Sensor Web Environment Research Articles

Spatio-Temporal Risk Assessment Process Modeling for Urban Hazard Events in Sensor Web Environment

Immediate risk assessment and analysis are crucial in managing urban hazard events (UHEs). However, it is a challenge to develop an immediate risk assessment process (RAP) that can integrate distributed sensors and data to determine the uncertain model parameters of facilities, environments, and populations. To solve this problem, this paper proposes a RAP modeling method within a unified spatio-temporal framework and forms a 10-tuple process information description structure based on a Meta-Object Facility (MOF). A RAP is designed as an abstract RAP chain that collects urban information resources and performs immediate risk assessments. In addition, we propose a prototype system known as Risk Assessment Process Management (RAPM) to achieve the functions of RAP modeling, management, execution and visualization. An urban gas leakage event is simulated as an example in which individual risk and social risk are used to illustrate the applicability of the RAP modeling method based on the 10-tuple metadata framework. The experimental results show that the proposed RAP immediately assesses risk by the aggregation of urban sensors, data, and model resources. Moreover, an extension mechanism is introduced in the spatio-temporal RAP modeling method to assess risk and to provide decision-making support for different UHEs.

An SDI Approach for Big Data Analytics: The Case on Sensor Web Event Detection and Geoprocessing Workflow

In the big data era, scientific and social data could complement each other for enhanced data analysis and scientific discovery. Such capabilities could be achieved by taking an infrastructure-based approach, compared to existing algorithm-based approaches. This paper investigates how scientific and social data could work together in a spatial data infrastructure (SDI) enabled by interoperable services. It takes a human-as-sensor perspective and treats the social data as a special kind of sensor data, which could be mined and used for event detection in the Sensor Web environment. Sensor Web, social data mining, and geoprocessing workflows are combined together for timely decision support from social and sensor data. The result is an SDI approach for big data analytics. A use case on haze-related data mining and analysis illustrates the applicability of the approach.

Task-oriented Sensor Web data processing for environmental monitoring

With the rapid development of geospatial service and sensor technologies, large volumes of geospatial data have been collected using various sensor networks, and accessible on the Web. Traditional geospatial data processing could be task-oriented, since a geoprocessing task can be described by a set of action steps, implemented as a workflow, and executed using distributed geoprocessing services. Tasks facilitate the expression of user requirements and capture the problem solving knowledge of users. In this paper, the task-oriented approach is extended to the OGC Sensor Web environment. It highlights how the event-driven technologies adopted by the Sensor Web can be leveraged with geoprocessing workflows to support environment monitoring tasks. The proposal of a Task Model Language (TaskML) and task trigger mechanism allows environmental events to be plugged into an existing model builder, GeoJModelBuilder. Tasks can be created in a stepwise manner, and their execution priority can be updated automatically using triggers. Compared to the traditional “reactive” task enactment mode, the trigger-augmented task can support “active” environmental monitoring. Use cases on PM2.5/PM10 monitoring demonstrate the applicability of the approach.

An Observation Capability Metadata Model for EO Sensor Discovery in Sensor Web Enablement Environments

Accurate and fine-grained discovery by diverse Earth observation (EO) sensors ensures a comprehensive response to collaborative observation-required emergency tasks. This discovery remains a challenge in an EO sensor web environment. In this study, we propose an EO sensor observation capability metadata model that reuses and extends the existing sensor observation-related metadata standards to enable the accurate and fine-grained discovery of EO sensors. The proposed model is composed of five sub-modules, namely, ObservationBreadth, ObservationDepth, ObservationFrequency, ObservationQuality and ObservationData. The model is applied to different types of EO sensors and is formalized by the Open Geospatial Consortium Sensor Model Language 1.0. The GeosensorQuery prototype retrieves the qualified EO sensors based on the provided geo-event. An actual application to flood emergency observation in the Yangtze River Basin in China is conducted, and the results indicate that sensor inquiry can accurately achieve fine-grained discovery of qualified EO sensors and obtain enriched observation capability information. In summary, the proposed model enables an efficient encoding system that ensures minimum unification to represent the observation capabilities of EO sensors. The model functions as a foundation for the efficient discovery of EO sensors. In addition, the definition and development of this proposed EO sensor observation capability metadata model is a helpful step in extending the Sensor Model Language (SensorML) 2.0 Profile for the description of the observation capabilities of EO sensors.

