Data Fusion Solution Research Articles

All-weather precipitable water vapor map reconstruction using data fusion and machine learning-based spatial downscaling

Precipitable water vapor (PWV) detection with high spatial resolution and high accuracy is of significant importance for contributing to extreme weather events monitoring and forecasting. Current PWV products, however, suffer from limitations of spatial and temporal discontinuities, low accuracy, and coarse spatial resolution. To overcome this problem, a data fusion and machine learning-based spatial downscaling solution is proposed. At first, spatially complete PWV maps are generated by integrating calibrated PWV of Moderate Resolution Imaging Spectroradiometer (MODIS) and the ERA5 PWV data from 2018 to 2022. Subsequently, three spatial downscaling models based on Gradient Boosting Decision Tree (GBDT), Multi-layer Perceptron Neural Network (MLPNN), and Random Forest (RF), respectively, are developed to produce high-quality, all-weather PWV considering the land-cover type. It has been verified that the high-quality all-weather PWV maps generated by the GBDT, MLPNN, and RF models exhibit strong agreement with Global Navigation Satellite System (GNSS) PWV estimates. The correlation coefficients are 0.95, 0.87, and 0.87, while the overall Bias is 0.29 mm, 0.67 mm, and 0.35 mm, and the root mean square errors (RMSE) are 1.74 mm, 2.98 mm, and 3.06 mm, respectively. These results significantly enhance the accuracy of MODIS PWV products (R2 = 0.73, RMSE = 5.64 mm, Bias = 3.05 mm). Notably, the GBDT model outperforms the other models in terms of performance. Compared to MODIS PWV, the new PWV map with a data fusion and machine learning-based spatial downscaling approach effectively utilizes the advantage of satellite-based and reanalyzed PWV products, providing continuous, detailed, and reasonable variation in time and space. Moreover, it is less influenced by seasonal changes. The new PWV map has a promising application for regional hydrology and meteorology.

Insights into Internet of Medical Things (IoMT): Data fusion, security issues and potential solutions

The Internet of Medical Things (IoMT) has created a wide range of opportunities for knowledge exchange in numerous industries. The opportunities include patient empowerment, healthcare collaboration, medical education and training, remote monitoring and telemedicine, customized treatment plans, data sharing for innovation, continuous medical learning, supply chain management, public health initiatives, wearable health devices, and quality improvement initiatives. However, the adoption of IoMT faces numerous challenges regarding interoperability, data privacy, security, regulatory, and infrastructure costs. This paper aims to address the implications of data fusion in IoMT, as well as the associated security challenges and their potential solutions, which are lacking in the literature. Data collected from IoMT devices has a direct impact on the accuracy of predictions because of its quality, quantity, and relevance. With an accuracy of 99.53 % to 99.99 %, the Epilepsy seizure detector-based Naive Bayes (ESDNB) algorithm is found to be the most effective for detecting epileptic seizures in IoMT networks. However, the way data are stored must also undergo a major revolution, and all phases—collection, protection, and storage—need to be improved. The standardization of architecture and security measures may improve the detection of security threats and compromises. Methods to detect malware in cross platforms is also an avenue for future research that can effectively tackle the heterogeneity of the IoMT systems. Cryptography and blockchain technology have shown to be promising ways to increase the security of an IoMT-based system. The findings of this review will assist a wide variety of stakeholders in the healthcare ecosystem.

Secure blockchain assisted Internet of Medical Things architecture for data fusion enabled cancer workflow

