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Disease surveillance systems allow public health agencies to respond to emerging diseases before they become widespread. Developing such systems requires identifying optimal ways to monitor in the context of an epidemic outbreak; this problem is known as sensor selection. Contact networks represent the dynamics of interaction in a population and are used to model how a disease spreads in a population and to explore strategies of sensor selection. We evaluated five sensor selection strategies on their ability to provide an early warning of a COVID-like outbreak in synthetic contact networks encapsulated in four network scenarios. Three of these scenarios assessed different aspects of community structure. The fourth scenario employed a contact network representing the population and interactions of 6.8 million people in New York City, constructed from an agent-based simulation using census and transportation data. This scenario exemplifies how sensor selection strategies may perform in a real-world, urban context. Our findings suggest that the choice of the optimal strategy depends heavily on the community structure of the network. Strategies that select highly connected nodes or maximize network coverage are the optimal surveillance strategy for outbreak detection in many network community structures. However, a naive implementation of these strategies may fail to provide an early warning at all—including in the New York City scenario. Moreover, these methods are impractical for real-world use as they require knowledge of the underlying contact network. Instead, a selection strategy that starts with a set of random nodes and then performs a random walk through a chain of neighbors reliably provides early warnings without requiring prior knowledge of the network. We find this method, called "random chain", to be the most pragmatic for implementation in a real-world disease surveillance context. • Disease surveillance is important for early detection and response to outbreaks. • Evaluated 5 sensor selection strategies for early warning of a COVID-like outbreak. • The optimal strategy depends heavily on the community structure of the network. • Find "random chain" strategy to be the most pragmatic for real-world surveillance. [ABSTRACT FROM AUTHOR]

Crop height and biomass are the two important phenotyping traits to screen forage population types at local and regional scales. This study aims to compare the performances of multispectral and RGB sensors onboard drones for quantitative retrievals of forage crop height and biomass at very high resolution. We acquired the unmanned aerial vehicle (UAV) multispectral images (MSIs) at 1.67 cm spatial resolution and visible data (RGB) at 0.31 cm resolution and measured the forage height and above-ground biomass over the alfalfa (Medicago sativa L.) breeding trials in the Canadian Prairies. (1) For height estimation, the digital surface model (DSM) and digital terrain model (DTM) were extracted from MSI and RGB data, respectively. As the resolution of the DTM is five times less than that of the DSM, we applied an aggregation algorithm to the DSM to constrain the same spatial resolution between DSM and DTM. The difference between DSM and DTM was computed as the canopy height model (CHM), which was at 8.35 cm and 1.55 cm for MSI and RGB data, respectively. (2) For biomass estimation, the normalized difference vegetation index (NDVI) from MSI data and excess green (ExG) index from RGB data were analyzed and regressed in terms of ground measurements, leading to empirical models. The results indicate better performance of MSI for above-ground biomass (AGB) retrievals at 1.67 cm resolution and better performance of RGB data for canopy height retrievals at 1.55 cm. Although the retrieved height was well correlated with the ground measurements, a significant underestimation was observed. Thus, we developed a bias correction function to match the retrieval with the ground measurements. This study provides insight into the optimal selection of sensor for specific targeted vegetation growth traits in a forage crop. [ABSTRACT FROM AUTHOR]

In the era of smart manufacturing, modern manufacturing systems face high demands for enhancing process performance and reducing machine downtime. Sensors and process data are essential for successfully implementing data-driven approaches to guarantee robust and reliable process monitoring, tool conditioning, or quality assurance. However, the accuracy and performance of such approaches are highly dependent on the quality of the gathered sensor data and influenced by the implemented data acquisition and processing methods. For this purpose, this work proposes a lightweight sensor ontology to provide a comprehensive overview to characterize underlying relationships between the physical environment and the quality of the data sets. The extended sensor ontology, in combination with domain knowledge, aims to support engineers in fully exploiting the potential of sensor data to obtain trustworthy data sets in forming technologies. As a result, this approach can improve the implementation of automated and data-driven process monitoring of forming systems and tools. [ABSTRACT FROM AUTHOR]

Abstract We address the sensor selection problem where linear measurements under correlated noise are gathered at the selected nodes to estimate the unknown parameter. Since finding the best subset of sensor nodes that minimizes the estimation error requires a prohibitive computational cost especially for a large number of nodes, we propose a greedy selection algorithm that uses the log-determinant of the inverse estimation error covariance matrix as the metric to be maximized. We further manipulate the metric by employing the QR and LU factorizations to derive a simple analytic rule which enables an efficient selection of one node at each iteration in a greedy manner. We also make a complexity analysis of the proposed algorithm and compare with different selection methods, leading to a competitive complexity of the proposed algorithm. For performance evaluation, we conduct numerical experiments using randomly generated measurements under correlated noise and demonstrate that the proposed algorithm achieves a good estimation accuracy with a reasonable selection complexity as compared with the previous novel selection methods.

The fused deposition modeling (FDM)-type three-dimensional (3D) printer is a popular choice in manufacturing facilities due to its capability of printing complex-shaped objects with simple machine control. To monitor the operational state of such 3D printing systems and detect faults, analog sensor signals can be collected and analyzed in real-time. Several research works have used traditional sensor types to monitor machinery movement, such as acceleration and temperature sensor signals, and have applied dimension reduction for efficient analysis. However, since the quality of operational state monitoring easily varies depending on the sensor information obtained, identifying meaningful sensor types at the sensor installation and data preparation stage is crucial for efficient data collection and analysis, prior to performing real-time status monitoring in 3D printing systems. In this study, we analyzed the relative importance of different sensor types for improving state monitoring performance and efficiency through statistical inference. It was evident that analyzing a set of five magnetic sensor signals was more effective and efficient for support vector machine-based classification of working stages and autoencoder-based fault detection than analyzing the entire set of signals, which includes 3-axis acceleration, 3-axis Euler angle, 3-axis magnetic field, temperature, and overall current sensors. By efficiently monitoring current working stages and detecting faults, this proposed strategy not only enhances the printing speed and product quality of FDM 3D printers but also improves the efficiency of original data storage in cloud services. This facilitates the control and remote monitoring of multiple 3D printers simultaneously. [ABSTRACT FROM AUTHOR]

