3,778 research outputs found
Context Aware Computing for The Internet of Things: A Survey
As we are moving towards the Internet of Things (IoT), the number of sensors
deployed around the world is growing at a rapid pace. Market research has shown
a significant growth of sensor deployments over the past decade and has
predicted a significant increment of the growth rate in the future. These
sensors continuously generate enormous amounts of data. However, in order to
add value to raw sensor data we need to understand it. Collection, modelling,
reasoning, and distribution of context in relation to sensor data plays
critical role in this challenge. Context-aware computing has proven to be
successful in understanding sensor data. In this paper, we survey context
awareness from an IoT perspective. We present the necessary background by
introducing the IoT paradigm and context-aware fundamentals at the beginning.
Then we provide an in-depth analysis of context life cycle. We evaluate a
subset of projects (50) which represent the majority of research and commercial
solutions proposed in the field of context-aware computing conducted over the
last decade (2001-2011) based on our own taxonomy. Finally, based on our
evaluation, we highlight the lessons to be learnt from the past and some
possible directions for future research. The survey addresses a broad range of
techniques, methods, models, functionalities, systems, applications, and
middleware solutions related to context awareness and IoT. Our goal is not only
to analyse, compare and consolidate past research work but also to appreciate
their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201
Km4City Ontology Building vs Data Harvesting and Cleaning for Smart-city Services
Presently, a very large number of public and private data sets are available
from local governments. In most cases, they are not semantically interoperable
and a huge human effort would be needed to create integrated ontologies and
knowledge base for smart city. Smart City ontology is not yet standardized, and
a lot of research work is needed to identify models that can easily support the
data reconciliation, the management of the complexity, to allow the data
reasoning. In this paper, a system for data ingestion and reconciliation of
smart cities related aspects as road graph, services available on the roads,
traffic sensors etc., is proposed. The system allows managing a big data volume
of data coming from a variety of sources considering both static and dynamic
data. These data are mapped to a smart-city ontology, called KM4City (Knowledge
Model for City), and stored into an RDF-Store where they are available for
applications via SPARQL queries to provide new services to the users via
specific applications of public administration and enterprises. The paper
presents the process adopted to produce the ontology and the big data
architecture for the knowledge base feeding on the basis of open and private
data, and the mechanisms adopted for the data verification, reconciliation and
validation. Some examples about the possible usage of the coherent big data
knowledge base produced are also offered and are accessible from the RDF-Store
and related services. The article also presented the work performed about
reconciliation algorithms and their comparative assessment and selection
Cross-Platform Presentation of Interactive Volumetric Imagery
Volume data is useful across many disciplines, not just medicine.
Thus, it is very important that researchers have a simple and
lightweight method of sharing and reproducing such volumetric
data. In this paper, we explore some of the challenges associated
with volume rendering, both from a classical sense and from the
context of Web3D technologies. We describe and evaluate the pro-
posed X3D Volume Rendering Component and its associated styles
for their suitability in the visualization of several types of image
data. Additionally, we examine the ability for a minimal X3D node
set to capture provenance and semantic information from outside
ontologies in metadata and integrate it with the scene graph
Handling Live Sensor Data on the Semantic Web
The increased linking of objects in the Internet of Things and the ubiquitous flood of data and information require new technologies in data processing and data storage in particular in the Internet and the Semantic Web.
Because of human limitations in data collection and analysis, more and more automatic methods are used. Above all, these sensors or similar data producers are very accurate, fast and versatile and can also provide continuous monitoring even places that are hard to reach by people.
The traditional information processing, however, has focused on the processing of documents or document-related information, but they have different requirements compared to sensor data. The main focus is static information of a certain scope in contrast to large quantities of live data that is only meaningful when combined with other data and background information.
The paper evaluates the current status quo in the processing of sensor and sensor-related data with the help of the promising approaches of the Semantic Web and Linked Data movement. This includes the use of the existing sensor standards such as the Sensor Web Enablement (SWE) as well as the utilization of various ontologies.
Based on a proposed abstract approach for the development of a semantic application, covering the process from data collection to presentation, important points, such as modeling, deploying and evaluating semantic sensor data, are discussed.
