scholarly journals Pemantauan Kualitas Udara Terintegrasi dengan Semantic Web Of Thing

2020 ◽  
Vol 4 (2) ◽  
pp. 153
Author(s):  
M. Udin Harun Al Rasyid ◽  
Rengga Asmara ◽  
Hendi Yanuar Setianto
Keyword(s):  
Social Media ◽  
Semantic Web ◽  
Real Time ◽  
Linked Data ◽  
Structured Data ◽  
Sensor Data ◽  
Sensor Web ◽  
Semantic Sensor Web ◽  
Living Organisms ◽  
Set Up

Abstrak: Udara merupakan salah satu sumber daya alam yang paling penting bagi keberadaan makhluk hidup di bumi ini. Semua organisme hidup membutuhkan kualitas udara yang baik bebas dari gas berbahaya untuk melanjutkan hidup mereka. Beberapa organisasi telah membuat sistem monitoring dengan struktur data yang berbeda tanpa adanya standar penyamaan. Di sisi lain, manusia masih membutuhkan waktu untuk menafsirkan data-data sensor untuk mendapatkan informasi. Linked Data merupakan metode untuk merepresentasikan dan menghubungkan data terstruktur pada web. Data terstruktur tersebut diintegrasikan dengan Semantic Sensor Web (SSW) yang dipublikasikan pada beberapa format sehingga mudah dibaca mesin dan dapat dihubungkan ke data terstruktur lainnya. Kemudian, untuk menyajikan data yang aktual, sistem monitoring didesain untuk menerima data secara terus-menerus, diquery secara real-time dan dibagikan melalui sosial media.   Kata kunci: Linked Data, Pemantauan Kualitas Udara, Semantic Web, Sosial Media.   Abstract: Air is one of the most essential natural resources for the existence and survival of the entire life on this planet. all living organisms need good quality of air which is free of harmful gases to continue their life. Some organizations have set up monitoring systems with different data structures without an equalization standard. On the other hand, humans still need time to interpret sensor data to get information. Linked Data is a method for representing and connecting structured data on the web. The structured data is integrated with the Semantic Sensor Web (SSW) which is published in several formats so that it is easy to read and can be connected to other structured data. Then, to present the actual data, the monitoring system is designed to receive data continuously, queried in real time and shared through social media   Keywords: Air Quality Monitoring, Linked Data, Semantic Web, Social Media

Developing a Smart Semantic Web With Linked Data and Models for Near-Real-Time Monitoring of Red Tides in the Eastern Gulf of Mexico

2016 ◽  
Vol 10 (3) ◽  
pp. 1282-1290 ◽  
Author(s):  
Chuanmin Hu ◽  
Brock Murch ◽  
Alina A. Corcoran ◽  
Lianyuan Zheng ◽  
Brian B. Barnes ◽  
...  
Keyword(s):  
Semantic Web ◽  
Gulf Of Mexico ◽  
Real Time ◽  
Linked Data ◽  
Red Tides ◽  
Real Time Monitoring ◽  
Eastern Gulf Of Mexico

scholarly journals Real Time Measurement for Spring-Mass System: The Graphical and Mathematical Representations

2021 ◽  
Vol 7 (1) ◽  
pp. 74
Author(s):  
Nurul Hidayat ◽  
Erni Yulianti
Keyword(s):  
Real Time ◽  
Physical Phenomenon ◽  
Sensor Data ◽  
Mass Motion ◽  
Mass System ◽  
Mathematical Representations ◽  
Harmonic Motion ◽  
Simple Harmonic Motion ◽  
Spring Force ◽  
Set Up

