Topology patterns of a community network: Guifi.net

This paper presents a measurement study of the topology and its effect on usage of Guifi.net, a large-scale community network. It focuses on the main issues faced by community network and lessons to consider for its future growth in order to preserve its scalability, stability and openness. The results show the network topology as an atypical high density Scale-Free network with critical points of failure and poor gateway selection or placement. In addition we have found paths with a large number of hops i.e. large diameter of the graph, and specifically long paths between leaf nodes and web proxies. The usage analysis using a widespread web proxy service confirms that these topological properties have an impact on the user experience

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

Understanding collaboration in volunteer computing systems

Volunteer computing is a paradigm in which devices participating in a distributed environment share part of their resources to help others perform their activities. The effectiveness of this computing paradigm depends on the collaboration attitude adopted by the participating devices. Unfortunately for software designers it is not clear how to contribute with local resources to the shared environment without compromising resources that could then be required by the contributors. Therefore, many designers adopt a conservative position when defining the collaboration strategy to be embedded in volunteer computing applications. This position produces an underutilization of the devices’ local resources and reduces the effectiveness of these solutions. This article presents a study that helps designers understand the impact of adopting a particular collaboration attitude to contribute with local resources to the distributed shared environment. The study considers five collaboration strategies, which are analyzed in computing environments with both, abundance and scarcity of resources. The obtained results indicate that collaboration strategies based on effort-based incentives work better than those using contribution-based incentives. These results also show that the use of effort-based incentives does not jeopardize the availability of local resources for the local needs.Peer ReviewedPostprint (published version

Estimación automática de grupos en entornos de aprendizaje cooperativo con aplicaciones sensibles al contexto

