The Semantic Web of Things (SWoT) merges the Internet of Things with knowledge representation and reasoning techniques borrowed from the SemanticWeb, in order to improve resource management and discovery. This paper proposes a SWoT framework in Wireless Sensor Networks (WSNs) enabling cooperative discovery of sensors and actuators. A backwardcompatible extension of the Constrained Application Protocol (CoAP) makes possible to use semantic matchmaking via nonstandard reasoning to better characterize the resource discovery. The framework also integrates nimble data stream mining to detect and annotate high-level events through raw data gathered from the environment. A cooperative environmental monitoring case study in Hybrid Sensor and Vehicular Networks (HSVN) is presented together with experiments on a real testbed to assess feasibility and benefits of proposal.
Cooperative Semantic Sensor Networks for pervasive computing contexts / Ruta, Michele; Scioscia, Floriano; Pinto, Agnese; Gramegna, Filippo; Ieva, Saverio; Loseto, Giuseppe; DI SCIASCIO, Eugenio. - (2017), pp. 38-43. (Intervento presentato al convegno The 7th IEEE International Workshop on Advances in Sensors and Interfaces, IWASI 2017 tenutosi a Vieste, Italy nel June 15-16, 2017) [10.1109/IWASI.2017.7974209].
Cooperative Semantic Sensor Networks for pervasive computing contexts
RUTA, Michele;SCIOSCIA, Floriano;PINTO, Agnese;GRAMEGNA, Filippo;IEVA, Saverio;LOSETO, Giuseppe;DI SCIASCIO, Eugenio
2017-01-01
Abstract
The Semantic Web of Things (SWoT) merges the Internet of Things with knowledge representation and reasoning techniques borrowed from the SemanticWeb, in order to improve resource management and discovery. This paper proposes a SWoT framework in Wireless Sensor Networks (WSNs) enabling cooperative discovery of sensors and actuators. A backwardcompatible extension of the Constrained Application Protocol (CoAP) makes possible to use semantic matchmaking via nonstandard reasoning to better characterize the resource discovery. The framework also integrates nimble data stream mining to detect and annotate high-level events through raw data gathered from the environment. A cooperative environmental monitoring case study in Hybrid Sensor and Vehicular Networks (HSVN) is presented together with experiments on a real testbed to assess feasibility and benefits of proposal.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.