An asynchronous Geoprocessing Workflow and its application to an Antarctic ozone monitoring and mapping service

The integration of Sensor Web Enablement services with other Open Geospatial Consortium (OGC) Web Services as Geospatial Processing Workflows (GPW) is essential for future Sensor Web application scenarios. With the help of GPW technology, distributed and heterogeneous OGC Web Services can be organized and integrated as compound Web Service applications that can direct complicated earth observation tasks. Under the Sensor Web environment, asynchronous communications between Sensor Web Services are common. We have proposed an asynchronous GPW architecture for the integration of Sensor Web Services into a Web Service Business Process Execution Language workflow technology. We designed a Sensor Information Accessing and Processing workflow, an asynchronous GPW instance, to take an experiment of observing and mapping ozone over Antarctica. Based on our results, our proposed asynchronous workflow method shows the advantages of taking environmental monitoring and mapping tasks.

A Dynamic Observation Capability Index for Quantitatively Pre-Evaluating Diverse Optical Imaging Satellite Sensors

Choosing a capable satellite sensor from a mass of homogeneous sensors to meet the requirements of observation tasks in various application scenarios is one of the basic challenges faced by the collaborative observation in an Earth Observation Sensor Web environment. This paper analyzed five main factors affecting the observation capability of optical imaging satellite sensors. This study proposed the concept of dynamic observation capability index (DOCI), which denotes the continuously changing observation performance of diverse sensors in various applications. A higher DOCI demonstrates stronger observation capability. The DOCI model consists of five subcapabilities: spatial-temporal covering capabilities (Coverage), thematic observation capability (Theme), environmental capability (Radiation), attribute capability (SpaceTime), and quality capability (Accuracy). We discussed the assessment methods on the basis of the DOCI model. To verify the proposed DOCI method, seven sensors (AVHRR/3, BGIS-2000, Hyperion, MERSI-1, MODIS, OLI, and SeaWiFS) were used in four different observation task scenarios: normalized difference vegetation index measurement, snow cover monitoring, oil spill detection, and vegetation-type mapping. The results showed that the changes in the observation capability of different sensors in different scenarios can be effectively assessed and modeled using the DOCI index, thus aiding in the scientific pre-evaluation of homogeneous optical sensors. DOCI can also be used as a quantitative, comprehensive, and all-purpose prior assessment method in web-based sensor planning.

An Object Model for Integrating Diverse Remote Sensing Satellite Sensors: A Case Study of Union Operation

In the Earth Observation sensor web environment, the rapid, accurate, and unified discovery of diverse remote sensing satellite sensors, and their association to yield an integrated solution for a comprehensive response to specific emergency tasks pose considerable challenges. In this study, we propose a remote sensing satellite sensor object model, based on the object-oriented paradigm and the Open Geospatial Consortium Sensor Model Language. The proposed model comprises a set of sensor resource objects. Each object consists of identification, state of resource attribute, and resource method. We implement the proposed attribute state description by applying it to different remote sensors. A real application, involving the observation of floods at the Yangtze River in China, is undertaken. Results indicate that the sensor inquirer can accurately discover qualified satellite sensors in an accurate and unified manner. By implementing the proposed union operation among the retrieved sensors, the inquirer can further determine how the selected sensors can collaboratively complete a specific observation requirement. Therefore, the proposed model provides a reliable foundation for sharing and integrating multiple remote sensing satellite sensors and their observations.

Geospatial Web-based Sensor Information Model for Integrating Satellite Observation

Flood observation-supporting satellite sensors (FO-SSs) are the important geospatial resources to flood disaster management. In the geospatial sensor Web environment, effective flood disaster management requires on-demand integration of instant observation information from the diverse web-ready FO-SSs. We propose a sharable and interoperable Earth Observation Satellite Sensor Information (SSI) model for satellite observations integration. The SSI model reuses and extends the existing metadata standards to be a standard metadata-filled description framework. The SensorModel prototype serving here is to apply the SSI model into FO-SSs, retrieve and visualize the qualified Web-ready FO-SSs. An experiment on the flood emergency in the middle reaches of Yangtze River basin in China is conducted. The results show that, three kinds of users including FO-SSs providers, common emergency responders, and emergency managers can benefit from the proposed FO-SSI model, and the managers can reliably and comprehensively integrate the satisfied Web-ready FO-SSs to guide the scheduling of the required observation activities.

