1,508 research outputs found
A Role-Based Approach for Orchestrating Emergent Configurations in the Internet of Things
The Internet of Things (IoT) is envisioned as a global network of connected
things enabling ubiquitous machine-to-machine (M2M) communication. With
estimations of billions of sensors and devices to be connected in the coming
years, the IoT has been advocated as having a great potential to impact the way
we live, but also how we work. However, the connectivity aspect in itself only
accounts for the underlying M2M infrastructure. In order to properly support
engineering IoT systems and applications, it is key to orchestrate
heterogeneous 'things' in a seamless, adaptive and dynamic manner, such that
the system can exhibit a goal-directed behaviour and take appropriate actions.
Yet, this form of interaction between things needs to take a user-centric
approach and by no means elude the users' requirements. To this end,
contextualisation is an important feature of the system, allowing it to infer
user activities and prompt the user with relevant information and interactions
even in the absence of intentional commands. In this work we propose a
role-based model for emergent configurations of connected systems as a means to
model, manage, and reason about IoT systems including the user's interaction
with them. We put a special focus on integrating the user perspective in order
to guide the emergent configurations such that systems goals are aligned with
the users' intentions. We discuss related scientific and technical challenges
and provide several uses cases outlining the concept of emergent
configurations.Comment: In Proceedings of the Second International Workshop on the Internet
of Agents @AAMAS201
A Semantic Collaboration Method Based on Uniform Knowledge Graph
The Semantic Internet of Things is the extension of the Internet of Things and the Semantic Web, which aims to build an interoperable collaborative system to solve the heterogeneous problems in the Internet of Things. However, the Semantic Internet of Things has the characteristics of both the Internet of Things and the Semantic Web environment, and the corresponding semantic data presents many new data features. In this study, we analyze the characteristics of semantic data and propose the concept of a uniform knowledge graph, allowing us to be applied to the environment of the Semantic Internet of Things better. Here, we design a semantic collaboration method based on a uniform knowledge graph. It can take the uniform knowledge graph as the form of knowledge organization and representation, and provide a useful data basis for semantic collaboration by constructing semantic links to complete semantic relation between different data sets, to achieve the semantic collaboration in the Semantic Internet of Things. Our experiments show that the proposed method can analyze and understand the semantics of user requirements better and provide more satisfactory outcomes
Medical data processing and analysis for remote health and activities monitoring
Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions
Semantic IoT Solutions - A Developer Perspective
Semantic technologies have recently gained significant support in a number of communities,
in particular the IoT community. An important problem to be solved is that, on the one hand,
it is clear that the value of IoT increases significantly with the availability of information from
a wide variety of domains. On the other hand, existing solutions target specific applications
or application domains and there is no easy way of sharing information between the
resulting silos. Thus, a solution is needed to enable interoperability across information silos.
As there is a huge heterogeneity regarding IoT technologies on the lower levels, the
semantic level is seen as a promising approach for achieving interoperability (i.e. semantic
interoperability) to unify IoT device description, data, bring common interaction, data
exploration, etc.This work has received funding from the European Unionâs Horizon 2020 research and innovation programme under grant agreements No.732240 (SynchroniCity) and No. 688467 (VICINITY); from ETSI under Specialist Task Forces 534, 556, 566 and 578. This work is partially funded by Hazards SEES NSF Award EAR 1520870, and KHealth NIH 1 R01 HD087132-01
SeLoC-ML: Semantic Low-Code Engineering for Machine Learning Applications in Industrial IoT
Internet of Things (IoT) is transforming the industry by bridging the gap
between Information Technology (IT) and Operational Technology (OT). Machines
are being integrated with connected sensors and managed by intelligent
analytics applications, accelerating digital transformation and business
operations. Bringing Machine Learning (ML) to industrial devices is an
advancement aiming to promote the convergence of IT and OT. However, developing
an ML application in industrial IoT (IIoT) presents various challenges,
including hardware heterogeneity, non-standardized representations of ML
models, device and ML model compatibility issues, and slow application
development. Successful deployment in this area requires a deep understanding
of hardware, algorithms, software tools, and applications. Therefore, this
paper presents a framework called Semantic Low-Code Engineering for ML
Applications (SeLoC-ML), built on a low-code platform to support the rapid
development of ML applications in IIoT by leveraging Semantic Web technologies.
SeLoC-ML enables non-experts to easily model, discover, reuse, and matchmake ML
models and devices at scale. The project code can be automatically generated
for deployment on hardware based on the matching results. Developers can
benefit from semantic application templates, called recipes, to fast prototype
end-user applications. The evaluations confirm an engineering effort reduction
by a factor of at least three compared to traditional approaches on an
industrial ML classification case study, showing the efficiency and usefulness
of SeLoC-ML. We share the code and welcome any contributions.Comment: Accepted by the 21st International Semantic Web Conference (ISWC2022
Geospatial information infrastructures
Manual of Digital Earth / Editors: Huadong Guo, Michael F. Goodchild, Alessandro Annoni .- Springer, 2020 .- ISBN: 978-981-32-9915-3Geospatial information infrastructures (GIIs) provide the technological, semantic,organizationalandlegalstructurethatallowforthediscovery,sharing,and use of geospatial information (GI). In this chapter, we introduce the overall concept and surrounding notions such as geographic information systems (GIS) and spatial datainfrastructures(SDI).WeoutlinethehistoryofGIIsintermsoftheorganizational andtechnologicaldevelopmentsaswellasthecurrentstate-of-art,andreïŹectonsome of the central challenges and possible future trajectories. We focus on the tension betweenincreasedneedsforstandardizationandtheever-acceleratingtechnological changes. We conclude that GIIs evolved as a strong underpinning contribution to implementation of the Digital Earth vision. In the future, these infrastructures are challengedtobecomeïŹexibleandrobustenoughtoabsorbandembracetechnological transformationsandtheaccompanyingsocietalandorganizationalimplications.With this contribution, we present the reader a comprehensive overview of the ïŹeld and a solid basis for reïŹections about future developments
User-defined semantics for the design of IoT systems enabling smart interactive experiences
© The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.Automation in computing systems has always been considered a valuable solution to unburden the user. Internet of Things (IoT) technology best suits automation in different domains, such as home automation, retail, industry, and transportation, to name but a few. While these domains are strongly characterized by implicit user interaction, more recently, automation has been adopted also for the provision of interactive and immersive experiences that actively involve the users. IoT technology thus becomes the key for Smart Interactive Experiences (SIEs), i.e., immersive automated experiences created by orchestrating different devices to enable smart environments to fluidly react to the final usersâ behavior. There are domains, e.g., cultural heritage, where these systems and the SIEs can support and provide several benefits. However, experts of such domains, while intrigued by the opportunity to induce SIEs, are facing tough challenges in their everyday work activities when they are required to automate and orchestrate IoT devices without the necessary coding skills. This paper presents a design approach that tries to overcome these difficulties thanks to the adoption of ontologies for defining Event-Condition-Action rules. More specifically, the approach enables domain experts to identify and specify properties of IoT devices through a user-defined semantics that, being closer to the domain expertsâ background, facilitates them in automating the IoT devices behavior. We also present a study comparing three different interaction paradigms conceived to support the specification of user-defined semantics through a âtransparentâ use of ontologies. Based on the results of this study, we work out some lessons learned on how the proposed paradigms help domain experts express their semantics, which in turn facilitates the creation of interactive applications enabling SIEs.Peer reviewedFinal Published versio
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