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Extending INSPIRE to the internet of things through sensorthings API
Abstract and Figures
Spatial Data Infrastructures (SDI) established during the past two decades “unlocked” heterogeneous geospatial datasets. The European Union INSPIRE Directive laid down the foundation of a pan-European SDI where thousands of public sector data providers make their data, including sensor observations, available for cross-border and cross-domain reuse. At the same time, SDIs should inevitably adopt new technology and standards to remain fit for purpose and address in the best possible way the needs of different stakeholders (government, businesses and citizens). Some of the recurring technical requirements raised by SDI stakeholders include: (i) the need for adoption of RESTful architectures; together with (ii) alternative (to GML) data encodings, such as JavaScript Object Notation (JSON) and binary exchange formats; and (iii) adoption of asynchronous publish–subscribe-based messaging protocols. The newly established OGC standard SensorThings API is particularly interesting to investigate for INSPIRE, as it addresses together all three topics. In this manuscript, we provide our synthesised perspective on the necessary steps for the OGC SensorThings API standard to be considered as a solution that meets the legal obligations stemming out of the INSPIRE Directive. We share our perspective on what should be done concerning: (i) data encoding; and (ii) the use of SensorThings API as a download service.
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... Recently, SensorThings API has been used by several domains. For example, SensorThings has been used as a service for managing heterogenous air quality sensor data in the European Union Infrastructure for Spatial Information in the European Community (INSPIRE) (Kotsev et al. 2018) and managing COVID-19 statistics . Additionally, the SensorThings API is expandable to manage dynamic time-series datasets in the CityGML 3D city models; the systematic study on this topic was conducted by Santhanavanich and Coors (2021). ...
In this chapter, we present an approach of enriching photogrammetric point clouds with semantic information extracted from images of digital cameras or smartphones to enable a later automation of BIM modelling with object-oriented models. Based on the DeepLabv3+ architecture, we extract building components and objects of interiors in full 3D. During the photogrammetric reconstruction, we project the segmented categories derived from the images into the point cloud. Based on the semantic information, we align the point cloud, correct the scale and extract further information. The combined extraction of geometric and semantic information yields a high potential for automated BIM model reconstruction.
... Recently, SensorThings API has been used by several domains. For example, SensorThings has been used as a service for managing heterogenous air quality sensor data in the European Union Infrastructure for Spatial Information in the European Community (INSPIRE) (Kotsev et al. 2018) and managing COVID-19 statistics . Additionally, the SensorThings API is expandable to manage dynamic time-series datasets in the CityGML 3D city models; the systematic study on this topic was conducted by Santhanavanich and Coors (2021). ...
The safety, proper maintenance, and renovation of tunnel structures have become a critical problem for urban management in view of the aging of tunnels. Tunnel inspection and inventory are regulated by construction laws and must be carried out at regular intervals. Advances in digitalization and machine vision technologies enable the development of an automated and BIM-based system to collect data from tunnel surfaces. In this study, a tunnel inspection system using vision-based systems and the related principles are introduced to measure the tunnel surfaces efficiently. In addition, the main components and requirements for subsystems are presented, and different challenges in data acquisition and point cloud generation are explained based on investigations during initial experiments.
... Recently, SensorThings API has been used by several domains. For example, SensorThings has been used as a service for managing heterogenous air quality sensor data in the European Union Infrastructure for Spatial Information in the European Community (INSPIRE) (Kotsev et al. 2018) and managing COVID-19 statistics . Additionally, the SensorThings API is expandable to manage dynamic time-series datasets in the CityGML 3D city models; the systematic study on this topic was conducted by Santhanavanich and Coors (2021). ...
Energy-efficient heating and cooling systems as well as intelligent systems for energy distribution are urgently required in order to be able to meet the ambitious goals of the European Union to reduce greenhouse gas emissions. The present article is intended to show that intelligent system extensions for the area of heating, cooling and electricity production for the industrial sector can lead to significant increase in efficiency. For this purpose, a simulation study for the expansion of a combined heat and power (CHP) plant with 2 MW thermal output using a 1.4 MW absorption chiller has been carried out. This shows that a heat-controlled CHP unit can significantly increase its running time. A system model was created for the initial situation and validated with existing measurement data. In the second step, this model was expanded to include the ACM module. The simulation was able to prove that in the event of a system expansion, the run time of the CHP unit can be increased by 35%. In addition to then increase of energy efficiency in the supply system, the analysis also focuses on the efficiency of the energy distribution via thermal networks in an industrial environment. The presented paper therefore also highlights the optimization potentials in the operation of thermal supply networks for industrial applications. For this purpose, a mathematical model has been developed which in addition to the components of the thermal network itself also comprises the producers and consumers. The specific construction of thermal networks for the supply of industrial properties requires adapted solutions for the simulation of such systems. Therefore, amongst other things, in the paper, solutions are shown for the modelling of direct flow local heating networks as well as for the operation of a cascade-controlled pump group.