Metadata Harvesting and Registration in a Geospatial Sensor Web Registry

AbstractA Sensor Web registry acts as a broker in a service‐oriented environment to publish and discover Sensor Web resources (e.g. sensors, sensor services, observations, and alerts). The Catalogue Service for the Web (CSW) developed by the Open Geospatial Consortium (OGC) defines a standard interface and protocol for publishing and discovering geospatial resources. This article adopts the CSW for the development of a Sensor Web registry. Metadata for Sensor Web resources are registered into the catalogue information model – the ebXML Registry Information Model (ebRIM). In the Sensor Web environment, sensor observations can be available in real‐time or near‐real‐time, and thus metadata registered in CSW need to be updated frequently. This article proposes an incremental harvesting approach, which can harvest the updated metadata efficiently. A prototypical implementation is provided to demonstrate the applicability of the approach.

Integration of hydrological observations into a Spatial Data Infrastructure under a Sensor Web environment

Various sensors connected to the World Wide Web are used to obtain real-time hydrological observations. Thus, real-time management and utilization of such distributed in situ observations in the cyber-physical environment becomes possible. A Sensor Observation Service (SOS) chaining Web Feature Service (WFS) method is proposed to integrate geographical reference observation data collected by a hydrological Sensor Web into a virtual globe. This method hides the complexity of a series of information and service models in the Sensor Web realm to enable the integration of heterogeneous distributed hydrological data sources into a Spatial Data Infrastructure (SDI). The core components – a dynamic schema transformer and automatic information extractor – were designed and implemented. The SOS schema is matched to WFS schema that uses the schema transformer dynamically. The information extractor extracts and serves features automatically, conforming to standard SOS operations for observation retrieval and insertion. Feasibility experiments conducted on the Jinsha River tested this proposed method. Results show that the proposed approach allows the integration of SOS servers into legacy applications that have a higher degree of availability within many SDIs. However, this is accompanied with the drawback that only a limited part of the SOS functionality is available to clients.

Using SensorML to construct a geoprocessing e-Science workflow model under a sensor web environment

Many achievements in web-based geoprocessing focus on logically chaining Open Geospatial Consortium (OGC) web services, for example using Business Process Execution Language to orchestrate web services that are interfaced through the OGC Web Processing Service. For e-Science application in a sensor web environment, how to internally integrate the sensor system, observation, and processes (physical and non-physical) as a geoprocessing e-Science workflow model is a critical issue. The OGC Sensor Model Language offers the possibility to construct a geoprocessing e-Science workflow model in the form of observation processes. We propose a construction method for a geoprocessing e-Science workflow model that integrates logical and physical processes into a composite process chain for sensor observations. The three phases of geoprocessing e-Science workflow creation are abstract process chain modeling, process chain instantiation, and process chain workflow execution. An experiment on chaining-related sub-processes for deriving the Normalized Difference Vegetation Index of Hubei Province (China) was conducted to verify the feasibility of the proposed workflow model.

Flood detection and mapping of the Thailand Central plain using RADARSAT and MODIS under a sensor web environment

Flooding in general is insignificant event worldwide and also in Thailand. The Central plain, the Northern plain and the northeast of Thailand are frequently flooded areas, caused by yearly monsoons. The Thai government has extra expenditure to provide disaster relief and for the restoration of flood affected structures, persons, livestock, etc. Current flood detection in real time or near real time has become a challenge in the flood emergency response. In this paper, an automatic instant time flood detection approach consisting of a data retrieval service, flood sensor observation service (SOS), flood detection web processing service (WPS) under a sensor web environment, is presented to generate dynamically real-time flood maps. A scenario of a RADARSAT and MODIS sensor web data service for flood detection cover of the Thailand Central plain is used to test the feasibility of the proposed framework. MODIS data are used to overview the wide area, while RADARSAT data are used to classify the flood area. The proposed framework using the transactional web coverage service (WCS-T) for instant flood detection processes dynamic real-time remote sensing observations and generates instant flood maps. The results show that the proposed approach is feasible for automatic instant flood detection.

