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Video streams are becoming ubiquitous in smart cities and traffic monitoring. Recent advances in computer vision with deep neural networks enable querying a rich set of visual features from these video streams. However, it is challenging to deploy these queries on edge devices due to the resource intensive nature of the computing operations of this sort. Hence, this paper will demonstrate our approach in pushing these computing operations closer to the video stream sources via autonomous stream fusion agents. These agents will facilitate an edge computing paradigm that enables edge devices to utilize its computing resources to serve federated queries over video streams. Our demonstration shows that edge devices can significantly alleviate the bottleneck of the centralized server in dealing with distributed video streams.

International audience; Data owners are creating an ever richer set of information resources online, and these are being used for more and more applications. Spatial data on the Web is becoming ubiquitous and voluminous with the rapid growth of location-based services, spatial technologies, dynamic location-based data and services published by different organizations. However, the heterogeneity and the peculiarities of spatial data, such as the use of different coordinate reference systems, make it difficult for data users, Web applications, and services to discover, interpret and use the information in the large and distributed system that is the Web. To make spatial data more effectively available, this paper summarizes the work of the joint W3C/OGC Working Group on Spatial Data on the Web that identifies 14 best practices for publishing spatial data on the Web. The paper extends that work by presenting the identified challenges and rationale for selection of the recommended best practices, framed by the set of principles that guided the selection. It describes best practices that are employed to enable publishing, discovery and retrieving (querying) spatial data on the Web, and identifies some areas where a best practice has not yet emerged.

The wide adoption of increasingly cheap and computationally powerful single-board computers, has triggered the emergence of new paradigms for collaborative data processing among IoT devices. Motivated by the billions of ARM chips having been shipped as IoT gateways so far, our paper proposes a novel continuous federation approach that uses RDF Stream Processing (RSP) engines as autonomous processing agents. These agents can coordinate their resources to distribute processing pipelines by delegating partial workloads to their peers via subscribing continuous queries. Our empirical study in “cooperative sensing” scenarios with resourceful experiments on a cluster of Raspberry Pi nodes shows that the scalability can be significantly improved by adding more autonomous agents to a network of edge devices on demand. The findings open several new interesting follow-up research challenges in enabling semantic interoperability for the edge computing paradigm.

Recently, many approaches have been proposed to manage sensor data using semantic web technologies for effective heterogeneous data integration. However, our empirical observations revealed that these solutions primarily focused on semantic relationships and unfortunately paid less attention to spatio&ndash, temporal correlations. Most semantic approaches do not have spatio&ndash, temporal support. Some of them have attempted to provide full spatio&ndash, temporal support, but have poor performance for complex spatio&ndash, temporal aggregate queries. In addition, while the volume of sensor data is rapidly growing, the challenge of querying and managing the massive volumes of data generated by sensing devices still remains unsolved. In this article, we introduce EAGLE, a spatio&ndash, temporal query engine for querying sensor data based on the linked data model. The ultimate goal of EAGLE is to provide an elastic and scalable system which allows fast searching and analysis with respect to the relationships of space, time and semantics in sensor data. We also extend SPARQL with a set of new query operators in order to support spatio&ndash, temporal computing in the linked sensor data context.

To enable efficiency in stream processing, the evaluation of a query is usually performed over bounded parts of (potentially) unbounded streams, i.e., processing windows “slide” over the streams. To avoid inefficient re-evaluations of already evaluated parts of a stream in respect to a query, incremental evaluation strategies are applied, i.e., the query results are obtained incrementally from the result set of the preceding processing state without having to re-evaluate all input buffers. This method is highly efficient but it comes at the cost of having to maintain processing state, which is not trivial, and may defeat performance advantages of the incremental evaluation strategy. In the context of RDF streams the problem is further aggravated by the hard-to-predict evolution of the structure of RDF graphs over time and the application of sub-optimal implementation approaches, e.g., using relational technologies for storing data and processing states which incur significant performance drawbacks for graph-based query patterns. To address these performance problems, this paper proposes a set of novel operator-aware data structures coupled with incremental evaluation algorithms which outperform the counterparts of relational stream processing systems. This claim is demonstrated through extensive experimental results on both simulated and real datasets.

