Semantic Sensor Network Ontology Research Articles

Design and Implementation of a Malfunction Detection System for Livestock Ventilation Devices in Smart Poultry Farms

Smart livestock farming aims to improve the productivity of livestock through the provision of optimal housing, and it is developed using various sensors and actuators. Ventilation systems play a crucial role in smart livestock farming, including disease prevention and the processing of pollutants (ammonia and hydrogen sulfide) that are severely detrimental to livestock growth. Malfunctions in animal housing ventilation systems lead to mass mortality events. To address such issues, this study reports the design and implementation for a smart detection system for malfunctions in the ventilation devices installed in animal housing. This system is based on recurrent neural networks (RNNs) and implements the ontology method, considering sensor and controller data as the standard. A semantic sensor network ontology founded on a knowledge base was used to detect malfunctions, and stimulus-sensor-observation patterns were used to determine a sensor network within the smart barn. System activation and RNN model tests were used to test the malfunction detection system, and the error between actual data and predicted values was found to be 0.06889. These findings provide insight into the development of autonomous detection systems for device malfunctions and are essential for the development of smart livestock farming technologies.

Semantic sensor network ontology based decision support system for forest fire management

The forests are significant assets for every country. When it gets destroyed, it may negatively impact the environment, and forest fire is one of the primary causes. Fire weather indices are widely used to measure fire danger and issue bushfire warnings. It can also be used to predict the demand for emergency management resources. Sensor networks have grown in popularity in data collection and processing capabilities for various applications in industries such as medical, environmental monitoring, home automation, etc. Semantic sensor networks can collect various climatic circumstances like wind speed, temperature, and relative humidity. However, estimating fire weather indices is challenging due to the various issues involved in processing the data streams generated by the sensors. Hence, the importance of forest fire detection has increased day by day. The underlying Semantic Sensor Network (SSN) ontologies are built to allow developers to create rules for calculating fire weather indices and the converted dataset into Resource Description Framework (RDF). This research describes the various steps in developing rules for calculating fire weather indices. Besides, this work presents a Web-based mapping interface to help users visualize the changes in fire weather indices over time. With the help of the inference rule, it designed a decision support system using the SSN ontology and query on it through SPARQL. The proposed fire management system acts according to the situation and supports reasoning and the general semantics of the open world, followed by all the ontologies.

Observational/hydrographic data of the South Atlantic Ocean published as LOD

This article describes the publication of occurrences of Southern Elephant Seals Mirounga leonina (Linnaeus, 1758) as Linked Open Data in two environments (marine and coastal). The data constitutes hydrographic measurements of instrumented animals and observation data collected during censuses between 1990 and 2017. The data scheme is based on the previously developed ontology BiGe-Onto and the new version of the Semantic Sensor Network ontology (SSN). We introduce the network of ontologies used to organize the data and the transformation process to publish the dataset. In the use case, we develop an application to access and analyze the dataset. The linked open dataset and the related visualization tool turned data into a resource that can be located by the international community and thus increase the commitment to its sustainability. The data, coming from Península Valdés (UNESCO World Heritage), is available for interdisciplinary studies of management and conservation of marine and coastal protected areas which demand reliable and updated data.

Service-oriented architecture for Internet of Things: A semantic approach

The Internet of Things (IoT) aids an interconnection between systems, humans, and services to develop computation-intensive autonomous applications. Service-oriented architecture (SOA) concepts have been used as defacto software architecture to develop IoT-based systems. Though SOA facilitates various benefits, several challenges exist for integration with IoT-based systems, including configurability, interoperability, and manageability. The ontology-based approach provides a semantically enriched and rigorous platform for interoperating different heterogeneous IoT-based systems and applications. The semantic modelling research has focused on IoT resource management without concerning the method of accessing and utilizing IoT information. In this context, an IoT-based large-scale SOA (IoT-LSS) ontology is proposed that supports interoperability, heterogeneity, flexibility, manageability, extendibility, scalability. This ontology integrates the concept of large-scale SOA (LSS) with semantic sensor network (SSN) ontology. Clinical decision support system (CDSS) ontology has considered as a case study to illustrate the proposed ontology. A detailed comparative analysis between the proposed system and several existing IoT-based service ontologies has been performed based on different characteristics. This analysis shows that IoT-LSS supports dynamic features and exposes to service with its different mechanisms. This article presents an ontological framework for the IoT service-based environment to efficiently use for service composition mechanisms in the large service domain.