Besides the related work on current and future developments on known diffculties of RDF/OWL, such as the handling of time, space and physical units, a sample application demonstrates the key points.
In addition, techniques for the spread of information, such as polling, notifying or streaming are handled to provide examples of data stream management systems (DSMS) for processing real-time data.
Finally, the overview points out remaining weaknesses and therefore enables the improvement of existing solutions in order to easily develop semantic sensor applications in the future
Linking Moving Object Databases with Ontologies
This work investigates the supporting role of ontologies for supplementing the information contained in moving object databases. Details of the spatial representation as well as the sensed location of moving objects are frequently stored within a database schema. However, this knowledge lacks the semantic detail necessary for reasoning about characteristics that are specific to each object. Ontologies contribute semantic descriptions for moving objects and provide the foundation for discovering similarities between object types. These similarities can be drawn upon to extract additional details about the objects around us. The primary focus of the research is a framework for linking ontologies with databases. A major benefit gained from this kind of linking is the augmentation of database knowledge and multi-granular perspectives that are provided by ontologies through the process of generalization. Methods are presented for linking based on a military transportation scenario where data on vehicle position is collected from a sensor network and stored in a geosensor database. An ontology linking tool, implemented as a stand alone application, is introduced. This application associates individual values from the geosensor database with classes from a military transportation device ontology and returns linked value-class pairs to the user as a set of equivalence relations (i.e., matches). This research also formalizes a set of motion relations between two moving objects on a road network. It is demonstrated that the positional data collected from a geosensor network and stored in a spatio-temporal database, can provide a foundation for computing relations between moving objects. Configurations of moving objects, based on their spatial position, are described by motion relations that include isBehind and inFrontOf. These relations supply a user context about binary vehicle positions relative to a reference object. For example, the driver of a military supply truck may be interested in knowing what types of vehicles are in front of the truck. The types of objects that participate in these motion relations correspond to particular classes within the military transportation device ontology. This research reveals that linking a geosensor database to the military transportation device ontology will facilitate more abstract or higher-level perspectives of these moving objects, supporting inferences about moving objects over multiple levels of granularity. The details supplied by the generalization of geosensor data via linking, helps to interpret semantics and respond to user questions by extending the preliminary knowledge about the moving objects within these relations
An Ontology for Product-Service Systems
Industries are transforming their business strategy from a product-centric to a more service-centric nature by bundling products and services into integrated solutions to enhance the relationship between their customers. Since Product- Service Systems design research is currently at a rudimentary stage, the development of a robust ontology for this area would be helpful. The advantages of a standardized ontology are that it could help researchers and practitioners to communicate their views without ambiguity and thus encourage the conception and implementation of useful methods and tools. In this paper, an initial structure of a PSS ontology from the design perspective is proposed and evaluated
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
Edge-to-cloud sensing and actuation semantics in the industrial Internet of Things
There are billions of devices worldwide deployed, connected, and communicating to other systems. Sensors and actuators, which can be stationary or movable devices. These Edge devices are considered part of the Internet of Things (IoT) devices, which can be referred to as a tier of the Computing Continuum paradigm. There are two main concerns at stake in the success of this ecosystem. The interoperability between devices and systems is the first. Mainly, because most of them communicate uniquely and differently from each other, leading to heterogeneous data. The second issue is the lack of decision-making capacity to conduct actuations, such as communicating through different computing tiers based on latency constraints due to a certain measured factor. In this article, we propose an ontology to improve device interoperability in the IoT. In addition, we also explain how to ease data communication between Computing Continuum devices, providing tools to enhance data management and decision-making. A use case is also presented, using the automotive industry, where quickness in maneuver determination is key to avoid accidents. It is exemplified using two Raspberry Pi devices, connected using different networks and choosing the appropriate one depending on context-aware conditions.This work is partially funded by: Industrial Doctorates (2019 DI 001) from Generalitat de Catalunya. The SUDOQU project (PID2021-127181OB-I00) from MCIN/AEI. FEDER âUna manera de hacer Europaâ; and project 2017-SGR-1749 from Generalitat de Catalunya. Also with the support of inLab FIB at UPC and Worldsensing.Peer ReviewedPostprint (published version
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