... Recently, SensorThings API has been used by several domains. For example, SensorThings has been used as a service for managing heterogenous air quality sensor data in the European Union Infrastructure for Spatial Information in the European Community (INSPIRE) (Kotsev et al. 2018) and managing COVID-19 statistics . Additionally, the SensorThings API is expandable to manage dynamic time-series datasets in the CityGML 3D city models; the systematic study on this topic was conducted by Santhanavanich and Coors (2021). ...
Effective government management, convenient public services, and sustainable industrial development are achieved by the thorough utilization and management of green, renewable resources. The research and the study of meteorological data and its effect on devising renewable solutions as a replacement for nonrenewable ones is the motive of researchers and city planners. Sources of energy like wind and solar are free, green, and popularly being integrated into sustainable development and city planning to preserve environmental quality. Sensor networks have become a convenient tool for environmental monitoring. Wind energy generated through the use and maintenance of wind turbines requires knowledge of wind parameters such as speed and direction for proper maintenance. An augmented reality (AR) tool for interactive visualization and exploration of future wind nature analyses for experts is still missing. Existing solutions are limited to graphs, tabular data, two-dimensional space (2D) maps, globe view, and GIS tool designed for the desktop and not adapted with AR for easy, interactive mobile use. This work aims to provide a novel AR-based mobile supported application (App) that serves as a bridge between three-dimensional space (3D) temporal wind dataset visualization and predictive analysis through machine learning (ML). The proposed development is a dynamic application of AR supported with ML. It provides a user interactive designed approach, presenting a multilayered infrastructure process accessed through a mobile AR platform that supports 3D visualization of temporal wind data through future wind analysis. Thus, a novel AR visualization App with the prediction of wind nature using ML algorithms would provide city planners with advanced knowledge of wind conditions and help in easy decision-making with interactive 3D visualization.
... Recently, SensorThings API has been used by several domains. For example, SensorThings has been used as a service for managing heterogenous air quality sensor data in the European Union Infrastructure for Spatial Information in the European Community (INSPIRE) (Kotsev et al. 2018) and managing COVID-19 statistics . Additionally, the SensorThings API is expandable to manage dynamic time-series datasets in the CityGML 3D city models; the systematic study on this topic was conducted by Santhanavanich and Coors (2021). ...
Buildings are accountable for about one third of the greenhouse gas emissions in Germany. An important step toward the reduction of greenhouse gases is to decarbonize the power productions and heating systems. However, in an energy system with a high share of renewable energy sources, large shares of energy have to be stored in summer for the winter season. Chemical energy storages, in this case hydrogen, can provide these qualities and offer diverse opportunities for coupling different sectors.
In this work, a simulation model is introduced which combines a PEM electrolyzer, a hydrogen compression, a high-pressure storage, and a PEM fuel cell for power and heat production. Applied on a building cluster in a rural area with existing PV modules, this system is optimized for operation as a district heating system based on measured and forecasted data. Evolutionary algorithms were used to determine the optimized system parameters.
The investigated system achieves an overall heat demand coverage of 63%. However, the local hydrogen production is not sufficient to meet the fuel cell demand. Several refills of the storage tanks with delivered hydrogen would be necessary within the year studied.
... Recently, SensorThings API has been used by several domains. For example, SensorThings has been used as a service for managing heterogenous air quality sensor data in the European Union Infrastructure for Spatial Information in the European Community (INSPIRE) (Kotsev et al. 2018) and managing COVID-19 statistics . Additionally, the SensorThings API is expandable to manage dynamic time-series datasets in the CityGML 3D city models; the systematic study on this topic was conducted by Santhanavanich and Coors (2021). ...