Semantic framework of event detection in emergency situations for smart buildings

Multimedia Sensor Networks (MSNs) have enhanced the ability to analyze the environment and provide responses based on its current status. Generally, MSNs are composed of scalar and multimedia sensors that have fixed locations. However, given the advancement of smart mobile device technologies, it is currently possible to dynamically integrate mobile sensors into MSNs. In this paper, we propose a formal platform to manage MSNs and the data gathered from them to detect complex events. Our main contributions include: M2SSN ​− ​Onto, a Mobile and Multimedia Semantic Sensor Networks Ontology; Py-CEMiD, an engine for detecting complex events and generate reactions to them; a mobile device location engine to locate mobile sensors; and a proof-of-concept in the context of detecting emergency situations in smart buildings. Several scenarios are validated for emergency events, combining simulated sensor measurements with real measurements of mobile devices. Results show complex events can be detected in near real time (less than 1 ​s).

An Automatic Ontology-Based Approach to Support Logical Representation of Observable and Measurable Data for Healthy Lifestyle Management: Proof-of-Concept Study.

BackgroundLifestyle diseases, because of adverse health behavior, are the foremost cause of death worldwide. An eCoach system may encourage individuals to lead a healthy lifestyle with early health risk prediction, personalized recommendation generation, and goal evaluation. Such an eCoach system needs to collect and transform distributed heterogenous health and wellness data into meaningful information to train an artificially intelligent health risk prediction model. However, it may produce a data compatibility dilemma. Our proposed eHealth ontology can increase interoperability between different heterogeneous networks, provide situation awareness, help in data integration, and discover inferred knowledge. This “proof-of-concept” study will help sensor, questionnaire, and interview data to be more organized for health risk prediction and personalized recommendation generation targeting obesity as a study case.ObjectiveThe aim of this study is to develop an OWL-based ontology (UiA eHealth Ontology/UiAeHo) model to annotate personal, physiological, behavioral, and contextual data from heterogeneous sources (sensor, questionnaire, and interview), followed by structuring and standardizing of diverse descriptions to generate meaningful, practical, personalized, and contextual lifestyle recommendations based on the defined rules.MethodsWe have developed a simulator to collect dummy personal, physiological, behavioral, and contextual data related to artificial participants involved in health monitoring. We have integrated the concepts of “Semantic Sensor Network Ontology” and “Systematized Nomenclature of Medicine—Clinical Terms” to develop our proposed eHealth ontology. The ontology has been created using Protégé (version 5.x). We have used the Java-based “Jena Framework” (version 3.16) for building a semantic web application that includes resource description framework (RDF) application programming interface (API), OWL API, native tuple store (tuple database), and the SPARQL (Simple Protocol and RDF Query Language) query engine. The logical and structural consistency of the proposed ontology has been evaluated with the “HermiT 1.4.3.x” ontology reasoner available in Protégé 5.x.ResultsThe proposed ontology has been implemented for the study case “obesity.” However, it can be extended further to other lifestyle diseases. “UiA eHealth Ontology” has been constructed using logical axioms, declaration axioms, classes, object properties, and data properties. The ontology can be visualized with “Owl Viz,” and the formal representation has been used to infer a participant’s health status using the “HermiT” reasoner. We have also developed a module for ontology verification that behaves like a rule-based decision support system to predict the probability for health risk, based on the evaluation of the results obtained from SPARQL queries. Furthermore, we discussed the potential lifestyle recommendation generation plan against adverse behavioral risks.ConclusionsThis study has led to the creation of a meaningful, context-specific ontology to model massive, unintuitive, raw, unstructured observations for health and wellness data (eg, sensors, interviews, questionnaires) and to annotate them with semantic metadata to create a compact, intelligible abstraction for health risk predictions for individualized recommendation generation.

