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Emerging distributed intelligence paradigms for the Internet of Things (IoT) call for flexible and dynamic reconfiguration of elementary services, resources and devices. In order to achieve such capability, this paper faces complex interoperability and autonomous decision problems by proposing a thorough framework based on the integration of the Semantic Web of Things (SWoT) and Social Internet of Things (SIoT) paradigms. SWoT enables low-power knowledge representation and autonomous reasoning at the edge of the network through carefully optimized inference services and engines. This layer provides service/resource management and discovery primitives for a decentralized collaborative social protocol in the IoT, based on the Linked Data Notifications(LDN) over Linked Data Platform on Constrained Application Protocol (LDP-CoAP). The creation and evolution of friend and follower relationships between pairs of devices is regulated by means of novel dynamic models assessing trust as a usefulness reputation score. The close SWoT-SIoT integration overcomes the functional limitations of existing proposals, which focus on either social device or semantic resource management only. A smart mobility case study on Plug-in Electric Vehicles (PEVs) illustrates the benefits of the proposal in pervasive collaborative scenarios, while experiments show the computational sustainability of the dynamic relationship management approach. [ABSTRACT FROM AUTHOR]

The W3C linked building data group is working on modeling the information for integrating building information with building life cycle data using Semantic Web technologies. The community has proposed a set of semantic models such as ifcOWL and Building Topology Ontology (BOT), to model various applications across Architecture, Engineering, Construction, and Operation (AECO) domain. On the other hand, the Semantic Web of Things (SWoT) group proposed standard semantic models such as M3-lite and BOSH ontologies for describing the sensor networks, observations, and sensor measurements. Both the aforementioned domains have their own siloed applications and with the evolution of the smart home domain, there is a need to combine the knowledge of building information with the sensor knowledge to develop cross-domain applications. However, in order to develop such downstream applications leveraging advantages from both domains requires interoperable knowledge. This paper proposes an interoperable ontology, Building Topology Ontology for Smart Homes (BOTSH), with the aim of aligning the building domain with sensors domain semantic models. The BOTSH ontology facilitates capturing knowledge from both domains and helps in developing cross-domain applications. The potential of the proposed model was demonstrated using a real-life building model based on the competency questions framed by the domain experts. [ABSTRACT FROM AUTHOR]

Emerging distributed intelligence paradigms for the Internet of Things (IoT) call for flexible and dynamic reconfiguration of elementary services, resources and devices. In order to achieve such capability, this paper faces complex interoperability and autonomous decision problems by proposing a thorough framework based on the integration of the Semantic Web of Things (SWoT) and Social Internet of Things (SIoT) paradigms. SWoT enables low-power knowledge representation and autonomous reasoning at the edge of the network through carefully optimized inference services and engines. This layer provides service/resource management and discovery primitives for a decentralized collaborative social protocol in the IoT, based on the Linked Data Notifications(LDN) over Linked Data Platform on Constrained Application Protocol (LDP-CoAP). The creation and evolution of friend and follower relationships between pairs of devices is regulated by means of novel dynamic models assessing trust as a usefulness reputation score. The close SWoT-SIoT integration overcomes the functional limitations of existing proposals, which focus on either social device or semantic resource management only. A smart mobility case study on Plug-in Electric Vehicles (PEVs) illustrates the benefits of the proposal in pervasive collaborative scenarios, while experiments show the computational sustainability of the dynamic relationship management approach.

Building rules on ontology is the main task of mining the logical layer of the Semantic Web. A major effort has been made to develop algorithms capable of efficiently processing relational data and complex background knowledge. One of the promising technologies used in this effort is inductive logic programming (ILP). Steam boilers are an important equipment in power plants, and boiler trips can lead to a complete shutdown of the plant. Thus, it is essential to detect possible boiler trips in critical times to maintain normal and safe operating conditions. To address this challenge, the automatic rule-learning approach is used in this study to diagnose rule extraction. The learning examples are event sequences obtained by simulating an industrial steam boiler model. The ontology is considered as prior conceptual knowledge in ILP to induce supervision rules. The latter are eventually introduced into a scenario recognition system capable of continuously analyzing the event flow arriving at the supervisory center and alerting the operator when a fault situation is detected. [ABSTRACT FROM AUTHOR]