For intelligent mobility concepts in growing urban environments, positioning of transportation vehicles and generally moving objects is a fundamental prerequisite. Global Navigation Satellite Systems (GNSS) are commonly used for this purpose, but especially in urban environments under certain conditions, they offer limited accuracy due to buildings, tunnels, etc. that can deviate or mask the satellite signals. The use of existing built-in sensors of the vehicle and the installation of additional sensors can be utilized to describe the movement of the vehicle independently of GNSS. This conforms to the concept of dead reckoning (DR). Both systems (GNSS and DR) can be integrated and prepared to work together since they compensate their respective weaknesses efficiently. In this study, a method to integrate different inertial sensors (gyroscope and accelerometer) and GNSS is investigated. Pedelecs usually do not have many inbuilt additional sensors like it is the case in cars; therefore, additional low-cost sensors have to be used. An extended Kalman filter (EKF) is the base of calculations to perform data integration. Driving tests are realized to check the performance of the integration model. The results show that positioning in situations where GNSS data is not available can be done through dead reckoning for a short period of time. The weak point hereby is the calibration of the accelerometer. Inaccurate accelerometer data cause increasing inaccuracy of the position due to the double integration of the acceleration over time.
... Recently, SensorThings API has been used by several domains. For example, SensorThings has been used as a service for managing heterogenous air quality sensor data in the European Union Infrastructure for Spatial Information in the European Community (INSPIRE) (Kotsev et al. 2018) and managing COVID-19 statistics . Additionally, the SensorThings API is expandable to manage dynamic time-series datasets in the CityGML 3D city models; the systematic study on this topic was conducted by Santhanavanich and Coors (2021). ...
Controlled natural ventilation in office buildings can ensure the indoor thermal comfort while reducing the life cycle energy consumption for ventilation, compared to mechanical ventilation systems (e.g. HVAC). Natural ventilation is mostly used in moderate climate zones where air conditioning is not a standard. During intermediate seasons, buildings with HVAC systems can additionally use natural ventilation to reduce energy consumption. However, in dense urban areas, natural ventilation can be problematic in terms of acoustic comfort. Here, a box-type window can serve as a compromise between thermal and acoustic comfort. Due to the more complex handling of the box-type window, an automated (electric driven) novel box-type window approach was developed within the imaF project, a part of the iCity initiative. The following article describes the basics of automated natural ventilation, acoustic characterization as well as architectural integration of this window type and the optimization of the airflow through box-type windows. The results show that the proposed geometry can provide sound insulation while providing an appropriate air exchange rate.
The Web of Things is an improvement on the Internet of Things (IoT) that incorporates smart objects into both the web architecture (application) and the internet (network). WoT applications are inescapable in residential homes and communities. The intent behind WoT applications is to increase sustainable development for reducing resource consumption. The Web of Things (WoT) aims to create a decentralized Internet of Things. Edge computing addresses IoT computing demands by reducing the escalation in resource congestion situations. In edge computing data is placed closed to the end users which diverts computation load from the centralized data centers. Furthermore, the dispersed structure balances network traffic and minimizes traffic peaks in IoT networks. Therefore, resulting in reducing transmission delays between edge servers and end users which improves response times for real-time WoT applications. Low battery supply to nodes with enough power resources can increase the lifespan of the individual nodes by moving processing and communication overhead from the nodes. This paper integrates WoT and edge computing and compares their functionalities. In addition, it demonstrates how edge computing enhances WoT performance and concentrates on transmission, storage, and computation aspects. Furthermore, for performance evaluation it categorizes edge computing based on different architectures. Moreover, the challenges of Web of Things and edge computing have been discussed in terms of bandwidth, latency, energy, and cost. Finally, advantages of the Web of Things and edge computing have been discussed.
Instrumentation and measurement technologies are currently playing a key role in the monitoring, assessment, and protection of water resources. The whole water sector involves multiple technological contexts for the monitoring of the resource, given the broad multidisciplinary context, which covers water from its natural domains up to the various man-made infrastructures. Water cycle management refers to a very complex framework, which requires reliable technological responses to the questions raised in meteorology, hydrology, water resources management, hydraulic engineering, and, more in general, environmental management, with the related societal implications. Measurement techniques and sensing methods for observing water systems are rapidly evolving, requiring a continuous update in measurement technologies and methods.
It is clear that effective and sustainable planning of the water cycle management requires the design and implementation of a systematic monitoring approach. In particular, instrumentation and measurement technologies have a pervasive presence in all the necessary aspects of the assessment, monitoring, and control of water systems. Thus, the assessment of the water resource and its relationship with the various environmental stressors, including the anthropic pressures on it, requires adequate knowledge, technologies, and infrastructures to deal with the challenges of today. It is also important to underline that this aspect applies to both quantitative and qualitative monitoring activities, being the threats to the quality of the resource also indirectly affecting its availability and quantity.
