SEMANTIC HYPERCAT

The rapidly increasing number of sensor networks and smart devices contributed to the generation of a huge number of information. Information that is generated by several sources and is available indifferent formats highlights interoperability as one of the key preconditions for the success of the Internet of Things (IoT). Hypercat is a specification defining a JSON-based catalogue, designed to serve the needs of the industry. In this thesis, I extend the existing work on semantic enrichment of Hypercat by defining a JSON-LD based catalogue. The proposed JSON-LD specification offers a mapping mechanism among JSON and JSON-LD catalogues, while highlighting the fact that JSON-LD could be seamlessly adopted by the Hypercat community

Enabling the use of a planning agent for urban traffic management via enriched and integrated urban data

Improving a city’s infrastructure is seen as a crucial part of its sustainability, leading to efficiencies and opportunities driven by technology integration. One significant step is to support the integration and enrichment of a broad variety of data, often using state of the art linked data approaches. Among the many advantages of such enrichment is that this may enable the use of intelligent processes to autonomously manage urban facilities such as traffic signal controls. In this paper we document an attempt to integrate sets of sensor and historical data using a data hub and a set of ontologies for the data. We argue that access to such high level integrated data sources leads to the enhancement of the capabilities of an urban transport operator. We demonstrate this by documenting the development of a planning agent which uses such data as inputs in the form of logic statements, and when given traffic goals to achieve, outputs complex traffic signal strategies which help transport operators deal with exceptional events such as road closures or road traffic saturation. The aim is to create an autonomous agent which reacts to commands from transport operators in the face of exceptional events involving saturated roads, and creates, executes and monitors plans to deal with the effects of such events. We evaluate the intelligent agent in a region of a large urban area, under the direction of urban transport operators

The BIG IoT API: semantically enabling IoT interoperability

Today, internet of things (IoT) platforms offer proprietary interfaces and protocols. To enable interoperable interaction with those platforms we present the generic BIG IoT API that employs a novel approach for self-description and semantic annotation to fully adapt arbitrary IoT platforms. We have deployed this approach for multiple platforms from the mobility domain.Peer ReviewedPostprint (author's final draft

Water utility decision support through the semantic web of things

Urban environments are urgently required to become smarter. However, building advanced applications on the Internet of Things requires seamless interoperability. This paper proposes a water knowledge management platform which extends the Internet of Things towards a Semantic Web of Things, by leveraging the semantic web to address the heterogeneity of web resources. Proof of concept is demonstrated through a decision support tool which leverages both the data-driven and knowledge-based programming interfaces of the platform. The solution is grounded in a comprehensive ontology and rule base developed with industry experts. This is instantiated from GIS, sensor, and EPANET data for a Welsh pilot. The web service provides discoverability, context, and meaning for the sensor readings stored in a scalable database. An interface displays sensor data and fault inference notifications, leveraging the complementary nature of serving coherent lower and higher-order knowledge

Towards Data Sharing across Decentralized and Federated IoT Data Analytics Platforms

In the past decade the Internet-of-Things concept has overwhelmingly entered all of the fields where data are produced and processed, thus, resulting in a plethora of IoT platforms, typically cloud-based, that centralize data and services management. In this scenario, the development of IoT services in domains such as smart cities, smart industry, e-health, automotive, are possible only for the owner of the IoT deployments or for ad-hoc business one-to-one collaboration agreements. The realization of "smarter" IoT services or even services that are not viable today envisions a complete data sharing with the usage of multiple data sources from multiple parties and the interconnection with other IoT services. In this context, this work studies several aspects of data sharing focusing on Internet-of-Things. We work towards the hyperconnection of IoT services to analyze data that goes beyond the boundaries of a single IoT system. This thesis presents a data analytics platform that: i) treats data analytics processes as services and decouples their management from the data analytics development; ii) decentralizes the data management and the execution of data analytics services between fog, edge and cloud; iii) federates peers of data analytics platforms managed by multiple parties allowing the design to scale into federation of federations; iv) encompasses intelligent handling of security and data usage control across the federation of decentralized platforms instances to reduce data and service management complexity. The proposed solution is experimentally evaluated in terms of performances and validated against use cases. Further, this work adopts and extends available standards and open sources, after an analysis of their capabilities, fostering an easier acceptance of the proposed framework. We also report efforts to initiate an IoT services ecosystem among 27 cities in Europe and Korea based on a novel methodology. We believe that this thesis open a viable path towards a hyperconnection of IoT data and services, minimizing the human effort to manage it, but leaving the full control of the data and service management to the users' will

Building the Future Internet through FIRE

The Internet as we know it today is the result of a continuous activity for improving network communications, end user services, computational processes and also information technology infrastructures. The Internet has become a critical infrastructure for the human-being by offering complex networking services and end-user applications that all together have transformed all aspects, mainly economical, of our lives. Recently, with the advent of new paradigms and the progress in wireless technology, sensor networks and information systems and also the inexorable shift towards everything connected paradigm, first as known as the Internet of Things and lately envisioning into the Internet of Everything, a data-driven society has been created. In a data-driven society, productivity, knowledge, and experience are dependent on increasingly open, dynamic, interdependent and complex Internet services. The challenge for the Internet of the Future design is to build robust enabling technologies, implement and deploy adaptive systems, to create business opportunities considering increasing uncertainties and emergent systemic behaviors where humans and machines seamlessly cooperate

A semantically enriched Hypercat-enabled internet of things data hub

Large volumes of data is generated from the increasing num-ber of sensor networks and smart devices. Such data is generated and published in multiple formats, thus highlighting the significance of inter-operability for the success of what has come to be known as the Internet of Things (IoT). The BT Hypercat Data Hub provides a focal point for the sharing and consumption of available datasets from a wide range of sources. In this work, we present a series of optimizations applied on the BT Hypercat Data Hub that enabled scalable SPARQL query answering over relational databases and an access control mechanism that filters SPARQL results based on user's subscriptions.Full Tex