The relationship between estimating traffic flow and evaluating pollutant emissions lies in understanding how vehicular traffic patterns affect air quality. Traffic flow estimation is a complex field that involves a variety of analytical techniques to understand, predict, and manage the flow of vehicles on road networks. Different types of analyses commonly employed in this area are statistical analysis (e.g., descriptive statistics, inferential statistics, time series analysis), mathematical modeling (macroscopic models, microscopic models, mesoscopic models), computational methods (e.g., simulation modeling, machine learning, and AI techniques), geospatial analysis (e.g., geographic information systems (GISs), spatial data analysis), network analysis (e.g., graph theory and network flow models). In sensor network setups, the strategic placement of sensors is crucial, primarily due to the challenges posed by limited energy supplies, restricted storage capabilities, and the demands on processing and communication, all of which significantly impact maintenance costs and hardware limitations. To mitigate the burden on processing and communication, it is essential to deploy a limited number of sensors strategically. In practical applications, achieving an optimal layout of physical sensors (i.e., placing sensors within the network in such a way as to meet a specific optimality criterion, such as identifying the minimum number of sensors required to ensure the ability to design reliable state observers capable of reconstructing the network's state based on the available data) is essential for the accurate monitoring of large-scale systems, including traffic flow or the distribution networks of water and gas. In the context of traffic systems, addressing the challenge of full link flow observability, that is, the ability to accurately monitor and assess the flow of entities (i.e., vehicles) across all the links or pathways within a network, entails selecting the smallest number of traffic sensors from a larger set to install. The goal is to choose a subset of p sensors, which may include redundancies, from a pool of n > > p potential sensors. This is conducted to maintain the structural observability of the entire traffic network. This concept pertains to deducing the complete internal state (traffic volume on each road link in the network) from external outputs and inputs (measurements from sensors). The traditional concept of system observability serves as a criterion for sensor placement. This article presents the development of a simulated annealing heuristic to address the selection problem. The selected sensors are then applied to construct a Luenberger observer, a mathematical construct used in control theory to accurately estimate the internal state of a dynamic system based on its inputs and outputs. Numerical simulations are carried out to demonstrate the effectiveness of this method, and a performance analysis using a digital twin of a transport network, designed using the Aimsun Next software, are also carried out to assess traffic flow and associated pollutant emissions. In particular, we examine a traffic network comprising 21 roads. We address the sensor selection problem by identifying an optimal set of six sensors, which facilitates the design of a Luenberger observer. This observer enables the reconstruction of traffic flow across the network with minimal estimation error. Furthermore, by integrating this observer with data from the Aimsun Next software, we assess the pollutant emissions related to traffic flow. The results indicate a high accuracy in estimating pollutant levels. [ABSTRACT FROM AUTHOR]

In the context of Industry 4.0, managing large amounts of data is essential to ensure informed decision-making in intelligent production environments. It enables, for example, predictive maintenance, which is essential for anticipating and identifying causes of failures in machines and equipment, optimizing processes, and promoting proactive management of human, financial, and material resources. However, generating accurate information for decision-making requires adopting suitable data preprocessing and analysis techniques. This study explores the identification of machine failures based on synthetic industrial data. Initially, we applied the feature selection techniques Principal Component Analysis (PCA), Minimum Redundancy Maximum Relevance (mRMR), Neighborhood Component Analysis (NCA), and Denoising Autoencoder (DAE) to the collected data and compared their results. In the sequence, a comparison among three widely known machine learning classifiers, namely Random Forest (RF), Support Vector Machine (SVM), and Multilayer Perceptron neural network (MLP), was conducted, with and without considering feature selection. The results showed that PCA and RF were superior to the other techniques, allowing the classification of failures with rates of 0.98, 0.97, and 0.98 for the accuracy, precision, and recall metrics, respectively. Thus, this work contributes by solving an industrial problem and detailing techniques to identify the most relevant variables and machine learning algorithms for predicting machine failures that negatively impact production planning. The findings provided by this study can assist industries in giving preference to employing sensors and collecting data that can contribute more effectively to machine failure predictions. [ABSTRACT FROM AUTHOR]

Steel-concrete composite structures (SCCS) have been widely used as primary load-bearing components in large-scale civil infrastructures. As the basis of the co-working ability of steel plate and concrete, the bonding status plays an essential role in guaranteeing the structural performance of SCCS. Accordingly, efficient non-destructive testing (NDT) on interfacial debondings in SCCS has become a prominent research area. Multi-channel analysis of surface waves (MASW) has been validated as an effective NDT technique for interfacial debonding detection for SCCS. However, the feasibility of MASW must be validated using experimental measurements. This study establishes a high-frequency data synchronous acquisition system with 32 channels to perform comparative verification experiments in depth. First, the current sensing approaches for high-frequency vibration and stress waves are summarized. Secondly, three types of contact sensors, namely, piezoelectric lead-zirconate-titanate (PZT) patches, accelerometers, and ultrasonic transducers, are selected for MASW measurement. Then, the selection and optimization of the force hammer head are performed. Comparative experiments are carried out for the optimal selection of ultrasonic transducers, PZT patches, and accelerometers for MASW measurement. In addition, the influence of different pasting methods on the output signal of the sensor array is discussed. Experimental results indicate that optimized PZT patches, acceleration sensors, and ultrasonic transducers can provide efficient data acquisition for MASW-based non-destructive experiments. The research findings in this study lay a solid foundation for analyzing the recognition accuracy of contact MASW measurement using different sensor arrays. [ABSTRACT FROM AUTHOR]

