamiento autónomo de la red, como la gestión de la capacidad.Postprint (published version