In today’s digital healthcare landscape, numerous clinical institutions collaborate to enhance healthcare quality in a ubiquitous fog and cloud environment. Data fusion plays a vital role in healthcare collaboration, enabling the integration of diverse healthcare sources. The primary advantage is the improvement of healthcare treatments and the availability of services throughout the network. However, despite these benefits, there is room for improvement in addressing various security issues regarding collaboration among clinical healthcare institutions to meet data fusion requirements. The primary challenge lies in processing lung cancer workflow data fusion on heterogeneous nodes while ensuring security in fog cloud networks. As a result, security emerges as a critical issue in the digital healthcare system operating within this ubiquitous environment. We present the secure Blockchain Internet of Medical Things (BIoMT) architecture for lung cancer workflow data fusion processing in fog cloud networks. The BIoMT architecture introduces the Blockchain Data Fusion Secure (BDFS) algorithm framework, which consists of task scheduling and blockchain validation schemes. The study aims to minimize the makespan of the lung workflow tasks based on security and deadline constraints in fog and cloud networks. We consider security at an advanced level, where runtime ransomware attacks are also identified in fog and cloud networks. Simulation results demonstrate that BDFS outperforms all existing BIoMT architectures regarding workflow processing while adhering to the specified constraints. Overall, the BDFS algorithm presented in the BIoMT architecture provides an efficient and secure solution for lung cancer workflow data fusion in fog cloud networks, contributing to the advancement of digital healthcare systems in a ubiquitous environment.

Principal component analysis approach for detecting faults in rotary machines based on vibrational and electrical fused data

Rotating machines are frequently used in industrial applications. However, due to their severity, mechanical failures such as rotor imbalance, shaft imbalance, pulley imbalance, structural breakage, and bearing imbalance can lead to unplanned shutdowns. While vibration analysis-based condition monitoring techniques can detect and diagnose many early errors, certain mechanical faults have associated vibration characteristics that make it difficult to identify and distinguish these faults. To address this issue, this paper proposes a method based on data fusion for vibrational and electrical signatures to achieve new fused signatures for healthy and different faulty cases. The weighted decision fusion method generates the fused decision by weighting and combining the output of multiple sensors. Conventional vibration evaluation parameters diagnose unbalance, pulley misalignment, belt damage, and combined faults. However, these parameters have more dimensions and correlated features for some faulty cases, such as unbalance and misalignment. Therefore, the Principal Component Analysis (PCA) was applied to reduce the dimensionality of evaluating parameters and preserve almost all data variation. The PCA produces uncorrelated Principal Components (PCs) for each case. A backpropagation neural network (BPNN) was constructed to construct an integrated fault diagnosis framework. The first and second PC was inserted as input parameters in the training set of BPNN. It was observed that BPNN achieves 2.1762×10-10 Mean Squared Error (MSE) demonstrates superior data fusion solutions and PCA in the condition monitoring of rotating machines. Overall, this study proposes an effective method for diagnosing mechanical faults in rotating machines, which can improve reliability and reduce downtime in industrial applications.

Multi-Sensor Data Fusion Solutions for Blind and Visually Impaired: Research and Commercial Navigation Applications for Indoor and Outdoor Spaces

Several assistive technology solutions, targeting the group of Blind and Visually Impaired (BVI), have been proposed in the literature utilizing multi-sensor data fusion techniques. Furthermore, several commercial systems are currently being used in real-life scenarios by BVI individuals. However, given the rate by which new publications are made, the available review studies become quickly outdated. Moreover, there is no comparative study regarding the multi-sensor data fusion techniques between those found in the research literature and those being used in the commercial applications that many BVI individuals trust to complete their everyday activities. The objective of this study is to classify the available multi-sensor data fusion solutions found in the research literature and the commercial applications, conduct a comparative study between the most popular commercial applications (Blindsquare, Lazarillo, Ariadne GPS, Nav by ViaOpta, Seeing Assistant Move) regarding the supported features as well as compare the two most popular ones (Blindsquare and Lazarillo) with the BlindRouteVision application, developed by the authors, from the standpoint of Usability and User Experience (UX) through field testing. The literature review of sensor-fusion solutions highlights the trends of utilizing computer vision and deep learning techniques, the comparison of the commercial applications reveals their features, strengths, and weaknesses while Usability and UX demonstrate that BVI individuals are willing to sacrifice a wealth of features for more reliable navigation.