We address the sensor selection problem where linear measurements under correlated noise are gathered at the selected nodes to estimate the unknown parameter. Since finding the best subset of sensor nodes that minimizes the estimation error requires a prohibitive computational cost especially for a large number of nodes, we propose a greedy selection algorithm that uses the log-determinant of the inverse estimation error covariance matrix as the metric to be maximized. We further manipulate the metric by employing the QR and LU factorizations to derive a simple analytic rule which enables an efficient selection of one node at each iteration in a greedy manner. We also make a complexity analysis of the proposed algorithm and compare with different selection methods, leading to a competitive complexity of the proposed algorithm. For performance evaluation, we conduct numerical experiments using randomly generated measurements under correlated noise and demonstrate that the proposed algorithm achieves a good estimation accuracy with a reasonable selection complexity as compared with the previous novel selection methods. [ABSTRACT FROM AUTHOR]

The progress towards fully autonomous mobility is significantly impacted by the integration of evolving technologies in connected and automated mobility (CAM). Connected and automated vehicles (CAVs) have the potential to revolutionize our transportation system by improving efficiency, safety, and environmental sustainability. Automated shuttles and public buses, smart traffic signals, intelligent passenger cars, and smart roundabouts are just a few examples of technologies that are being planned and actively researched for integration into transportation systems. Sensors are essential in making this possible. This article provides a structured overview of research on the selection and positioning of sensors on- and off-vehicle to achieve cooperative, connected, and automated mobility. The general integration and usage of sensors in vehicles and infrastructure is described, a detailed taxonomy of the examined research is provided, and future research directions are presented, involving solutions for quantification of sensor performance and addressing current trends. The findings of this article also highlight numerous challenges in creating a universal framework, the lack of application of novel machine learning methods, and the complexity of modeling multi-sensor settings.

While new production areas (greenfields) have state-of-the-art technologies for implementing digitalization, existing production areas (brownfields) and devices must first be upgraded with technologies before digitalization can be implemented. The aim of this research work is to use a case study to identify the differences in the implementation of machine learning (ML) projects in brownfields and greenfields. For this purpose, an ML application for the detection of changeover times on milling machines is implemented and analyzed in the brownfield and greenfield scenarios as well as a combined scenario. Particular attention is paid to the selection of sensors and features. It was found that the abundant availability of features in the greenfield scenario poses pitfalls when creating ML projects if the underlying sensors cannot be checked for their suitability. For the changeover detector use case, the best model quality was achieved for the combined scenario, followed by the greenfield scenario. [ABSTRACT FROM AUTHOR]

The real-time monitoring of heat fluxes on the surface of a flight vehicle is vital for safe reentry and efficient attitude control. However, conducting measurements during flight is a challenge. In this study, a real-time heat flux estimation method using inverse heat conduction was proposed, and internally mounted sensors were used for measurements. First, the time-varying samples of heat flux on the surface and temperature on the inner wall were generated along a variety of reentry trajectories. Second, a sensor selection algorithm based on feature importance was applied to select optimal sensor mounting locations. Finally, a prediction model was built using the random forest algorithm to estimate surface heat flux with measured temperatures from the mounted sensors. The proposed method was employed to predict the real-time heat flux of a reentry capsule during return flight. The results show that the proposed method can predict heat flux in real time with a prediction error of less than 0.2%. Further, the sensor selection algorithm enhanced prediction efficiency by reducing the number of necessary sensors. [ABSTRACT FROM AUTHOR]

Abstract Inferring the source of a diffusion in a large network of agents is a difficult but feasible task, if a few agents act as sensors revealing the time at which they got hit by the diffusion. One of the main limitations of current source identification algorithms is that they assume full knowledge of the contact network, which is rarely the case, especially for epidemics, where the source is called patient zero. Inspired by recent implementations of contact tracing algorithms, we propose a new framework, which we call Source Identification via Contact Tracing Framework (SICTF). In the SICTF, the source identification task starts at the time of the first hospitalization, and initially we have no knowledge about the contact network other than the identity of the first hospitalized agent. We may then explore the network by contact queries, and obtain symptom onset times by test queries in an adaptive way, i.e., both contact and test queries can depend on the outcome of previous queries. We also assume that some of the agents may be asymptomatic, and therefore cannot reveal their symptom onset time. Our goal is to find patient zero with as few contact and test queries as possible. We implement two local search algorithms for the SICTF: the LS algorithm, which has recently been proposed by Waniek et al. in a similar framework, is more data-efficient, but can fail to find the true source if many asymptomatic agents are present, whereas the LS+ algorithm is more robust to asymptomatic agents. By simulations we show that both LS and LS+ outperform previously proposed adaptive and non-adaptive source identification algorithms adapted to the SICTF, even though these baseline algorithms have full access to the contact network. Extending the theory of random exponential trees, we analytically approximate the source identification probability of the LS/ LS+ algorithms, and we show that our analytic results match the simulations. Finally, we benchmark our algorithms on the Data-driven COVID-19 Simulator (DCS) developed by Lorch et al., which is the first time source identification algorithms are tested on such a complex dataset.