In collaborative learning scenarios, the use of computers and communication networks to facilitate collaboration is becoming popular, the Computer-Supported Collaborative Learning (CSCL). In face-to-face CSCL scenarios, participants are grouped for learning activities. The location information of the participants, very useful for computational support, may involve additional configuration work for students or teachers themselves. Mobile devices that facilitate this cooperation have evolved into ubiquitous computing. Sensing devices can capture context information that allows automate the detection of the location of users and objects involved in the learning scenario and use this contextual information to improve the support offered by computers. This thesis deals in detail the problem of computational support to the detection and management of learning groups in faceto-face CSCL environments. Our research has focused on the proposal of a system that automates the management of groups in these scenarios collecting and processing contextual information from sensors. To do this we have proposed a context model for this use and we have identified what information is most relevant to this domain application. This process of modeling and identification have been theoretical ---from conceptual frameworks that have allowed us to define a model of context--- and experimental ---from assessing the quality, reliability and sensitivity of contextual information in realistic environments---. Then we have verified how this contextual information fits the contextual model. Contextual information can pass through several stages before being used. First, the contextual information collected by the sensors could be conditioned and filtered to treat quality and uncertainty. Then it is supplied to an intelligent system that learns behavior patterns of groups and students. This intelligent system requires two different operating processes: training and estimation. We have proposed specific training and assessment processes for prediction and management of groups. The output also could be conditioned as it was done with the input. Finally, we used traces of contextual information in real scenarios ---real students doing group learning activities--- to validate the system. In this validation we have taken into account both the effectiveness and their impact on the activity of students and groups. From this impact assessment we have identified patterns in the contextual information and in the behavior that have allowed us to design a system of quality, error and uncertainty management in the group estimation and a filtering system and interpolation of contextual information ambiguous, missing or erroneous, and filtering and interpolation system of ambiguous, missing or erroneous contextual information. Our thesis is that to provide computational support to the detection and management of learning groups in face-to-face CSCL environments we need three basic functionalities: 1) the collection and filtering of changes in contextual information in realtime for each student and adjust them in the context model, 2) the transformation of this contextual information and thier historical to group membership by an intelligent algorithm and 3) the quality management of group estimations to minimize the impact on activity of students because of the uncertainty of these estimations.En escenaris d’aprenentatge col·laboratiu, s'ha introduït l’ús d’ordinadors i xarxes de comunicacions per facilitar aquesta col·laboració, l’anomenat “Computer-Supported Collaborative Learning” (CSCL). En escenaris CSCL presencials els participants s’agrupen per realitzar activitats d’aprenentatge. Aquesta informació sobre la disposició dels participants, de gran utilitat per al suport computacional, pot suposar un treball addicional de configuració per als propis estudiants o els professors. Els dispositius electrònics per facilitar aquesta cooperació han evolucionat fins la computació ubiqua, en la que dispositius sensors poden captar informació que permet a las aplicacions informàtiques automatitzar la detecció de la ubicació dels usuaris i objectes participants en l’escenari d’aprenentatge i usar aquesta informació contextual per millorar el suport ofert per els dispositius.Aquesta tesis tracta amb detall el problema del suport computacional a la detecció i gestió de grups d’aprenentatge en entorns CSCL presencials. La nostra investigació s’ha centrat en proposar un sistema que a partir de la recollida d’informació contextual proporcionada per sensors, automatitzi la gestió de grups en aquests escenaris CSCL presencials.Per açò hem proposat un modelo de context per aquest ús i hem identificat quina informació es mes rellevant per aquest proposit. Aquest modelat i identificació han estat tant teòrics ---a partir de marcs conceptuals que ens han permès definir un modelo de context--- com experimentals ---a partir de l’avaluació de la qualitat, fiabilitat i sensibilitat de la informació contextual en entorns realistes---. Finalment hem verificat cóm aquest informació contextual del nostre escenari s’adapta a aquest model.La informació contextual passa per varies fases per poder ser usada. Primer aquesta informació percebuda per els sensors es pot condicionar i filtrar per tractar la seva qualitat i incertesa. A continuació es subministra a un sistema intel·ligent que aprèn els patrons de funcionament dels grups i estudiants. Aquest sistema intel·ligent requereix dos processos diferents de funcionament: l’entrenament i l’estimació. Nosaltres hem proposat uns processos d’entrenament i estimació específics per la predicció i gestió de grups. També es pot tornar a condicionar la sortida igual que s’ha fet amb l’entrada.Finalment, per la validació del sistema hem utilitzat traces d’informació contextual d’escenaris reals ---amb estudiants reals realitzant activitats d’aprenentatge en grup---. En aquesta validació hem tingut present tant l’eficàcia del sistema como el seu impacte en l’activitat dels estudiants i grups. A partir d’aquest impacte hem identificat patrons en la informació contextual y en el comportament que ens ha permès introduir un sistema de gestió de la qualitat, errors i incertesa en la estimació, així com un sistema de filtratge i interpolació de la informació contextual ambigua, inexistent o errònia.La nostra tesis es que per proporcionar suport computacional a la detecció i estimació de grups de treball en activitats d'aprenentatge presencial en entorns CSCL són necessàries tres funcionalitats bàsiques: 1) la recol·lecció i filtrat en temps real dels canvis de la informació contextual de cada estudiant i recollir-los en el model contextual, 2) la transformació d'aquesta informació contextual el seu històric a informació contextual de grup per part d'un algoritme intel·ligente i 3) la gestió de la qualitat de les estimacions de grup per minimitzar l'impacte en l'atenció dels estudiants degut a la incertesa d'aquestes estimacions.En escenarios de aprendizaje colaborativo, se ha introducido el uso de ordenadores y redes de comunicación para facilitar esta colaboración, el llamado “Computer-Supported Collaborative Learning” (CSCL). En escenarios CSCL presenciales los participantes se agrupan para realizar actividades de aprendizaje. La información sobre la disposición de los participantes, de gran utilidad para dar soporte computacional, puede suponer un trabajo adicional de configuración para los propios estudiantes o los profesores. Los dispositivos electrónicos para facilitar esta cooperación han evolucionado hasta la computación ubicua, en que dispositivos sensores pueden captar información que permite a las aplicaciones informáticas automatizar la detección de la ubicación de los usuarios y objetos participantes en el escenario de aprendizaje y usar esa información contextual para mejorar el soporte ofrecido.Este trabajo trata con detalle el problema del soporte computacional a la detección y gestión de grupos de aprendizaje en entornos CSCL presenciales. Nuestra investigación se ha centrado en proponer un sistema que a partir de la recogida de información contextual proveniente de sensores, automatice la gestión de grupos en estos escenarios CSCL presenciales.Para ello hemos propuesto un modelo de contexto para este uso y hemos identificado qué información es más relevante para este propósito. Este modelado e identificación han sido tanto teóricos ---a partir de marcos conceptuales que nos han permitido definir un modelo de contexto--- como experimentales ---a partir de la evaluación de la calidad, fiabilidad y sensibilidad de la información contextual en entornos realistas---. Finalmente hemos verificado cómo esta información contextual de nuestro escenario se adapta a este modelo.La información contextual pasa por varias fases para ser usada. Primero esta información percibida por los sensores se puede acondicionar y filtrar para tratar su calidad e incertidumbre. A continuación se suministra a un sistema inteligente que aprende los patrones de funcionamiento de los grupos y estudiantes. Este sistema inteligente requiere dos procesos diferentes de funcionamiento: el entrenamiento y la estimación. Nosotros hemos propuesto unos procesos de entrenamiento y estimación específicos para la predicción y gestión de grupos. También se puede volver a acondicionar la salida como ya se ha hecho con la entrada.Finalmente, para la validación del sistema hemos utilizado trazas de información contextual de escenarios reales ---con estudiantes reales realizando actividades de aprendizaje en grupo---. En esta validación hemos tenido en cuenta tanto la eficacia del sistema como su impacto en la actividad de los estudiantes y grupos. A partir de este impacto hemos identificado ciertos patrones en la información contextual y en el comportamiento que nos ha permitido introducir un sistema de gestión de la calidad, errores e incertidumbre en la estimación así como un sistema de filtrado e interpolación de la información contextual ambigua, inexistente o errónea.Nuestra tesis es que para proporcionar soporte computacional a la detección y estimación de grupos de trabajo en actividades de aprendizaje presenciales en entornos CSCL son necesarias tres funcionalidades básicas: 1) la recolección y filtrado en tiempo real de los cambios de la información contextual de cada estudiante y recogerlos en el modelo contextual, 2) la transformación de esta información contextual y su histórico a información contextual de grupo por parte de un algoritmo inteligente y 3) la gestión de la calidad de las estimaciones de grupo para minimizar el impacto en la atención de los estudiantes debido a la incertidumbre de estas estimaciones.Postprint (published version