Compact representations for efficient storage of semantic sensor data

Nowadays, there is a rapid increase in the number of sensor data generated by a wide variety of sensors and devices. Data semantics facilitate information exchange, adaptability, and interoperability among several sensors and devices. Sensor data and their meaning can be described using ontologies, e.g., the Semantic Sensor Network (SSN) Ontology. Notwithstanding, semantically enriched, the size of semantic sensor data is substantially larger than raw sensor data. Moreover, some measurement values can be observed by sensors several times, and a huge number of repeated facts about sensor data can be produced. We propose a compact or factorized representation of semantic sensor data, where repeated measurement values are described only once. Furthermore, these compact representations are able to enhance the storage and processing of semantic sensor data. To scale up to large datasets, factorization based, tabular representations are exploited to store and manage factorized semantic sensor data using Big Data technologies. We empirically study the effectiveness of a semantic sensor’s proposed compact representations and their impact on query processing. Additionally, we evaluate the effects of storing the proposed representations on diverse RDF implementations. Results suggest that the proposed compact representations empower the storage and query processing of sensor data over diverse RDF implementations, and up to two orders of magnitude can reduce query execution time.

Internet of Things Multi-protocol Interoperability with Syntactic Translation Capability

Because Internet of Things (IoT) systems contain different devices, infrastructures, and data formats; its success depends on the realization of full interoperability among these systems. Interoperability is a communication challenge that affects all the layers of the system. In this paper, a transparent translator to solve interoperability issues in two layers of an IoT system is proposed. The communication protocol layer is the first layer. In this layer, it is necessary to overcome the difference between the interaction patterns, such as request/response and publish/subscribe. The second layer includes the syntactic layer, which refers to data encoding. This type of interoperability is achieved through the semantic sensor network (SSN) ontology. Tests and evaluations of the proposed translator in comparison with a similar translator were performed using the constrained application protocol (CoAP), message queuing telemetry transport (MQTT) protocol, and hypertext transfer (HTTP) protocol, in addition to different data formats, such as JSON, CSV, and XML. The results reveal the efficiency of the proposed method in terms of application protocol interoperability. In addition, the suggested translator has the added feature that it supports different data encoding standards as compared to the other translator.

An Ontology-Based Framework for Integrating Remote Sensing Imagery, Image Products, and In Situ Observations

Along with the rapid development of remote sensing satellites and sensor network technology, vast amounts of remote sensing imagery and in situ observations have been accumulated. Further, various researchers and agencies have released a variety of thematic image products. These heterogeneous observations are therefore difficult to utilize comprehensively. In this study, an ontology-based framework for integrating remote sensing imagery, image products, and in situ observations was developed. It was extended based on the Semantic Sensor Network (SSN) ontology in the Web Ontology Language (OWL). The detailed process of ontology construction and rule establishment was demonstrated. Combined with some actual remote sensing imagery, image products, and in situ observations, semantic queries based on DL Query and SPARQL were conducted to establish the rationality and feasibility of the ontology and framework.

Semantic Annotation of Web of Things Using Entity Linking

The web of things (WoT) improves syntactic interoperability between internet of things (IoT) devices by leveraging web standards. However, the lack of a unified WoT data model remains a challenge for the semantic interoperability. Fortunately, semantic web technologies are taking this challenge over by offering numerous semantic vocabularies like the semantic sensor networks (SSN) ontology. Although it enables the semantic interoperability between heterogeneous devices, the manual annotation hinders the scalability of the WoT. As a result, the automation of the semantic annotation of WoT devices becomes a prior issue for researchers. This paper proposes a method to improve the semi-automatic semantic annotation of web of things (WoT) using the entity linking task and the well-known ontologies, mainly the SSN.