We validate the OpenSense framework for IMU-based joint angle estimation and furthermore analyze the framework's ability for sensor selection and optimal positioning during activities of daily living (ADL). Personalized musculoskeletal models were created from anthropometric data of 19 participants. Quaternion coordinates were derived from measured IMU data and served as input to the simulation framework. Six ADLs, involving upper and lower limbs were measured and a total of 26 angles analyzed. We compared the joint kinematics of IMU-based simulations with those of optical marker-based simulations for most important angles per ADL. Additionally, we analyze the influence of sensor count on estimation performance and deviations between joint angles, and derive the best sensor combinations. We report differences in functional range of motion (fRoMD) estimation performance. Results for IMU-based simulations showed MAD, RMSE, and fRoMD of 4.8°, 6.6°, 7.2° for lower limbs and for lower limbs and 9.2°, 11.4°, 13.8° for upper limbs depending on the ADL. Overall, sagittal plane movements (flexion/extension) showed lower median MAD, RMSE, and fRoMD compared to transversal and frontal plane movements (rotations, adduction/abduction). Analysis of sensor selection showed that after three sensors for the lower limbs and four sensors for the complex shoulder joint, the estimation error decreased only marginally. Global optimum (lowest RMSE) was obtained for five to eight sensors depending on the joint angle across all ADLs. The sensor combinations with the minimum count were a subset of the most frequent sensor combinations within a narrowed search space of the 5% lowest error range across all ADLs and participants. Smallest errors were on average < 2° over all joint angles. Our results showed that the open-source OpenSense framework not only serves as a valid tool for realistic representation of joint kinematics and fRoM, but also yields valid results for IMU sensor selection for a comprehensive set of ADLs involving upper and lower limbs. The results can help researchers to determine appropriate sensor positions and sensor configurations without the need for detailed biomechanical knowledge.

The measurement of respiratory volume based on upper body movements by means of a smart shirt is increasingly requested in medical applications. This research used upper body surface motions obtained by a motion capture system, and two regression methods to determine the optimal selection and placement of sensors on a smart shirt to recover respiratory parameters from benchmark spirometry values. The results of the two regression methods (Ridge regression and the least absolute shrinkage and selection operator (Lasso)) were compared. This work shows that the Lasso method offers advantages compared to the Ridge regression, as it provides sparse solutions and is more robust to outliers. However, both methods can be used in this application since they lead to a similar sensor subset with lower computational demand (from exponential effort for full exhaustive search down to the order of O (n2)). A smart shirt for respiratory volume estimation could replace spirometry in some cases and would allow for a more convenient measurement of respiratory parameters in home care or hospital settings. [ABSTRACT FROM AUTHOR]

In the context of Industry 4.0, managing large amounts of data is essential to ensure informed decision-making in intelligent production environments. It enables, for example, predictive maintenance, which is essential for anticipating and identifying causes of failures in machines and equipment, optimizing processes, and promoting proactive management of human, financial, and material resources. However, generating accurate information for decision-making requires adopting suitable data preprocessing and analysis techniques. This study explores the identification of machine failures based on synthetic industrial data. Initially, we applied the feature selection techniques Principal Component Analysis (PCA), Minimum Redundancy Maximum Relevance (mRMR), Neighborhood Component Analysis (NCA), and Denoising Autoencoder (DAE) to the collected data and compared their results. In the sequence, a comparison among three widely known machine learning classifiers, namely Random Forest (RF), Support Vector Machine (SVM), and Multilayer Perceptron neural network (MLP), was conducted, with and without considering feature selection. The results showed that PCA and RF were superior to the other techniques, allowing the classification of failures with rates of 0.98, 0.97, and 0.98 for the accuracy, precision, and recall metrics, respectively. Thus, this work contributes by solving an industrial problem and detailing techniques to identify the most relevant variables and machine learning algorithms for predicting machine failures that negatively impact production planning. The findings provided by this study can assist industries in giving preference to employing sensors and collecting data that can contribute more effectively to machine failure predictions.

The quality of myrrh decoction pieces can be influenced by factors such as origin, source, and processing methods. The quality of myrrh in the market varies greatly, and adulteration is a serious issue, highlighting the urgent need for improved quality control measures. This study explores the integration of GC–MS analysis and sensor selection in electronic nose technology for the improved classification of myrrh decoction pieces. GC–MS analysis revealed the presence of 130 volatile compounds in the six myrrh samples, primarily composed of alkene compounds, and each sample exhibited variations in composition. An electronic nose system was designed using a sensor array consisting of six sensors selected from twelve sensors capable of detecting volatile compounds consistent with myrrh composition, including WO3 quantum dots, Fe2O3 hollow nanorods, ZnFe2O4 nanorods, SnO2 nanowires, and two commercially available sensors. The sensors exhibited distinct response patterns to the myrrh samples, indicating their suitability for myrrh analysis. Various sensor parameters, including response, response and recovery time, integral area, and slope, were computed to characterize the sensors' performance. These parameters provided valuable insight into the sensor–gas interactions and the unique chemical profiles of the myrrh samples. The LDA model demonstrated high accuracy in differentiating between the myrrh types, utilizing the discriminative features captured by the sensor array, with a classification accuracy of 90% on the testing set. This research provides a comprehensive evaluation method for the quality control of myrrh pieces and a scientific basis for the development and utilization of myrrh. [ABSTRACT FROM AUTHOR]

Objective functions for sensor selection are investigated in linear time-invariant systems with a large number of sensor candidates. This study compared the performance of sensor sets obtained using three types of D-optimality-based indices as objective functions for sensor selection based on the greedy method. The compared indices are computed based on the snapshot-to-snapshot Fisher information matrix, the observability Gramian and the Kalman filter-based matrix. Both random systems and systems with eigenmodes are considered, indices for selecting the best-performing sensor set for each are identified, as well as computational complexity and corresponding wall clock times. The sensor optimized for each index works best for that index, as expected. We also clarified the trend of the sensor sets selected by the greedy method based on each objective function in terms of the other objective function.