L’ús d’Atenea per co-avaluació entre grups

Atenea pot ser de gran utilitat per fer certes activitats de co-avaluació. Les co-avaluacions en una forma tradicional poden ser complexes de gestionar. L'exemple mes clar pot ser que un grup avaluï un document, pòster o presentació d'altres grups. Aquí presentarem com fer-ho amb l'activitat Fòrum. Encara Atenea té l'activitat taller per fer-ho a nosaltres ens ha donat molt bons resultats l'ús del Fòrum.Peer Reviewe

A role-based software architecture to support mobile service computing in IoT scenarios

The interaction among components of an IoT-based system usually requires using low latency or real time for message delivery, depending on the application needs and the quality of the communication links among the components. Moreover, in some cases, this interaction should consider the use of communication links with poor or uncertain Quality of Service (QoS). Research efforts in communication support for IoT scenarios have overlooked the challenge of providing real-time interaction support in unstable links, making these systems use dedicated networks that are expensive and usually limited in terms of physical coverage and robustness. This paper presents an alternative to address such a communication challenge, through the use of a model that allows soft real-time interaction among components of an IoT-based system. The behavior of the proposed model was validated using state machine theory, opening an opportunity to explore a whole new branch of smart distributed solutions and to extend the state-of-the-art and the-state-of-the-practice in this particular IoT study scenario.Peer ReviewedPostprint (published version

L’ús d’Atenea per gestionar l’assignació de temes, horaris, grups,...

Atenea pot ser de gran utilitat per fer certes assignacions. Assignacions que en una forma tradicional poden ser complexes. Els exemples mes clars poden ser: con assignar algun tema de treballar a cada estudiant, com assignar un horari de tutoria, consulta o demostracions a cada estudiant, com assignar els estudiants a grups de treball,... Per aquestes tasques podem utilitzar l’eina Consulta i que els estudiants participen de forma activa en aquestes assignacions.Peer Reviewe

Predicting topology propagation messages in mobile ad hoc networks: The value of history

This research was funded by the Spanish Government under contracts TIN2016-77836-C2-1-R,TIN2016-77836-C2-2-R, and DPI2016-77415-R, and by the Generalitat de Catalunya as Consolidated ResearchGroups 2017-SGR-688 and 2017-SGR-990.The mobile ad hoc communication in highly dynamic scenarios, like urban evacuations or search-and-rescue processes, plays a key role in coordinating the activities performed by the participants. Particularly, counting on message routing enhances the communication capability among these actors. Given the high dynamism of these networks and their low bandwidth, having mechanisms to predict the network topology offers several potential advantages; e.g., to reduce the number of topology propagation messages delivered through the network, the consumption of resources in the nodes and the amount of redundant retransmissions. Most strategies reported in the literature to perform these predictions are limited to support high mobility, consume a large amount of resources or require training. In order to contribute towards addressing that challenge, this paper presents a history-based predictor (HBP), which is a prediction strategy based on the assumption that some topological changes in these networks have happened before in the past, therefore, the predictor can take advantage of these patterns following a simple and low-cost approach. The article extends a previous proposal of the authors and evaluates its impact in highly mobile scenarios through the implementation of a real predictor for the optimized link state routing (OLSR) protocol. The use of this predictor, named OLSR-HBP, shows a reduction of 40–55% of topology propagation messages compared to the regular OLSR protocol. Moreover, the use of this predictor has a low cost in terms of CPU and memory consumption, and it can also be used with other routing protocols.Peer ReviewedPostprint (published version

Energy-Aware Topology Control Strategy for Human-Centric Wireless Sensor Networks

The adoption of mobile and ubiquitous solutions that involve participatory or opportunistic sensing increases every day. This situation has highlighted the relevance of optimizing the energy consumption of these solutions, because their operation depends on the devices’ battery lifetimes. This article presents a study that intends to understand how the prediction of topology control messages in human-centric wireless sensor networks can be used to help reduce the energy consumption of the participating devices. In order to do that, five research questions have been defined and a study based on simulations was conducted to answer these questions. The obtained results help identify suitable mobile computing scenarios where the prediction of topology control messages can be used to save energy of the network nodes. These results also allow estimating the percentage of energy saving that can be expected, according to the features of the work scenario and the participants behavior. Designers of mobile collaborative applications that involve participatory or opportunistic sensing, can take advantage of these findings to increase the autonomy of their solutions.Fil: Meseguer, Roc . Universidad Politecnica de Catalunya; EspañaFil: Molina, Carlos. Universitat Rovira I Virgili; EspañaFil: Ochoa, Sergio F.. Universidad de Chile; ChileFil: Santos, Rodrigo Martin. Universidad Nacional del Sur. Departamento de Ingenieria Electrica y de Computadoras. Laboratorio de Sistemas Digitales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Instituto de Investigación en Ingeniería Eléctrica; Argentin