This paper proposes a novel Semantic Web of Things 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 framework also integrates ecient data stream mining to analyze raw data gathered from the environment and detect high-level events, annotating them with machine-understandable metadata. A case study about cooperative environmental risk monitoring and prevention in Hybrid Sensor and Vehicular Networks is presented and experimental performance results on a real testbed are provided.

A CoAP-based framework for collaborative sensing in the Semantic Web of Things

LOSETO, Giuseppe;
2017

Abstract

This paper proposes a novel Semantic Web of Things 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 framework also integrates ecient data stream mining to analyze raw data gathered from the environment and detect high-level events, annotating them with machine-understandable metadata. A case study about cooperative environmental risk monitoring and prevention in Hybrid Sensor and Vehicular Networks is presented and experimental performance results on a real testbed are provided.
Semantic Web of Things
CoAP
Collaborative sensing
Resource discovery
Matchmaking
Data mining
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.12572/7610
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