Mathematics is the language of physics. The best way to describe a physical phenomenon is by describing its mathematical representations. In addition, viewing the graphical diagram of the corresponding mathematical expression is crucial to deeply understand the physical events. Therefore, setting simple experiments in real time to (1) observe the phenomena, (2) view the related diagrams, and (3) extract the mathematical representations is required. In this study, the real time and simple experimental set-up (consisting of ultrasonic sensor HC-SR04 connected to an Arduino Uno board) was designed to perceive the motion of a spring-mass system. The spring force, which is equal to the object’s weight, and displacement or spring elongation data were recorded for the object (with varying mass) attached to the spring. A small external downward force was given to stimulate the simple harmonic motion of the vertical spring-mass system. The displacement as the function of time of the spring-mass motion was recorded. With those measurements, the sinusoidal patterns, representing the simple harmonic motion characteristics, were also observed. The spring constants were 6.35(2) N/m and 6.26(1) N/m for the displacements measured by sensor and ruler, respectively. The periods form the angular frequency of the displacement function and from the spring constant (acquired from sensor data fitting) showed consistent results with very high accuracy. This simple experimental set-up is believed to fulfill the technological-based learning demand.

Kinematic SAMI : a new real-time multi-sensor data assimilation strategy for nonlinear modal identification

2020 ◽  
Vol 21 (4) ◽  
pp. 413
Author(s):  
Adrien Goeller ◽  
Jean-Luc Dion ◽  
Ronan Le Breton ◽  
Thierry Soriano
Keyword(s):  
Real Time ◽  
High Speed ◽  
Natural Frequencies ◽  
Modal Identification ◽  
Modal Testing ◽  
Sensor Data ◽  
Contactless Measurement ◽  
Vibration Level ◽  
Set Up ◽  
Real Time Identification

In many engineering applications, the vibration analysis of a structure requires the set up of a large number of sensors. These studies are mostly performed in post processing and based on linear modal analysis. However, many studied devices highlight that modal parameters depend on the vibration level non linearities and are performed with sensors as accelerometers that modify the dynamics of the device. This work proposes a significant evolution of modal testing based on the real time identification of non linear parameters (natural frequencies and damping) tracked with a linear modal basis. This method, called Kinematic-SAMI (for multiSensors Assimilation Modal Identification) is assessed firstly on a numerical case with known non linearities and secondly in the framework of a classical cantilever beam with contactless measurement technique (high speed and high resolution cameras). Finally, the efficiency and the limits of the method are discussed.

scholarly journals A Methodology for Heterogeneous Sensor Data Organization and Near Real-Time Data Sharing by Adopting OGC SWE Standards

2019 ◽  
Vol 8 (4) ◽  
pp. 167 ◽  
Author(s):  
Bartolomeo Ventura ◽  
Andrea Vianello ◽  
Daniel Frisinghelli ◽  
Mattia Rossi ◽  
Roberto Monsorno ◽  
...  
Keyword(s):  
Real Time ◽  
Sensor Data ◽  
Weather Data ◽  
Time Data ◽  
Heterogeneous Sensors ◽  
Heterogeneous Datasets ◽  
Real Time Data ◽  
Sensor Registration ◽  
Set Up ◽  
Minimum Interaction

Finding a solution to collect, analyze, and share, in near real-time, data acquired by heterogeneous sensors, such as traffic, air pollution, soil moisture, or weather data, represents a great challenge. This paper describes the solution developed at Eurac Research to automatically upload data, in near real-time, by adopting Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) standards to guarantee interoperability. We set up a methodology capable of ingesting heterogeneous datasets to automatize observation uploading and sensor registration, with minimum interaction required of the user. This solution has been successfully tested and applied in the Long Term (Socio-)Ecological Research (LT(S)ER) Matsch-Mazia initiative, and the code is accessible under the CC BY 4.0 license.

scholarly journals SREQP: A Solar Radiation Extraction and Query Platform for the Production and Consumption of Linked Data from Weather Stations Sensors