Semantic interoperability for the Internet of Things (IoT) is enabled by standards and technologies from the Semantic Web. As recent research suggests a move towards decentralised IoT architectures, we have investigated the scalability and robustness of RDF (Resource Description Framework)engines that can be embedded throughout the architecture, in particular at edge nodes. RDF processing at the edge facilitates the deployment of semantic integration gateways closer to low-level devices. Our focus is on how to enable scalable and robust RDF engines that can operate on lightweight devices. In this paper, we have first carried out an empirical study of the scalability and behaviour of solutions for RDF data management on standard computing hardware that have been ported to run on lightweight devices at the network edge. The findings of our study shows that these RDF store solutions have several shortcomings on commodity ARM (Advanced RISC Machine) boards that are representative of IoT edge node hardware. Consequently, this has inspired us to introduce a lightweight RDF engine, which comprises an RDF storage and a SPARQL processor for lightweight edge devices, called RDF4Led. RDF4Led follows the RISC-style (Reduce Instruction Set Computer) design philosophy. The design constitutes a flash-aware storage structure, an indexing scheme, an alternative buffer management technique and a low-memory-footprint join algorithm that demonstrates improved scalability and robustness over competing solutions. With a significantly smaller memory footprint, we show that RDF4Led can handle 2 to 5 times more data than popular RDF engines such as Jena TDB (Tuple Database) and RDF4J, while consuming the same amount of memory. In particular, RDF4Led requires 10%&ndash, 30% memory of its competitors to operate on datasets of up to 50 million triples. On memory-constrained ARM boards, it can perform faster updates and can scale better than Jena TDB and Virtuoso. Furthermore, we demonstrate considerably faster query operations than Jena TDB and RDF4J.

RDF stores provide a simple abstraction for publishing and querying data, that is becoming a norm in data sharing practice. They also empower the decentralised architecture of data publishing for the Web or IoT-driven systems. Such architecture shares a lot in common with blockchain infrastructure and technologies. Therefore, there are emerging interests in marrying RDF stores and blockchain to realise desirable but speculative benefits of blockchain-powered data sharing. This paper presents the first RDF store with blockchain that enables lightweight edge devices to control of the data sharing processes (personal, IoT data). Our novel approach on the deep integration of the storage design for RDF store enables the ability to enforce controlling measures on access methods and auditing policies over data elements via smart contracts before they fetched from the sources to the consumers. Our experiments show that the prototype system delivers an effective performance for a processing load of 1 billion triples on a small network of lightweight nodes which costs less than a commodity PC.

Semantic interoperability for the Internet of Things(IoT) is being enabled by standards and technologies from the Semantic Web. As recent research suggests a move towards decentralised IoT architectures, our focus is on how to enable scalable and robust RDF engines that can be embedded throughout the architecture, in particular at edge nodes. RDF processing at edge enables the creation of semantic integration gateways for locally connected low-level devices. We introduce a lightweight RDF engine, which comprises of RDF storage and SPARQL processor, for the lightweight edge devices, called RDF4Led. RDF4Led follows the RISCstyle (Reduce Instruction Set Computer) design philosophy. The design comprises a flash-aware storage structure, an indexing scheme and a low-memory-footprint join algorithm which improves scalability as well as robustness over competing solutions. With a significantly smaller memory footprint, we show that RDF4Led can handle 2 to 5 times more data than RDF engines such as Jena TDB and Virtuoso. On three types of ARM boards, RDF4Led requires 10--30% memory of its competitors to operate up to 30 million triples dataset; it can perform faster updates and can scale better than Jena TDB and Virtuoso. Furthermore, we demonstrate considerably faster query operations than Jena TDB.