This book provides an updated framework of observational techniques, sensing technologies, and
water management and protection data processing. In data analytics, attention is given to the synergy between different sensing systems and between measurements and modeling approaches. The coexistence in this book of measurement techniques, sensing methods, and data science implications for the observation of water systems, emphasize the strong link between measurement aspects and computational and modeling
The present volume provides a portrait of current measurement technologies and data analysis approaches for water systems monitoring and management, also offering insights into the enabling technologies that are today fostering the concept of smart water systems.
The 23 chapters of this book are organized to give a survey of current technologies and available methods for the assessment and monitoring of water resources in multiple domains. In particular, the selected contributions are intended to cover the following thematic areas: (i) remote sensing methods; (ii) instrumentation for direct water sensing; (iii) water sensor networks and ICT infrastructures; (iv) geophysical techniques; (v) synergy between measurements and modeling.
For more details, please visit the Springer website:
https://link.springer.com/book/10.1007/978-3-031-08262-7
Water management associations are responsible for monitoring large catchments and water bodies and thus have a great need for various hydrological measurement data. Most often, this comprises meteorological parameters as well as physical and chemical observations of water reservoirs, rivers and dams. In this context, low-cost sensors provide a great potential for densifying comprehensive monitoring systems. Typically, such a system requires the interoperable sharing of hydrological observation data as well as an efficient communication between the sensor devices. To establish smart water monitoring solutions, a sensor network infrastructure is needed that facilitates the management of a large amount of observation data and applies modern Internet of Things (IoT) communication approaches. This chapter aims to provide guidance in setting up a measurement data infrastructure that relies on both, standards of the well-established Sensor Web Enablement framework of the Open Geospatial Consortium (OGC) and IoT technologies. We introduce a concept for a modular water monitoring system that combines Sensor Web and IoT technologies to integrate sensor data in Spatial (Research) Data Infrastructures. Furthermore, the feasibility of this approach will be demonstrated with the presentation of an operational deployment at a German water management association.
The Internet of Things (IoT) is an infrastructure that interconnects uniquely-identifiable devices using the Internet. By interconnecting everyday appliances, various monitoring, and physical mashup applications can be constructed to improve human's daily life. In general, IoT devices provide two main capabilities: sensing and tasking capabilities. While the sensing capability is similar to the World-Wide Sensor Web, this research focuses on the tasking capability. However, currently, IoT devices created by different manufacturers follow different proprietary protocols and are locked in many closed ecosystems. This heterogeneity issue impedes the interconnection between IoT devices and damages the potential of the IoT. To address this issue, this research aims at proposing an interoperable solution called tasking capability description that allows users to control different IoT devices using a uniform web service interface. This paper demonstrates the contribution of the proposed solution by interconnecting different IoT devices for different applications. In addition, the proposed solution is integrated with the OGC SensorThings API standard, which is a Web service standard defined for the IoT sensing capability. Consequently, the Extended SensorThings API can realize both IoT sensing and tasking capabilities in an integrated and interoperable manner.
The emergence of the Internet of Things has accelerated the development of smart cities. Currently, an increasing number of smart city applications comprising various scenarios and use cases is being deployed and tested. However, to realize a smart city, it is necessary to have converging solutions as opposed to niche, detached solutions. Smart city platforms need to take into account existing services and infrastructures. Furthermore, in the context of a smart city it is important to account for various kinds of stakeholders with a variety of requirements with regard to data ownership, legal and organizational frameworks as well as commercial interests. We present the ALMANAC Smart City Platform as a solution which handles the described heterogeneity. Firstly, the platform allows the development of smart city applications by creating interoperability among devices and services. Secondly, the platform is deployed in a de-centralized, federated way that allows to map the organizational requirements to the deployed city systems. We introduce the concept of federation for smart cities and describe its implementation in the ALMANAC Platform through a case study which simulates a scenario in the waste management domain. This scenario models realworld requirements of a waste management company and the municipality of Turin.
