Spatial Database Organization for Multi-attribute Sensor Data Representation

Architectural concepts for managing biomedical sensor data utilised for medical diagnosis and patient remote care.

MATEC Web of Conferences ◽

10.1051/matecconf/201821005016 ◽

2018 ◽

Vol 210 ◽

pp. 05016

Author(s):

Mariusz Chmielewski ◽

Damian Frąszczak ◽

Dawid Bugajewski

Keyword(s):

Data Processing ◽

Data Acquisition ◽

Large Scale ◽

Data Representation ◽

Medical Diagnostics ◽

Mobile Technologies ◽

Sensor Data ◽

Biomedical Data ◽

Support Tool ◽

Decision Algorithms

This paper discusses experiences and architectural concepts developed and tested aimed at acquisition and processing of biomedical data in large scale system for elderly (patients) monitoring. Major assumptions for the research included utilisation of wearable and mobile technologies, supporting maximum number of inertial and biomedical data to support decision algorithms. Although medical diagnostics and decision algorithms have not been the main aim of the research, this preliminary phase was crucial to test capabilities of existing off-the-shelf technologies and functional responsibilities of system’s logic components. Architecture variants contained several schemes for data processing moving the responsibility for signal feature extraction, data classification and pattern recognition from wearable to mobile up to server facilities. Analysis of transmission and processing delays provided architecture variants pros and cons but most of all knowledge about applicability in medical, military and fitness domains. To evaluate and construct architecture, a set of alternative technology stacks and quantitative measures has been defined. The major architecture characteristics (high availability, scalability, reliability) have been defined imposing asynchronous processing of sensor data, efficient data representation, iterative reporting, event-driven processing, restricting pulling operations. Sensor data processing persist the original data on handhelds but is mainly aimed at extracting chosen set of signal features calculated for specific time windows – varying for analysed signals and the sensor data acquisition rates. Long term monitoring of patients requires also development of mechanisms, which probe the patient and in case of detecting anomalies or drastic characteristic changes tune the data acquisition process. This paper describes experiences connected with design of scalable decision support tool and evaluation techniques for architectural concepts implemented within the mobile and server software.

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BIM in People2People and Things2People Interactive Process

Sensors ◽

10.3390/s20102982 ◽

2020 ◽

Vol 20 (10) ◽

pp. 2982

Author(s):

Bruno Mataloto ◽

João C. Ferreira ◽

Ricardo Resende ◽

Rita Moura ◽

Sílvia Luís

Keyword(s):

User Behavior ◽

3D Visualization ◽

Research Work ◽

Data Representation ◽

Sensor Data ◽

University Campus ◽

Shared Spaces ◽

Interactive 3D ◽

Interaction Approach ◽

Real Time Information

In this research work, we present an IoT solution to environment variables using a LoRa transmission technology to give real-time information to users in a Things2People process and achieve savings by promoting behavior changes in a People2People process. These data are stored and later processed to identify patterns and integrate with visualization tools, which allow us to develop an environmental perception while using the system. In this project, we implemented a different approach based on the development of a 3D visualization tool that presents the system collected data, warnings, and other users’ perception in an interactive 3D model of the building. This data representation introduces a new People2People interaction approach to achieve savings in shared spaces like public buildings by combining sensor data with the users’ individual and collective perception. This approach was validated at the ISCTE-IUL University Campus, where this 3D IoT data representation was presented in mobile devices, and from this, influenced user behavior toward meeting campus sustainability goals.