International audience; The joint W3C (World Wide Web Consortium) and OGC (Open Geospatial Consortium) Spatial Data on the Web (SDW) Working Group developed a set of ontologies to describe sensors, actuators, samplers as well as their observations, actuation, and sampling activities. The ontologies have been published both as a W3C recommendation and as an OGC implementation standard. The set includes a lightweight core module called SOSA (Sensor, Observation, Sampler, and Actuator) available at: http://www.w3.org/ns/sosa/, and a more expressive extension module called SSN (Semantic Sensor Network) available at: http://www.w3.org/ns/ssn/. Together they describe systems of sensors and actuators, observations, the used procedures, the subjects and their properties being observed or acted upon, samples and the process of sampling, and so forth. The set of ontologies adopts a modular architecture with SOSA as a self-contained core that is extended by SSN and other modules to add expressivity and breadth. The SOSA/SSN ontologies are able to support a wide range of applications and use cases, including satellite imagery, large-scale scientific monitoring, industrial and household infrastructures, social sensing, citizen science, observation-driven ontology engineering, and the Internet of Things. In this paper we give an overview of the ontologies and discuss the rationale behind key design decisions, reporting on the differences between the new SSN ontology presented here and its predecessor [9] developed by the W3C Semantic Sensor Network Incubator group (the SSN-XG). We present usage examples and describe alignment modules that foster interoperability with other ontologies.

Large RDF interconnected datasets, especially in the form of open as well as enterprise knowledge graphs, are constructed and consumed in several domains. Reasoning over such large knowledge graphs poses several performance challenges. In practice, although there has been some prior work on scalable approaches to RDF reasoning, the interest in this field started gathering momentum with the rising popularity of modern big data processing systems (e.g., Hadoop, Spark). In this chapter, we cover five main categories of distributed RDF reasoning systems: (1) Peer-to-Peer RDF reasoning systems, (2) NoSQL-based RDF reasoning systems, (3) Hadoop-based RDF reasoning systems, (4) Spark-based RDF reasoning systems, and (5) shared memory RDF reasoning systems.

With increasing sizes of RDF datasets, executing complex queries on a single node has turned to be impractical especially when the node’s main memory is dwarfed by the volume of the dataset. Therefore, there was a crucial need for distributed systems with a high degree of parallelism that can satisfy the performance demands of complex SPARQL queries. In this chapter, we give an overview of various techniques and systems for efficiently querying large RDF datasets in distributed environments.

We are witnessing a paradigm shift, where real-time, time-dependent data is becoming ubiquitous. As Linked Data facilitates the data integration process among heterogenous data sources, RDF Stream Data has the same goal with respect to data streams. It bridges the gap between stream and more static data sources. To support the processing on RDF stream data, there is a need on investigating how to extend RDF to model and represent stream data. Then, from the RDF-based data representation, the query model processing models need to be defined to build the stream processing engine that is tailored for streaming data. This chapter provides an overview on how such requirements are addressed in the current state-of-the-art of RDF Stream Data processing.

The wide adoption of the RDF data model has called for efficient and scalable RDF query processing schemes. As a response to this call, a number of centralized RDF query processing systems have been designed to tackle this challenge. In these systems, the storage and query processing of RDF datasets are managed on a single node. In this chapter, we give an overview of various techniques and systems for centrally querying RDF datasets.

The nature of Web data is changing. The popularity of news feeds and social media, the rise of the Web of Things, and the adoption of sensor technologies are examples of streaming data that reached the Web scale. The different nature of streaming data calls for specific solutions to problems like data integration and analytics. There is a need for streaming-specific Web resources: new vocabularies to describe, find and select streaming data sources, and systems that can cooperate dynamically to solve stream processing tasks. To foster interoperability between these streaming services on the Web, we propose the Vocabulary & Catalog of Linked Streams (VoCaLS). VoCaLS is a three-module ontology to (i) publish streaming data following Linked Data principles, (ii) describe streaming services and (iii) track the provenance of stream processing.

The ever-increasing growth of the Internet of Things (IoT) has attracted a considerable amount of research attention from the Semantic Web community in order to address the challenge of poor interoperability. However, our survey of research work has shown that the goal of providing an intelligent pro- cessing and analysis engine for IoT has still not been fully achieved. Central to this problem is the requirement for a semantic spatio-temporal query processing engine that is able to not only analyze spatio-temporal correlations in a massive amount of IoT data, but that can also generate an effective query plan for a given query to execute in a timely manner. Needless to say, query planning for the multidimensional data like IoT is a costly operation. The most known techniques are either based on the cost model or by using spatio-temporal data statistics and heuristics. In this paper, we propose an alternative solution that uses query similarity identification in conjunction with machine learning techniques to recommend a previously generated query plan to the optimizer for a given query. Our approach also aims to predict the query execution time for the purposes of workload management and capacity planning. Our extensive experiments indicate the efficiency of our learning approach with an impressive prediction accuracy on test queries.