A Human-Prosthetic Interface (HPI) serves to estimate and realise the limb pose intended by the human user, using the information obtained from sensors worn by the user. In recent studies, the HPI maps multi-joint limb poses (i.e. coordinated movement of the body and limbs) to the inputs of multiple sensors. This is in contrast to the conventional methods where each degree of freedom of the powered prosthesis is mapped to the input of one/a pair of sensors. In this approach, it is necessary to systematically select sensors that carry the most information for the intended set of poses, to improve system accuracy and/or minimise the number of sensors, thus the complexity, in the prosthetic system. In this paper, sensor selection process is systematically formulated to maximise the information contained in the input features for a given number of sensors. Most importantly, it accounts for composite features, which are features requiring information from multiple sensors. Such composite features exist and are important in HPIs as we seek to capture coordinated motion involving movements of multiple limb and body segments. A non-convex optimisation problem is formulated which accounts for the constraint introduced by the composite features. A projection matrix is utilised as the optimisation variable to select intended features for evaluation. The problem is solved by the proposed Sensor Selection with Composite Features (SS-CF) algorithm which adapts convex-relaxation techniques. The SS-CF is benchmarked against HPI with expert-selected sensors in the literature and against a greedy heuristic method. The outcome demonstrated the efficacy of the SS-CF algorithm.

Radio emitter localization based on Received Signal Strength (RSS) is promising in large-scale Internet of Things (IoT) and wireless sensor networks (WSNs) for its low hardware and computation costs. To improve its local-ization accuracy and reduce the system energy consump-tion, we propose an improved RSS localization algorithm based on the joint sensor selection and semidefinite pro-gramming (SDP). An initial position estimate is first ob-tained using RSSs available at a random set of sensors. A refined sensor set is then selected to complete the sec-ond estimation by analyzing the geometric structure of sensing network. Performance of the method is evaluated in terms of localization accuracy and execution time, and compared with existing methods. Extensive simulations demonstrate that the proposed approach achieves a locali-zation accuracy of approximately 1.5 m with 8 to 10 sen-sors. The method outperforms the second-order cone pro-gramming (SOCP) and the least squared relative error (LSRE)-based SDP algorithms in terms of both the loca-tion and the transmit power estimation accuracy.

Abstract In this paper, we propose a computationally efficient sensor selection approach for maneuvering target tracking using a sensor network with communication bandwidth constraints, given limited prior information on the target maneuvering models. We formulate the stochastic sensor selection problem as a linear programming problem which consists of two easily implementable steps. First, the Cramér–Rao lower bound corresponding to the sensor subset is derived as the objective function of the proposed sensor selection method based on a partially observable Markov decision process. Second, the target trajectory is modeled by a function of time to enable online target tracking which is free of the conventional, a priori Markov modeling of the target dynamics. We demonstrate the effectiveness of our method through several numerical examples.

Digital Twins are being used more and more frequently and provide information from the Real Twin for different applications. Measurements on the Real Twin are required to obtain information, which in many cases requires the installation of supplementary sensors. For their conception and design, it is particularly important that the measuring principles are selected purposefully and the appropriate sensors are integrated at the goal-oriented measuring positions without impairing the functions and other properties of the Real Twin by the integration of these sensors. In this article, a "Design for Digital Twin" approach is discussed for the systematic procedure and demonstrated using a multi-staged gearbox as a concrete example. The approach focuses on the mechanical and hardware side of the Real Twin. For the systematic conception and design of the Digital Twin solution, an understanding of the stakeholder demands and the expected use cases is necessary. Based on the stakeholder demands and use cases, the relevant product properties can be determined. Using the relevant properties, an iterative process of conception, design, and analysis takes place. The conception is carried out by means of target-oriented cause–effect analyses, taking into account systemic interrelations of the Real Twin components and systematics for the selection of measurement principles. Systemic considerations, combined with an effect graph, allow for the analysis and evaluation of disturbing factors. [ABSTRACT FROM AUTHOR]

The continuous trend towards digitalization of products and processes in mechanical engineering requires an ever-increasing amount of sensor data. In order to acquire this data, sensory functions must be integrated into products and processes. This integration of sensory functions does not only include the actual sensor integration but also the preceding sensor selection as well as other supporting steps. It takes place in the field of tension between mechanical engineering, electrical engineering and information technology. In order to analyse which systematic approaches and tools are available for developers and which topics are covered in current research, a systematic review of literature is conducted in accordance with the PRISMA 2020 statement. It is found that only a few systematic approaches and tools for sensor selection and integration are described in literature. Furthermore, there seems to be no uniform understanding of terms in this context. These results contribute to the identification of research gaps in the field of systematic approaches and tools for the integration of sensory functions. [ABSTRACT FROM AUTHOR]

While new production areas (greenfields) have state-of-the-art technologies for implementing digitalization, existing production areas (brownfields) and devices must first be upgraded with technologies before digitalization can be implemented. The aim of this research work is to use a case study to identify the differences in the implementation of machine learning (ML) projects in brownfields and greenfields. For this purpose, an ML application for the detection of changeover times on milling machines is implemented and analyzed in the brownfield and greenfield scenarios as well as a combined scenario. Particular attention is paid to the selection of sensors and features. It was found that the abundant availability of features in the greenfield scenario poses pitfalls when creating ML projects if the underlying sensors cannot be checked for their suitability. For the changeover detector use case, the best model quality was achieved for the combined scenario, followed by the greenfield scenario.

Unit-to-unit variation among the production chambers is a long-lasting challenge for Fault Detection and Classification (FDC) development in the semiconductor industry. Currently, various methods are applied for knowledge transfer among chambers and generalized FDC model development. However, the existing methods cannot give a quantitative or qualitative measure for cross-chamber data transferability evaluation. This research proposes a novel methodology for data transferability evaluation and important sensor screening, which can serve as a data quality evaluation tool for any FDC model. In this research, firstly, Time Series Alignment Kernel (TSAK) is incorporated into Multidomain Discriminant Analysis (MDA) algorithm to achieve sensor-based domain generalization. Then, domain-invariant features are directly extracted for sensor visualization. After that, Fisher’s criterion ratios of the labeled good wafer samples and defective ones are computed based on the domain-invariant features of each sensor to quantitatively estimate how easy it is to transfer knowledge of each sensor among chambers, i.e., data transferability evaluation. Lastly, the proposed method develops a Recursive Feature Elimination (RFE)-based sensor selection algorithm to qualitatively analyze the importance of each sensor channel and identify the critical sensor subset. In this study, validation of the proposed method is based on two open-source datasets from real production lines. [ABSTRACT FROM AUTHOR]

