Developing an open data portal for the ESA climate change initiative

We introduce the rationale for, and architecture of, the European Space Agency Climate Change Initiative (CCI) Open Data Portal (http://cci.esa.int/data/). The Open Data Portal hosts a set of richly diverse datasets – 13 “Essential Climate Variables” – from the CCI programme in a consistent and harmonised form and to provides a single point of access for the (>100 TB) data for broad dissemination to an international user community. These data have been produced by a range of different institutions and vary across both scientific and spatio-temporal characteristics. This heterogeneity of the data together with the range of services to be supported presented significant technical challenges. An iterative development methodology was key to tackling these challenges: the system developed exploits a workflow which takes data that conforms to the CCI data specification, ingests it into a managed archive and uses both manual and automatically generated metadata to support data discovery, browse, and delivery services. It utilises both Earth System Grid Federation (ESGF) data nodes and the Open Geospatial Consortium Catalogue Service for the Web (OGC-CSW) interface, serving data into both the ESGF and the Global Earth Observation System of Systems (GEOSS). A key part of the system is a new vocabulary server, populated with CCI specific terms and relationships which integrates OGC-CSW and ESGF search services together, developed as part of a dialogue between domain scientists and linked data specialists. These services have enabled the development of a unified user interface for graphical search and visualisation – the CCI Open Data Portal Web Presence

A Model for Energy-Awareness in Federated Cloud Computing Systems with Service-Level Agreements

International audienceAs data centers increase in size and computational capac- ity, numerous infrastructure issues become critical. Energy efficient is one of these issues because of the constantly increasing power consump- tion of CPUs, memory, and storage devices. A study shows that the whole energy consumed by data centers will be extremely high and it is like to overtake airlines in terms of carbon emissions. In that scenario, Cloud computing is gaining popularity since it can help companies to reduce costs and carbon footprint, usually distributing execution of ser- vices across distributed data centers. The research aims of this work are to propose and evaluate a Model for Federated Clouds that takes into account power consumption and Quality of Service (QoS) requirements. In our model, the energy reduction shall not result in negative impacts to the agreements between Cloud users and Cloud providers. Therefore, the model should ensure both energy-efficiency and QoS parameters, which sets up possibly conflicting objectives

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

Context caches in the clouds

In context-aware systems, the contextual information about human and computing situations has a strong temporal aspect i.e. it remains valid for a period of time. This temporal property can be exploited in caching mechanisms that aim to exploit such locality of reference. However, different types of contextual information have varying temporal validity durations and a varied spectrum of access frequencies as well. Such variation affects the suitability of a single caching strategy and an ideal caching mechanism should utilize dynamic strategies based on the type of context data, quality of service heuristics and access patterns and frequencies of context consuming applications. This paper presents an investigation into the utility of various context-caching strategies and proposes a novel bipartite caching mechanism in a Cloud-based context provisioning system. The results demonstrate the relative benefits of different caching strategies under varying context usage scenarios. The utility of the bipartite context caching mechanism is established both through simulation and deployment in a Cloud platform

Energy conservation in mobile devices and applications: A case for context parsing, processing and distribution in clouds

Context information consumed and produced by the applications on mobile devices needs to be represented, disseminated, processed and consumed by numerous components in a context-aware system. Significant amounts of context consumption, production and processing takes place on mobile devices and there is limited or no support for collaborative modelling, persistence and processing between device-Cloud ecosystems. In this paper we propose an environment for context processing in a Cloud-based distributed infrastructure that offloads complex context processing from the applications on mobile devices. An experimental analysis of complexity based context-processing categories has been carried out to establish the processing-load boundary. The results demonstrate that the proposed collaborative infrastructure provides significant performance and energy conservation benefits for mobile devices and applications

MARGOT: Dynamic IoT Resource Discovery for HADR Environments

Smart City services leverage sophisticated IT architectures whose assets are deployed in dynamic and heterogeneous computing and communication scenarios. Those services are particularly interesting for Humanitarian Assistance and Disaster Relief (HADR) operations in urban environments, which could improve Situation Awareness by exploiting the Smart City IT infrastructure. To this end, an enabling requirement is the discovery of the available Internet-of-Things (IoT) resources, including sensors, actuators, services, and computing resources, based on a variety of criteria, such as geographical location, proximity, type of device, type of capability, coverage, resource availability, and communication topology / quality of network links. To date, no single standard has emerged that has been widely adopted to solve the discovery challenge. Instead, a variety of different standards have been proposed and cities have either adopted one that is convenient or reinvented a new standard just for themselves. Therefore, enabling discovery across different standards and administrative domains is a fundamental requirement to enable HADR operations in Smart Cities. To address these challenges, we developed MARGOT (Multi-domain Asynchronous Gateway Of Things), a comprehensive solution for resource discovery in Smart City environments that implements a distributed and federated architecture and supports a wide range of discovery protocols