Gear Wear Detection Based on Statistic Features and Heuristic Scheme by Using Data Fusion of Current and Vibration Signals

Kinematic chains are ensembles of elements that integrate, among other components, with the induction motors, the mechanical couplings, and the loads to provide support to the industrial processes that require motion interchange. In this same line, the induction motor justifies its importance because this machine is the core that provides the power and generates the motion of the industrial process. However, also, it is possible to diagnose other types of faults that occur in other elements in the kinematic chain, which are reflected as problems in the motor operation. With this purpose, the coupling between the motor and the final load in the chain requires, in many situations, the use of a gearbox that balances the torque–velocity relationship. Thus, the gear wear in this component is addressed in many works, but the study of gradual wear has not been completely covered yet at different operating frequencies. Therefore, in this work, a methodology is proposed based on statistical features and genetic algorithms to find out those features that can best be used for detecting the gradual gear wear of a gearbox by using the signals, measured directly in the motor, from current and vibration sensors at different frequencies. The methodology also makes use of linear discriminant analysis to generate a bidimensional representation of the system conditions that are fed to a neural network with a simple structure for performing the classification of the condition. Four uniform gear wear conditions were tested, including the healthy state and three gradual conditions: 25%, 50%, and 75% wear in the gear teeth. Because of the sampling frequency, the number of sensors, the time for data acquisition, the different operation frequencies analyzed, and the computation of the different statistical features, meant that a large amount of data were generated that needed to be fused and reduced. Therefore, the proposed methodology provides an excellent generalized solution for data fusion and for minimizing the computational burden required. The obtained results demonstrate the effectiveness of fault gradualism detection for the proposed approach.

Data fusion and machine learning for ship fuel efficiency modeling: Part I – Voyage report data and meteorological data

The International Maritime Organization has been promoting energy-efficient operational measures to reduce ships' bunker fuel consumption and the accompanying emissions, including speed optimization, trim optimization, weather routing, and the virtual arrival policy. The theoretical foundation of these measures is a model that can accurately forecast a ship's bunker fuel consumption rate according to its sailing speed, displacement/draft, trim, weather conditions, and sea conditions. Voyage report is an important data source for ship fuel efficiency modeling but its information quality on weather and sea conditions is limited by a snapshotting practice with eye inspection. To overcome this issue, this study develops a solution to fuse voyage report data and publicly accessible meteorological data and constructs nine datasets based on this data fusion solution. Eleven widely-adopted machine learning models were tested over these datasets for eight 8100-TEU to 14,000-TEU containerships from a global shipping company. The best datasets found reveal the benefits of fusing voyage report data and meteorological data, as well as the practically acceptable quality of voyage report data. Extremely randomized trees (ET), AdaBoost (AB), Gradient Tree Boosting (GB) and XGBoost (XG) present the best fit and generalization performances. Their R2 values over the best datasets are all above 0.96 and even reach 0.99 to 1.00 for the training set, and 0.74 to 0.90 for the test set. Their fit errors on daily bunker fuel consumption are usually between 0.5 and 4.0 ton/day. These models have good interpretability in explaining the relative importance of different determinants to a ship's fuel consumption rate.

Mobile Crowd-sourced Data Fusion and Urban Traffic Estimation

Urban traffic estimation is one of the critical tasks for intelligent transportation systems (ITS). To estimate traffic condition, accurately and timely traffic data must be sensed frequently at every location around the city utilizing multimedia data fusion and analytics. This paper proposes a novel approach to urban traffic data collection and analysis leveraging crowd-sourced data from drivers and mobile users. Concretely, we have proposed solutions for mobile crowd-sourced data fusion to which just the right traffic data is collected automatically by GPS modules equipped in mobile devices. In addition, mechanisms for data validation and analytics for traffic estimation have been devised. Consequently, a mobile application is developed and provided to public users so that they can conveniently collect and share traffic data to the system. Besides, users can access traffic information and ITS services such as routing recommendation freely. The proposed system has been deployed for a real-world application in Ho Chi Minh City (HCMC), the largest city in Vietnam. Experimental results from real-field data confirm the feasibility, effectiveness and efficiency of the proposed approaches.

A Review of Multisensor Data Fusion Solutions in Smart Manufacturing: Systems and Trends.