The join operator is a core component of an RDF Stream Processing engine. The join operations usually dominate the processing load of a query execution plan. Due to the constantly updating nature of continuous queries, the query optimiser has to frequently change the optimal execution plan for a query. However, optimising the join executing plan for every execution step might be prohibitively expensive, hence, dynamic optimisation of continuous join operations is still a challenging problem so far. Therefore, this paper proposes the first adaptive optimisation approach towards this problem in the context of RDF Stream Processing. The approach comes with two dynamic cost-based optimisation algorithms which use a light-weight process to search for the best execution plan for every execution step. The experiments show the encouraging results towards this direction.

The term Provenance refers to the origin of information and is used to describe where and how the data was obtained. Provenance is versatile and could include various types of information, such as the source of the data, information on the processes that led to a certain result, date of creation or last modification, and authorship. Recording and managing the provenance of data is of paramount importance, as it allows supporting trust mechanisms, access control and privacy policies, digital rights management, quality management and assessment, in addition to reputability, reliability and accountability of data sources.

Standards and benchmarking have traditionally been used as the main tools to formally define and provably illustrate the level of the adequacy of systems to address the new challenges. In this chapter, we discuss benchmarks for RDF query engines and instance matching systems. In practice, benchmarks are used to inform users of the strengths and weaknesses of competing tools and approaches, but more importantly, they encourage the advancement of technology by providing both academia and industry with clear targets for performance and functionality.

International audience; The Sensor, Observation, Sample, and Actuator (SOSA) ontology provides a formal but lightweight general-purpose specification for modellingthe interaction between the entities involved in the acts of observation, actuation, and sampling. SOSA is the result of rethinking the W3C-XG Semantic Sensor Network (SSN) ontology based on changes in scope and target audience, technical developments, and lessons learned over the past years. SOSA also acts as a replacement of SSN’s Stimulus Sensor Observation (SSO) core. It has been developed by the first joint working group of the Open Geospatial Consortium (OGC) and the World Wide Web Consortium (W3C) on Spatial Data on the Web. In this work, we motivate the need for SOSA, provide an overview of the main classes and properties, and briefly discuss its integration with the new release of the SSN ontology as well as various other alignments to specifications such as OGC’s Observations and Measurements (O&M), Dolce-Ultralite (DUL), and other prominent ontologies. We will also touch upon common modelling problems and application areas related to publishing and searching observation, sampling, and actuation data on the Web. The SOSA ontology and standard can be accessed at https://www.w3.org/TR/vocab-ssn/.

The W3C Semantic Sensor Network Incubator group (the SSN-XG) produced an OWL 2 ontology to describe sensors and observations — the SSN ontology, available at http://purl.oclc.org/NET/ssnx/ssn. The SSN ontology can describe sensors in terms of capabilities, measurement processes, observations and deployments. This article describes the SSN ontology. It further gives an example and describes the use of the ontology in recent research projects.

The Internet of Things (IoT) with billions of connected devices has been generating an enormous amount of data every hour. Connecting every data item generated by IoT to the rest of the digital world to turn this data into meaningful actions will create new capabilities, richer experiences, and unprecedented economic opportunities for businesses, individuals, and countries. However, providing an integrated view for exploring and querying such data at real-time is extremely challenging due to its Big Data natures: big volume, fast real-time update and messy data sources. To address this challenge, we provide a unified integrated and live view for heterogeneous IoT data sources using Linked Data, called the Graph of Things (GoT). GoT is backed by a scalable and elastic software stack to deal with billions of records of historical and static datasets in conjunction with millions of triples being fetched and enriched to connect to GoT per hour in real time. GoT makes approximately a half of million stream data sources queryable via a SPARQL endpoint and a continuous query channel that enable us to create a live explorer of GoT (http://graphofthings.org/) with just HTML and Javascript.

