# A Survey of Techniques for Discovering, Using, and Paying for Third-Party IoT Sensors
## 3 Related Work for Sharing IoT Devices
### Related Research for Describing IoT Devices and Their Data  
Describing IoT devices and their data can be achieved via using techniques that use an ontology, a data model, or a schema. Most relevant papers use ontologies to describe IoT devices and their data. The most commonly used ontologies in the related work are Semantic Sensor Network (SSN) [16] and Sensor Observation Sampling Actuator (SOSA) [18], which are World Wide Web Consortium (W3C) recommendations that were developed by the W3C semantic sensor network incubator group (SSN-XG) [16; 19; 20]. SSN/SOSA are semantic-based ontologies that present a standard machine-readable semi-autonomous or even autonomous system to find, assemble, process, analyze, and proceed on IoT devices. The SSN ontology can be seen from four main perspectives, which are (1) the sensor perspective, which focuses on what senses, how it senses, and what is sensed; (2) the observation perspective, which focuses on observation data and related IoT device descriptions; (3) the system perspective, which focuses on deployments and systems that IoT devices belong to; and (4) the feature and property perspective, which focuses on what senses a particular property or what observations have been made about a property. SSN/SOSA can describe heterogeneous IoT devices and their data. Many researchers have used SSN/SOSA ontologies (modified or as is) in their work, such as [PERSON] et al. [13], [PERSON] et al. [21], [PERSON] et al. [22], and [PERSON] et al. [23]. The Machine-to-Machine Measurements (M3) ontology [24] has been proposed to solve some of the limitations of the existing standards (e.g., SSN [16], oneM2M [25], and ETSI M2M [26]). M3 is a semantic-based ontology, and it can describe heterogeneous IoT devices and their data. Also, it provides a unified description for sensors, data, and measurements, but it is limited to measurement name, device name, and unit of measurement. The authors have provided a link for the complete unified description, but it is not reachable. Other researchers who have used the M3 ontology for their work, such as the authors of [27; 28], have modified the M3 ontology to fit their objectives for providing a unified semantic search engine for smart cities and the IoT. MyOntoSens [29] provides a semantic open data model for IoT devices and their data. The data model is designed to deal with the heterogeneity of IoT devices and their data. Although this data model was proposed mainly for the WirelessSensor Networks (WSNs) domain, the model was designed based on other ontologies such as SSN [16], which makes it suitable for the IoT as well. IoT-Lite [20] provides an instantiation of SSN [16] that allows for the interoperability and discovery of IoT devices by using lightweight semantics. IoT-Lite focuses on three key concepts, which are objects, devices, and services. It provides fast processing times for semantic queries by reducing the complexity of concepts and relationships. [PERSON] et al. [15] have proposed data modelling and annotation to describe the observation and measurement of streaming data. The data stream schema includes value, time, location, type, and unit of IoT data. Although this semantic model describes data observation and measurements, it does not cover describing IoT devices. [PERSON] et al. [30] have proposed a semantic resource (IoT device) ontology that describes a physical device and its characteristics. This ontology has four main concepts, which are (1) physical characteristics, including IDs, energy, and processing time; (2) working features, including accuracy, latency, and measurement; (3) deployment, including geographical location; and (4) interests, including device property. The Open Geospatial Consortium's (OGCs) Sensor Web Enablement (SWE) group have proposed the Sensor Model Language (SensorML) for describing and discovering IoT devices [31]. The ontologies/models proposed in [15; 20; 30; 31] cannot deal with heterogeneous devices and their data. [PERSON] et al.