Manufacturing companies increasingly become “smarter” as a result of the Industry 4.0 revolution. Multiple sensors are used for industrial monitoring of machines and workers in order to detect events and consequently improve the manufacturing processes, lower the respective costs, and increase safety. Multisensor systems produce big amounts of heterogeneous data. Data fusion techniques address the issue of multimodality by combining data from different sources and improving the results of monitoring systems. The current paper presents a detailed review of state-of-the-art data fusion solutions, on data storage and indexing from various types of sensors, feature engineering, and multimodal data integration. The review aims to serve as a guide for the early stages of an analytic pipeline of manufacturing prognosis. The reviewed literature showed that in fusion and in preprocessing, the methods chosen to be applied in this sector are beyond the state-of-the-art. Existing weaknesses and gaps that lead to future research goals were also identified.

A kernel transfer learning based multi-sensor surface reconstruction framework for reverse engineering

Reverse engineering (RE) has become increasingly important in the product development cycle and product design process, and it is used wildly for several years in industry. However, there is still room for increasing in the performance of RE. Indeed, this is the case in particular with the improvement of the quality of the initially measured data to increase the scope of applications in areas where the accuracy of the reconstructed model is a key element. The increasing requirements for high efficiency and accuracy in RE can be reached by the development of multi-sensor reconstruction techniques. Unlike existing data fusion solutions, this research redefines multi-sensor surface reconstruction as a transfer learning problem. A kernel transfer learning based multi-sensor reconstructed framework is proposed to transfer the common knowledge from the source data to the target data to take advantage of different sensors. A special combination kernel composed of a task kernel and a transfer kernel is designed to extract the transferable information and task-dependent information of two metrology data. The transferable common features are learned by training the task kernel using both datasets. And the conditional difference is captured by the transfer kernel learned on the probe points. Experiments are conducted on both simulated data and real surfaces to verify the effectiveness of the proposed framework. The merits and demerits are also studied comprehensively by statistical analysis and convergence analysis.

Over-the-Air Computation via Broadband Channels

Over-the-air computation (AirComp) has been recognized as a low-latency solution for wireless sensor data fusion, where multiple sensors send their measurement signals to a receiver simultaneously for computation. Most existing work only considered performing AirComp over a single frequency channel. However, for a sensor network with a massive number of nodes, a single frequency channel may not be sufficient to accommodate the large number of sensors, and the AirComp performance will be very limited. So it is highly desirable to have more frequency channels for large-scale AirComp systems to benefit from multi-channel diversity. In this letter, we propose an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${M}$ </tex-math></inline-formula> -frequency AirComp system, where each sensor selects a subset of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${M}$ </tex-math></inline-formula> frequencies and broadcasts its signal over these channels under a certain power constraint. We derive the optimal sensors’ transmission and receiver’s signal processing methods separately, and develop an algorithm for joint design to achieve the best AirComp performance. Numerical results show that increasing one frequency channel can improve the AirComp performance by threefold compared to the single-frequency case.

Solutions for Data management and Correlative Data Fusion by ZEISS

Solutions for Data management and Correlative Data Fusion by ZEISS

Blockchain-Enabled HMM Model for Sports Performance Prediction

The historical training or exam data of an athlete produced in the past sport exercise or test activities have provided a promising way to objectively and accurately evaluate the real-time sport performance of the athlete. However, the continuous generation of sport training or exam data has placed a heavy transmission and processing burden on the traditional centralized data processing paradigm (e.g., cloud platform). Considering this drawback, a decentralized blockchain-based athlete sport data transmission and utilization solution is proposed in this research work. Moreover, the available athlete sport data produced in past sport exercise or test activities is often sparse and time-related, which call for a robust and time-aware data fusion and processing solution. In this situation, HMM model is employed in this article to cope with the data sparsity and dynamics and further make accurate sports performance prediction for athletes accordingly. Finally, we design a set of experiments on a real-world dataset to validate the feasibility of our proposal in terms of effectiveness and efficiency.

