Stefan Herle

Enhancing the OGC WPS interface with GeoPipes support for real-time geoprocessing

ABSTRACT Real-time geospatial information is used in various applications such as risk management or alerting services. Especially, the rise of new sensing technologies also increases the demand for processing the data in real time. Today’s spatial data infrastructures, however, do not meet the requirements for real-time geoprocessing. The OpenGIS® Web Processing Service (WPS) is not designed to process real-time workflows. It has some major drawbacks in asynchronous processing and cannot handle (geo) data streams out of the box. In previous papers, we introduced the GeoPipes approach to share spatiotemporal data in real time. We implemented the concept extending the Message Queue and Telemetry Transport (MQTT) protocol by a spatial and temporal dimension, which we call GeoMQTT. In this paper, we demonstrate the integration of the GeoPipes idea in the WPS interface to expose standardized real-time geoprocessing services. The proof of the concept is illustrated in some exemplary real-time geo processes.

GeoPipes using GeoMQTT

The integration of common OpenGIS Web Services (OWS) into the Internet of Things and Service (IoTS) paradigm is a difficult task since they are based on HTTP with all its weak points. E.g. coupling small sensing devices or real-time processes with these services takes an enormous effort due to the different domain requirements. This paper focuses on extending existing geo web services with a push-based messaging mechanism to overcome their major drawbacks. We introduce the concept of GeoPipes and an exemplary implementation of them using the GeoMQTT protocol. The latter one is an extension of the MQTT protocol which is presented in this paper. Application examples show that with this concept a lot of technological issues can be solved easier.

Smart sensor-based geospatial architecture for dike monitoring

Artificial hydraulic structures like dams or dikes used for water level regulations or flood prevention are continuously under the influence of the weather and variable river regimes. Thus, ongoing monitoring and simulation is crucial in order to determine the inner condition. Potentially life-threatening situations, in extreme case a failure, must be counteracted by all available means. Nowadays flood warning systems rely exclusively on water level forecast without considering the state of the structure itself. Area-covering continuous knowledge of the inner state including time dependent changes increases the capability of recognizing and locating vulnerable spots for early treatment. In case of a predicted breach, advance warning time for alerting affected citizens can be extended. Our approach is composed of smart sensors integrated in a service-oriented geospatial architecture to monitor and simulate artificial hydraulic structures continuously. The sensors observe the inner state of the construction like the soil moisture or the stress and deformation over time but also various external influences like water levels or wind speed. They are interconnected in distributed network architecture by a so-called sensor bus system based on lightweight protocols like Message Queue Telemetry Transport for Sensor Networks (MQTT-SN). These sensor data streams are transferred into an OGC Sensor Web Enablement (SWE) data structure providing high-level geo web services to end users. Bundled with 3rd party geo web services (WMS etc.) powerful processing and simulation tools can be invoked using the Web Processing Service (WPS) standard. Results will be visualized in a geoportal allowing user access to all information.

GeoSpatial IoT - the Need for an Event-Driven Spatial Data Infrastructure

The nature of contemporary Spatial Data Infrastructures lies in the provision of geospatial information in an on-demand fashion. Though recent applications identified t he n eed t o react to real-time information in a time-critical way. In particular, research efforts in the field of geospatial Internet of Things have identified substantial gaps in this context, ranging from a lack of standardization for event-based architectures to the meaningful handling of real-time information as ”events”. This manuscript presents work in the field of Event-driven Spatial Data Infrastructures with a particular focus on sensor networks and the devices capturing in-situ measurements. The current landscape of Spatial Data Infrastructures is outlined and used as the basis for identifying existing gaps that retain certain geospatial applications from using real-time information. We present a selection of approaches developed in different research projects to overcome these gaps. Being designed for specific application domains, these approaches share commonalities as well as orthogonal solutions and can build the foundation of an overall Event-driven Spatial Data Infrastructure.

Geospatial IoT—The Need for Event-Driven Architectures in Contemporary Spatial Data Infrastructures

The nature of contemporary spatial data infrastructures lies in the provision of geospatial information in an on-demand fashion. Although recent applications identified the need to react to real-time information in a time-critical way, research efforts in the field of geospatial Internet of Things in particular have identified substantial gaps in this context, ranging from a lack of standardisation for event-based architectures to the meaningful handling of real-time information as “events”. This manuscript presents work in the field of event-driven architectures as part of spatial data infrastructures with a particular focus on sensor networks and the devices capturing in-situ measurements. The current landscape of spatial data infrastructures is outlined and used as the basis for identifying existing gaps that retain certain geospatial applications from using real-time information. We present a selection of approaches—developed in different research projects—to overcome these gaps. Being designed for specific application domains, these approaches share commonalities as well as orthogonal solutions and can build the foundation of an overall event-driven spatial data infrastructure.

A Scalable Architecture for Real-Time Stream Processing of Spatiotemporal IoT Stream Data—Performance Analysis on the Example of Map Matching

Scalable real-time processing of large amounts of data has become a research topic of particular importance due to the continuously rising amount of data that is generated by devices equipped with sensing components. While existing approaches allow for fault-tolerant and scalable stream processing, we present a pipeline architecture that consists of well-known open source tools to specifically integrate spatiotemporal internet of things (IoT) data streams. In a case study, we utilize the architecture to tackle the online map matching problem, a pre-processing step for trajectory mining algorithms. Given the rising amount of vehicle location data that is generated on a daily basis, existing map matching algorithms have to be implemented in a distributed manner to be executable in a stream processing framework that provides scalability. We demonstrate how to implement state-of-the-art map matching algorithms in our distributed stream processing pipeline and analyze measured latencies.