Semantic-based privacy settings negotiation and management

By 2020, an individual is expected to own an average of 6.58 devices that share and integrate a wealth of personal user data. The management of privacy preferences across these devices is a complex task for which users are ill-equipped, which increases privacy risks. In this paper we propose an approach that exploits Semantic Web (SW) technology to manage the user’s IoT privacy preferences and negotiate the permissions for data sharing with third parties. SW technology comprises a web of data that can be processed by machines through a formal, universally shared representation. In our approach, SW enables a lightweight and interoperable communication between a Personal Data Manager (PDM) and the Third Parties (TPs) that request access to the user’s personal data. The PDM can handle multiple heterogeneous personal IoT devices and manages the negotiation process between the user and the TPs in a way that can relieve users from the burden of specifying their privacy requirement for each TP. The core of the approach is the definition of the Privacy Preference for IoT (PPIoT) Ontology which is based on the Privacy Preference Ontology, the W3C Semantic Sensor Network Ontology, the Fair Information Practices (FIP) principles, and state-of-the-art recommendation techniques for privacy protection in the IoT. This ontology aims to capture the complexity of privacy management in the IoT paradigm in light of the recent General Data Protection Regulation (GDPR) of the European Union. Along with presenting the ontology, in this paper we will provide an example on how to use the PPIoT ontology for the management of privacy preferences in the fitness IoT domain and we will show how the PDM handles the process of negotiation between the user and the TPs. The approach is based on an interactive PPIoT-based Privacy Preference Model (PPM) that meets the requirements of the GDPR to have transparent and simple TP privacy policies. Finally, we will report the results of an evaluation on a mockup fitness app that implements this PPM. The main contributions of this paper are: (i) to propose an ontology for privacy preference in the IoT context, which covers a knowledge gap in existing literature and can be used for IoT privacy management, (ii) to propose an interactive PPIoT-based Privacy Preference Model, which is in accordance with the GDPR objectives.

A mobile health monitoring-and-treatment system based on integration of the SSN sensor ontology and the HL7 FHIR standard

BackgroundMobile health (MH) technologies including clinical decision support systems (CDSS) provide an efficient method for patient monitoring and treatment. A mobile CDSS is based on real-time sensor data and historical electronic health record (EHR) data. Raw sensor data have no semantics of their own; therefore, a computer system cannot interpret these data automatically. In addition, the interoperability of sensor data and EHR medical data is a challenge. EHR data collected from distributed systems have different structures, semantics, and coding mechanisms. As a result, building a transparent CDSS that can work as a portable plug-and-play component in any existing EHR ecosystem requires a careful design process. Ontology and medical standards support the construction of semantically intelligent CDSSs.MethodsThis paper proposes a comprehensive MH framework with an integrated CDSS capability. This cloud-based system monitors and manages type 1 diabetes mellitus. The efficiency of any CDSS depends mainly on the quality of its knowledge and its semantic interoperability with different data sources. To this end, this paper concentrates on constructing a semantic CDSS based on proposed FASTO ontology.ResultsThis realistic ontology is able to collect, formalize, integrate, analyze, and manipulate all types of patient data. It provides patients with complete, personalized, and medically intuitive care plans, including insulin regimens, diets, exercises, and education sub-plans. These plans are based on the complete patient profile. In addition, the proposed CDSS provides real-time patient monitoring based on vital signs collected from patients’ wireless body area networks. These monitoring include real-time insulin adjustments, mealtime carbohydrate calculations, and exercise recommendations. FASTO integrates the well-known standards of HL7 fast healthcare interoperability resources (FHIR), semantic sensor network (SSN) ontology, basic formal ontology (BFO) 2.0, and clinical practice guidelines. The current version of FASTO includes 9577 classes, 658 object properties, 164 data properties, 460 individuals, and 140 SWRL rules. FASTO is publicly available through the National Center for Biomedical Ontology BioPortal at https://bioportal.bioontology.org/ontologies/FASTO.ConclusionsThe resulting CDSS system can help physicians to monitor more patients efficiently and accurately. In addition, patients in rural areas can depend on the system to manage their diabetes and emergencies.

Towards Semantic Sensor Data: An Ontology Approach.