Semantic‐Based Sports Music Information Fusion and Retrieval in Wireless Sensor Networks

The wireless sensor network has developed rapidly in recent years. It is formed by the intersection of multiple disciplines. It integrates embedded technology, sensor technology, distributed technology, wireless communication technology, and modern networks. It is a brand new information acquisition platform. The characteristics of sensor networks determine that information fusion technology is a hot spot in the research of wireless sensor networks. Information fusion can achieve high performance and low cost in terms of energy and communication, which is of great significance to the research of sensor networks. This paper is aimed at studying the semantic‐based sports music information fusion and retrieval research in wireless sensor networks. WSNs may face various attacks including eavesdropping attacks, replay attacks, Sybil attacks, and DOS attacks. Therefore, they are designing sensor network solutions. It is necessary to consider the network security issues. This article summarizes and analyzes the existing WSN security data fusion solutions for this issue and compares them by classification. This paper proposes methods and theories such as the spatial correlation detection algorithm, CBA algorithm, FTD algorithm, and DFWD algorithm, which enriches the research of information fusion and retrieval in wireless sensor networks, which is of exploratory significance, and it also establishes this problem. The model was studied, and reliable data was obtained. The experimental results of this paper show that when using these methods to diagnose faults in WSN, the correct rate of model diagnosis is higher than 77%.

A survey on data fusion: what for? in what form? what is next?

Data fusion is the process of merging records from multiple sources which represent the same real-world object into a single representation. This review of the literature concerns Data Fusion in the context of data integration, i.e., the integration of structured and semi-structured data from the same domain, and provides an overview of this field of research. We present why data fusion is becoming increasingly necessary, what it is used for (What for?), what methods and solutions for data fusion have been proposed in the literature (In what form?), what research challenges are still open in the data fusion area and what future research directions could usefully take (What is next?)

Real time localization solution for land vehicle application using low-cost integrated sensors with GPS

The technique proposed in this research demonstrates a real time nonlinear data fusion solution based on extremely low-cost and grade inertial sensors for land vehicle navigation. Here, the utilized nonlinear multi-sensor data fusion (MSDF) is based on the combination between extremely low-cost micro electrical mechanical systems (MEMS) inertial, heading, pressure, and speed sensors in addition to satellite-based navigation system. The integrated navigation system fuses position and velocity states from the Global Positioning System (GPS), the velocity measurements from an odometer, heading angle observation from a magnetometer and navigation states from an inertial navigation system (INS). The implemented system performance is assessed through the post-processing of collected raw measurements and real time experimental work. The system that runs the real-time experiments is established on three connected platforms, two of them are based on a 32-bit ARMTM core and the third one is based 16-bit AVR ATMEL microcontroller. This microcontroller is connected to an on-board diagnostics (OBD) shield to collect the vehicle speed measurements. The raw data obtained from the integrated sensors is saved and post processed in MATLAB®. In normal conditions, the estimated position errors are reduced through the usage of INS/GPS integration with heading observation angle from a magnetometer. In GPS-denied environments, the integrated system uses the observations from INS, magnetometer in addition to the velocity from odometer to ensure a continuous and accurate navigation solution. A complementary filter (CF) is implemented to estimate and improve the pitch and roll angles calculations. In addition to that, an unscented Kalman filter (UKF) is used cascaded with the designed CF to complete the designed sensors fusion algorithm. Experimental results show that the designed MSDF can achieve a good level of accuracy and a continuous localization solution of a land vehicle in different GPS availability cases and can be implemented on the available in the market processors to be run in real time.

A Multisensory Edge-Cloud Platform for Opportunistic Radio Sensing in Cobot Environments

