Sensor-based Linked Open Rules (S-LOR): An Automated Rule Discovery Approach for IoT Applications and its use in Smart Cities

Amelie Gyrard 1, 2 Martin Serrano 1 Joao Bosco Soumya Kanti Datta 3 Muhammad Intizar
1 DERI-NUIG - Digital Enterprise Research Institute
2 Mines Saint-Étienne MSE - École des Mines de Saint-Étienne
3 Eurecom [Sophia Antipolis]
Abstract : This paper introduces an automated rule discovery approach for IoT device data (S-LOR: Sensor-based Linked Open Rules) and its use in smart cities. S-LOR is built following Linked Open Data (LOD) Standards and provides support for semantics-based mechanisms to share, reuse and execute logical rules for interpreting data produced by IoT systems. S-LOR follows LOD principles for data re-usability, semantics-based reasoning and interoperability. In this paper, S-LOR main capability is demonstrated in the context of enabling semantics-based reasoning mechanisms and tools according to application-demand and user requirements. S-LOR (i) supports an automated interpretation of IoT data by executing rules, and (ii) allows an automated rule discovery interface. The implemented S-LOR mechanism can automatically process and interpret data from IoT devices by using rule-based discovery paradigm. Its extension called Linked Open Reasoning (LOR) enables and encourages re-usability of reasoning mechanisms and tools for different IoT smart city applications. The use cases described in this paper fits in the domain of smart city applications within Internet of Things deployed systems.
Keywords : Semantic Web of Things Internet of Things Semantic Web Technologies Reasoning Knowledge
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Conference papers
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Amelie Gyrard, Martin Serrano, Joao Bosco, Soumya Kanti Datta, Muhammad Intizar. Sensor-based Linked Open Rules (S-LOR): An Automated Rule Discovery Approach for IoT Applications and its use in Smart Cities. 3rd International ACM Smart City Workshop (AW4city) in conjunction with 26th International World Wide Web Conference (WWW 2017), Apr 2017, Perth, Australia. ⟨10.1145/3038912.3038914⟩. ⟨emse-01644351⟩

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