Multiphase sampling using expected value of information

This paper explores multiphase or infill sampling to reduce uncertainty after an initial sample has been taken and analysed to produce a map of the probability of some hazard. New observations are iteratively added by maximising the global expected value of information of the points. This is equivalent to minimisation of global misclassification costs. The method accounts for measurement error and different costs of type I and type II errors. Constraints imposed by a mobile sensor web can be accommodated using cost distances rather than Euclidean distances to decide which sensor moves to the next sample location. Calculations become demanding when multiple sensors move simultaneously. In that case, a genetic algorithm can be used to find sets of suitable new measurement locations. The method was implemented using R software for statistical computing and contributed libraries and it is demonstrated using a synthetic data set

The design of a Bayesian Network for mobility management in Wireless Sensor Networks

Mobility in Wireless Sensor Networks (WSNs) is achieved by attaching sensors to mobile objects such as animals (Juang et al. 2002), people (Campbell et al. 2008), and robots (Dantu et al. 2005). Currently, the research about WSN management is mainly focused on energy management functions to control how sensors should use their power; fault management functions to solve sensor problems; quality of services (QoS) management functions to quantify and control the performance; and mobility management functions to detect the sensor movement so that the network wireless connectivity is always maintained (Wang et al. 2010; Ruiz et al. 2003). However, the sensor mobility has not only an impact on the network connectivity, but also on the network spatial coverage. In mobile WSNs, the extension of the spatial coverage is often changing, and as a result, the region of interest might be inaccurately sensed by the mobile sensors. Therefore, the representation of a movement context is important to avoid making interpretations and decisions outside of the situation in which the WSN is capturing information; and make possible to decide where, when and how the sensing is performed in order to obtain the most suitable spatial coverage of a region of interest. This paper proposes a Bayesian network (BN) approach for making explicit the structural and parametric components of a movement context using WSN metadata. The aim is to infer mobility management requirements when a spatial coverage is incorrectly covering a Region of Interest (ROI), regardless the network connectivity. The BN approach provides several advantages regarding to the probabilistic representation of a movement context, the inference of mobility management requirements based on such a context, and the dynamic updating of the movement context every time new metadata are retrieved from the WSN. Previous research works in WSNs have used a similar approach focusing on energy management (Elnahrawy and Nath 2004) and prediction of sensor movement directions (Coles et al. 2009). The main contribution of our work is the analysis of how well a ROI is being covered by mobile sensors, and what are the requirements to improve that coverage given a movement context. A controlled experiment was carried out and the results show that, when the ROI is not being sufficiently covered by a WSN, the BN can probabilistically infer different mobility management requirements, based on a given movement context. Two movement contexts have been used to illustrate this approach. They are related to whether the sensing is being carried out in an emergency situation or not

Aportes para el estudio de situaciones de vulnerabilidad social en áreas inundables : El caso del arroyo Regimiento, Partido de La Plata

El presente trabajo comprende la presentación y desarrollo metodológico de un estudio que aborda el análisis de situaciones de vulnerabilidad social en asentamientos de sectores de bajos recursos sobre áreas inundables de alta fragilidad ambiental con la finalidad última de proponer lineamientos de planificación ambiental que incorporen estudios de riesgo para la localización o modificación de los usos de suelo urbanos en esas áreas. Se toma como caso de estudio la cuenca alta del Arroyo Regimiento en el Partido de La Plata por ser uno de los cursos de agua más afectado en la inundación de 2013, menos estudiados en el partido y con un importante avance en la ocupación irregular. Entre las variables consideradas se mencionan la identificación de áreas expuestas a inundaciones, la identificación de transformaciones en los usos de suelo, la caracterización de la población allí asentada y el análisis de instrumentos de planificación en relación al tratamiento de estas áreas. Se efectuará su estudio a partir de la sistematización de información bibliográfica, documentos de investigación, utilización de fuentes periodísticas, datos censales, relevamiento in situ y elaboración de cartografía temática en SIGFil: Pérez Ballari, Andrea. Universidad Nacional de La Plata. Facultad de Humanidades y Ciencias de la Educación. Instituto de Investigaciones en Humanidades y Ciencias Sociales (UNLP-CONICET); Argentina.Fil: Plot, Beatriz. Universidad Nacional de La Plata. Facultad de Humanidades y Ciencias de la Educación. Instituto de Investigaciones en Humanidades y Ciencias Sociales (UNLP-CONICET); Argentina