The current state of the art in RDF Stream Processing (RSP) proposes several models and implementations to combine Semantic Web technologies with Data Stream Management System (DSMS) operators like windows. Meanwhile, only a few solutions combine Semantic Web and Complex Event Processing (CEP), which includes relevant features, such as identifying sequences of events in streams. Current RSP query languages that support CEP features have several limitations: EP-SPARQL can identify sequences, but its selection and consumption policies are not all formally defined, while C-SPARQL offers only a naive support to pattern detection through a timestamp function. In this work, we introduce an RSP query language, called RSEP-QL, which supports both DSMS and CEP operators, with a special interest in formalizing CEP selection and consumption policies. We show that RSEP-QL captures EP-SPARQL and C-SPARQL, and offers features going beyond the ones provided by current RSP query languages.

This paper presents a solution to the Grand Challenge using CQELS (Continuous Query Evaluation over Linked Stream), a general execution framework to build RDF Stream Processing engines to answer continuous analytical queries. It provides an efficient execution architecture whereby incremental computing algorithms can be implemented to boost the performance. Our experimental results show strong effects of the implemented approach as CQELS outperforms a base-line implementation which recomputes on every incoming input.

Sensor technology and sensor networks have evolved so rapidly that they are now considered a core driver of the Internet of Things (IoT), however data analytics on IoT streams is still in its infancy. This paper introduces an approach to sensor data analytics for the Internet of Things by using the OpenIoT1 middleware ; real time event processing and clustering algorithms have been used for this purpose. The OpenIoT platform has been extended to support stream processing and thus we demonstrate its flexibility in enabling real time on-demand application domain analytics. We use mobile crowd-sensed data, provided in real time from wearable sensors, to analyse and infer air quality conditions. This experimental evaluation has been implemented using the design principles and methods for IoT data interoperability specified by the OpenIoT project. We describe an event and clustering analytics server that acts as an interface for novel analytical IoT services. The approach presented in this paper also demonstrates how sensor data acquired from mobile devices can be integrated within IoT platforms to enable analytics on data streams. It can be regarded as a valuable tool to understand complex phenomena, e.g., air pollution dynamics and its impact on human health.

Recently, many approaches have been proposed to manage sensor data using Semantic Web technologies for effective heterogeneous data integration. However, our research survey revealed that these solutions primarily focused on semantic relationships and still paid less attention to its temporal-spatial correlation. Most semantic approaches do not have spatiotemporal support. Some of them have served limitations on providing full spatiotemporal support but have poor performance for complex spatiotemporal aggregate queries. In addition, while the volume of sensor data is rapidly growing, a challenge of querying and managing the massive volumes of data generated by sensing devices still remains unsolved. In this paper, we propose a spatiotemporal query engine for sensor data based on Linked Data model. The ultimate goal of our approach is to provide an elastic and scalable system which allows fast searching and analysis on the relationships of space, time and semantic in sensor data. We also introduce a set of new query operators in order to support spatiotemporal computing in linked sensor data context.

This paper introduces the stack for service delivery models and interoperability in the Internet of Things. The main characteristics and functional layers of the IoT stack are described. The applicability of the IoT stack is described based on particular use cases and deployed pilots. The validation of the IoT stack in terms of functionality and adaptation at different IoT particular areas is based on the Virtual Development Kit (VDK) developed and implemented within the framework of the OpenIoT project—OpenIoT project is the awarded Internet of Things open-source rookie of the year by BlackDuck Software Co. ( www.github.com/OpenIotOrg ). The methods and standards that boosted OpenIoT-VDK implementation are described in this paper. An instance of the OpenIoT-VDK process is described as the practical use case demonstrating being an IoT platform with autonomic behavior. OpenIoT-VDK creates IoT instances, analyzes the IoT stack dependence, and resolves them following interoperability principles. The OpenIoT-VDK instance deploys IoT service delivery models facilitating the validation of use cases by using the OpenIoT platform. As proof of concept, a delivered IoT service using open data from OpenIoT local instantiation is described.

