Internet of things

Manual of Digital Earth / Editors: Huadong Guo, Michael F. Goodchild, Alessandro Annoni .- Springer, 2020 .- ISBN: 978-981-32-9915-3Digital Earth was born with the aim of replicating the real world within the digital world. Many efforts have been made to observe and sense the Earth, both from space (remote sensing) and by using in situ sensors. Focusing on the latter, advances in Digital Earth have established vital bridges to exploit these sensors and their networks by taking location as a key element. The current era of connectivity envisions that everything is connected to everything. The concept of the Internet of Things(IoT)emergedasaholisticproposaltoenableanecosystemofvaried,heterogeneous networked objects and devices to speak to and interact with each other. To make the IoT ecosystem a reality, it is necessary to understand the electronic components, communication protocols, real-time analysis techniques, and the location of the objects and devices. The IoT ecosystem and the Digital Earth (DE) jointly form interrelated infrastructures for addressing today’s pressing issues and complex challenges. In this chapter, we explore the synergies and frictions in establishing an efficient and permanent collaboration between the two infrastructures, in order to adequately address multidisciplinary and increasingly complex real-world problems. Although there are still some pending issues, the identified synergies generate optimism for a true collaboration between the Internet of Things and the Digital Earth

Virtual Knowledge Graphs: An Overview of Systems and Use Cases

In this paper, we present the virtual knowledge graph (VKG) paradigm for data integration and access, also known in the literature as Ontology-based Data Access. Instead of structuring the integration layer as a collection of relational tables, the VKG paradigm replaces the rigid structure of tables with the flexibility of graphs that are kept virtual and embed domain knowledge. We explain the main notions of this paradigm, its tooling ecosystem and significant use cases in a wide range of applications. Finally, we discuss future research directions

Sharing Human-Generated Observations by Integrating HMI and the Semantic Sensor Web

Current “Internet of Things” concepts point to a future where connected objects gather meaningful information about their environment and share it with other objects and people. In particular, objects embedding Human Machine Interaction (HMI), such as mobile devices and, increasingly, connected vehicles, home appliances, urban interactive infrastructures, etc., may not only be conceived as sources of sensor information, but, through interaction with their users, they can also produce highly valuable context-aware human-generated observations. We believe that the great promise offered by combining and sharing all of the different sources of information available can be realized through the integration of HMI and Semantic Sensor Web technologies. This paper presents a technological framework that harmonizes two of the most influential HMI and Sensor Web initiatives: the W3C’s Multimodal Architecture and Interfaces (MMI) and the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) with its semantic extension, respectively. Although the proposed framework is general enough to be applied in a variety of connected objects integrating HMI, a particular development is presented for a connected car scenario where drivers’ observations about the traffic or their environment are shared across the Semantic Sensor Web. For implementation and evaluation purposes an on-board OSGi (Open Services Gateway Initiative) architecture was built, integrating several available HMI, Sensor Web and Semantic Web technologies. A technical performance test and a conceptual validation of the scenario with potential users are reported, with results suggesting the approach is soun

Mapping for the future: Business intelligence tool to map regional housing stock

The amount of data available and the lack of data integration represent an increasing challenge to effective planning for government agencies. Integration of data from multiple sources has the potential to enable a user to draw valuable insights, which can be used to enhance service targeting and delivery, and to improve program evaluation. In recognition of the need to improve data integration the University of Wollongong and the NSW Office of Environment and Heritage (OEH) partnered to create an integrated housing stock database for the Illawarra region. The database serves as the backbone for an online and interactive Housing Stock Mapping Dashboard (HSMD). It assembled multilevel granular information (including at the Statistical Area Level 1 (SA1) and Local Government Area (LGA) level) collected from multiple historical programs by multiple agencies. This centralised, integrated data repository can help agencies understand the existing housing stock, and improve access to information to support evidence-based policy. This paper presents a model of how data can be integrated from multiple agencies to provide an online collaboration platform. The platform, HSMD, was designed to demonstrate to government, industry, and the research community the opportunity of data integration and advanced analytics. Potential applications of the HSMD include characterisation of the existing housing stock according to a range of building attributes, for instance the presence of ceiling insulation or rainwater tanks. Comparison of these attributes with energy consumption data can indicate the influence of the attribute, or the impact of a specific intervention. This can help policy makers understand uptake and penetration of previous rebate schemes

Smart City Ontologies and Their Applications: A Systematic Literature Review

The increasing interconnections of city services, the explosion of available urban data, and the need for multidisciplinary analysis and decision making for city sustainability require new technological solutions to cope with such complexity. Ontologies have become viable and effective tools to practitioners for developing applications requiring data and process interoperability, big data management, and automated reasoning on knowledge. We investigate how and to what extent ontologies have been used to support smart city services and we provide a comprehensive reference on what problems have been addressed and what has been achieved so far with ontology-based applications. To this purpose, we conducted a systematic literature review finalized to presenting the ontologies, and the methods and technological systems where ontologies play a relevant role in shaping current smart cities. Based on the result of the review process, we also propose a classification of the sub-domains of the city addressed by the ontologies we found, and the research issues that have been considered so far by the scientific community. We highlight those for which semantic technologies have been mostly demonstrated to be effective to enhance the smart city concept and, finally, discuss in more details about some open problems

Quarterly Report (QR3)

In the third quarter, the project has built on the first public release (v0.1), providing additional functionality leading up to the 1.0 production release expected at the end of Q4. An additional public, preview release (v0.2) was produced and a subsequent release (v0.3) is expected just after the close of Q3. The releases are progressing well, with v0.3 expected to be nearly feature-complete, lacking only storage functionalities. The project is well-positioned to release the StratusLab v1.0 distribution at PM12 with the complete set of expected features