Transformaciones espaciales en la frontera socio-productiva del periurbano platense

El presente trabajo está dirigido a analizar y reflexionar acerca de las principales transformaciones espaciales dadas en el sector florihortícola del periurbano platense, en la última década, considerando dicho espacio como una frontera socio-productiva. La misma se caracteriza por ser un espacio dinámico y mutante, en el cual conviven y se entremezclan usos propios de lo urbano y lo rural, generándose en el mismo conflictos socio-ambientales derivados de la incidencia de los distintos actores que en él intervienen. La fragilidad de tal frontera, especialmente dada por albergar en su interior los suelos productivos más importantes a escala regional y encontrarse constantemente a expensas del avance de la mancha urbana de la ciudad platense, requiere de acciones tendientes a lograr la sustentabilidad ambiental de dicho espacio

etos para la investigación en infraestructuras de datos espaciales (IDE)

En los últimos años se ha producido un crecimiento sin precedentes del volumen, valor y uso de la información geográfica o georreferenciada. De hecho, la aparición del prefijo ‘geo’ junto a la más variada terminología (geomarketing, geovisualización, geoinformación, geociencias, etc.) evidencia la importancia de la referencia geográfica (Vintimilla y Ballari, 2009). Esta importancia se hace especialmente visible en los procesos de toma decisiones (Nebert, 2004) como el ordenamiento territorial, la gestión de emergencias, el manejo de recursos naturales y el estudio de impacto ambiental. Dada la influencia multidisciplinar de estas decisiones, la información requerida suele ser producida y gestionada por diferentes instituciones, volviéndose esencial el descubrimiento, acceso, integración y uso de la geoinformación proveniente de fuentes diversas (Nebert,2004). Las infraestructuras de datos espaciales (IDE) facilitan el acceso a geoinformación proveniente de fuentes diferentes, a través del establecimiento de normativas y del desarrollo de geoservicios web estandarizados. Los principales geoservicios de una IDE son los catálogos como metadatos, la visualización de cartografía online y el acceso a los datos mismos para su análisis espacial. Las IDE permiten,a través de la web, descubrir la geoinfor- mación existente en diferentes instituciones y acceder a ella de forma estandarizada. La investigación en IDE se ha centrado, por un lado, en los aspectos institucionales para lograr acuerdos y políticas que permitan compartir geoinformación, y por otro lado, en el desarrollo tecnológico y de estándares para los geoservicios. Sin embargo, un cambio en la dirección de la investigación en IDE se está evidenciando motivado por tres factores principales de cambio. El primero es el crecimiento sin precedentes en el uso de la geoinformación, que ha permitido situar a la geografía como el eje central para integrar cualquier tipo de información (Craglia et al., 2008). El segundo es la innovación tecnológica de los sensores de monitoreo que facilita el acceso, a un coste relativamente bajo,de datos dinámicos y en tiempo real (Bröring et al., 2011; Nittel, 2009). Finalmente, el tercero, es el avance de la web 2.0 que posiciona a los ciudadanos como participantes activos en la creación de la geoinformación (Goodchild,2007)