Mobile devices are becoming a central data integration hub for personal information. Thus, an up-to-date, comprehensive and consolidated view of this information across heterogeneous personal information spaces is required. Linked Data offers various solutions for integrating personal information, but none of them comprehensively addresses the specific resource constraints of mobile devices. To address this issue, this paper presents a unified data integration framework for resource-constrained mobile devices. Our generic, extensible framework not only provides a unified view of personal data from different personal information data spaces but also can run on a user's mobile device without any external server. To save processing resources, we propose a data normalisation approach that can deal with ID-consolidation and ambiguity issues without complex generic reasoning. This data integration approach is based on a triple storage for Android devices with small memory footprint. We evaluate our framework with a set of experiments on different devices and show that it is able to support complex queries on large personal data sets of more than one million triples on typical mobile devices with very small memory footprint.

Linked Stream Data extends the Linked Data paradigm to dynamic data sources. It enables the integration and joint processing of heterogeneous stream data with quasi-static data from the Linked Data Cloud in near-real-time. Several Linked Stream Data processing engines exist but their scalability still needs to be in improved in terms of (static and dynamic) data sizes, number of concurrent queries, stream update frequencies, etc. So far, none of them supports parallel processing in the Cloud, i.e., elastic load profiles in a hosted environment. To remedy these limitations, this paper presents an approach for elastically parallelizing the continuous execution of queries over Linked Stream Data. For this, we have developed novel, highly efficient, and scalable parallel algorithms for continuous query operators. Our approach and algorithms are implemented in our CQELS Cloud system and we present extensive evaluations of their superior performance on Amazon EC2 demonstrating their high scalability and excellent elasticity in a real deployment.

The Web has long exceeded its original purpose of a distributed hypertext system and has become a global, data sharing and processing platform. This development is confirmed by remarkable milestones such as the Semantic Web, Web services, social networks and mashups. In parallel with these developments on the Web, the Internet of Things (IoT), i.e., sensors and actuators, has matured and has become a major scientific and economic driver. Its potential impact cannot be overestimated-for example, in logistics, cities, electricity grids and in our daily life, in the form of sensor-laden mobile phones-and rivals that of the Web itself. While the Web provides ease of use of distributed resources and a sophisticated development and deployment infrastructure, the IoT excels in bringing real-time information from the physical world into the picture. Thus a combination of these players seems to be the natural next step in the development of even more sophisticated systems of systems. While only starting, there is already a significant amount of sensor-generated, or more generally dynamic information, available on the Web. However, this information is not easy to access and process, depends on specialised gateways and requires significant knowledge on the concrete deployments, for example, resource constraints and access protocols. To remedy these problems and draw on the advantages of both sides, we try to make dynamic, online sensor data of any form as easily accessible as resources and data on the Web, by applying well-established Web principles, access and processing methods, thus shielding users and developers from the underlying complexities. In this paper we describe our Linked Stream Middleware (LSM, http://lsm.deri.ie/), which makes it easy to integrate time-dependent data with other Linked Data sources, by enriching both sensor sources and sensor data streams with semantic descriptions, and enabling complex SPARQL-like queries across both dataset types through a novel query processing engine, along with means to mashup the data and process results. Most prominently, LSM provides (1) extensible means for real-time data collection and publishing using a cloud-based infrastructure, (2) a Web interface for data annotation and visualisation, and (3) a SPARQL endpoint for querying unified Linked Stream Data and Linked Data. We describe the system architecture behind LSM, provide details of how Linked Stream Data is generated, and demonstrate the benefits and efficiency of the platform by showcasing some experimental evaluations and the system's interface.

The W3C Semantic Sensor Network Incubator group (the SSN-XG) produced an OWL~2 ontology to describe sensors and observations --- the SSN ontology. The SSN ontology can describe sensors in terms of capabilities, measurement processes, observations and deployments. This article describes the SSN ontology. It further gives an example and describes the use of the ontology in recent research projects.

