EAGLE—A Scalable Query Processing Engine for Linked Sensor Data

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–temporal correlations. Most semantic approaches do not have spatio–temporal support. Some of them have attempted to provide full spatio–temporal support, but have poor performance for complex spatio–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–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–temporal computing in the linked sensor data context.EC/H2020/732679/EU/ACTivating InnoVative IoT smart living environments for AGEing well/ACTIVAGEEC/H2020/661180/EU/A Scalable and Elastic Platform for Near-Realtime Analytics for The Graph of Everything/SMARTE

Programming patterns and development guidelines for Semantic Sensor Grids (SemSorGrid4Env)

The web of Linked Data holds great potential for the creation of semantic applications that can combine self-describing structured data from many sources including sensor networks. Such applications build upon the success of an earlier generation of 'rapidly developed' applications that utilised RESTful APIs. This deliverable details experience, best practice, and design patterns for developing high-level web-based APIs in support of semantic web applications and mashups for sensor grids. Its main contributions are a proposal for combining Linked Data with RESTful application development summarised through a set of design principles; and the application of these design principles to Semantic Sensor Grids through the development of a High-Level API for Observations. These are supported by implementations of the High-Level API for Observations in software, and example semantic mashups that utilise the API

A Linked-Data Model for Semantic Sensor Streams

This paper describes a semantic modelling scheme, a naming convention and a data distribution mechanism for sensor streams. The proposed solutions address important challenges to deal with large-scale sensor data emerging from the Internet of Things resources. While there are significant numbers of recent work on semantic sensor networks, semantic annotation and representation frameworks, there has been less focus on creating efficient and flexible schemes to describe the sensor streams and the observation and measurement data provided via these streams and to name and resolve the requests to these data. We present our semantic model to describe the sensor streams, demonstrate an annotation and data distribution framework and evaluate our solutions with a set of sample datasets. The results show that our proposed solutions can scale for large number of sensor streams with different types of data and various attributes

Energy-Efficient Data Acquisition in Wireless Sensor Networks through Spatial Correlation

The application of Wireless Sensor Networks (WSNs) is restrained by their often-limited lifetime. A sensor node's lifetime is fundamentally linked to the volume of data that it senses, processes and reports. Spatial correlation between sensor nodes is an inherent phenomenon to WSNs, induced by redundant nodes which report duplicated information. In this paper, we report on the design of a distributed sampling scheme referred to as the 'Virtual Sampling Scheme' (VSS). This scheme is formed from two components: an algorithm for forming virtual clusters, and a distributed sampling method. VSS primarily utilizes redundancy of sensor nodes to get only a subset to sense the environment at any one time. Sensor nodes that are not sensing the environment are in a low-power sleep state, thus conserving energy. Furthermore, VSS balances the energy consumption amongst nodes by using a round robin method

Linked Stream Data: A Position Paper

The amount of sensors publishing data on the Web is increasing as a result of the online availability of Sensor Web platforms that provide support for this task. With such increase in sensor data publication, new challenges arise for the identification, discovery and access to this data. Following the set of best practices to publish and link structured data on the web proposed by the Linked Data community, in this paper we introduce the concept of Linked Stream Data, a way in which the Linked Data principles can be applied to stream data and be part of the Web of Linked Data

Decentralized linked open data in constrained wireless sensor networks

Data generated by sensors in Internet of Things ecosystems contains lots of valuable information, which is often not used to its full potential. This is mainly due to the fact that data is stored in proprietary storages and formats. Manufacturers of sensor devices often offer closed platforms to view and manage the data, which limits their reusability. Moreover, questions start to raise about true data ownership over data generated from monitoring our everyday lives. In order to overcome these issues several initiatives have emerged in the past to hand over data to the rightful owner. One of these initiatives is Solid, currently focusing on socially linked data. However, never before did one apply the Solid principles to Internet of Things data. Therefore, in this paper, a novel approach is presented where sensor data is handled from sensor to storage using open data formats and standards to ensure interoperability and reusability. It is shown that combining existing concepts can be helpful in designing decentralized Internet of Things data storages, on top of which data can be incorporated into the Linked Open Data cloud. This has been done by comparing the overhead of a regular approach, using Linked Open Data concepts on top of a sensor device, to an approach that was optimized for device management in constrained Internet of Things networks