IoT-Stream: A Lightweight Ontology for Internet of Things Data Streams

Abstract

In recent years, the development and deployment of Internet of Things (IoT) devices has led to the generation of large volumes of real world data. Analytical models can be used to extract meaningful insights from this data. However, most of IoT data is not fully utilised, which is mainly due to interoperability issues and the difficulties to analyse data collected by heterogeneous resources. To overcome this heterogeneity, semantic technologies are used to create common models to share various data originated from heterogeneous sources. However, semantics add further overhead to data delivery, and the processing time to annotate the data with the model can increase the latency and complexity in publishing and querying the annotated data. In this paper, we present a lightweight semantic model to annotate IoT streams. The metadata descriptions that are provided in the models are used for search and discovery of the data using various attributes such as value and type. The proposed model extends commonly used ontologies such as W3C/OGC SSN ontology and its recent lightweight core, SOSA, and includes concepts to describe streaming IoT data. We also show use cases, tools and applications where the proposed model has been used.

SOSA-SHACL: Shapes Constraint for the Sensor, Observation, Sample, and Actuator Ontology

“…The SOSA ontology 2 [9] is a W3C/OGC standard developed using a subset of SSN's entities [3,11] and contains concepts, properties, and annotations for describing sensors, observations, actuators, samples, features of interest, observable properties, and observation procedures. It is extensively used in the Semantic Web for applications ranging from representing data-streams [1,5], smart-city data [6,7], communication technologies and protocols [2,14], and various kinds of geospatial information [12,13,15,16]. The key uses of the ontology are: (1) linking observations (sosa:Observation) to sensors (sosa:Sensor) that measured them and the property (sosa:ObservableProperty) that was observed;…”

Semantic and Syntactic Interoperability for Agricultural Open-Data Platforms in the Context of IoT Using Crop-Specific Trait Ontologies

“…Utilizing IoT data, which are obtained from heterogeneous data sources, requires more complex operations and technical skills due to interoperability issues. Considering the heterogeneity of IoT data, semantic web technologies can be used for sharing data gathered from heterogeneous IoT devices by generating "common models" [22] of domains of knowledge-i.e., an ontology, shared and explicit conceptualization of the knowledge and of the relationships of the concepts composing a domain [23,24]. The diversity of IoT devices from several vendors can uncover semantic and syntactic errors [20].…”

SensorStream: An XES Extension for Enriching Event Logs with IoT-Sensor Data

“…One domain where process orchestration/execution [24] and IoT meet is the manufacturing domain [25,29]. Here context is mainly derived from sensors that monitor the execution environment and resources during execution [4,13,31,33]. In domains as manufacturing, process orchestration/execution [23][24][25]29,31] often relies on Internet of Things (IoT) technology.…”

“…In domains as manufacturing, process orchestration/execution [23][24][25]29,31] often relies on Internet of Things (IoT) technology. While IoT actuators can be used to automate process tasks, IoT sensors and tags can be used to closely monitor the execution environment and involved resources [4,13,31,33]. IoT technology can therefore capture the context in which certain process tasks are performed, which is a particularly important factor for techniques such as process mining [7,32] to better understand and analyze the manufacturing processes.…”

“…Depending on logging, knowledge about the executed processes and process models, and physical aspects such as the placement of sensors or their orientation, sensors can be directly assigned to individual events or traces or neither. In addition, IoT data is often ad hoc, highly variable, contains data quality issues, and has varying degrees of semantification [5,13].…”