4.2. Interoperable IoCT Using STA
In order to integrate multiple COVID-19 sensor systems, the OGC STA was used to support the interoperability between the sensing layer, cloud data management, and cleaning risk assessment application. Figure 7 shows an example of the OGC STA data model being used in the cleaning scenario for a specific Thing—an IndoorGML cell.
Every IndoorGML cell has a Location in space and time. This geospatial encoding was performed by GeoJSON (Geographical JavaScript Object Notation) [59]. Every sensor was referenced by the IndoorGML cell in which the sensor was installed. Each Thing can have multiple Datastreams, which are collections of Observation entities grouped together using the same Observed Property. For the cleaning use case, a different Datastream for each sensor’s phenomenon was used. Each Datastream contained a Sensor and an ObservedProperty. This refers to the instruments that can observe a phenomenon. For this paper, eight different Datastreams were defined, including, proximity, density, and coughs. An ObservedProperty specifies the phenomenon and also contains the unit of measurement. A Datastream can have several Observations, and they dictate the value for the phenomena encoded by the OGC Observations and Measurements (OM). For our example, this can refer to the values taken from a sensor measurement. FeatureOfInterest identifies the characteristics of the Thing. The Thing entity is an IndoorGML cell and the FeatureOfInterest entity describes the characteristics of this cell. For example, Figure 7 shows that “Duration” spent by a smartphone user in a room recorded within the proximity of a BLE beacon is considered a Datastream entity that kept the “Time” duration in seconds as an ObservedProperty. This Datastream entity used “Smartphones” as the sensor entity to keep Observations which are the duration of time that users spend in each cell in seconds.
Table 2 lists all Datastream entities that were used together with their sensing profile, whereby each property indicates the type of format that was encoded. For this research, all of the observations were sent to the Amazon IoT Core using smartphones and the Jetson Xavier NX development kit [49]. The next step was to map observations to an instance of the OGC STA endpoint using the Amazon Lambda functions. Interested readers can see and test the JSON payloads that were used to send all eight types of observations in Supplementary Materials.