An approach to pervasive monitoring in dynamic learning contexts : data sensing, communication support and awareness provision

It is within the capabilities of current technology to support the emerging learning paradigms. These paradigms suggest that today’s learning activities and environments are pervas ive and require a higher level of dynamism than the traditional learning contexts. Therefore, we have to rethink our approach to learning and use technology not only as a digital information support, but also as an instrument to reinforce knowledge, foster collaboration, promote creativity and provide richer learning experiences. Particularly, this thesis was motivated by the rapidly growing number of smartphone users and the fact that these devices are increasingly becoming more and more resource-rich, in terms of their communication and sensing technologies, display capabilities battery autonomy, etc. Hence, this dissertation benefits from the ubiquity and development of mobile technology, aiming to bridge the gap between the challenges posed by modern learning requirements and the capabilities of current technology. The sensors embedded in smartphones can be used to capture diverse behavioural and social aspects of the users. For example, using microphone and Bluetooth is possible to identify conversation patterns, discover users in proximity and detect face-to-face meetings. This fact opens up exciting possibilities to monitor the behaviour of the user and to provide meaningful feedback. This feedback offers useful information that can help people be aware of and reflect on their behaviour and its effects, and take the necessary actions to improve them. Consequently, we propose a pervasive monitoring system that take advantage of the capabilities of modern smartphones, us ing them to s upport the awarenes s provis ion about as pects of the activities that take place in today’s pervas ive learning environments. This pervasive monitoring system provides (i) an autonomous sensing platform to capture complex information about processes and interactions that take place across multiple learning environments, (ii) an on-demand and s elf-m anaged communication infras tructure, and (ii) a dis play facility to provide “awarenes s inform ation” to the s tudents and/or lecturers. For the proposed system, we followed a research approach that have three main components. First, the description of a generalized framework for pervasive sensing that enables collaborative sensing interactions between smartphones and other types of devices. By allowing complex data capture interactions with diverse remote sensors, devices and data sources, this framework allows to improve the information quality while saving energy in the local device. Second, the evaluation, through a real-world deployment, of the suitability of ad hoc networks to support the diverse communication processes required for pervasive monitoring. This component also includes a method to improve the scalability and reduce the costs of these networks. Third, the design of two awareness mechanisms to allow flexible provision of information in dynamic and heterogeneous learning contexts. These mechanisms rely on the use of smartphones as adaptable devices that can be used directly as awareness displays or as communication bridges to enable interaction with other remote displays available in the environment. Diverse aspects of the proposed system were evaluated through a number of simulations, real-world experiments, user studies and prototype evaluations. The experimental evaluation of the data capture and communication aspects of the system provided empirical evidence of the usefulness and suitability of the proposed approach to support the development of pervasive monitoring solutions. In addition, the proof-of-concept deployments of the proposed awareness mechanisms, performed in both laboratory and real-world learning environments, provided quantitative and qualitative indicators that such mechanisms improve the quality of the awareness information and the user experienceLa tecnología moderna tiene capacidad de dar apoyo a los paradigmas de aprendizaje emergentes. Estos paradigmas sugieren que las actividades de aprendizaje actuales, caracterizadas por la ubicuidad de entornos, son más dinámicas y complejas que los contextos de aprendizaje tradicionales. Por tanto, tenemos que reformular nuestro acercamiento al aprendizaje, consiguiendo que la tecnología sirva no solo como mero soporte de información, sino como medio para reforzar el conocimiento, fomentar la colaboración, estimular la creatividad y proporcionar experiencias de aprendizaje enriquecedoras. Esta tesis doctoral está motivada por el vertiginoso crecimiento de usuarios de smartphones y el hecho de que estos son cada vez más potentes en cuanto a tecnologías de comunicación, sensores, displays, autonomía energética, etc. Por tanto, esta tesis aprovecha la ubicuidad y el desarrollo de esta tecnología, con el objetivo de reducir la brecha entre los desafíos del aprendizaje moderno y las capacidades de la tecnología actual. Los sensores integrados en los smartphones pueden ser utilizados para reconocer diversos aspectos del comportamiento individual y social de los usuarios. Por ejemplo, a través del micrófono y el Bluetooth, es posible determinar patrones de conversación, encontrar usuarios cercanos y detectar reuniones presenciales. Este hecho abre un interesante abanico de posibilidades, pudiendo monitorizar aspectos del comportamiento del usuario y proveer un feedback significativo. Dicho feedback, puede ayudar a los usuarios a reflexionar sobre su comportamiento y los efectos que provoca, con el fin de tomar medidas necesarias para mejorarlo. Proponemos un sistema de monitorización generalizado que aproveche las capacidades de los smartphones para proporcionar información a los usuarios, ayudándolos a percibir y tomar conciencia sobre diversos aspectos de las actividades que se desarrollan en contextos de aprendizaje modernos. Este sistema ofrece: (i) una plataforma de detección autónoma, que captura información compleja sobre los procesos e interacciones de aprendizaje; (ii) una infraestructura de comunicación autogestionable y; (iii) un servicio de visualización que provee “información de percepción” a estudiantes y/o profesores. Para la elaboración de este sistema nos hemos centrado en tres áreas de investigación. Primero, la descripción de una infraestructura de detección generalizada, que facilita interacciones entre smartphones y otros dispositivos. Al permitir interacciones complejas para la captura de datos entre diversos sensores, dispositivos y fuentes de datos remotos, esta infraestructura consigue mejorar la calidad de la información y ahorrar energía en el dispositivo local. Segundo, la evaluación, a través de pruebas reales, de la idoneidad de las redes ad hoc como apoyo de los diversos procesos de comunicación requeridos en la monitorización generalizada. Este área incluye un método que incrementa la escalabilidad y reduce el coste de estas redes. Tercero, el diseño de dos mecanismos de percepción que permiten la provisión flexible de información en contextos de aprendizaje dinámicos y heterogéneos. Estos mecanismos descansan en la versatilidad de los smartphones, que pueden ser utilizados directamente como displays de percepción o como puentes de comunicación que habilitan la interacción con otros displays remotos del entorno. Diferentes aspectos del sistema propuesto han sido evaluados a través de simulaciones, experimentos reales, estudios de usuarios y evaluaciones de prototipos. La evaluación experimental proporcionó evidencia empírica de la idoneidad del sistema para apoyar el desarrollo de soluciones de monitorización generalizadas. Además, las pruebas de concepto realizadas tanto en entornos de aprendizajes reales como en el laboratorio, aportaron indicadores cuantitativos y cualitativos de que estos mecanismos mejoran la calidad de la información de percepción y la experiencia del usuario.Postprint (published version