We study the problem of selecting a subset of sensor nodes in sensor networks in order to maximize the performance of parameter estimation. To achieve a low-complexity sensor selection algorithm, we propose a greedy iterative algorithm that allows us to select one sensor node at a time so as to maximize the log-determinant of the inverse of the estimation error covariance matrix without resort to direct minimization of the estimation error. We apply QR factorization to the observation matrix in the log-determinant to derive an analytic selection rule which enables a fast selection of the next node at each iteration. We conduct the extensive experiments to show that the proposed algorithm offers a competitive performance in terms of estimation performance and complexity as compared with previous sensor selection techniques and provides a practical solution to the selection problem for various network applications. [ABSTRACT FROM AUTHOR]

Radio emitter localization based on Received Signal Strength (RSS) is promising in large-scale Internet of Things (IoT) and wireless sensor networks (WSNs) for its low hardware and computation costs. To improve its localization accuracy and reduce the system energy consumption, we propose an improved RSS localization algorithm based on the joint sensor selection and semidefinite programming (SDP). An initial position estimate is first obtained using RSSs available at a random set of sensors. A refined sensor set is then selected to complete the second estimation by analyzing the geometric structure of sensing network. Performance of the method is evaluated in terms of localization accuracy and execution time, and compared with existing methods. Extensive simulations demonstrate that the proposed approach achieves a localization accuracy of approximately 1.5 m with 8 to 10 sensors. The method outperforms the second-order cone programming (SOCP) and the least squared relative error (LSRE)-based SDP algorithms in terms of both the location and the transmit power estimation accuracy. [ABSTRACT FROM AUTHOR]

Optimization of sensor selection has been studied to monitor complex and large-scale systems with data-driven linear reduced-order modeling. An algorithm for greedy sensor selection is presented under the assumption of correlated noise in the sensor signals. A noise model is given using truncated modes in reduced-order modeling, and sensor positions that are optimal for generalized least squares estimation are selected. The determinant of the covariance matrix of the estimation error is minimized by efficient one-rank computations in both underdetermined and overdetermined problems. The present study also reveals that the objective function with correlated noise is neither submodular nor supermodular. Several numerical experiments are conducted using randomly generated data and real-world data. The results show the effectiveness of the selection algorithm in terms of accuracy in the estimation of the states of large-dimensional measurement data.

Abstract We address the problem of selecting a given number of sensor nodes in wireless sensor networks where noise-corrupted linear measurements are collected at the selected nodes to estimate the unknown parameter. Noting that this problem is combinatorial in nature and selection of sensor nodes from a large number of nodes would require unfeasible computational cost, we propose a greedy sensor selection method that seeks to choose one node at each iteration until the desired number of sensor nodes are selected. We first apply the QR factorization to make the mean squared error (MSE) of estimation a simplified metric which is iteratively minimized. We present a simple criterion which enables selection of the next sensor node minimizing the MSE at iterations. We discuss that a near-optimality of the proposed method is guaranteed by using the approximate supermodularity and also make a complexity analysis for the proposed algorithm in comparison with different greedy selection methods, showing a reasonable complexity of the proposed method. We finally run extensive experiments to investigate the estimation performance of the different selection methods in various situations and demonstrate that the proposed algorithm provides a good estimation accuracy with a competitive complexity when compared with the other novel greedy methods.

Nowadays, with the aging of the bridges and the advancements in technology, load testing has emerged as an effective method to assess existing concrete bridges with missing information, or where analytical methods do not provide an accurate assessment. Two types of load tests are identified: diagnostic load tests and proof load tests. Both rely on field measurements of parameters or structural responses of the bridge during the test. A diagnostic load test measures the response of the bridge so that analytical models can be calibrated and evaluated. In a proof load test, the bridge directly demonstrates that it can carry a certain load. Since large loads are applied, the bridge needs to be carefully monitored. In this case, monitoring the measurements provide a warning to avoid damage. This paper reviews the literature on reported load tests and the measurement techniques used during these tests. It also includes a review of traditional and recently developed sensing technologies. Finally, the measurement requirements for diagnostic and proof load tests are given as well as a flow chart to guide engineers in the selection process of appropriate monitoring and measurement techniques during load tests. This paper can serve engineers during the preparation of a load test. [ABSTRACT FROM AUTHOR]

Estimation of tool wear in precision machining is vital in the traditional subtractive machining industry to reduce processing cost, improve manufacturing efficiency and product quality. In this vein, fusion of time and frequency‐domain features of commonly sensed signals can provide an early indication of tool wear and improve its prediction accuracy for prognostics and health management. This paper presents a data‐driven methodology and a complete tool chain for the inference of precision machining tool wear from fused machine measurements, such as cutting force, power, audio and vibration signals, and quantify the usefulness of each measurement. Indicators of tool wear are extracted from time‐domain signal statistics, frequency‐domain analysis, and time‐frequency domain analysis. Correlation coefficients between the extracted features (indicators) and the tool wear are used to select the most informative features. Principal Component Analysis and Partial Least‐Squares are used to reduce the dimensionality of the feature space. Regression models, including linear regression, support vector regression, Decision tree regression, neural network regression and Gaussian process regression, are used to predict the tool wear using data from a Haas milling machine performing spiral boss face milling. The performance of the regression models based on subsets of sensors validates the preliminary estimates about the saliency of the sensors. The experimental results show that the proposed methods can predict the machine tool wear precisely, with readily available sensor measurements. Neural network and Gaussian process regression were able to achieve good estimates of tool wear at different machine operating conditions. The most informative signal in predicting tool wear was shown to be the vibration signal. Time‐frequency domain features were the most informative features among the combination of features of three domains. In addition, using partial least squares components extracted from the original features of signals led to higher prediction accuracy. [ABSTRACT FROM AUTHOR]