Real-Time On-Demand Motion Video Change Detection in the Sensor Web Environment

Detecting motion-based video change, such as different types of motion video or applications using different change detection algorithms in a Web system, is difficult. This paper designs and implements architecture for a real-time or near real-time on-demand motion video change detection system using the Sensor Observation Service (SOS) and the Web Processing Service (WPS) in the Sensor Web environment. Real-time or near real-time includes sensors that obtain motion video data, SOS provides motion video data to WPS and WPS processes this data. Three solution methods are introduced: the GetObservation operation of SOS by transaction, dynamical interaction between SOS and WPS, and WPS real-time or near real-time processing. On-demand means that a developer can choose different motion video change detection algorithms under different applications or different conditions. For this purpose, a flexible, standards-based and service-oriented WPS architecture is designed, which consists of three layers: the WPS interface layer, the field interface layer and the implementation layer. To test the proposed approach, a video change detection case of monitoring a road situation is shown, which was a demonstration for Open Geospatial Consortium Web Service phase 7. The results demonstrate that the proposed approach is feasible.

Automatic On-Demand Data Feed Service for AutoChem Based on Reusable Geo-Processing Workflow

A new method for serving observational data, based on a reusable geo-processing workflow (GPW), is proposed in order to meet the requirements of the automatic AutoChem data feed service in the sensor web environment. A service-oriented architecture (SOA) is adopted. The system has three roles: the GPW provider, registry center, and portal. The system has GPW design, deployment, registration, search, and invocation functions. The AutoChem data discovery, processing, and integration GPW is implemented by chaining NASA EOS ClearingHOuse data service system, the George Mason University (GMU) Catalogue Service for the Web, the GMU Web Coverage Service, and a series of preprocessing services wrapped with the Web Processing Service. The reused data feed flows have been tested using data from the NASA Aura Ozone Monitoring Instrument. Test results show that the proposed data feed service can also be used by the NASA Aqua Atmospheric Infrared Sounder and the Aura Microwave Limb Sounder. Its architecture is more flexible, and it performs more automatically than the traditional method.

An automatic SWILC classification and extraction for the AntSDI under a Sensor Web environment

How to update spatial information in real time or near real time has become a bottleneck in the construction of the Antarctica Spatial Data Infrastructure (AntSDI). An automatic sensor processing workflow (SPW) approach consisting of sensor discovery and retrieval service; snow, water, ice, land and cloud (SWILC) Web processing service (WPS); and an SPW engine under the Sensor Web environment is used in this paper to generate live SWILC maps dynamically. A prototype consisting of a sensor processing model designer, a model instantiation service, and an SPW engine is presented. A scenario of an Earth Observing-1 (EO-1) Sensor Web data service for SWILC classification and feature extraction in Antarctica is used to test the feasibility of the proposed framework. The integration of SPW with Google Earth and updating of the spatial information for AntSDI using the Transactional Web Feature Service (WFS-T) are evaluated. The results show that the proposed approach is feasible for the update of spatial data in Antarctica.

Geo-processing workflow driven wildfire hot pixel detection under sensor web environment

Integrating Sensor Web Enablement (SWE) services with Geo-Processing Workflows (GPW) has become a bottleneck for Sensor Web-based applications, especially remote-sensing observations. This paper presents a common GPW framework for Sensor Web data service as part of the NASA Sensor Web project. This abstract framework includes abstract GPW model construction, GPW chains from service combination, and data retrieval components. The concrete framework consists of a data service node, a data processing node, a data presentation node, a Catalogue Service node, and a BPEL engine. An abstract model designer is used to design the top level GPW model, a model instantiation service is used to generate the concrete Business Process Execution Language (BPEL), and the BPEL execution engine is adopted. This framework is used to generate several kinds of data: raw data from live sensors, coverage or feature data, geospatial products, or sensor maps. A prototype, including a model designer, model instantiation service, and GPW engine-BPELPower is presented. A scenario for an EO-1 Sensor Web data service for wildfire hot pixel detection is used to test the feasibility of the proposed framework. The execution time and influences of the EO-1 live Hyperion data wildfire classification service framework are evaluated. The benefits and high performance of the proposed framework are discussed. The experiments of EO-1 live Hyperion data wildfire classification service show that this framework can improve the quality of services for sensor data retrieval and processing.