A Review of the Enviro-Net Project

Ecosystems monitoring is essential to properly understand their development and the effects of events, both climatological and anthropological in nature. The amount of data used in these assessments is increasing at very high rates. This is due to increasing availability of sensing systems and the development of new techniques to analyze sensor data. The Enviro-Net Project encompasses several of such sensor system deployments across five countries in the Americas. These deployments use a few different ground-based sensor systems, installed at different heights monitoring the conditions in tropical dry forests over long periods of time. This paper presents our experience in deploying and maintaining these systems, retrieving and pre-processing the data, and describes the Web portal developed to help with data management, visualization and analysis.Comment: v2: 29 pages, 5 figures, reflects changes addressing reviewers' comments v1: 38 pages, 8 figure

Wireless in-situ sensor network for agriculture and water monitoring on a river basin scale in Southern Finland: evaluation from a data user's perspective

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Geosensors to Support Crop Production: Current Applications and User Requirements

Sensor technology, which benefits from high temporal measuring resolution, real-time data transfer and high spatial resolution of sensor data that shows in-field variations, has the potential to provide added value for crop production. The present paper explores how sensors and sensor networks have been utilised in the crop production process and what their added-value and the main bottlenecks are from the perspective of users. The focus is on sensor based applications and on requirements that users pose for them. Literature and two use cases were reviewed and applications were classified according to the crop production process: sensing of growth conditions, fertilising, irrigation, plant protection, harvesting and fleet control. The potential of sensor technology was widely acknowledged along the crop production chain. Users of the sensors require easy-to-use and reliable applications that are actionable in crop production at reasonable costs. The challenges are to develop sensor technology, data interoperability and management tools as well as data and measurement services in a way that requirements can be met, and potential benefits and added value can be realized in the farms in terms of higher yields, improved quality of yields, decreased input costs and production risks, and less work time and load

Support Vector Methods for Higher-Level Event Extraction in Point Data

Phenomena occur both in space and time. Correspondingly, ability to model spatiotemporal behavior translates into ability to model phenomena as they occur in reality. Given the complexity inherent when integrating spatial and temporal dimensions, however, the establishment of computational methods for spatiotemporal analysis has proven relatively elusive. Nonetheless, one method, the spatiotemporal helix, has emerged from the field of video processing. Designed to efficiently summarize and query the deformation and movement of spatiotemporal events, the spatiotemporal helix has been demonstrated as capable of describing and differentiating the evolution of hurricanes from sequences of images. Being derived from image data, the representations of events for which the spatiotemporal helix was originally created appear in areal form (e.g., a hurricane covering several square miles is represented by groups of pixels). ii Many sources of spatiotemporal data, however, are not in areal form and instead appear as points. Examples of spatiotemporal point data include those from an epidemiologist recording the time and location of cases of disease and environmental observations collected by a geosensor at the point of its location. As points, these data cannot be directly incorporated into the spatiotemporal helix for analysis. However, with the analytic potential for clouds of point data limited, phenomena represented by point data are often described in terms of events. Defined as change units localized in space and time, the concept of events allows for analysis at multiple levels. For instance lower-level events refer to occurrences of interest described by single data streams at point locations (e.g., an individual case of a certain disease or a significant change in chemical concentration in the environment) while higher-level events describe occurrences of interest derived from aggregations of lower-level events and are frequently described in areal form (e.g., a disease cluster or a pollution cloud). Considering that these higher-level events appear in areal form, they could potentially be incorporated into the spatiotemporal helix. With deformation being an important element of spatiotemporal analysis, however, at the crux of a process for spatiotemporal analysis based on point data would be accurate translation of lower-level event points into representations of higher-level areal events. A limitation of current techniques for the derivation of higher-level events is that they imply bias a priori regarding the shape of higher-level events (e.g., elliptical, convex, linear) which could limit the description of the deformation of higher-level events over time. The objective of this research is to propose two newly developed kernel methods, support vector clustering (SVC) and support vector machines (SVMs), as means for iii translating lower-level event points into higher-level event areas that follow the distribution of lower-level points. SVC is suggested for the derivation of higher-level events arising in point process data while SVMs are explored for their potential with scalar field data (i.e., spatially continuous real-valued data). Developed in the field of machine learning to solve complex non-linear problems, both of these methods are capable of producing highly non-linear representations of higher-level events that may be more suitable than existing methods for spatiotemporal analysis of deformation. To introduce these methods, this thesis is organized so that a context for these methods is first established through a description of existing techniques. This discussion leads to a technical explanation of the mechanics of SVC and SVMs and to the implementation of each of the kernel methods on simulated datasets. Results from these simulations inform discussion regarding the application potential of SVC and SVMs

