Ontology-Based Consistent Specification of Sensor Data Acquisition Plans in Cross-Domain IoT Platforms

Nowadays there is an high number of IoT applications that seldom can interact with each other because developed within different Vertical IoT Platforms that adopt different standards. Several efforts are devoted to the construction of cross-layered frameworks that facilitate the interoperability among cross-domain IoT platforms for the development of horizontal applications. Even if their realization poses different challenges across all layers of the network stack, in this paper we focus on the interoperability issues that arise at the data management layer. Specifically, starting from a flexible multi-granular Spatio-Temporal-Thematic data model according to which events generated by different kinds of sensors can be represented, we propose a Semantic Virtualization approach according to which the sensors belonging to different IoT platforms and the schema of the produced event streams are described in a Domain Ontology, obtained through the extension of the well-known Semantic Sensor Network ontology. Then, these sensors can be exploited for the creation of Data Acquisition Plans by means of which the streams of events can be filtered, merged, and aggregated in a meaningful way. A notion of consistency is introduced to bind the output streams of the services contained in the Data Acquisition Plan with the Domain Ontology in order to provide a semantic description of its final output. When these plans meet the consistency constraints, it means that the data they handle are well described at the Ontological level and thus the data acquisition process over passed the interoperability barriers occurring in the original sources. The facilities of the StreamLoader prototype are finally presented for supporting the user in the Semantic Virtualization process and for the construction of meaningful Data Acquisition Plans

Ontology-based Consistent Specification and Scalable Execution of Sensor Data Acquisition Plans in Cross-Domain loT Platforms

Nowadays there is an increased number of vertical Internet of Things (IoT) applications that have been developed within IoT Platforms that often do not interact with each other because of the adoption of different standards and formats. Several efforts are devoted to the construction of software infrastructures that facilitate the interoperability among heterogeneous cross-domain IoT platforms for the realization of horizontal applications. Even if their realization poses different challenges across all layers of the network stack, in this thesis we focus on the interoperability issues that arise at the data management layer. Starting from a flexible multi-granular Spatio-Temporal-Thematic data model according to which events generated by different kinds of sensors can be represented, we propose a Semantic Virtualization approach according to which the sensors belonging to different IoT platforms and the schema of the produced event streams are described in a Domain Ontology, obtained through the extension of the well-known ontologies (SSN and IoT-Lite ontologies) to the needs of a specific domain. Then, these sensors can be exploited for the creation of Data Acquisition Plans (DAPs) by means of which the streams of events can be filtered, merged, and aggregated in a meaningful way. Notions of soundness and consistency are introduced to bind the output streams of the services contained in the DAP with the Domain Ontology for providing a semantic description of its final output. The facilities of the \streamLoader prototype are finally presented for supporting the domain experts in the Semantic Virtualization of the sensors and for the construction of meaningful DAPs. Different graphical facilities have been developed for supporting domain experts in the development of complex DAPs. The system provides also facilities for their syntax-based translations in the Apache Spark Streaming language and execution in real time in a distributed cluster of machines

Developing IoT Spark-Streaming Applications by Means of streamLoader

Nowadays there is a great interest in the development of cross-domain IoT applications that are able to gather an high number of observations taken from sensors belong to heterogeneous IoT platforms and efficiently combine them in flows that can be exploited for conducting analysis. However, their realization poses several concerns. From one side, graphical interfaces are needed for facilitating the specification and composition of the data acquisition operations. From the other side, facilities are required to manage big data streams, to check the application correctness, and to set up and configure clusters of machines. Starting from the graphical facilities of the StreamLoader system for the development of Data Acquisition Plans (DAPs) from sensors belonging to heterogeneous IoT platforms, in this paper we show the approach developed for translating DAPs in a Spark-Streaming application. Experiments are shown for assessing the scalability of the developed applications