Linked Stream Data, i.e., the RDF data model extended for representing stream data generated from sensors social network applications, is gaining popularity. This has motivated considerable work on developing corresponding data models associated with processing engines. However, current implemented engines have not been thoroughly evaluated to assess their capabilities. For reasonable systematic evaluations, in this work we propose a novel, customizable evaluation framework and a corresponding methodology for realistic data generation, system testing, and result analysis. Based on this evaluation environment, extensive experiments have been conducted in order to compare the state-of-the-art LSD engines wrt. qualitative and quantitative properties, taking into account the underlying principles of stream processing. Consequently, we provide a detailed analysis of the experimental outcomes that reveal useful findings for improving current and future engines.

Linked Stream Data has emerged as an effort to represent dynamic, time-dependent data streams following the principles of Linked Data. Given the increasing number of available stream data sources like sensors and social network services, Linked Stream Data allows an easy and seamless integration, not only among heterogenous stream data, but also between streams and Linked Data collections, enabling a new range of real-time applications.

Providing adequate support for context acquisition and management is a non-trivial task that complicates application development. To mitigate this, existing middleware systems rely on a centralized context service that manages the context of all devices located within a certain area. However, in many cases, centralized context management impacts privacy as it requires users to share their context with a possibly untrusted device. In this paper, we show how this problem can be avoided by a peer-based approach to context management. To validate the approach, we have implemented it on top of the BASE middleware and used it extensively in the PECES European research project. The evaluation shows that given a customizable implementation, the approach provides high flexibility while being suitable for a broad spectrum of devices.

There are millions of sensors being deployed all over the world. Data generated by these sensors is provided in different formats and interfaces and is rarely associated with semantics that describe its meaning. The heterogeneity and lack of semantic descriptions pose a big barrier for accessing sensor data and combining it with other data sources.The Live Linked Open Sensor Database project is the first project to provide a live database of semantically enriched sensor data, where each sensor reading is extended by adding proper metadata and by linking it to resources in the Linked Open Data Cloud. Currently, the database provides information of approximately 200,000 sensors and we are currently working on expanding it to incorporate even more data sources.

The use of RDF data published on the Web for applications is still a cumbersome and resource-intensive task due to the limited software support and the lack of standard programming paradigms to deal with everyday problems such as combination of RDF data from dierent sources, object identifier consolidation, ontology alignment and mediation, or plain querying and filtering tasks. In this paper we present a framework, Semantic Web Pipes, that supports fast implementation of Semantic data mash-ups while preserving desirable properties such as abstraction, encapsulation, component-orientation, code re-usability and maintainability which are common and well supported in other application areas.

In this demo we present a first implementation of Semantic Web Pipes, a powerful tool to build RDF-based mashups. Semantic Web pipes are defined in XML and when executed they fetch RDF graphs on the Web, operate on them, and produce an RDF output which is itself accessible via a stable URL. Humans can also use pipes directly thanks to HTML wrapping of the pipe parameters and outputs. The implementation we will demo includes an online AJAX pipe editor and execution engine. Pipes can be published and combined thus fostering collaborative editing and reuse of data mashups.

Within the vision of ubiquitous multimedia, using multimedia sensor nodes, such as audio, image, and video sensor nodes, in wireless sensor networks can dramatically enhance the capability of sensor networks for event description. Different kinds of holes can easily appear in sensor networks. For example, tens of sensor nodes can consist of a hole in a small area because of overload. Efficiently and reliably transmit these multimedia streaming data back to the base station while bypassing these static or dynamic holes is one of the key challenge issues. In this paper, we propose a new Two Phase geographical Greedy Forwarding (TPGF) routing algorithm for exploring one or multiple optimized hole-bypassing transmission paths. We demonstrate the execution of TPGF routing algorithm by using our implemented simulator NetTopo. Comparison and simulation results show that our algorithm can effectively solve the identified problems and is better than the famous GPSR routing algorithm in both functionality and performance aspects.

The Internet and the World Wide Web have transformed modern life by connecting the real world. With the massive connectivity extension of the Net to a much wider world wide web of the manifold physical objects, we are on the verge of the next evolution of the Internet, so called the Internet of Everything (IoE). However, enabling seamless interoperability is still a biggest challenge towards building the next generation of IoE applications. Believing in Linked Data as a promising solution to address this challenge, we propose the idea of “Linking Everything” by extending Linked Data Principles to interlink “everything” into a hypergraph. Via this book chapter, we present how to make this hypergraph programmable via the emerging Semantic Web technologies.