To improve the robustness and reliability of multi-sensor navigation and reduce the uncertainties and complexity of sensor management in challenging environment, a resilient interactive sensor-independent-update (ISIU) method is proposed. Inspired by the interactive cooperation theory, the contributions can be divided into two aspects. Firstly the priority of trust of navigation sensors is introduced into the information fusion in the form of transition probability matrix defined by Markov chain. Secondly every observable sensor is integrated with the propagated system in an elemental filter with sensor-independent-update structure. The multi-sensor integration is implemented in state estimation domain enhanced by interactive information fusion rather than in measurement domain implemented in traditional filter method. The overall estimation is determined by the weighted sum of average from every filter estimate. This weight of every model is dynamic updated by the prior transition information and posterior model likelihood. The same independent structure is also applied to adopt new available sensor to realize plug-and-play navigation. The kinematic vehicle experiment in sub-urban and urban canyon environment verified the superiority of the proposed method. The ISIU method shows better accuracy and reliability compared to classical Kalman filters. The introduction of priority of sensors and decoupled measurement update process make it robust and insensitive to sensor measurement noise and outliers. The interactive sensor-independent-update structure has the natural function of fault detection and exclusion without additional operations. The effect of dynamic sensor selection is achieved in this processing. The proposed ISIU method is pretty suitable for resilient navigation in challenging environments. [ABSTRACT FROM AUTHOR]

In this paper, we propose a computationally efficient sensor selection approach for maneuvering target tracking using a sensor network with communication bandwidth constraints, given limited prior information on the target maneuvering models. We formulate the stochastic sensor selection problem as a linear programming problem which consists of two easily implementable steps. First, the Cramér–Rao lower bound corresponding to the sensor subset is derived as the objective function of the proposed sensor selection method based on a partially observable Markov decision process. Second, the target trajectory is modeled by a function of time to enable online target tracking which is free of the conventional, a priori Markov modeling of the target dynamics. We demonstrate the effectiveness of our method through several numerical examples. [ABSTRACT FROM AUTHOR]

The measurement of respiratory volume based on upper body movements by means of a smart shirt is increasingly requested in medical applications. This research used upper body surface motions obtained by a motion capture system, and two regression methods to determine the optimal selection and placement of sensors on a smart shirt to recover respiratory parameters from benchmark spirometry values. The results of the two regression methods (Ridge regression and the least absolute shrinkage and selection operator (Lasso)) were compared. This work shows that the Lasso method offers advantages compared to the Ridge regression, as it provides sparse solutions and is more robust to outliers. However, both methods can be used in this application since they lead to a similar sensor subset with lower computational demand (from exponential effort for full exhaustive search down to the order of O (n2)). A smart shirt for respiratory volume estimation could replace spirometry in some cases and would allow for a more convenient measurement of respiratory parameters in home care or hospital settings.

The quality of myrrh decoction pieces can be influenced by factors such as origin, source, and processing methods. The quality of myrrh in the market varies greatly, and adulteration is a serious issue, highlighting the urgent need for improved quality control measures. This study explores the integration of GC–MS analysis and sensor selection in electronic nose technology for the improved classification of myrrh decoction pieces. GC–MS analysis revealed the presence of 130 volatile compounds in the six myrrh samples, primarily composed of alkene compounds, and each sample exhibited variations in composition. An electronic nose system was designed using a sensor array consisting of six sensors selected from twelve sensors capable of detecting volatile compounds consistent with myrrh composition, including WO3 quantum dots, Fe2O3 hollow nanorods, ZnFe2O4 nanorods, SnO2 nanowires, and two commercially available sensors. The sensors exhibited distinct response patterns to the myrrh samples, indicating their suitability for myrrh analysis. Various sensor parameters, including response, response and recovery time, integral area, and slope, were computed to characterize the sensors’ performance. These parameters provided valuable insight into the sensor–gas interactions and the unique chemical profiles of the myrrh samples. The LDA model demonstrated high accuracy in differentiating between the myrrh types, utilizing the discriminative features captured by the sensor array, with a classification accuracy of 90% on the testing set. This research provides a comprehensive evaluation method for the quality control of myrrh pieces and a scientific basis for the development and utilization of myrrh.

The choice and placement of sensors and actuators is an essential factor determining the performance that can be realized using feedback control. This determination is especially important, but difficult, in the context of controlling transitional flows. The highly non-normal nature of the linearized Navier–Stokes equations makes the flow sensitive to small perturbations, with potentially drastic performance consequences on closed-loop flow control. Full-information controllers, such as the linear quadratic regulator (LQR), have demonstrated some success in reducing transient energy growth and suppressing transition; however, sensor-based output feedback controllers with comparable performance have been difficult to realize. In this study, we propose two methods for sensor selection that enable sensor-based output feedback controllers to recover full-information control performance: one based on a sparse controller synthesis approach, and one based on a balanced truncation procedure for model reduction. Both approaches are investigated within linear and nonlinear simulations of a sub-critical channel flow with blowing and suction actuation at the walls. We find that sensor configurations identified by both approaches allow sensor-based static output feedback LQR controllers to recover full-information LQR control performance, both in reducing transient energy growth and suppressing transition. Further, our results indicate that both the sensor selection methods and the resulting controllers exhibit robustness to Reynolds number variations. [ABSTRACT FROM AUTHOR]