Mobile sensor networks for environmental monitoring

Vulnerability to natural disasters and the human pressure on natural resources have increased the need for environmental monitoring. Proper decisions, based on real-time information gathered from the environment, are critical to protecting human lives and natural resources. To this end, mobile sensor networks, such as wireless sensor networks, are promising sensing systems for flexible and autonomous gathering of such information. Mobile sensor networks consist of geographically deployed sensors very close to a phenomenon of interest. The sensors are autonomous, self-configured, small, lightweight and low powered, and they become mobile when they are attached to mobile objects such as robots, people or bikes. Research on mobile sensor networks has focused primarily on using sensor mobility to reduce the main sensor network limitations in terms of network topology, connectivity and energy conservation. However, how sensor mobility could improve environmental monitoring still remains largely unexplored. Addressing this requires the consideration of two main mobility aspects: sampling and mobility constraints. Sampling is about where mobile sensors should be moved, while mobility constraints are about how such movements should be handled, considering the context in which the monitoring is carried out. This thesis explores approaches for sensor mobility within a wireless sensor network for use in environmental monitoring. To achieve this goal, four sub-objectives were defined: Explore the use of metadata to describe the dynamic status of sensor networks. Develop a mobility constraint model to infer mobile sensor behaviour. Develop a method to adapt spatial sampling using mobile, constrained sensors. Extend the developed adaptive sampling method to monitoring highly dynamic environmental phenomena. Chapter 2 explores the use of metadata to describe the dynamic status of sensor networks. A context model was proposed to describe the general situation in which a sensor network is monitoring. The model consists of four types of contexts: sensor, network, sensing and organisation, where each of the contexts describes the sensor network from a different perspective. Metadata, which are descriptors of observed data, sensor configurations and functionalities, are used as parameters to describe what is happening in the different contexts. The results reveal that metadata are suitable for describing sensor network status within different contexts and reporting the status back to other components, systems or users. Chapter 3 develops a model which describes mobility constraints for inferring mobile sensor behaviour. The proposed mobility constraint model consists of three components: first, the context typology proposed in Chapter 2 to describe mobility constraints within the different contexts; second, a context graph, modelled as a Bayesian network, to encode dependencies of mobility constraints within the same or different contexts, as well as among mobility constraints and sensor behaviour; and third, contextual rules to encode how dependent mobility constraints are expected to constrain sensor behaviour. Metadata values for the monitored phenomenon and sensor properties are used to feed the context graph. They are propagated through the graph structure, and the contextual rules are used to infer the most suitable behaviour. The model was used to simulate the behaviour of a mobile sensor network to obtain a suitable spatial coverage in low and high fire risk scenarios. It was shown that the mobility constraint model successfully inferred behaviour, such as sleeping sensors, moving sensors and deploying more sensors to enhance spatial coverage. Chapter 4 develops a spatial sampling strategy for use with mobile, constrained sensors according to the expected value of information (EVoI) and mobility constraints. EVoI allows decisions to be made about the location to observe. It minimises the expected costs of wrong predictions about a phenomenon using a spatially aggregated EVoI criterion. Mobility constraints allow decisions to be made about which sensor to move. A cost-distance criterion is used to minimise unwanted effects of sensor mobility on the sensor network itself, such as energy depletion. The method was assessed by comparing it with a random selection of sample locations combined with sensor selection based on a minimum Euclidian distance criterion. The results demonstrate that EVoI enables selection of the most informative locations, while mobility constraints provide the needed context for sensor selection. Chapter 5 extends the method developed in Chapter 4 for the case of highly dynamic phenomena. It develops a method for deciding when and where to sample a dynamic phenomenon using mobile sensors. The optimisation criterion is to maximise the EVoI from a new sensor deployment at each time step. The method was demonstrated in a scenario in which a simulated fire in a chemical factory released polluted smoke into the open air. The plume varied in space and time because of variations in atmospheric conditions and could be only partially predicted by a deterministic dispersion model. In-situ observations acquired by mobile sensors were considered to improve predictions. A comparison with random sensor movements and the previous sensor deployment without performing sensor movements shows that the optimised sensor mobility successfully reduced risk caused by poor model predictions. Chapter 6 synthesises the main findings and presents my reflections on the implications of such findings. Mobile sensors for environmental monitoring are relevant to improving monitoring by selecting sampling locations that deliver the information that most improves the quality of decisions for protecting human lives and natural resources. Mobility constraints are relevant to managing sensor mobility within sampling strategies. The traditional consideration of mobility constraints within the field of computer sciences mainly leads to sensor self-protection rather than to the protection of human beings and natural resources. By contrast, when used for environmental monitoring, mobile sensors should above all improve monitoring performance, even thought this might produce negative effects on coverage, connectivity or energy consumption. Thus, mobility constraints are useful for reducing such negative effects without constraining the sampling strategy. Although sensor networks are now a mature technology, they are not yet widely used by surveyors and environmental scientists. The operational use of sensor networks in geo-information and environmental sciences therefore needs to be further stimulated. Although this thesis focuses on wireless sensor network, other types of informal sensor networks could be also relevant for environmental monitoring, such as smart phones, volunteer citizens and sensor web. Finally, the following recommendations are given for further research: extend the sampling strategy for dynamic phenomena to take account of mobility constraints; develop sampling strategies that take a decentralised approach; focus on mobility constraints related to connectivity and data transmission; elicit expert knowledge to reveal preferences for sensor mobility under mobility constraints within different types of environmental applications; and validate the proposed strategies in operational implementations. </p