Worker monitoring and protection in collaborative robot (cobots) industrial environments requires advanced sensing capabilities and flexible solutions to monitor the movements of the operator in close proximity of moving robots. Collaborative robotics is an active research area where Internet of Things (IoT) and novel sensing technologies are expected to play a critical role. Considering that no single technology can currently solve the problem of continuous worker monitoring, the article targets the development of an IoT multisensor data fusion (MDF) platform. It is based on an edge-cloud architecture that supports the combination and transformation of multiple sensing technologies to enable the passive and anonymous detection of workers. Multidimensional data acquisition from different IoT sources, signal preprocessing, feature extraction, data distribution, and fusion, along with machine learning (ML) and computing methods are described. The proposed IoT platform also comprises a practical solution for data fusion and analytics. It is able to perform opportunistic and real-time perception of workers by fusing and analyzing radio signals obtained from several interconnected IoT components, namely, a multiantenna WiFi installation (2.4-5 GHz), a sub-THz imaging camera (100 GHz), a network of radars (122 GHz) and infrared sensors (8-13 μm). The performance of the proposed IoT platform is validated through real use case scenarios inside a pilot industrial plant in which protective human-robot distance must be guaranteed considering latency and detection uncertainties.

Multiphysics anomaly map: A new data fusion workflow for geophysical interpretation

When collecting and processing geophysical data for exploration, the same geologic feature can generate a different response for each rock property being targeted. Typically, the units of these responses may differ by several orders of magnitude; therefore, the combination of geophysical data in integrated interpretation is not a straightforward process and cannot be performed by visual inspection only. The multiphysics anomaly map (MAM) that we have developed is a data fusion solution that consists of a spatial representation of the correlation between anomalies detected with different geophysical methods. In the MAM, we mathematically process geophysical data such as seismic attributes, gravity, magnetic, and resistivity before combining them in a single map. In each data set, anomalous regions of interest, which are problem-dependent, are selected by the interpreter. Selected anomalies are highlighted through the use of a logistic function, which is specially designed to clip large magnitudes and rescale the range of values, increasing the discrimination of anomalies. The resulting anomalies, named logistic anomalies, represent regions of large probabilities of target occurrence. This new solution highlights areas where individual interpretations of different geophysical methods correlate, increasing the confidence in the interpretation. We determine the effectiveness of our MAM with application to real data from onshore and offshore Brazil. In the onshore Recôncavo Basin, the MAM allows the interpreter to identify a channel where a drilled well found the largest sandstone thickness on the area. In a second example, from offshore Sergipe-Alagoas Basin, the MAM helps differentiate between a dry and an oil-bearing channel previously outlined in seismic data. Therefore, these outcomes indicate that the MAM is a valid interpretation tool that we believe can be applied to a wide range of geologic problems.

Optimal fusion of industrial data streams with different granularities

In the Industry 4.0 era, it is a common situation that measurements are available with different resolutions (or granularities). Multiresolution (or multi-granularity) data can arise, for instance, from aggregation rules applied over measurements collected in batches during non-overlapping periods of time, or in composite sampling schemes (e.g., using industrial auto-sampler devices). Current solutions for optimal data fusion, such as Kalman filters (KF) and Moving Horizon Estimation (MHE) approaches, assume that all variables are measured at the same resolution, even if their acquisition rates are different. Therefore, they are unable to account for the multiresolution structure of data. In this article we introduce a multiresolution framework for the optimal fusion of multiple signals at distinct resolutions. This approach is compared against the benchmark alternative using simulated case studies, where the superior signal processing capability of the novel methodology is demonstrated.

A Fuzzy Data Fusion Solution to Enhance the QoS and the Energy Consumption in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are formed of various nodes that gather parameters in a monitored environment. These nodes interact with each other or can be arranged into clusters controlled by a cluster head that has the task of rerouting the acquired data to a base station. Both the Quality of Service (QoS) and low data quality are common issues in WSNs, mainly prompted by the data fusion mechanism, where a certain amount of low-quality data may affect the overall fusion result negatively. In this paper, a fuzzy-based solution for data fusion in WSNs is presented to provide a better QoS and to reduce the energy consumption. The suggested approach can aggregate only true value rather than process the full data. This purpose is accomplished thanks to a Fuzzy Logic Controller (FLC) implemented within nodes. Besides, the data, which have been separated, are aggregated by a cluster head which also has the responsibility of determining the probability that an event has happened in the monitored environment. Finally, the base station estimates whether an event has occurred and, eventually, raises an appropriate alarm. The results of a real testbed scenario reveal that the proposed method achieves encouraging performance.