## 4 CAS Development
### CAS Design
#### 4.2.1 Ontology Conceptualization  
First, the ontology requirements specification was oriented to the development of one ontology to support the smart irrigation CAS. However, it should be noted that a general part of the ontology could be reusable for other use cases; therefore, we decided to materialize the implementation in two ontologies, CASO and IRRIG. In the following, a typical approach for the conceptualization is detailed, and then the decisions about the ontology modularization used to create CASO and IRRIG are described. Please note that our ontologies have already studied well-known ontologies about sensor measurements [12]. They choose to reuse SSN since this ontology provides a precise description of measurements based on data modeling patterns. SSN defines several patterns that help to understand the data modeling issues. The first step towards a conceptualization was to identify from the competency questions document those ontology elements that needed to be represented. More precisely, this step was carried out in the following order:  
* Identification of concepts: We want to model all the processes applied to sensor streams. They are mainly aggregations and threshold comparisons. The goal of the comparison is to derive qualitative (symbolic) data. To differentiate quantitative from symbolic computation, we need to use deduction. A deduction provides qualitative data as a result. These results are the entity states that should be defined in some referential item. For example, maize growth stages are states defined in a certain thesaurus. In most cases, the state of an entity is derived because one value is above or below a threshold. We model thresholds as state boundaries. * Identification of relations: To characterize a deduction, we need two new relations: (1) a relation that links a deduction to its resulting state and (2) a relation that links a deduction to the period when this deduction is valid, i.e., the period when the entity is associated with a state. * Identification of cardinality restrictions applied for relations: This step also involves a decision on whether the relation at hand should be defined as functional. For example, the relation between a deduction and its result is functional. A deduction has only one result: the state of the entity derived by the deduction. * Identification of individuals: Following the SSN pattern, these entities are characteristics of the feature of interest [3]. They are modeled as an instance of the class **ssn:Property**, which are linked to an instance of the class **ssoa:FeatureOfInterest** by the object property ssn:hasProperty. To specialize an SSN to the irrigation use case, the involved properties in the use case, for example, air temperature or crop stage, have been defined as instances of properties. Next, ontologies and particular ontology elements were identified to be reused. The following selected ontologies have been developed and maintained by standardization organizations. * The W3C & OGC Semantic Sensor Network (SSN) ontology describes sensors, observations, samples and actuators [3]. Smart Appliances REFerence (SAREF) ([[http://www.w3](http://www.w3) id.org/saref]([http://www.w3](http://www.w3) id.org/saref)) is an ontology for smart appliances that contributes to semantic interoperability in the IoT domain [4]. This ETSI recommendation focuses on the representation of appliances and devices together with their functions, commands, services, states and profiles (It is necessary to mention that CASO and IRRIG reuse some concepts from SAREF and SAREF4 AGRI, which are unavailable in the latest published versions of these ontologies, but it is known by the authors that they will be available in the next versions. SAREF4 AGRI is an extension of SAREF for agriculture.). * The W3C PROV ontology represents provenance information [19]. * The W3C SKOS vocabulary describes knowledge organization systems. It will be useful in defining properties and their states [20].