scholarly journals Thinger.io: An Open Source Platform for Deploying Data Fusion Applications in IoT Environments

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1044 ◽  
Author(s):  
Alvaro Luis Bustamante ◽  
Miguel Patricio ◽  
José Molina
Keyword(s):  
Data Fusion ◽  
Open Source ◽  
Cost Effective ◽  
Geographic Area ◽  
Sensor Data ◽  
Sensor Technology ◽  
Huge Amount ◽  
Effective Manner ◽  
Fusion Applications ◽  
High Level

In the last two decades, data and information fusion has experienced significant development due mainly to advances in sensor technology. The sensors provide a continuous flow of data about the environment in which they are deployed, which is received and processed to build a dynamic estimation of the situation. With current technology, it is relatively simple to deploy a set of sensors in a specific geographic area, in order to have highly sensorized spaces. However, to be able to fusion and process the information coming from the data sources of a highly sensorized space, it is necessary to solve certain problems inherent to this type of technology. The challenge is analogous to what we can find in the field of the Internet of Things (IoT). IoT technology is characterized by providing the infrastructure capacity to capture, store, and process a huge amount of heterogeneous sensor data (in most cases, from different manufacturers), in the same way that it occurs in data fusion applications. This work is not simple, mainly due to the fact that there is no standardization of the technologies involved (especially within the communication protocols used by the connectable sensors). The solutions that we can find today are proprietary solutions that imply an important dependence and a high cost. The aim of this paper is to present a new open source platform with capabilities for the collection, management and analysis of a huge amount of heterogeneous sensor data. In addition, this platform allows the use of hardware-agnostic in a highly scalable and cost-effective manner. This platform is called Thinger.io. One of the main characteristics of Thinger.io is the ability to model sensorized environments through a high level language that allows a simple and easy implementation of data fusion applications, as we will show in this paper.

Sensor Integration and Data Fusion Theory

2010 ◽  
pp. 160-188
Author(s):  
Zude Zhou ◽  
Huaiqing Wang ◽  
Ping Lou
Keyword(s):  
Data Fusion ◽  
Error Compensation ◽  
Intelligent Manufacturing ◽  
Sensor Integration ◽  
Sensor Data ◽  
Sensor Technology ◽  
Fusion Technology ◽  
Knowledge Based ◽  
Manufacturing Intelligence ◽  
Single Sensor

In previous chapters, the engineering scientific foundations of manufacturing intelligence (such as the knowledge-based system, Multi-Agent system, data mining and knowledge discovery, and computing intelligence) have been discussed in detail. Sensor integration and data fusion is another important theory of manufacturing intelligence. With the development of integrated systems, there is an urgent requirement for improving system automaticity and intelligence. Without improvement, the complexity and scale of systems are increased. Such systems need to be more sensitive to their work environment and independent state, and obviously, single sensor technology hardly meets these requirements. Multi-sensor and data fusion technology are therefore employed in automatic and intelligent manufacturing as it is more comprehensive and accurate than traditional single sensor technology if the information redundancy and complementarity are used reasonably. In theory, the outputs of multi-sensors are mutually validated. Multi-sensor integration is a brand new concept for intelligent manufacturing, and without doubt, sensor integration-based intelligent manufacturing is the development orientation of manufacturing in the future. With reference to the information fusion problem of the multi-sensor integration system, the development state, technical background, application scope and basic meaning of the multi-sensor integration and the data fusion are first reviewed in this chapter. Secondly the classification, level, system structure and function model of the data fusion system is discussed. The theoretical method of the data fusion is then introduced, and finally, attention is paid to cutting tool condition detection, machine thermal error compensation and online detection and error compensation because those are the main applications of multi-sensor data fusion technology in intelligent manufacturing.

Breathing New Life to Aging Pipeline Infrastructure Using Unique Wireline Inspection Techniques and Pipe-Lining Technology

2018 ◽  
Author(s):  
Clay Goudy ◽  
Alex Gutiérrez
Keyword(s):  
Cost Effective ◽  
Steel Pipe ◽  
Quality Data ◽  
Central Component ◽  
Efficient Manner ◽  
Southern Mexico ◽  
Inspection Method ◽  
Effective Manner ◽  
Pipe Replacement ◽  
High Level