Mismatch between media coverage and research on invasive species: The case of wild boar (Sus scrofa) in Argentina

Invasive species are a pervasive driver of global change with increasing media coverage. Media coverage and framing can influence both invasive species management and policies, as well as shed light on research needs. Using the wild boar (Sus scrofa) invasion in Argentina as a case study, we conducted a content analysis of media coverage and scientific articles. Specifically, we compared news and scientific articles based on their emphasis: ecological, economic, and health impacts and the overall perception portrayed in the news: “positive” when the articles emphasized benefits from wild boar and “negative” when focused on damage and/or loss. A literature search using Google news, Web of Science, Scielo, and Google Scholar yielded a total of 194 news articles and 37 research papers on wild boar in Argentina. More than half of the news articles focused on economic impacts of wild boar (56%) such as sport hunting, illegal hunting, and road accidents; while 27% focused on ecological impacts, and 10% on health impacts. In contrast, the majority of the scientific articles (65%) focused on ecological impacts of wild boar on native species and ecosystems; while 21% were related to health impacts and only 8.3% of scientific articles were related to economic impacts. This mismatch between media and science reveals a disconnection between social and scientific interests in wild boar and their management in Argentina, and it provides insights to research needs and prevention of management conflicts. Additionally, we found that 66.8% of news articles focused on “negative” aspects of wild boar, while 33.2% of news articles portrayed “positive” perceptions. This finding is very important because the management of invasive species such as wild boar usually requires lethal techniques, and the success of the programs depend on favorable social and political support. Good science communication is therefore key to helping scientists and managers perform more effective management actions.Fil: Ballari, Sebastián A.. Administración de Parques Nacionales. Parque Nacional "Nahuel Huapi"; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte; ArgentinaFil: Barrios Garcia Moar, Maria Noelia. Administración de Parques Nacionales. Parque Nacional "Nahuel Huapi"; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte; Argentin

The Interoperability of Wireless Sensor Networks

The interoperability of heterogeneous sensor networks is needed for the achievement of a world integrated sensing system. The aim of this paper is to describe the results of an exploratory study which has been carried out to determine the role of metadata in an interoperability model for Wireless Sensor Networks. This model includes a description of the observations, processes, functionalities, status and configuration of a network to help improving the knowledge of a network itself, as well as to ensure the integration with other sensor networks. The results demonstrate the use of metadata to support different interoperability levels of Wireless Sensor Networks as a first step towards defining an interoperability model of Wireless Sensor Network