1,344 research outputs found
IoT and semantic web technologies for event detection in natural disasters
This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.Natural disasters cannot be predicted well in advance, but it is still possible to decrease the loss of life and mitigate the damages, exploiting some peculiarities that distinguish them. Smart collection, integration, and analysis of data produced by distributed sensors and services are key elements for understanding the context and supporting decision making process for disaster prevention and management. In this paper, we demonstrate how Internet of Things and Semantic Web technologies can be effectively used for abnormal event detection in the contest of an earthquake. In our proposal, a prototype system, which retrieves the data streams from IoT sensors and web services, is presented. In order to contextualize and give a meaning to the data, semantic web technologies are applied for data annotation. We evaluate our system performances by measuring the response time and other parameters that are important in a disaster detection scenario.Peer ReviewedPostprint (author's final draft
A Semantic IoT Early Warning System for Natural Environment Crisis Management
This work was supported in part by the European FP7 Funded Project TRIDEC under Grant 258723, the other project partners in helping to deliver the complete project Syste, in particular, GFZ, and the German Research Centre for Geosciences, Potsdam, Germany. The work of R. Tao was supported by the Queen Mary University of London for a Ph.D. studentship
A Semantic loT Early Warning System for Natural Environment Crisis Management
An early warning system (EWS) is a core type of data driven Internet of Things (IoTs) system used for environment disaster risk and effect management. The potential benefits of using a semantic-type EWS include easier sensor and data source plug-and-play, simpler, richer, and more dynamic metadata-driven data analysis and easier service interoperability and orchestration. The challenges faced during practical deployments of semantic EWSs are the need for scalable time-sensitive data exchange and processing (especially involving heterogeneous data sources) and the need for resilience to changing ICT resource constraints in crisis zones. We present a novel IoT EWS system framework that addresses these challenges, based upon a multisemantic representation model.We use lightweight semantics for metadata to enhance rich sensor data acquisition.We use heavyweight semantics for top level W3CWeb Ontology Language ontology models describing multileveled knowledge-bases and semantically driven decision support and workflow orchestration. This approach is validated through determining both system related metrics and a case study involving an advanced prototype system of the semantic EWS, integrated with a reployed EWS infrastructure
Internet of things for disaster management: state-of-the-art and prospects
Disastrous events are cordially involved with the momentum of nature. As such mishaps have been showing off own mastery, situations have gone beyond the control of human resistive mechanisms far ago. Fortunately, several technologies are in service to gain affirmative knowledge and analysis of a disaster's occurrence. Recently, Internet of Things (IoT) paradigm has opened a promising door toward catering of multitude problems related to agriculture, industry, security, and medicine due to its attractive features, such as heterogeneity, interoperability, light-weight, and flexibility. This paper surveys existing approaches to encounter the relevant issues with disasters, such as early warning, notification, data analytics, knowledge aggregation, remote monitoring, real-time analytics, and victim localization. Simultaneous interventions with IoT are also given utmost importance while presenting these facts. A comprehensive discussion on the state-of-the-art scenarios to handle disastrous events is presented. Furthermore, IoT-supported protocols and market-ready deployable products are summarized to address these issues. Finally, this survey highlights open challenges and research trends in IoT-enabled disaster management systems. © 2013 IEEE
empathi: An ontology for Emergency Managing and Planning about Hazard Crisis
In the domain of emergency management during hazard crises, having sufficient
situational awareness information is critical. It requires capturing and
integrating information from sources such as satellite images, local sensors
and social media content generated by local people. A bold obstacle to
capturing, representing and integrating such heterogeneous and diverse
information is lack of a proper ontology which properly conceptualizes this
domain, aggregates and unifies datasets. Thus, in this paper, we introduce
empathi ontology which conceptualizes the core concepts concerning with the
domain of emergency managing and planning of hazard crises. Although empathi
has a coarse-grained view, it considers the necessary concepts and relations
being essential in this domain. This ontology is available at
https://w3id.org/empathi/
Integration of Data Driven Technologies in Smart Grids for Resilient and Sustainable Smart Cities: A Comprehensive Review
A modern-day society demands resilient, reliable, and smart urban
infrastructure for effective and in telligent operations and deployment.
However, unexpected, high-impact, and low-probability events such as
earthquakes, tsunamis, tornadoes, and hurricanes make the design of such robust
infrastructure more complex. As a result of such events, a power system
infrastructure can be severely affected, leading to unprecedented events, such
as blackouts. Nevertheless, the integration of smart grids into the existing
framework of smart cities adds to their resilience. Therefore, designing a
resilient and reliable power system network is an inevitable requirement of
modern smart city infras tructure. With the deployment of the Internet of
Things (IoT), smart cities infrastructures have taken a transformational turn
towards introducing technologies that do not only provide ease and comfort to
the citizens but are also feasible in terms of sustainability and
dependability. This paper presents a holistic view of a resilient and
sustainable smart city architecture that utilizes IoT, big data analytics,
unmanned aerial vehicles, and smart grids through intelligent integration of
renew able energy resources. In addition, the impact of disasters on the power
system infrastructure is investigated and different types of optimization
techniques that can be used to sustain the power flow in the network during
disturbances are compared and analyzed. Furthermore, a comparative review
analysis of different data-driven machine learning techniques for sustainable
smart cities is performed along with the discussion on open research issues and
challenges
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