In multi-sensor systems (MSSs), sensor selection is a critical technique for obtaining high-quality sensing data. However, when the number of sensors to be selected is unknown in advance, sensor selection is essentially non-deterministic polynomial-hard (NP-hard), and finding the optimal solution is computationally unacceptable. To alleviate these issues, we propose a novel sensor selection approach based on evolutionary computational intelligence for tracking multiple targets in the MSSs. The sensor selection problem is formulated in a partially observed Markov decision process framework by modeling multi-target states as labeled multi-Bernoulli random finite sets. Two conflicting task-driven objectives are considered: minimization of the uncertainty in posterior cardinality estimates and minimization of the number of selected sensors. By modeling sensor selection as a multi-objective optimization problem, we develop a binary constrained evolutionary multi-objective algorithm based on non-dominating sorting and dynamically select a subset of sensors at each time step. Numerical studies are used to evaluate the performance of the proposed approach, where the MSS tracks multiple moving targets with nonlinear/linear dynamic models and nonlinear measurements. The results show that our method not only significantly reduces the number of selected sensors but also provides superior tracking accuracy compared to generic sensor selection methods. [ABSTRACT FROM AUTHOR]

Maneuvering target tracking in wireless sensor networks (WSNs) has been a prominent research topic over the past twenty years. In many situations, WSNs are vulnerable to natural or man-made interference, resulting in uncertainty problems. In this paper, we propose an information-quality-based sensor selection method with estimation feedback for maneuvering target tracking in uncertain WSNs. First, based on the Generalized Pseudo-Bayesian estimator of first order (GPB1) and the unscented information filter (UIF), a GPB1-UIF algorithm with estimation feedback is presented. Inspired by the theory of information quality, an information-quality metric framework is proposed for sensor selection in uncertain WSNs. And the information-theoretic selection metrics are redesigned based on consistency measuring operator. Moreover, without knowing the number of selected sensors a priori, a multi-objective optimization problem is considered to determine the final selection strategy. Simulation results demonstrate that the proposed approach outperform the previous sensor selection methods. [ABSTRACT FROM AUTHOR]

In this article the problem of data‐driven structural damage detection is considered exploiting historical data collected from a structure. First, a novel technique based on Kalman filtering and on a combination of regression trees theory from machine learning and auto‐regressive system identification from control theory is derived to build switching models that can be used to detect structural damages. A technique is also proposed leveraging principal component analysis together with the poly‐exponential approach to create nonlinear models to be used for structural damage detection. Finally, a novel sensors selection algorithm based on the notions of entropy and information gain from information theory is developed to reduce the number of sensors without affecting or even improving, as it happens in our experimental setup, the model accuracy. The presented techniques are validated on three independent experimental datasets, showing that the proposed algorithms outperform previous and classical approaches, improving the prediction accuracy and the damage detection sensitivity while reducing the number of sensors. [ABSTRACT FROM AUTHOR]

Obtaining appropriate temperature sensor locations is crucial for data-driven thermal error modeling. The pseudo-correlation and variable ranking will cause inappropriate sensor selection results. In this paper, a three-step sensor selection strategy based on the detrended cross-correlation coefficient is proposed to obtain a stable and robust set of thermal key points. Combined with sensor reduction and classification, 15 sensors are reduced to 9 and classified into 3 groups. Finally, three sensors are selected as thermal key points. The sensor selection results are applied to a support vector machine model for a CNC grinding machine. The modeling results of 49 predictions based on 7 speed spectrums show that the root mean square error and maximum error are less than 2.32 μm and 3.73 μm, respectively. Compared with two traditional methods, the proposed method has higher accuracy and stronger robustness, which is effective for sensor selection of thermal error modeling. [ABSTRACT FROM AUTHOR]

Wireless acoustic sensor network (WASN) has shown great potential for speech enhancement compared with traditional microphone arrays. As dealing with all sensor measurements demands a powerful centralized processor, a distributed framework for speech enhancement is developed in this work. We propose to suppress noises by jointly exploiting the temporal and spatial information of audio signals, based on the information weighted consensus (IWC) filtering. In order to reduce the computational cost as well as the transmission power, we activate only an informative subnetwork by maximizing the energy efficiency in the WASN, where the subnetwork connectivity is guaranteed by incorporating more constraints. To select sensors rapidly, we propose a near-optimal greedy method, which removes one sensor at each iteration until the subnetwork size equals a preset value. The proposed method can obtain the high-quality target signal in a distributed and energy-efficient way. Simulation and real-world experiment results confirm its validity. • A distributed frame-work for speech enhancement via temporal and spatial data fusion. • Sensor selection strategy to reduce power consumption in system. • A near-optimal greedy searching method for sensor selection. [ABSTRACT FROM AUTHOR]

We study the problem of sensor selection for distributed systems, where a large number of sensors are located spatially in many different locations. Specifically, we consider both perfect and packet-dropping communication channels. While the original problem is NP-hard, by adopting a sparse design, we can solve the problem via convex optimization and reduce the computation cost significantly. Our method not only handles correlated measurement noise but also can be easily extended to actuator selection or sensor-and-actuator (SaA) selection problems. Simulation shows that our sparsity-based approach performs similarly to the brute force optimal strategy while consuming significantly less computation time. Additionally, our method is shown to outperform the state-of-art method notably. [ABSTRACT FROM AUTHOR]

In the past few years, introducing ontology to describe the concepts and relationships between different entities in semantic sensor network enhances the interoperability between entities. Existing works mostly based on SPARQL retrieval ignore the user's specific requirements of sensor attributes. Therefore, the recommendation results cannot satisfy the user's needs. In this article, we propose a graph-based sensor recommendation model. The model mainly includes two parts: (1) Filtering nodes in data graph. In addition to using the traditional graph matching algorithm, we propose a threshold pruning algorithm to narrow the matching scope and improve the matching efficiency. (2) Recommending top- k sensors. We use the improved fast non-dominated sorting algorithm to obtain the local optimal solutions of sensor data set, and we apply the simple additive weight algorithm to characterize and sort local optional solutions. Finally, we recommend the top- k sensors to the user. By comparison, the graph-based sensor recommendation algorithm meets user's needs more than other algorithms, and experiments show that our model outperforms several baselines in terms of both response time and precision. [ABSTRACT FROM AUTHOR]