Mexico’s Energy Reform has opened up various interesting and unique opportunities for energy infrastructure. A CO2 pipeline project that was recently completed in southern Mexico provides a perfect example of how to breathe new life to deteriorated pipeline infrastructure — infrastructure that would have typically been written off. By coupling a unique pipeline inspection method with a novel lining system, two 28-kilometer (17 mile) pipelines were rehabilitated in record time and in a cost-effective manner. The project consisted of two 12 and 18-inch (300 and 450 millimeters) CO2 transport pipelines that had been out of service for 22 years and that are a central component for a high-profile fertilizer project. Replacing these deteriorated assets with a new transport pipeline was not an option due to time, environmental, permitting and budgetary constraints. The rehabilitated system had to offer a minimum 25-year service life required by the owner. To put this aging infrastructure back into service, it was essential to assess the condition of the pipelines with a high level of accuracy and precision which would allow for the rehabilitation of the pipeline and installation of an interactive liner to extend the system’s serviceable life for a minimum of 25 years. The challenge, however, was that these pipelines were non-piggable by traditional methods. By using a tethered MFL and Caliper ILI solution, the pipelines were each inspected in 13 separate sections with the level of detail necessary to assess the condition and suitability of the rehabilitation strategy selected for the project. Fast-track scheduling constraints required 24-hour data analysis turnaround of reports identifying and discriminating areas of modest and significant corrosion as well as deformations including areas of significant weld slag which could complicate the installation of the liners. Once high-quality data was available, pinpoint repairs were possible with a combination of carbon fiber reinforcement and steel pipe replacement. Afterwards, the pipelines were internally lined with a patented process that effectively provides a double containment system. A grooved liner and the host steel pipe create an annular space that is pressurized with air and remotely monitored. The system is able to detect even a small pressure drop in the annulus that would occur in case the integrity is breached, or a pinhole develops in the steel pipe. With the grooved liner, external repairs can be conducted while the line continues to operate without interrupting CO2 service to the plant. By applying these novel solutions, the rehabilitated pipelines will transport carbon dioxide to a revitalized fertilizer plant in a safe and efficient manner for the next 25 years.

Federated IMM-UKF Algorithm for Multi-Sensor Data Fusion

2013 ◽  
Vol 753-755 ◽  
pp. 2117-2120 ◽  
Author(s):  
Tian Lai Xu
Keyword(s):  
Data Fusion ◽  
Nonlinear Filtering ◽  
Adaptive Estimation ◽  
Estimation Method ◽  
Cost Effective ◽  
Estimation Algorithm ◽  
Sensor Data ◽  
Interacting Multiple Model ◽  
Integrated Navigation ◽  
Multiple Model

The accuracy of multi-sensor navigational data fusion by federated Kalman filter will be reduced in condition that the systems dynamics model is nonlinear and the noise statistical properties are unknown. To address this problem, a federated Interacting Multiple Model-Unscented Kalman Filteing (IMM-UKF) algorithm is presented. The UKF is a nonlinear estimation method which can achieve the accuracy at least to the second-order. The IMM estimation algorithm is one of the cost-effective adaptive estimation algorithm for systems involving parametric changes. The combination of IMM with UKF could deal with the problem of nonlinear filtering with uncertain noise. Simulation results show that the method can improve the accuracy of INS/GPS/odometer integrated navigation.

scholarly journals Vayu: An Open-Source Toolbox for Visualization and Analysis of Crowd-Sourced Sensor Data

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7726
Author(s):  
Sachit Mahajan
Keyword(s):  
Air Quality ◽  
Open Source ◽  
Time Series Data ◽  
Sensor Data ◽  
Quality Data ◽  
Sensor Technology ◽  
Data Conversion ◽  
Series Data ◽  
Monitoring Networks ◽  
Systematic Analysis

Recent advances in sensor technology and the availability of low-cost and low-power sensors have changed the air quality monitoring paradigm. These sensors are being widely used by scientists and citizens for monitoring air quality at finer spatial-temporal resolution. Such practices are opening up opportunities to enhance the traditional monitoring networks, but at the same time, these sensors are producing large data sets that can become overwhelming and challenging when it comes to the scientific tools and skills required to analyze the data. To address this challenge, an open-source, robust, and cross-platform sensor data analysis toolbox called Vayu is developed that allows researchers and citizens to do detailed and reproducible analyses of air quality data. Vayu combines the power of visualization and statistical analysis using a simple and intuitive graphical user interface. Additionally, it offers a comprehensive set of tools for systematic analysis such as data conversion, interpolation, aggregation, and prediction. Even though Vayu was developed with air quality research in mind, it can be used to analyze different kinds of time-series data.

scholarly journals Static, Portable and Smart Detection of Water Quality in Panchayath Distribution Tank

2019 ◽  
Vol 8 (2S5) ◽  
pp. 103-108
Keyword(s):  
Real Time ◽  
Low Cost ◽  
Cost Effective ◽  
Daily Basis ◽  
Quality Parameters ◽  
Sensor Data ◽  
Sensor Technology ◽  
Real Time System ◽  
Quality Of Water ◽  
Physical And Chemical

The significant crunch in the Current world is Water pollution. It has created an abundant influence on the Environment. With the intention of the non-toxic distribution of the water and its eminence should be monitored at real time. This paper suggested the smart detection with low cost real time system which is used to monitor the quality of water through IOT(internet of things). The system entail of different sensors which are used to measure the physical and chemical parameters of the water. The quality parameters are temperature, pH, turbidity, conductivity and Total dissolved solids of the water are measured. Commercially available products capable of monitoring such parameters are usually somewhat expensive and the data’s are collected by mobile van. Using Sensor technology provides a cost-effective and pre-eminent reliable as they can provide real time output. The measured values from the sensors can be observed by the core controller. The controller was programmed to monitor the distribution tank on a daily basis to hour basis monitoring. The TIVA C series is used as a core controller. The Controller is mounted on the side of the distribution tank. Finally, the sensor data from the controller is sent to Wi-Fi module through UART protocol. Wi-fi Module is connected to a public Wi-Fi system through which data is seen by the locals who are all connected to that Wi-Fi network.