Humans are feeling emotions every day, but they can still encounter difficulties understanding them. To better understand emotions, we integrated interdisciplinary knowledge about emotions from various domains such as neurosciences (e.g., neurobiology), physiology, and psychology (affective sciences, positive psychology, cognitive psychology, psychophysiology, neuropsychology, etc.). To organize the knowledge, we employ technologies such as Artificial Intelligence with Knowledge Graphs and Semantic Reasoning. Furthermore, Internet of Things (IoT) technologies can help to acquire physiological data knowledge. The goal of this paper is to aggregate the interdisciplinary knowledge and implement it within the Emotional Knowledge Graph (EmoKG). The Emotional Knowledge Graph is used within our naturopathy recommender system that suggests food to boost emotion (e.g., chocolate contains magnesium that is recommended when we feel depressed). The recommender system also answers a set of competency questions to easily retrieve emotional related-knowledge from EmoKG, such as what are the basic emotions and the more sophisticated ones, what are the neurotransmitters and hormones related to emotions, etc. To follow FAIR principles, EmoKG is mapped to existing knowledge bases found on the BioPortal biomedical ontology catalog such as SNOMEDCT, FMA, RXNORM, MedDRA, and also from emotion ontologies (when available online). We design the LOV4IoT-Emotion ontology catalog that encourages researchers from heterogeneous communities to apply FAIR principles by releasing online their (emotion) ontologies, datasets, rules, etc. The set of ontology codes shared online can be semi-automatically processed; if not available, the scientific publications describing the emotion ontologies are semi-automatically processed with Natural Language Processing (NLP) technologies. This research is also relevant for other use cases such as European projects (ACCRA for emotional robots to reduce the social isolation of aging people, StandICT for standardization, and AI4EU for Artificial Intelligence) and alliances for IoT such as AIOTI. The recommender system can be extended to address other advice such as aromatherapy and take into consideration medical devices to monitor patients' vital signals related to emotions and mental health. [ABSTRACT FROM AUTHOR]

The Semantic Web of Things (SWoT) integrates knowledge representation and reasoning techniques originally devised for the Semantic Web into Internet of Things architectures, in order to provide more advanced service/resource management and discovery. This paper proposes a novel SWoT framework, enabling collaborative discovery of sensors and actuators in pervasive contexts. It is based on a backward-compatible extension of the Constrained Application Protocol (CoAP), supporting advanced semantic matchmaking via non-standard inference services. The proposed mobile agent is able to discover devices and share smartphone embedded sensors in a peer-to-peer fashion. Efficient data stream mining is also integrated to analyze raw data gathered from the environment and detect high-level events, annotating them with machine-understandable metadata. Finally, a case study about cooperative environmental risk monitoring and prevention in Hybrid Sensor and Vehicular Networks is presented. Experimental performance results on a real testbed assess the approach. [ABSTRACT FROM AUTHOR]

In industry, dashboards are often used to monitor fleets of assets, such as trains, machines or buildings. In such industrial fleets, the vast amount of sensors evolves continuously, new sensor data exchange protocols and data formats are introduced, new visualization types may need to be introduced and existing dashboard visualizations may need to be updated in terms of displayed sensors. These requirements motivate the development of dynamic dashboarding applications. These, as opposed to fixed-structure dashboard applications, allow users to create visualizations at will and do not have hard-coded sensor bindings. The state-of-the-art in dynamic dashboarding does not cope well with the frequent additions and removals of sensors that must be monitored—these changes must still be configured in the implementation or at runtime by a user. Also, the user is presented with an overload of sensors, aggregations and visualizations to select from, which may sometimes even lead to the creation of dashboard widgets that do not make sense. In this paper, we present a dynamic dashboard that overcomes these problems. Sensors, visualizations and aggregations can be discovered automatically, since they are provided as RESTful Web Things on a Web Thing Model compliant gateway. The gateway also provides semantic annotations of the Web Things, describing what their abilities are. A semantic reasoner can derive visualization suggestions, given the Thing annotations, logic rules and a custom dashboard ontology. The resulting dashboarding application automatically presents the available sensors, visualizations and aggregations that can be used, without requiring sensor configuration, and assists the user in building dashboards that make sense. This way, the user can concentrate on interpreting the sensor data and detecting and solving operational problems early.

Ambient Intelligence aims at simplifying the interaction of a user with her surrounding context, minimizing the effort needed to increase comfort and assistance. Nevertheless, especially in built and structured environments, current technologies and market solutions are often far from providing the required levels of automation, coordination and adaptivity. Devices should interact autonomously, should reach opportunistic decisions and take actions accordingly. In particular, user activities and profiles should be among the manifold implicit factors to be taken into account in that process. Home and Building Automation (HBA) is one of the most relevant scenarios suffering from the poorness of the allowed system intelligence, automation and adaptivity. Devices are logically associated through static profiles defined at system deployment stage. The large majority of solutions are proprietary and centralized, and require manual configuration. This paper proposes a novel semantic-based framework complying with the emerging Social Internet of Things paradigm. Infrastructured spaces can be intended as populated by device agents organized in social networks, interacting autonomously and sharing information, cooperating and orchestrating resources. A service-oriented architecture allows collaborative dissemination, discovery and composition of service/resource descriptions. Semantic Web languages are adopted as knowledge representation layer and mobile-oriented implementations of non-monotonic inferences for semantic matchmaking are used to give decision capabilities to software agents. Finally, the Linked Data Platform (LDP) over the Constrained Application Protocol (CoAP) provides the knowledge organization and sharing infrastructure underpinning social object interactions. The framework has been implemented and tested in a home automation prototype integrating several communication protocols and off-the-shelf devices. Experiments advocate the effectiveness of the approach. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a novel Semantic Web of Things framework, enabling collaborative discovery of sensors and actuators in pervasive contexts. It is based on a backward-compatible extension of the Constrained Application Protocol (CoAP), supporting advanced semantic matchmaking via non-standard inference services. The framework also integrates efficient data stream mining to analyze raw data gathered from the environment and detect high-level events, annotating them with machine-understandable metadata. A case study about cooperative environmental risk monitoring and prevention in Hybrid Sensor and Vehicular Networks is presented and experimental performance results on a real testbed are provided. [ABSTRACT FROM AUTHOR]