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Research on a Rotating Machinery Fault Prognosis Method Using Three-Dimensional Spatial Representations

Shock and Vibration ◽

10.1155/2016/5710152 ◽

2016 ◽

Vol 2016 ◽

pp. 1-10

Author(s):

Xiaoni Dong ◽

Xiaodong Zhang ◽

Zaichao Ma ◽

Guangrui Wen ◽

Zhifen Zhang

Keyword(s):

Spatial Representation ◽

Three Dimensional ◽

Data Representation ◽

Rotating Machinery ◽

Process Models ◽

Sensor Data ◽

One Dimensional ◽

Time Domains ◽

Fault Prognosis ◽

Representation Method

Process models and parameters are two critical steps for fault prognosis in the operation of rotating machinery. Due to the requirement for a short and rapid response, it is important to study robust sensor data representation schemes. However, the conventional holospectrum defined by one-dimensional or two-dimensional methods does not sufficiently present this information in both the frequency and time domains. To supply a complete holospectrum model, a new three-dimensional spatial representation method is proposed. This method integrates improved three-dimensional (3D) holospectra and 3D filtered orbits, leading to the integration of radial and axial vibration features in one bearing section. The results from simulation and experimental analysis on a complex compressor show that the proposed method can present the real operational status and clearly reveal early faults, thus demonstrating great potential for condition-based maintenance prediction in industrial machinery.

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Integration of Semantics Into Sensor Data for the IoT

International Journal on Semantic Web and Information Systems ◽

10.4018/ijswis.2020100101 ◽

2020 ◽

Vol 16 (4) ◽

pp. 1-25

Author(s):

Besmir Sejdiu ◽

Florije Ismaili ◽

Lule Ahmedi

Keyword(s):

Data Stream ◽

Data Representation ◽

Research Field ◽

Sensor Data ◽

Stream Data ◽

Resource Saving ◽

Digital Resources ◽

Data Annotation ◽

Semantic Annotations ◽

The Internet Of Things

The internet of things (IoT) as an evolving technology represents an active scientific research field in recognizing research challenges associated with its application in various domains, ranging from consumer convenience, smart energy, and resource saving to IoT enterprises. Sensors are crucial components of IoT that relay the collected data in the form of the data stream for further processing. Interoperability of various connected digital resources is a key challenge in IoT environments. The enrichment of raw sensor data with semantic annotations using concept definitions from ontologies enables more expressive data representation that supports knowledge discovery. In this paper, a systematic review of integration of semantics into sensor data for the IoT is provided. The conducted review is focused on analyzing the main solutions of adding semantic annotations to the sensor data, standards that enable all types of sensor data via the web, existing models of stream data annotation, and the IoT trend domains that use semantics.

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Design of database systems for optimized spatio-temporal querying to facilitate monitoring, analysing and forecasting in the "Internet of Water"

10.5194/egusphere-egu2020-19171 ◽

2020 ◽

Author(s):

Erik Bollen ◽

Brianna R. Pagán ◽

Bart Kuijpers ◽

Stijn Van Hoey ◽

Nele Desmet ◽

...

Keyword(s):

Real Time ◽

Hybrid Approach ◽

Water System ◽

Database Systems ◽

Data Representation ◽

Water Systems ◽

Flow Path ◽

Graph Representation ◽

Sensor Data ◽

Spatio Temporal

Monitoring, analysing and forecasting water-systems, such as rivers, lakes and seas, is an essential part of the tasks for an environmental agency or government. In the region of Flanders, in Belgium, different organisations have united to create the &#8221;Internet of Water&#8221; (IoW). During this project, 2500 wireless water-quality sensors will be deployed in rivers, canals and lakes all over Flanders. This network of sensors will support a more accurate management of water systems by feeding real-time data. Applications include monitoring real-time water-flows, automated warnings and notifications to appropriate organisations, tracing pollution and the prediction of salinisation.Despite the diversity of these applications, they mostly rely on a correct spatial representation and fast querying of the flow path: where does water flow to, where can the water come from, and when does the water pass at certain locations? In the specific case of Flanders, the human-influenced landscape provides additional complexity with rivers, channels, barriers and even cycles. Numerous models and systems exist that are able to answer the above questions, even very precisely, but they often lack the ability to produce the results quickly enough for real-time applicability that is required in the IoW. Moreover, the rigid data representation makes it impossible to integrate new data sources and data types, especially in the IoW, where the data originates from vastly different backgrounds.In this research, we focus on the performance of spatio-temporal queries taking into account the spatial configuration of a strongly human-influenced water system and the real-time acquisition and processing of sensor data. The use of graph-database systems is compared with relational-database systems to store topologies and execute recursive path-tracing queries. Not only storing and querying are taken into account, but also the creation and updating of the topologies are an essential part. Moreover, the advantages of a hybrid approach that integrates the graph-based databases for spatial topologies with relational databases for temporal and water-system attributes are investigated. The fast querying of both upstream and downstream flow-path information is of great use in various applications (e.g., pollution tracking, alerting, relating sensor signals, &#8230;). By adding a wrapper library and creating a standardised result graph representation, the complexity is abstracted away from the individual applications.