The development of the Internet of Things (IoT) is enabling innovative and smarter domestic and commercial services. Due to the lack of skill and technology to make sense of IoT data, the full potential of the available information from IoT networks has not been achieved yet. It has long been recognised that standards for the interoperability and integration of data must play a key role in the value generated by IoT. Semantic technologies have been proposed to deal with data heterogeneity and to enable service interoperability in the IoT domain, and to underpin resource discovery, reasoning, and knowledge extraction. The Semantic Web research community has been attentive to the issues of data integration and interoperability raised in the IoT. Many efforts have constituted important milestones towards the integration of heterogeneous IoT platforms and applications. Linked Data model is now the preferred data model for semantic IoT data, and Resource Description Framework (RDF) engines have been proposed to be used as semantic integration gateways for the IoT. It has been proposed that a decentralised integration paradigm fits better with the distributed nature of autonomous deployment of smart IoT devices. The idea is that moving RDF data processing closer to the edge of the network will reduce network overhead/bottlenecks and enable flexible and continuous integration of new IoT devices/data sources. The state-of-the-art RDF engines suffer performance degradation when they are required to work on IoT edge devices that are significantly underequipped in terms of memory and CPU for supporting regular RDF engines. In this thesis, we develop a scalable RDF engine for lightweight IoT devices, namely, RDF4Led. Our approach focuses on minimising memory consumption, maximising scalability, and processing performance. RDF4Led is designed with a RISC-style (Reduced Instruction Set Computer) design that simplifies the implementation with the purpose of reducing code footprint and memory footprint. We carefully redesign the RDF storage, indexing scheme and buffer manager to optimise flash I/O. We improve the join algorithm so that it significantly lowers the memory consumption used to evaluate SPARQL queries. Furthermore, we demonstrate the feasibility of RDF4Led in different application scenarios. Firstly, we show how to use RDF4Led to enable the integration of different personal data spaces on mobile devices. We also demonstrate how to integrate RDF stores and blockchain to protect the ownership of the shared RDF data on IoT edge nodes. As a result, we introduce a novel distributed RDF store which leverages blockchain benefits for data publishers at the edges of networks. To the best of our knowledge, our system is the first of this kind. Finally, we introduce an federation mechanism to distribute the workload of RDF stream processing over IoT edge nodes. To enable such a mechanism, we integrate RDF4Led with CQELS to create Fed4Edge and RDF stream processing engine for IoT edge devices.

In industry, dashboards are often used to monitor fleets of assets, such as trains, machines or buildings. In such industrial fleets, the vast amount of sensors evolves continuously, new sensor data exchange protocols and data formats are introduced, new visualization types may need to be introduced and existing dashboard visualizations may need to be updated in terms of displayed sensors. These requirements motivate the development of dynamic dashboarding applications. These, as opposed to fixed-structure dashboard applications, allow users to create visualizations at will and do not have hard-coded sensor bindings. The state-of-the-art in dynamic dashboarding does not cope well with the frequent additions and removals of sensors that must be monitored&mdash, these changes must still be configured in the implementation or at runtime by a user. Also, the user is presented with an overload of sensors, aggregations and visualizations to select from, which may sometimes even lead to the creation of dashboard widgets that do not make sense. In this paper, we present a dynamic dashboard that overcomes these problems. Sensors, visualizations and aggregations can be discovered automatically, since they are provided as RESTful Web Things on a Web Thing Model compliant gateway. The gateway also provides semantic annotations of the Web Things, describing what their abilities are. A semantic reasoner can derive visualization suggestions, given the Thing annotations, logic rules and a custom dashboard ontology. The resulting dashboarding application automatically presents the available sensors, visualizations and aggregations that can be used, without requiring sensor configuration, and assists the user in building dashboards that make sense. This way, the user can concentrate on interpreting the sensor data and detecting and solving operational problems early.