In terms of current research problems on semantic Web of things (SWoT), main challenges include un-completeness and non-sharedness of its semantic system make it hard to support resource description and semantic interoperability between different domains. To solve these problems, this paper proposed a method of semantic interoperability which combined multi-domain ontologies with linked open data( LOD). Firstly,it annotated newly-deployed sensors in semi-automatic way and added related semantics to corresponding sensor data,so that machine could understand the semantics about data and reason on them. Secondly, it managed same-domain ontologies in manner of ontology-entity table to obtain formulism and consensus on ontologies. Thirdly, it obtained related semanteme of named-entities based on LOD, so their information could be enriched. Finally, it demonstrated a specific example to show the working mechanism of proposed method and made a comparison between proposed method and other classic methods. The result of simulation experiment reveals that the interoperability of multi-domain information can be better improved. [ABSTRACT FROM AUTHOR]

This column discusses the benefits of using Semantic Web technologies to get meaningful knowledge from sensor data. [ABSTRACT FROM PUBLISHER]

The intrinsic contradiction hidden in the Internet of things, which is the mismatching between the diversification of forms expressing the thing's information and the insufficiency of understanding ability of the agents using the thing's information, hinders its intelligent development. Adding a semantic annotation for the expression of thing's information based on ontology will improve the ability to understand, to reason and to obtain information for the agents, and will essentially promote the Internet of things to "semantic web of things". For semantic web of things, its open and dynamic natures inevitably determine it is an open service ecosystem, and whether the hyper world itself and the individual in their sub worlds are in continuous development and evolution, so the semantic web of things must be self-adaptive to provide semantic collaboration services. To achieve all, this is based on "sensing as a service". From different points of view, such as dynamic space, service collaboration and web-based context management services, we analyze semantic collaboration mechanism. Respectively put forward self-adaptiveness formulas and self-adaptive support framework of semantic web of things, propose the definition and architecture of semantic web of things and present sensing as a service model. This paper discusses semantic coordination mechanism and key support technologies for semantic web of things, which lay a theory and technology foundation for subsequent semantic collaboration application schemas, and the application experiments, validation and evaluation of semantic collaboration support system. [ABSTRACT FROM AUTHOR]

The Linked Data Platform (LDP) W3C Recommendation defined resource management primitives for HTTP only, pushing into the background not-negligible use cases related to Web of Things (WoT) scenarios where HTTP-based communication and infras- tructures are unfeasible. This paper proposes a mapping of the LDP specification for Constrained Application Protocol (CoAP) in order to publish Linked Data on the WoT. A general translation of LDP-HTTP requests and responses is provided, as well as a fully comprehensive framework for HTTP-to-CoAP proxying. The theoretical work is corroborated by an experimental campaign using the W3C Test Suite for LDP. [ABSTRACT FROM AUTHOR]

At present, the volume of ontology's Abox in semantic Web of things is always too large, which is not easy for users to understand or reason about ontology. To solve this problem, this paper proposed a method based on equivalence relation. Summary ontology's Abox through searching, mapping and deleting the equivalence individuals. It designed and implemented a prototype system of ontology Abox's summarization. Some experimental examples of public ontologies prove that on the premise of reserving the original semantics, this method can streamline the redundant assertions of ontology's Abox, decrease ontology's volume, reduce storage pressure, so that it can be more convenient for ontology understanding, reusing and other ontology-related operations. [ABSTRACT FROM AUTHOR]

The Internet of Everything and Semantic Web can be joined by giving more intelligence to pervasive systems. To that end, reasoning capabilities should be enabled even for very resource-constrained embedded devices. This paper presents Tiny-ME (the Tiny Matchmaking Engine), a matchmaking and reasoning engine for the Web Ontology Language (OWL), designed and implemented with a compact and portable C core. Main features are high resource efficiency and multiplatform support, spanning containerized microservices, desktops, mobile devices, and embedded boards. The OWLlink interface has been extended to enable non-standard reasoning services for matchmaking in Web, Cloud, and Edge computing. A prototype evaluation is proposed, including a case study on the Pixhawk Unmanned Aerial Vehicle (UAV) autopilot and performance highlights. [ABSTRACT FROM AUTHOR]

Welcome to this special issue of the Semantic Web (SWJ) journal. The special issue compiles four technical contributions that significantly advance the state-of-the-art in Semantic Web of Things for Industry 4.0 including the use of Semantic Web technologies and techniques in Industry 4.0 solutions. [ABSTRACT FROM AUTHOR]