2016 ◽  
Vol 2016 ◽  
pp. 1-18 ◽  
Author(s):  
José Luis Sánchez-Cervantes ◽  
Mateusz Radzimski ◽  
Cristian Aaron Rodriguez-Enriquez ◽  
Giner Alor-Hernández ◽  
Lisbeth Rodríguez-Mazahua ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Linked Data ◽  
Sensor Data ◽  
Multiple Sources ◽  
Analytic Hierarchy ◽  
Multiple Resources ◽  
Sensor Web ◽  
Production And Consumption ◽  
External Sources ◽  
Web Systems

Nowadays, solar radiation information is provided from sensors installed in different geographic locations and platforms of meteorological agencies. However, common formats such as PDF files and HTML documents to provide solar radiation information do not offer semantics in their content, and they may pose problems to integrate and fuse data from multiple resources. One of the challenges of sensors Web is the unification of data from multiple sources, although this type of information facilitates interoperability with other sensor Web systems. This research proposes architecture SREQP (Solar Radiation Extraction and Query Platform) to extract solar radiation data from multiple external sources and merge them on a single and unique platform. SREQP makes use of Linked Data to generate a set of triples containing information about extracted data, which allows final users to query data through a SPARQL endpoint. The conceptual model was developed by using known vocabularies, such as SSN or WGS84. Moreover, an Analytic Hierarchy Process was carried out for the evaluation of SREQP in order to identify and evaluate the main features of Linked-Sensor-Data and the sensor Web systems. Results from the evaluation indicated that SREQP contained most of the features considered essential in Linked-Sensor-Data and sensor Web systems.

What Counts? Reflections on the Multivalence of Social Media Data

2016 ◽  
Vol 2 (2) ◽  
pp. 19-38 ◽  
Author(s):  
Carolin Gerlitz
Keyword(s):  
Social Media ◽  
Structured Data ◽  
Social Media Data ◽  
Orders Of Worth ◽  
Social Media Platforms ◽  
Set Up ◽  
Empirical Experiment ◽  
Media Data

Abstract Social media platforms have been characterised by their programmability, affordances, constraints and stakeholders - the question of value and valuation of platforms, their data and features has, however, received less attention in platform studies. This paper explores the specific socio-technical conditions for valuating platform data and suggests that platforms set up their data to become multivalent, that is to be valuable alongside multiple, possibly conflicting value regimes. Drawing on both platform and valuation studies, it asks how the production, storing and circulation of data, its connection to user action and the various stakeholders of platforms contribute to its valuation. Platform data, the paper suggests, is the outcome of capture systems which allow to collapse action and its capture into pre-structured data forms which remain open to divergent interpretations. Platforms offer such grammars of action both to users and other stakeholders in frontand back-ends, inviting them to produce and engage with its data following heterogeneous orders of worth. Platform data can participate in different valuation regimes at the same time - however, the paper concludes, not all actors can participate in all modes of valuation, as in the end, it is the platform that sets the conditions for participation. The paper offers a conceptual perspective to interrogate what data counts by attending to questions of quantification, its entanglement with valuation and the various technologies and stakeholders involved. It finishes with an empirical experiment to map the various ways in which Instagram data is made to count.

scholarly journals Multi-Agent based MapReduce Model for Efficient Utilization of System Resources

2018 ◽  
Vol 11 (2) ◽  
pp. 504 ◽  
Author(s):  
Heena Kousar ◽  
B.R. Prasad Babu
Keyword(s):  
Social Media ◽  
Real Time ◽  
Historical Data ◽  
Sensor Data ◽  
Forecasting Model ◽  
Agent Based ◽  
Online Processing ◽  
Inflow Forecasting ◽  
System Resource ◽  
State Of Art