The need for constant monitoring of environmental conditions has produced an increase in the development of wireless sensor networks (WSN). The drive towards smart cities has produced the need for smart sensors to be able to monitor what is happening in our cities. This, combined with the decrease in hardware component prices and the increase in the popularity of open hardware, has favored the deployment of sensor networks based on open hardware. The new trends in Internet Protocol (IP) communication between sensor nodes allow sensor access via the Internet, turning them into smart objects (Internet of Things and Web of Things). Currently, WSNs provide data in different formats. There is a lack of communication protocol standardization, which turns into interoperability issues when connecting different sensor networks or even when connecting different sensor nodes within the same network. This work presents a sensorized platform proposal that adheres to the principles of the Internet of Things and the Web of Things. Wireless sensor nodes were built using open hardware solutions, and communications rely on the HTTP/IP Internet protocols. The Open Geospatial Consortium (OGC) SensorThings API candidate standard was used as a neutral format to avoid interoperability issues. An environmental WSN developed following the proposed architecture was built as a proof of concept. Details on how to build each node and a study regarding energy concerns are presented. Sensors 2015, 15 5556
The Sensor Observation Service (SOS) [3] provides access to near real-time environmental data, or observations, in a standardized way. Thereby, the SOS offers flexible spatial, temporal, and thematic filtering capabilities that enable clients to query and discover large sources of time series data over the Web. The SOS standard is already in version 2.0 [6] and applied in many projects and organizational infrastructures (see e.g., [4]). The data encoding leveraged by SOS is the Observations & Measurements (O&M) standard, which is been introduced in the INSPIRE data specification through the Guidelines for the use of O&M. O&M data can also be accessed through the Web Feature Service (WFS), which has been incorporated in INSPIRE [2] as an implementation of the INSPIRE Download Service [1]. However, the WFS interface is very generic and not optimized for O&M data access. So, an inclusion of the SOS in the INSPIRE Technical Guidance (TG) is desired. Hence, this work analyses the SOS specification on how it can be enhanced to conform to the implementation rules for INSPIRE download services.
The Open Geospatial Consortium (OGC) standards activities that focus on sensors and sensor networks comprise an OGC focus
area known as Sensor Web Enablement (SWE). Readers interested in greater technical and architecture details can download the
OGC SWE Architecture Discussion Paper titled “The OGC Sensor Web Enablement Architecture” (OGC document 06-021r1).
This document provides a high level overview if the Sensor Web Enablement work of the Open Geospatial Consortium. This paper provides a high level architecture and includes descriptions of the OGC sensor interface and encoding standards that have been approved or are soon to be approved.
Back in the 1990s, there were several barriers for accessing and using the spatial data and information necessary for environmental management and policy making in Europe. These included different data policies, encodings, formats and semantics, to name a few. Data was collected for, and applied to, domain specific use cases and comprehensive standards did not exist, all impacting on the re-usability of such public sector data. To release the potential of spatial data held by public authorities and improve evidence-based environmental policy making, action was needed at all levels (Local, Regional, National, European) to introduce more effective data and information management and to make data available for citizens’ interest. The INSPIRE Directive, the Infrastructure for Spatial Information in Europe, directly addresses this set of problems. The Directive came into force on 15 May 2007, with full implementation in every EU Member State required by 2021. It combines both, a legal and a technical framework for the EU Member States, to make relevant spatial data accessible and reused. Specifically, this has meant making data discoverable and interoperable through a common set of standards, data models and Internet services. The Directive’s data scope covers 34 themes of cross-sector relevance as a decentralised infrastructure where data remains at the place it can be best maintained. A great deal of experience has been gained by public administrations through its implementation. Due to its complexity and wide scope, this is taking place in a stepwise manner, with benefits already emerging as important deadlines approached. Efficient and effective coordination are following the participatory approach established in its design. It is timely to reflect on 10 years of progress of the “cultural change” which the European Spatial Data Infrastructure represents. We therefore, consider the lessons INSPIRE is offering for those interested in joined-up and federated approaches to geospatial data-sharing and semantic interoperability across borders and sectors. The approach itself is evolving through this experience.
One of the essential components for the construction of a geographic spatial data infrastructure at a regional, national or global level is the geographic information catalog server. But, for the catalog to be a useful component, it must enable access to geographic information metadata independently of the nature of search client applications, in other words, client applications do not need to be developed by the same company or same technology that implemented the server. In this sense, the contribution of the OpenGIS Consortium, an organization which promotes the standardization mechanisms for catalog services, is to provide the tool that makes possible this enterprise and technological independence. The objective of this paper is to review current approaches, provide some inside on the software implementation of catalogs, and show the applicability of these catalogs in real world scenarios.
Every 2 Days We Create As Much Information As We Did Up To
- Eric Schmidt
Eric Schmidt: Every 2 Days We Create As Much Information As We Did Up To 2003. Available online:
https://techcrunch.com/2010/08/04/schmidt-data/?guccounter=1 (accessed on 1 June 2018).