In order to optimize intelligent applications driven by various sensors, it is vital to properly interpret and reuse sensor data from different domains. The construction of semantic maps which illustrate the relationship between heterogeneous domain ontologies plays an important role in knowledge reuse. However, most mapping methods in the literature use the literal meaning of each concept and instance in the ontology to obtain semantic similarity. This is especially the case for domain ontologies which are built for applications with sensor data. At the instance level, there is seldom work to utilize data of the sensor instances when constructing the ontologies’ mapping relationship. To alleviate this problem, in this paper, we propose a novel mechanism to achieve the association between sensor data and domain ontology. In our approach, we first classify the sensor data by making them as SSN (Semantic Sensor Network) ontology instances, and map the corresponding instances to the concepts in the domain ontology. Secondly, a multi-strategy similarity calculation method is used to evaluate the similarity of the concept pairs between the domain ontologies at multiple levels. Finally, the set of concept pairs with a high similarity is selected by the analytic hierarchy process to construct the mapping relationship between the domain ontologies, and then the correlation between sensor data and domain ontologies are constructed. Using the method presented in this paper, we perform sensor data correlation experiments with a simulator for a real world scenario. By comparison to other methods, the experimental results confirm the effectiveness of the proposed approach.

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.

A novel ontology consistent with acknowledged standards in smart homes

With the development of Internet of Things, the Smart Home equipped with various sensors and devices has become a hot area attracting global attention and concern. In order to get a better understanding of ambient environments, adding semantics to sensor data plays a significant role. Researchers are attempting to build semantic models in order to satisfy their own requirements, which leads to little reusability between different models. This paper aims to provide a novel ontology which follows publicly acknowledged standards for achieving sensor data semantization in smart homes, including modeling sensors, context and activities with semantics. For keeping consistent with current accepted standards, the proposed ontology is based on the Semantic Sensor Network Ontology. In addition, we enrich the ontologies by incorporating spatiotemporal information and user profiles. The ontology is designed using Protégé and a use case is demonstrated to show the great potentiality in daily activity recognition in smart homes.

An Ontology-Driven Approach for Integrating Intelligence to Manage Human and Ecological Health Risks in the Geospatial Sensor Web.

Due to the rapid installation of a massive number of fixed and mobile sensors, monitoring machines are intentionally or unintentionally involved in the production of a large amount of geospatial data. Environmental sensors and related software applications are rapidly altering human lifestyles and even impacting ecological and human health. However, there are rarely specific geospatial sensor web (GSW) applications for certain ecological public health questions. In this paper, we propose an ontology-driven approach for integrating intelligence to manage human and ecological health risks in the GSW. We design a Human and Ecological health Risks Ontology (HERO) based on a semantic sensor network ontology template. We also illustrate a web-based prototype, the Human and Ecological Health Risk Management System (HaEHMS), which helps health experts and decision makers to estimate human and ecological health risks. We demonstrate this intelligent system through a case study of automatic prediction of air quality and related health risk.

The modular SSN ontology: A joint W3C and OGC standard specifying the semantics of sensors, observations, sampling, and actuation

The joint W3C (World Wide Web Consortium) and OGC (Open Geospatial Consortium) Spatial Data on the Web (SDW) Working Group developed a set of ontologies to describe sensors, actuators, samplers as well as their observations, actuation, and sampling activities. The ontologies have been published both as a W3C recommendation and as an OGC implementation standard. The set includes a lightweight core module called SOSA (Sensor, Observation, Sampler, and Actuator) available at: http://www.w3.org/ns/sosa/, and a more expressive extension module called SSN (Semantic Sensor Network) available at: http://www.w3.org/ns/ssn/. Together they describe systems of sensors and actuators, observations, the used procedures, the subjects and their properties being observed or acted upon, samples and the process of sampling, and so forth. The set of ontologies adopts a modular architecture with SOSA as a self-contained core that is extended by SSN and other modules to add expressivity and breadth. The SOSA/SSN ontologies are able to support a wide range of applications and use cases, including satellite imagery, large-scale scientific monitoring, industrial and household infrastructures, social sensing, citizen science, observation-driven ontology engineering, and the Internet of Things. In this paper we give an overview of the ontologies and discuss the rationale behind key design decisions, reporting on the differences between the new SSN ontology presented here and its predecessor [9] developed by the W3C Semantic Sensor Network Incubator group (the SSN-XG). We present usage examples and describe alignment modules that foster interoperability with other ontologies.