Providing behaviour awareness in collaborative project courses

Several studies show that awareness mechanisms can contribute to enhance the collaboration process among students and the learning experiences during collaborative project courses. However, it is not clear what awareness information should be provided to whom, when it should be provided, and how to obtain and represent such information in an accurate and understandable way. Regardless the research efforts done in this area, the problem remains open. By recognizing the diversity of work scenarios (contexts) where the collaboration may occur, this research proposes a behaviour awareness mechanism to support collaborative work in undergraduate project courses. Based on the authors previous experiences and the literature in the area, the proposed mechanism considers personal and social awareness components, which represent metrics in a visual way, helping students realize their performance, and lecturers intervene when needed. The trustworthiness of the mechanisms for determining the metrics was verified using empirical data, and the usability and usefulness of these metrics were evaluated with undergraduate students. Experimental results show that this awareness mechanism is useful, understandable and representative of the observed scenarios.Peer ReviewedPostprint (published version

Providing behaviour awareness in collaborative project courses

Several studies show that awareness mechanisms can contribute to enhance the collaboration process among students and the learning experiences during collaborative project courses. However, it is not clear what awareness information should be provided to whom, when it should be provided, and how to obtain and represent such information in an accurate and understandable way. Regardless the research efforts done in this area, the problem remains open. By recognizing the diversity of work scenarios (contexts) where the collaboration may occur, this research proposes a behaviour awareness mechanism to support collaborative work in undergraduate project courses. Based on the authors previous experiences and the literature in the area, the proposed mechanism considers personal and social awareness components, which represent metrics in a visual way, helping students realize their performance, and lecturers intervene when needed. The trustworthiness of the mechanisms for determining the metrics was verified using empirical data, and the usability and usefulness of these metrics were evaluated with undergraduate students. Experimental results show that this awareness mechanism is useful, understandable and representative of the observed scenarios.Peer ReviewedPostprint (published version

Proyecto ICARUS. Sistema de comunicación GPRS.

El presente proyecto desarrolla el sistema de comunicación de un UAV (Unmanned Aerial Vehicle). Los UAVs son aviones no tripulados con aplicaciones tanto en el ámbito militar como civil. El objetivo que pretendemos alcanzar es el desarrollo de un sistema para la transferencia de datos desde el UAV a través de la red GPRS, el cual se situará dentro del avión junto con los dispositivos de control del mismo. Estudiamos la infraestructura de la red GPRS y los diferentes protocolos de software que nos permitirán establecer la comunicación. Además se diseña el hardware necesario para tal fin. Este trabajo forma parte del proyecto ICARUS, en el cual se desarrollan diversos sistemas que se integrarán dentro de un UAV

OLSRp: predicting control information to achieve scalability in OLSR ad hoc networks

Scalability is a key design challenge that routing protocols for ad hoc networks must properly address to maintain the network performance when the number of nodes increases. We focus on this issue by reducing the amount of control information messages that a link state proactive routing algorithm introduces to the network. Our proposal is based on the observation that a high percentage of those messages is always the same. Therefore, we introduce a new mechanism that can predict the control messages that nodes need for building an accurate map of the network topology so they can avoid resending the same messages. This prediction mechanism, applied to OLSR protocol, could be used to reduce the number of messages transmitted through the network and to save computational processing and energy consumption. Our proposal is independent of the OLSR configuration parameters and it can dynamically self-adapt to network changes.Postprint (published version

