The exponential growth of connected devices is radically changing modern society. This phenomenon, also known as the Internet of Things, is conditioning an unprecedented technological and data revolution. Among the diverse technologies and conceptual fields that underlie the Internet of Things, IoT, are ubiquitous or pervasive computing, context awareness and semantics, which are complemented by sensor networks, embedded systems and wireless communication to grant devices or "things" the ability to measure information, interconnect and transfer it. The need to efficiently manage data collected in an IoT environment in order to adequately address problems arising from heterogeneity, dynamism and data size poses challenges to the scientific community. Some of these challenges are associated with the tasks of representation, interpretation and reasoning of data coming from sensors as well as its semantic integration with other open data on the Web. To face these challenges, this thesis presents the Semantic Context Model Extended with Linked Open Data for the Internet of Things, XSCM_4_IoT, supported by a 4-layer architecture and ontological context network CO_NET. XSCM_4_IoT, in addition to being semantic and extensible, is open, interoperable and user-centered. The semantic enrichment of contextual data using the Linked Open Data project, starting with the publishing of the ontological data and resources of CO_NET, is a distinctive feature of the model. The validation of XSCM_4_IoT in the setting of a Smart Campus permits the verification of its viability to manage context-sensitive data in an IoT environment.