SOSA: A lightweight ontology for sensors, observations, samples, and actuators

The Sensor, Observation, Sample, and Actuator (SOSA) ontology provides a formal but lightweight general-purpose specification for modellingthe interaction between the entities involved in the acts of observation, actuation, and sampling. SOSA is the result of rethinking the W3C-XG Semantic Sensor Network (SSN) ontology based on changes in scope and target audience, technical developments, and lessons learned over the past years. SOSA also acts as a replacement of SSN’s Stimulus Sensor Observation (SSO) core. It has been developed by the first joint working group of the Open Geospatial Consortium (OGC) and the World Wide Web Consortium (W3C) on Spatial Data on the Web. In this work, we motivate the need for SOSA, provide an overview of the main classes and properties, and briefly discuss its integration with the new release of the SSN ontology as well as various other alignments to specifications such as OGC’s Observations and Measurements (O&M), Dolce-Ultralite (DUL), and other prominent ontologies. We will also touch upon common modelling problems and application areas related to publishing and searching observation, sampling, and actuation data on the Web. The SOSA ontology and standard can be accessed at https://www.w3.org/TR/vocab-ssn/.

MSSN-Onto: An ontology-based approach for flexible event processing in Multimedia Sensor Networks

Multimedia Sensor Networks (MSNs) have gained much attention in recent years from the emerging trends of Internet of Things (IoT). They can be found in different scenarios in our everyday life (e.g., smart homes, smart buildings). Sensors in MSNs can have different capacities, produce multiple kinds of outputs, and have different output encoding formats. Thus, detecting complex events, which requires the aggregation of several sensor readings, can be difficult due to the lack of a generic model that can describe: (i) sensor networks infrastructure, (ii) individual sensor specificities, as well as (iii) multimedia data, while allowing the alignment with the application domain knowledge. In this study, we propose Multimedia Semantic Sensor Network Ontology (MSSN-Onto) to ensure MSNs modeling and provide both syntactic and semantic data interoperability for defining and detecting events in various domains. To show the readiness of MSSN-Onto, we used it as the core ontology of a dedicated framework (briefly defined here). We also adopted MSSN-Onto in HIT2GAP European Project. A prototype has been implemented to conduct a set of tests. Experimental results show that MSSN-Onto can be used to: (i) effectively model MSNs and multimedia data; (ii) define complex events; and (iii) allow to build an efficient event querying engine for MSNs.

SOSA: A Lightweight Ontology for Sensors, Observations, Samples, and Actuators

The Sensor, Observation, Sample, and Actuator (SOSA) ontology provides a formal but lightweight general-purpose specification for modeling the interaction between the entities involved in the acts of observation, actuation, and sampling. SOSA is the result of rethinking the W3C-XG Semantic Sensor Network (SSN) ontology based on changes in scope and target audience, technical developments, and lessons learned over the past years. SOSA also acts as a replacement of SSN’s Stimulus Sensor Observation (SSO) core. It has been developed by the first joint working group of the Open Geospatial Consortium (OGC) and the World Wide Web Consortium (W3C) on Spatial Data on the Web. In this work, we motivate the need for SOSA, provide an overview of the main classes and properties, and briefly discuss its integration with the new release of the SSN ontology as well as various other alignments to specifications such as OGC’s Observations and Measurements (O&M), Dolce-Ultralite (DUL), and other prominent ontologies. We will also touch upon common modeling problems and application areas related to publishing and searching observation, sampling, and actuation data on the Web. The SOSA ontology and standard can be accessed at https://www.w3.org/TR/vocab-ssn/.