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In many application domains sensor data contributes an important part to the situation awareness required for decision making. Examples range from environmental and climate change situations to industrial production processes. All these fields need to aggregate and fuse many data sources, the semantics of the data needs to be understood and the results must be presented to the decision makers in an accessible way. This process is already defined as the "sensor to decision chain" [11] but which solutions and technologies can be proposed for implementing it? Since the Internet of Things (IoT) is rapidly growing with an estimated number of 30 billion sensors in 2020, it offers excellent potential to collect time-series data for improving situational awareness. The IoT brings several challenges: caused by a splintered sensor manufacturer landscape, data comes in various structures, incompatible protocols and unclear semantics. To tackle these challenges a well-defined interface, from where uniform data can be queried, is necessary. The Open Geospatial Consortium (OGC) has recognized this demand and developed the Sensor-Things API (STA) standard, an open, unified way to interconnect devices throughout the IoT. Since its introduction in 2016, it has shown to be a versatile and easy to use standard for exchanging and managing sensor data. This paper proposes the STA as the central part for implementing the sensor to decision chain. Furthermore, it describes several projects that successfully implemented the architecture and identifies open issues with the SensorThings API that, if solved, would further improve the usability of the API.

The OGC Environmental Linked Feature Interoperability Experiment (ELFIE) sought to assess a suite of pre-existing OGC and W3C standards with a view to identifying best practice for exposing cross-domain links between environmental features and observations. Environmental domain models concerning landscape interactions with the hydrologic cycle served as the basis for this study, whilst offering a meaningful constraint on its scope. JSON-LD was selected for serialization; this combines the power of linked data with intuitive encoding. Vocabularies were utilized for the provision of the JSON-LD contexts; these ranged from common vocabularies such as schema.org to semantic representations of OGC/ISO observational standards to domain-specific feature models synonymous with the hydrological and geological domains. Exemplary data for the selected use cases was provided by participants and shared in static form via a GitHub repository. User applications were created to assess the validity of the proposed approach as it pertained to real-world situations. This process resulted in the identification of issues whose resolution is a prerequisite for wide-scale deployment and best practice definition. Addressing these issues will be the focus of future OGC Interoperability Experiments.