Linked Sensor Data Research Articles

Ontology Network-Based In-Situ Sensor Selection for Quality Management in Metal Additive Manufacturing

AbstractMetal additive manufacturing (MAM) offers a larger design space with greater manufacturability than traditional manufacturing. Despite continued advances, MAM processes still face huge uncertainty, resulting in variable part quality. Real-time sensing for MAM processing helps quantify uncertainty by detecting build failure and process anomalies. While the high volume of multidimensional sensor data—such as melt-pool geometries and temperature gradients—is beginning to be explored, sensor selection does not yet effectively link sensor data to part quality. To begin investigating such connections, we propose network-based models that capture in real-time (1) sensor data's association with process variables and (2) as-built part qualities’ association with related physical phenomena. These sensor models and networks lay the foundation for a comprehensive framework to monitor and manage the quality of MAM process outcomes.

Engagement and Participant Experiences With Consumer Smartwatches for Health Research: Longitudinal, Observational Feasibility Study.

BackgroundWearables provide opportunities for frequent health data collection and symptom monitoring. The feasibility of using consumer cellular smartwatches to provide information both on symptoms and contemporary sensor data has not yet been investigated.ObjectiveThis study aimed to investigate the feasibility and acceptability of using cellular smartwatches to capture multiple patient-reported outcomes per day alongside continuous physical activity data over a 3-month period in people living with knee osteoarthritis (OA).MethodsFor the KOALAP (Knee OsteoArthritis: Linking Activity and Pain) study, a novel cellular smartwatch app for health data collection was developed. Participants (age ≥50 years; self-diagnosed knee OA) received a smartwatch (Huawei Watch 2) with the KOALAP app. When worn, the watch collected sensor data and prompted participants to self-report outcomes multiple times per day. Participants were invited for a baseline and follow-up interview to discuss their motivations and experiences. Engagement with the watch was measured using daily watch wear time and the percentage completion of watch questions. Interview transcripts were analyzed using grounded thematic analysis.ResultsA total of 26 people participated in the study. Good use and engagement were observed over 3 months: most participants wore the watch on 75% (68/90) of days or more, for a median of 11 hours. The number of active participants declined over the study duration, especially in the final week. Among participants who remained active, neither watch time nor question completion percentage declined over time. Participants were mainly motivated to learn about their symptoms and enjoyed the self-tracking aspects of the watch. Barriers to full engagement were battery life limitations, technical problems, and unfulfilled expectations of the watch. Participants reported that they would have liked to report symptoms more than 4 or 5 times per day.ConclusionsThis study shows that capture of patient-reported outcomes multiple times per day with linked sensor data from a smartwatch is feasible over at least a 3-month period.International Registered Report Identifier (IRRID)RR2-10.2196/10238

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.

Biologging Tags Reveal Links Between Fine-Scale Horizontal and Vertical Movement Behaviors in Tiger Sharks (Galeocerdo cuvier)

An understanding of the role that large marine predators play in structuring trophic flow and nutrient cycling in marine ecosystems requires knowledge of their fine-scale (m-km) movement behaviours. In this study, biologging tags were used to reveal new insights into the three-dimensional fine-scale movement ecology of tiger sharks (Galeocerdo cuvier) at Ningaloo Reef, Western Australia. Tags deployed on 21 sharks in April-May 2017 for durations of 5-48 hours recorded both physical parameters such as depth and temperature, and, through the use of accelerometers, gyroscopes and compasses, in-situ measurements of animal trajectory and locomotion. Animal-borne-video enabled the validation of behavioural signatures, mapping of habitat, and recording of interactions with prey. Collectively, these data were used to examine the link between vertical (oscillations) and horizontal (tortuosity) movements, and link sensor data to prey interactions recorded by the video. This biologging approach revealed complex movements that would otherwise be invisible within the time-depth records provided by traditional tagging techniques. The rate of horizontal turning was not related to vertical oscillations, suggesting that vertical movements occur independently of searching behaviours in tiger sharks. These animals displayed tortuous movements possibly associated with prey searching for 27% of their tracks, and interactions with prey elicited varied responses including highly tortuous paths and burst movements. Accurate speed measurements and GPS anchor points will considerably enhance the value of magnetometer data in future studies by facilitating more accurate dead-reckoning and geo-referencing of area-restricted search behaviours.

The ACORN-SAT linked climate dataset

In 2012 the Australian Bureau of Meteorology published a dataset, ACORN-SAT, containing the homogenised daily temperature observations of 112 locations throughout Australia for the last 100 years. The dataset employs the latest analysis techniques and takes advantage of newly digitised observational data to monitor climate variability and change in Australia. The observations in ACORN-SAT were initially published only as comma separated values, whereas the metadata was published in a PDF report. In 2013 we converted the metadata and the observation data into RDF and published the result as Linked Open Data, accessible online via a pilot government linked data service built on the Linked Data API. In this article we describe the process of transforming the original tabular data into a Linked Sensor Data Cube (in: Proc. of the 5th International Workshop on Semantic Sensor Networks, SSN12, CEUR-WS.org, 2012, pp. 1-16) based on the W3C Semantic Sensor Network ontology (Web Semantics: Science, Services and Agents on the World Wide Web 17 (2012), 25-32) and the W3C RDF Data Cube vocabulary (The RDF Data Cube Vocabulary, W3C Recommendation, 16 January 2014). We further discuss how the dataset has since been used and interlinked with near-real time weather observations for the 112 sensing locations of the ACORN-SAT that are published by the Bureau of Meteorology. Both the original ACORN-SAT dataset and the weather observation data are accessible online at lab.environment.data.gov.au.

