A Logic-based CoAP Extension for Resource Discovery in Semantic Sensor Networks

Main restraints curbing a deep integration of Semantic Sensor Networks with complex and articulated architectures, basically reside in too elementary allowed discovery capabilities. Several studies agree advanced querying and retrieval mechanisms are needed to truly fulfill the potential of the SSN paradigm. This paper presents a novel SSN framework, supporting a resource discovery grounded on semantic-based matchmaking. Offered contributions are: a backward-compatible extension of Constrained Application Protocol (CoAP) resource discovery; data mining exploitation to detect high-level events from raw data; employment of non-standard inference services for retrieving and ranking resources; adoption of W3C standard SSN-XG ontology to annotate data, events and device features. The effectiveness of the proposed approach is motivated by a case study regarding fire risk prevention and air conditioning control in a university building.