<p>Recently with increased adoption of big data, Internet of Things and sensor technology by various organization for provisioning smart intelligent services for various application uses. Data processing on real-time social media and sensor data is been a key area of research in recent times and these data are massive and continuous. Smart application using sensor and social media data can be classified into three class: 1) online processing of streaming data; 2) online processing of historical data; and 3) hybrid processing of both. The existing model are designed considering stream or batch processing. For provisioning real-time processing MapReduce framework using Hadoop framework is considered by state-of-art technique for data inflow forecasting. However, the Hadoop based forecasting model are not efficient in fully utilizing system resource. Agent based MapReduce forecasting model is adopted by state-of-art technique to utilize system efficiently. However, they incurs high computation overhead, thus increase cost of computing cost. To overcome this work present an agent based Data Inflow Forecasting (DIF) model for both stream and non-stream (historical) data by using Multivariate Gaussian Mixture (MGM) model. This work present an Agent based MapReduce (AMR) framework to process data in real-time and utilize system resource efficiently. To provide scalability for processing social media and sensor data DIF-AMR model adopts cloud computing architecture. Experiment are conducted to evaluate performance of DIF-AMR of over existing model shows significant performance improvement in terms of computation time.</p>

scholarly journals Serverless GEO Labels for the Semantic Sensor Web

2020 ◽  
Author(s):  
Anika Graupner ◽  
Daniel Nüst
Keyword(s):  
Real World ◽  
Large Scale ◽  
Sensor Data ◽  
Quality Information ◽  
World Semantic ◽  
Semantic Web Technologies ◽  
Web Technologies ◽  
Sensor Web ◽  
Semantic Sensor Web ◽  
Cloud Infrastructures

As the amount of sensor data made available online increases, it becomes more difficult for users to identify useful datasets. Semantic web technologies improve discovery with meaningful ontologies, but the decision of suitability remains with the users. The GEO label provides a visual summary of the standardised metadata to aid users in this process. This work presents novel rules for deriving the information for the GEO label's multiple facets, such as user feedback or quality information, based on the Semantic Sensor Network Ontology and related ontologies. It enhances an existing implementation of the GEO label API to generate labels for resources of the Semantic Sensor Web. The prototype is deployed to serverless cloud infrastructures. We find that serverless GEO label generation is capable of handling two evaluation scenarios for concurrent users and burst generation. More real-world semantic sensor descriptions and an integration into large scale discovery platforms are needed to develop the presented solutions further.

Generating Linked Data in Real-time from Sensor Data Streams

2015 ◽  
pp. 103-126
Author(s):  
Nikolaos Konstantinou ◽  
Dimitrios-Emmanuel Spanos
Keyword(s):  
Real Time ◽  
Data Streams ◽  
Linked Data ◽  
Sensor Data

scholarly journals A Stream Processing System for Multisource Heterogeneous Sensor Data

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Liang Hu ◽  
Rui Sun ◽  
Feng Wang ◽  
Xiuhong Fei ◽  
Kuo Zhao
Keyword(s):  
Data Integration ◽  
Data Processing ◽  
Real Time ◽  
Rapid Development ◽  
Streaming Data ◽  
Sensor Data ◽  
Low Latency ◽  
Time System ◽  
Real Time System ◽  
Set Up

With the rapid development of the Internet of Things (IoT), a variety of sensor data are generated around everyone’s life. New research perspective regarding the streaming sensor data processing of the IoT has been raised as a hot research topic that is precisely the theme of this paper. Our study serves to provide guidance regarding the practical aspects of the IoT. Such guidance is rarely mentioned in the current research in which the focus has been more on theory and less on issues describing how to set up a practical system. In our study, we employ numerous open source projects to establish a distributed real time system to process streaming data of the IoT. Two urgent issues have been solved in our study that are (1) multisource heterogeneous sensor data integration and (2) processing streaming sensor data in real time manner with low latency. Furthermore, we set up a real time system to process streaming heterogeneous sensor data from multiple sources with low latency. Our tests are performed using field test data derived from environmental monitoring sensor data collected from indoor environment for system validation. The results show that our proposed system is valid and efficient for multisource heterogeneous sensor data integration and streaming data processing in real time manner.

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