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Development of an Internet of Civil Infrastructure Framework

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.684.583 ◽

2013 ◽

Vol 684 ◽

pp. 583-587 ◽

Cited By ~ 3

Author(s):

Der Cherng Liaw ◽

Tzu Hsuan Lin

Keyword(s):

Disaster Management ◽

Information Exchange ◽

Middle Layer ◽

Data Representation ◽

Sensor Data ◽

Civil Infrastructure ◽

Information Platform ◽

Civil Infrastructures ◽

Layered Architecture ◽

Novel Concept

Nowadays, the civil infrastructures are subjected to disasters such as fires, floods, and earthquakes. Recently, the novel concept of Internet of Things (IoT) is known to be useful for managing crisis situations via providing a good disaster management and emergency response information. This paper addresses the civil infrastructure issue of health monitoring and disaster management by introducing IoT technology. A concept of internet of civil infrastructure (IoCI) framework is also proposed in this paper. The proposed framework is a three layered architecture. Among them, the top layer is a wireless sensor network (WSN) client which is deployed in civil infrastructure to perform specific tasks such as sensing, data processing, and acknowledgement. The middle layer is an information exchange web service (IEWS) through which all the information such as sensor data, structural health and location are exchanged, while the remaining layer is a mobile device based information platform for data representation, control, and event notification.

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LOCALLY REFINED SPLINES REPRESENTATION FOR GEOSPATIAL BIG DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-3-w3-565-2015 ◽

2015 ◽

Vol XL-3/W3 ◽

pp. 565-570

Author(s):

T. Dokken ◽

V. Skytt ◽

O. Barrowclough

Keyword(s):

Degrees Of Freedom ◽

Spline Approximation ◽

Point Clouds ◽

Data Representation ◽

Ocean Floor ◽

Sensor Data ◽

B Spline ◽

B Splines ◽

Model Combining ◽

Elevation Model

When viewed from distance, large parts of the topography of landmasses and the bathymetry of the sea and ocean floor can be regarded as a smooth background with local features. Consequently a digital elevation model combining a compact smooth representation of the background with locally added features has the potential of providing a compact and accurate representation for topography and bathymetry. The recent introduction of Locally Refined B-Splines (LR B-splines) allows the granularity of spline representations to be locally adapted to the complexity of the smooth shape approximated. This allows few degrees of freedom to be used in areas with little variation, while adding extra degrees of freedom in areas in need of more modelling flexibility. In the EU fp7 Integrating Project IQmulus we exploit LR B-splines for approximating large point clouds representing bathymetry of the smooth sea and ocean floor. A drastic reduction is demonstrated in the bulk of the data representation compared to the size of input point clouds. The representation is very well suited for exploiting the power of GPUs for visualization as the spline format is transferred to the GPU and the triangulation needed for the visualization is generated on the GPU according to the viewing parameters. The LR B-splines are interoperable with other elevation model representations such as LIDAR data, raster representations and triangulated irregular networks as these can be used as input to the LR B-spline approximation algorithms. Output to these formats can be generated from the LR B-spline applications according to the resolution criteria required. The spline models are well suited for change detection as new sensor data can efficiently be compared to the compact LR B-spline representation.

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