Multi-Sensor Data Fusion (MSDF)

2017 ◽  
pp. 29-61
Author(s):  
Gouranga Charan Jena
Keyword(s):  
Data Fusion ◽  
Evaluation System ◽  
Functional Model ◽  
Cost Effective ◽  
The State ◽  
Sensor Data ◽  
Source Information ◽  
Fusion Model ◽  
Multi Sensor Data Fusion ◽  
Available Technology

The Data Fusion Model maintained by the JDL (Joint Directors of Laboratories) Data Fusion Group is the most widely-used method for categorizing data fusion-related functions. This paper discusses the current effort to revise and expand this model to facilitate the cost-effective development, acquisition, integration and operation of multi-sensor/multi-source systems. Data fusion involves combining information in the broadest sense to estimate or predict the state of some aspect of the universe. These may be represented in terms of attributive and relational states. If the job is to estimate the state of a people (or any other sentient beings), it can be useful to include consideration of informational and perceptual states in addition to the physical state. Developing cost-effective multi-source information systems requires a standard method for specifying data fusion processing and control functions, interfaces, and associated data bases. The lack of common engineering standards for data fusion systems has been a major impediment to integration and re-use of available technology. There is a general lack of standardized or even well-documented performance evaluation, system engineering methodologies, architecture paradigms, or multi-spectral models of targets and collection systems. In short, current developments do not lend themselves to objective evaluation, comparison or re-use. This paper reports on proposed revisions and expansions of the JDL Data Fusion model to remedy some of these deficiencies. This involves broadening the functional model and related taxonomy beyond the original military focus, and integrating the Data Fusion Tree Architecture model for system description, design and development.

scholarly journals An Improved Data Fusion Method Based on Weighted Belief Entropy considering the Negation of Basic Probability Assignment

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Yong Chen ◽  
Yongchuan Tang ◽  
Yan Lei
Keyword(s):  
Data Fusion ◽  
Evidence Theory ◽  
Mass Function ◽  
Sensor Data ◽  
Artificial Data ◽  
Probability Assignment ◽  
Basic Probability Assignment ◽  
Fusion Applications ◽  
Basic Probability ◽  
Belief Entropy

Uncertainty in data fusion applications has received great attention. Due to the effectiveness and flexibility in handling uncertainty, Dempster–Shafer evidence theory is widely used in numerous fields of data fusion. However, Dempster–Shafer evidence theory cannot be used directly for conflicting sensor data fusion since counterintuitive results may be attained. In order to handle this issue, a new method for data fusion based on weighted belief entropy and the negation of basic probability assignment (BPA) is proposed. First, the negation of BPA is applied to represent the information in a novel view. Then, by measuring the uncertainty of the evidence, the weighted belief entropy is adopted to indicate the relative importance of evidence. Finally, the ultimate weight of each body of evidence is applied to adjust the mass function before fusing by the Dempster combination rule. The validity of the proposed method is demonstrated in accordance with an experiment on artificial data and an application on fault diagnosis.

scholarly journals Object-Level Fusion for Surround Environment Perception in Automated Driving Applications

2017 ◽  
Author(s):  
Michael Aeberhard
Keyword(s):  
Data Fusion ◽  
Sensor Data ◽  
Driver Assistance ◽  
Automated Driving ◽  
Driver Assistance Systems ◽  
Sensor Data Fusion ◽  
Level Sensor ◽  
Environment Perception ◽  
High Level ◽  
Level Fusion

Driver assistance systems have increasingly relied on more sensors for new functions. As advanced driver assistance system continue to improve towards automated driving, new methods are required for processing the data in an efficient and economical manner from the sensors for such complex systems. In this thesis, an environment model approach for the detection of dynamic objects is presented in order to realize an effective method for sensor data fusion. A scalable high-level fusion architecture is developed for fusing object data from several sensors in a single system. The developed high-level sensor data fusion architecture and its algorithms are evaluated using a prototype vehicle equipped with 12 sensors for surround environment perception. The work presented in this thesis has been extensively used in several research projects as the dynamic object detection platform for automated driving applications on highways in real traffic. Contents Abbreviations VIII List of Symbols X Abs...

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