Group Prediction in Collaborative Learning

We propose an approach for predicting group formations, to address the problem of automating the incorporation of group awareness into CSCL applications. Contextual information can enable the construction of applications that effectively assist the group members to automatically communicate in synchronous and collocated collaborative learning activities. We used data traces collected from the study of students’ behavior to train and test an intelligent system. Results have shown that context-information can be effectively used as a basis for a middleware for a dynamic group management. Inferring group membership is technically viable and can be used in real world settings.Postprint (published version

Mobile Autonomous Sensing Unit (MASU): a framework that supports distributed pervasive data sensing

Pervasive data sensing is a major issue that transverses various research areas and application domains. It allows identifying people’s behaviour and patterns without overwhelming the monitored persons. Although there are many pervasive data sensing applications, they are typically focused on addressing specific problems in a single application domain, making them difficult to generalize or reuse. On the other hand, the platforms for supporting pervasive data sensing impose restrictions to the devices and operational environments that make them unsuitable for monitoring loosely-coupled or fully distributed work. In order to help address this challenge this paper present a framework that supports distributed pervasive data sensing in a generic way. Developers can use this framework to facilitate the implementations of their applications, thus reducing complexity and effort in such an activity. The framework was evaluated using simulations and also through an empirical test, and the obtained results indicate that it is useful to support such a sensing activity in loosely-coupled or fully distributed work scenarios.Peer ReviewedPostprint (published version

OIoT: a platform to manage opportunistic IoT communities

Opportunistic Internet of Things (IoT) extends the concept of opportunistic networking combining human users carrying mobile devices and smart things. It explores the relationships between humans and the opportunistic connection of smart objects. This paper presents a software infrastructure, named Opportunistic IoT Platform (OIoT), which helps developers to create and manage opportunistic IoT communities between smart devices. The platform enables the creation of opportunistic IoT communities that support the AllJoyn communications framework, for IoT devices and applications. Results from a preliminary evaluation of the OIoT platform indicate that this infrastructure is useful to manage and share data across opportunistic IoT communities.Peer ReviewedPostprint (published version

Supporting context-aware collaborative learning through automatic group formation

Collaborative learning is based on groups of students working together with traditional and computer-based tools or applications. We have found that to make these supporting applications more effective we need to address the problem of automating group awareness in CSCL applications by estimating group arrangements from location sensors and the history of interaction. This contextual information can enable the construction of applications that facilitate communication among group members in synchronous and collocated collaborative learning activities. We used data traces collected from the study of students‟ behavior to train and test an intelligent system. Results show that context-information can be effectively used as a basis for a middleware for automating group management. Inferring group membership is technically feasible, can be integrated in group-support applications and can be used in real-world settings.Postprint (published version

A technological overview of the guifi.net community network

This work presents a technological analysis of guifi.net, a free, neutral, and open-access community network. Guifi.net consists of more than 27,000 operational nodes, which makes it the world’s largest community network in terms of the number of nodes and coverage area. This paper describes the characteristics of the network, the link level topology, its growth over a decade, and its resilience in terms of availability and reachability of network nodes. Our study is based on open data published by guifi.net regarding its nodes and wireless links, monitoring information, community database, and its web portal. The data includes historical information that covers the lifetime of the network. The scale and diversity of the network requires a separate analysis of the subsets of the entire dataset by area or by separating the core from the leaf nodes. This shows some degree of diversity in local characteristics caused by several demographic, geographic, technological, and network design factors. We focus on the following aspects: technological network diversity, topology characteristics, evolution of the network over time, analysis of robustness, and its effect on networking service availability. In addition, we analyse how the community, the technology used, the geographical region where the network is deployed, and its self-organised structure shape the network properties and determine its strengths and weaknesses. The study demonstrates that the guifi.net community network is diverse in technological choices for hardware, link protocols, and channels and uses a combination of routing protocols yet provides a common private IP network. The graph topology follows a power- law distribution for links in regions up to a few thousand Km 2 , limited to the scope of wireless links. Network growth has two aspects: a geographic growth of the network core using long distance links with wireless or fibre, and the local growth in density with leaf low-cost leaf nodes. The resilience of the network derived from the nodes’ uptime and the structure of the graph varies across different regions with more fragile leafs than core nodes and diverse degrees of graph resilience to random failures or coordinated attacks, such as natural causes, depending on the network planning, structure, and maturity. The guifi.net community network results from a loosely coupled and decentralised organic growth that exhibits large local differences, diverse growth, and maturity under a common community license and social network.Peer ReviewedPostprint (author's final draft