SEMDPA

The Internet of Things (IoT) vision is to connect uniquely identifiable devices that surround us to the Internet, which is best described through ontologies. Thereby, new emerging technologies such as wireless sensor networks (WSN) are recognized as an essential enabling component of the IoT today. Hence, given the increasing interest to provide linked sensor data through the Web either following the Semantic Web Enablement (SWE) standard or the Linked Data approach, there is a need to also explore those data for potential hidden knowledge through data mining techniques utilized by a domain ontology. Following that rationale, a new lightweight IoT architecture SEMDPA has been developed. It supports linking sensors and other devices, as well as people via a single web by mean of a device-person-activity (DPA) crossroad ontology. The architecture is validated by mean of three rich-in-semantic services: contextual data mining over WSN, semantic WSN web enablement, and Linked WSN data. SEMDPA could be easily extensible to capture semantics of input sensor data from other domains as well.

Mathematical models for the trimethylamine (TMA) formation on packed cod fish fillets at different temperatures

The microbial formation of trimethylamine (TMA) can be used as a quality indicator compound to predict the freshness of fish during its shelf life. In a supply chain with fluctuating temperatures, mathematical models will be valuable tools to simulate this formation as a function of temperature and time. These models are essential to link sensor data on the formation of TMA to the actual freshness of fish. Existing models for the formation of TMA in fish needed improvements and secondary models for the effect of temperature on the formation of TMA are lacking in the literature. Three different approaches were evaluated on their ability to simulate the experimental observed TMA formation at 4 different temperatures (0, 5, 10 and 15°C). In the first approach the existing models were improved and the temperature effect was modelled by an empirical model using four parameters. This model is able to simulate the TMA formation at static temperatures. Since TMA is produced on fresh cod fillets by the micro-organisms Shewanella putrefaciens and Photobacterium phosphoreum the microbial Baranyi–Roberts model was initially used for modelling the TMA formation, but this model was found to be too complex (too many correlated parameters that could not be estimated). In the third approach it was seen that a simplified Baranyi–Roberts model with only three parameters could be used to predict the TMA formation with equal accuracy. The influence of the temperature on the parameter μmax was modelled using the extended square root model of Ratkowsky and the differences in TMA formation profiles of different batches could be described by the batch specific parameter N0 representing the initial quality. The presented dynamic model is valuable in predicting the formation of TMA in a fresh fish supply chain with dynamic temperatures. This model has the potential to be used to link sensor data of TMA in the headspace to the actual freshness status of the fish.

Design principles for utility-driven services and cloud-based computing modelling for the Internet of Things

By following an analysis of the state of the art in the convergence of cloud computing and the Internet of Things (IoT), this paper presents design principles for the IoT in cloud environments. A framework for on-demand establishment of IoT services based on the automated formulation of societies of internet-connected objects is described and the interactions between architecture modules are explained in detail to validate this approach. The main building blocks of the functional framework and its operational components follow the utility-driven cloud-based computing model. The framework leverages well-known technologies (i.e. linked sensor data) and standards (notably the W3C semantic sensor networks ontology). Finally, an example for service formulation and delivery of services for a smart campus scenario is provided and discussed. This paper also introduces some experiment results using the utility-driven service formulation model for mobile applications.

A RESTful proxy and data model for linked sensor data

The vision of a Digital Earth calls for more dynamic information systems, new sources of information, and stronger capabilities for their integration. Sensor networks have been identified as a major information source for the Digital Earth, while Semantic Web technologies have been proposed to facilitate integration. So far, sensor data are stored and published using the Observations & Measurements standard of the Open Geospatial Consortium (OGC) as data model. With the advent of Volunteered Geographic Information and the Semantic Sensor Web, work on an ontological model gained importance within Sensor Web Enablement (SWE). In contrast to data models, an ontological approach abstracts from implementation details by focusing on modeling the physical world from the perspective of a particular domain. Ontologies restrict the interpretation of vocabularies toward their intended meaning. The ongoing paradigm shift to Linked Sensor Data complements this attempt. Two questions have to be addressed: (1) how to refer to changing and frequently updated data sets using Uniform Resource Identifiers, and (2) how to establish meaningful links between those data sets, that is, observations, sensors, features of interest, and observed properties? In this paper, we present a Linked Data model and a RESTful proxy for OGC's Sensor Observation Service to improve integration and inter-linkage of observation data for the Digital Earth.

Assessing Quality in the Web of Linked Sensor Data

Assessing the quality of sensor data available on the Web is essential in order to identify reliable information for decision-making. This paper discusses how provenance of sensor observations and previous quality ratings can influence quality assessment decisions.

A 3D-LOCATION SYSTEM OPTIMIZED FOR SIMPLE STATIC AND MOBILE DEVICES

This article focuses on 3D-location systems based on ultrasound time-of-flight (TOF) measurements. Commonly, location systems localize mobile devices with base stations. However, our concept is primarily oriented to simple static devices, especially sensors measuring local physical quantities. Normally the sensor-identification, established at system setup, allows to link sensor data and sensor location. Using locally measured TOFs of ultrasonic signals instead of an explicit identification, the device directly transmits measurement data together with information about its own location. This enables a receiving base station to identify the device by calculating its location. Moreover, this principle can also be applied for mobile devices.