Technological Advances in Wireless Sensor Network Systems for Urban Drainage Monitoring

Urban drainages are important for evacuation of waste water in cities. It helps for the smooth running of the daily activities in the city and prevents proliferation of diseases. Drainage systems and construction methods have not evolved much in the past years. Due to population growth, urbanization and climatic changes, our urban drainages have become inefficient. Localized heavy rainfall causes overflow of drains that lead to floods resulting in major infrastructural damages and loss of lives. Obstruction due to solid waste prevents effective waste water evacuation. In this paper existing drainage monitoring systems are identified and their monitoring methods and technologies are analysed. Current drainage water monitoring methods such as the Rational method, the Modified Rational method, the SCS Runoff method, the Saint-Venant equation and the Manning’s equation are not reliable and only provide estimated value for peak discharge and mean water velocity. Wireless sensor network systems for monitoring drains and rivers in different regions such as Birmingham, Brazil, Philippines and Mississippi are thoroughly discussed. Wireless sensors and microprocessor platforms that may be used for the urban drainage monitoring are evaluated. A systematic review of the research challenges for real-time monitoring of urban drainages is carried out. Furthermore, possible solutions that use advanced sensor technologies to detect overflow and obstruction in urban drainages are analysed. Indeed this paper provides a comprehensive assessment of technological advances in urban drainage monitoring systems. Keywords: wireless sensor networks, urban drainage monitoring, water flow monitoring, overflow detection, obstruction detectio

Aggregate Farming in the Cloud: The AFarCloud ECSEL project

Farming is facing many economic challenges in terms of productivity and cost-effectiveness. Labor shortage partly due to depopulation of rural areas, especially in Europe, is another challenge. Domain specific problems such as accurate monitoring of soil and crop properties and animal health are key factors for minimizing economical risks, and not risking human health. The ECSEL AFarCloud (Aggregate Farming in the Cloud) project will provide a distributed platform for autonomous farming that will allow the integration and cooperation of agriculture Cyber Physical Systems in real-time in order to increase efficiency, productivity, animal health, food quality and reduce farm labor costs. Moreover, such a platform can be integrated with farm management software to support monitoring and decision-making solutions based on big data and real-time data mining techniques.publishedVersio

Simultaneous measurements of soil moisture and streamflow in small catchments reveal varied coupling across sites, seasons, and timescales

Soil moisture is an important component in the interaction of terrestrial and aquatic systems, as it may play a role in regulating streamflow and the delivery of nutrients from soils to streams. There are few studies that collect in situ soil moisture and stream discharge data simultaneously at the same location across different land uses at a fine enough temporal resolution to understand processes at sub-daily timescales. I examined the relationship between soil moisture and streamflow over varying timescales using concurrent, high temporal frequency (one hour) in situ measurements of soil volumetric water content and stream discharge at five headwater catchments with different land use characteristics. I found that soil moisture and streamflow appear to be coupled, and that antecedent moisture conditions and seasonal change in temperature and precipitation regulated this coupling. Furthermore, each site/land use had a different coupling relationship and the antecedent requirements to induce coupling differed by site. I also found that depth in the soil profile, timescale, and site specific characteristics all played a role in streamflow coupling. Simultaneous measurement of streamflow and soil moisture across different spatial and temporal scales is key to understanding the actual physical connectivity between terrestrial and aquatic systems. Strategic placement of in situ sensor networks will allow us to better understand the interactions among atmosphere, land, and water that couple soils and surface waters