IoT data processing pipeline in FoF perspective

With the development in the contemporary industry, the concepts of ICT and IoT are gaining more importance, as they are the foundation for the systems of the future. Most of the current solutions converge into transforming the traditional industry in new smart interconnected factories, aware of its context, adaptable to different environments and capable of fully using its resources. However, the full potential for ICT manufacturing has not been achieved, since there is not a universal or standard architecture or model that can be applied to all the existing systems, to tackle the heterogeneity of the existing devices. In a common factory, exists a large amount of information that needs to be processed into the system in order to define event rules accordingly to the related contextual knowledge, to later execute the needed actions. However, this information is sometimes heterogeneous, meaning that it cannot be accessed or understood by the components of the system. This dissertation analyses the existing theories and models that may lead to seamless and homogeneous data exchange and contextual interpretation. A framework based on these theories is proposed in this dissertation, that aims to explore the situational context formalization in order to adequately provide appropriate actions

Knowledge-Driven Harmonization of Sensor Observations: Exploiting Linked Open Data for IoT Data Streams

The rise of the Internet of Things leads to an unprecedented number of continuous sensor observations that are available as IoT data streams. Harmonization of such observations is a labor-intensive task due to heterogeneity in format, syntax, and semantics. We aim to reduce the effort for such harmonization tasks by employing a knowledge-driven approach. To this end, we pursue the idea of exploiting the large body of formalized public knowledge represented as statements in Linked Open Data

Continuous Monitoring of Environmental Disturbances by Cumulative Sums of Dense SAR Satellite Timeseries

Climate change together with growing socio-economic pressures are leading to a significant increase in alterations to natural ecosystems. The alteration of natural cycles and dynamics through the direct destruction or continuous degradation, are threatening the conservation of these natural spaces on a global scale. Satellite remote sensing is a suitable solution for large-scale monitoring and evaluation of natural landscapes under threat, as it provides a consistent source of information for both historical and updated environmental studies. However, most current remote sensing-based environmental monitoring tools still present certain limitations which hinder access to continuous and real-time information. The design and development of new methods and approaches to environmental remote sensing is required to mitigate the current environmental degradation trends. This thesis analyses the current challenges associated with environmental monitoring to focus on the development of new change detection methods applied to the study of environmental disturbances in highly dynamic natural ecosystems. By exploiting the frequent monitoring capabilities of Synthetic Aperture Radar (SAR) dense timeseries, this research introduces new approaches based on Cumulative Sum (CuSum) strategies for continuous and near-real-time investigation. These approaches have been applied to monitor permanent and cyclical disturbances in highly threatened forest and wetland ecosystems. The main scientific contribution of this thesis is the introduction of three novel SAR-based change detection approaches capable of exploiting dense satellite imagery time series for continuous and near-real-time monitoring. The outcome of this research provides environmental managers with a fully operational alternative tool capable of rapid and continuous monitoring of environmental dynamics

Improving Spatial and Temporal Coverage in Earth Observations through Inter-Sensor Data Harmonization

Data harmonization in remote sensing enhances the quality and utility of image data allowing unified big data to emerge from multiple sensors having different spatial and spectral properties. It works toward creating a single source of truth about targets of interest. A path to implementation will allow civil and commercial sensors to work together improving spatial and temporal coverage using current assets and then merge seamlessly with future sensors and constellations. Differences in design and function of various optical remote-sensing systems can be minimized by imaging reference ground sites containing targets designed specifically for radiometric, spectroscopic and spatial performance assessment among disparate sensors. The process allows difference in characteristics to be effectively identified revealing information for improved data integration. In this presentation we describe applications toward harmonization using an affordable, autonomous, and responsive ground site target configuration and operation for supporting vicarious calibration and sensor performance assessment of Earth-imaging satellites and constellations. The Specular Array Calibration (SPARC) method, is an adaptable in-flight calibration system that uses ground-based convex mirrors to create small reference targets capturing radiometric, spatial, spectral, geometric, and temporal characteristics of individual sensors for transforming distinct sensors and constellation into a harmonious Earth-monitoring system. The approach minimizes inaccuracies and inconstancies between data sources by equalizing radiometric content through a common traceability path and characterizing spatial effects impacting data interoperability