The Semantic Web of Things (SWoT) integrates knowledge representation and reasoning techniques originally devised for the Semantic Web into Internet of Things architectures, in order to provide more advanced service/resource management and discovery. This paper proposes a novel SWoT framework, enabling collaborative discovery of sensors and actuators in pervasive contexts. It is based on a backward-compatible extension of the Constrained Application Protocol (CoAP), supporting advanced semantic matchmaking via non-standard inference services. The proposed mobile agent is able to discover devices and share smartphone embedded sensors in a peer-to-peer fashion. Efficient data stream mining is also integrated to analyze raw data gathered from the environment and detect high-level events, annotating them with machineunderstandable metadata. Finally, a case study about cooperative environmental risk monitoring and prevention in Hybrid Sensor and Vehicular Networks is presented. Experimental performance results on a real testbed assess the approach.

CoAP-based Collaborative Sensor Networks in the Semantic Web of Things

Giuseppe Loseto;
2019

Abstract

The Semantic Web of Things (SWoT) integrates knowledge representation and reasoning techniques originally devised for the Semantic Web into Internet of Things architectures, in order to provide more advanced service/resource management and discovery. This paper proposes a novel SWoT framework, enabling collaborative discovery of sensors and actuators in pervasive contexts. It is based on a backward-compatible extension of the Constrained Application Protocol (CoAP), supporting advanced semantic matchmaking via non-standard inference services. The proposed mobile agent is able to discover devices and share smartphone embedded sensors in a peer-to-peer fashion. Efficient data stream mining is also integrated to analyze raw data gathered from the environment and detect high-level events, annotating them with machineunderstandable metadata. Finally, a case study about cooperative environmental risk monitoring and prevention in Hybrid Sensor and Vehicular Networks is presented. Experimental performance results on a real testbed assess the approach.
Semantic Web of Things
CoAP
Collaborative sensing
Resource discovery
Matchmaking
Data mining
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.12572/7667
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
social impact