Optimal Sensor Location Research Articles

Twenty-Five Years of Sensor Array and Multichannel Signal Processing: A review of progress to date and potential research directions

In this article, a general introduction to the area of sensor array and multichannel signal processing is provided, including associated activities of the IEEE Signal Processing Society (SPS) Sensor Array and Multichannel (SAM) Technical Committee (TC). The main technological advances in five SAM subareas made in the past 25 years are then presented in detail, including beamforming, direction-of-arrival (DOA) estimation, sensor location optimization, target/source localization based on sensor arrays, and multiple-input multiple-output (MIMO) arrays. Six recent developments are also provided at the end to indicate possible promising directions for future SAM research, which are graph signal processing (GSP) for sensor networks; tensor-based array signal processing, quaternion-valued array signal processing, 1-bit and noncoherent sensor array signal processing, machine learning and artificial intelligence (AI) for sensor arrays; and array signal processing for next-generation communication systems.

Optimal Deployment of Sensors along Freeway Corridors for Traffic Accident Detection

Sensor deployment is important to the detection performance of traffic accidents on freeways. However, the temporally and spatially varying effects of accident risks are seldom considered in sensor deployment, which can induce a decrease in accident detection accuracy and detection timeliness. To address this problem, this paper proposes an optimal deployment method of sensors according to the temporal-spatial effect of accidents on traffic flow. Considering the uncertainty of traffic accidents, the temporal-spatial distribution of accident risk is estimated according to historical accident data. Then the kinematic wave model and Van Aerder model are introduced to analyze the temporal-spatial effect of accidents on traffic flow. To obtain the optimal locations of sensors, the sensor deployment problem is converted into a coverage problem on accident risk in time and space, and an optimal deployment model of sensors is built by considering the temporal-spatial effect of accident risk, the coverage rate on accident risk, the detection timeliness of the accident, and deployment cost. The particle swarm optimization algorithm is introduced to solve it. A case study is carried out to validate the proposed method using consistency, sensitivity, reliability, and comparison experiments. The results show that the proposed method is effective and reliable under different traffic volumes, and it outperforms the uniform spacing method in traffic accident detection.

Optimal sensor location for adaptive control system in tropical smart greenhouse

The uniform control in greenhouse with technology capabilities is seemingly still difficult to be obtained due to the accuracy uncertainty of the data in certain locations. Considering this case, it is highly necessary to choose the right location for the sensor installation. This study aimed to determine sensor placement locations to support precision control activities, using an arch-type smart greenhouse measuring 8×24 m<sup>2</sup> as the research location. Air temperature was calculated from 12 locations and analyzed for all possible combinations to designate the best sensor point according to the number of sensors. The analysis was conducted using the error-based method to ascertain the number and location of sensors that represent the smart greenhouse. The best location and number of sensors are identified with performance value under 10% and recommended for developing an adaptive control system.

Study on choosing mobile sensor location to improve the prediction accuracy of indoor temperature distribution

Nonuniform indoor temperature distributions have been widely introduced due to the increasing concerns brought about by the trade-off between building energy consumption and thermal comfort. Related to this, obtaining real-time indoor temperature field is crucial for control and regulation. Previously, a rapid temperature prediction algorithm based on the contribution ratio of indoor climate (CRI) and sensor information was proposed. Compared with fixed sensors, mobile sensors have better moving flexibilities and higher prediction accuracies. Given that considerable data can be obtained from one sensor, the chosen mobile sensor location has a strong correlation with the prediction accuracy. However, only a few studies have investigated the impact of the sensor location. Thus, this study aimed to determine the optimal mobile sensor location for improving prediction accuracy using the proposed algorithm. Two differently scaled rooms were numerically built, and 1.2 m high points were set as the selected locations. After experimental validation of the simulation results, three factors (moving path, supply/return air, and air velocity) were investigated to determine their effects on prediction accuracy. The results showed that the air velocity was the only significantly impactful factor. Furthermore, two principles of choosing location were proposed: (1) when the percentage of dominant velocity is high, all points should have one identical dominant direction, and (2) when the percentage of dominant velocity is low, points should have a similar actual direction. These principles were verified in both models with a prediction accuracy greater than 85%.

NSGA-II parameterization for the optimal pressure sensor location in water distribution networks

ABSTRACT The optimal location of pressure sensors is typicallysolved using heuristic algorithms. Non-dominated Sorting Genetic Algorithm II (NSGA-II) is one of the most used algorithms in the water industry, requiring a preliminary parameter tuning process. The lack of guidelines on how to tune model parameters generally limits the use of these algorithms by researchers or practitioners and, as such, fails to be used in real-life problems. The current paper explores different NSGA-II parameterizations for the optimal location of pressure sensors by using a multi-objective optimization methodology applied to a real distribution network. Results show that (i) both the uniform and simulated binary crossover operators (depending on the internal parameters) produce the best results, being the former recommended since it does not require further parameter tuning; (ii) polynomial mutation with lower probability value should be chosen; and (iii) the distribution indices of polynomial mutation have a minor effect on NSGA-II performance.

Optimal number and locations of automatic vehicle identification sensors considering link travel time estimation

AbstractThe problem of optimally locating Automatic Vehicle Identification (AVI) sensors on a traffic network for travel time estimation has been a topic of growing interests in recent years. Even though great progresses have been made on AVI sensor deployment for path‐level travel time estimation, very few contributions exist in the literatures that address the AVI sensor deployment for link‐level travel time estimation on an urban network. In this paper, considering the link travel time estimation, two deployment sub‐problems are addressed: (1) where to deploy a certain number of AVI sensors? (2) What is a cost‐effective number of AVI sensors to deploy? To address the first problem, a potential game of sensors is developed to find their optimal locations which maximize the objective function that consists of estimation coverage and estimation accuracy. Then, based on the optimal locations, an incremental search method is proposed to find the optimal number of sensors considering the cost. The case in Shanghai shows the proposed game‐theoretic method is superior to other two heuristic algorithms. Moreover, compared to the real‐world sensor locations, the optimally redeployed locations improve both the estimation coverage and estimation accuracy. The case in Xuancheng City validates the proposed incremental search uses less computations to find an optimal number that close to the global optimal number solved from the brute‐force search.

Gaussian mixture models for the optimal sparse sampling of offshore wind resource

Abstract. Wind resource assessment is a crucial step for the development of offshore wind energy. It relies on the installation of measurement devices, whose placement is an open challenge for developers. Indeed, the optimal sensor placement for field reconstruction is an open challenge in the field of sparse sampling. As for the application to offshore wind field reconstruction, no similar study was found, and standard strategies are based on semi-empirical choices. In this paper, a sparse sampling method using a Gaussian mixture model on numerical weather prediction data is developed for offshore wind reconstruction. It is applied to France's main offshore wind energy development areas: Normandy, southern Brittany and the Mediterranean Sea. The study is based on 3 years of Météo-France AROME's data, available through the MeteoNet data set. Using a Gaussian mixture model for data clustering, it leads to optimal sensor locations with regards to wind field reconstruction error. The proposed workflow is described and compared to state-of-the-art methods for sparse sampling. It constitutes a robust yet simple method for the definition of optimal sensor siting for offshore wind field reconstruction. The described method applied to the study area output sensor arrays of respectively seven, four and four sensors for Normandy, southern Brittany and the Mediterranean Sea. Those sensor arrays perform approximately 20 % better than the median Monte Carlo case and more than 30 % better than state-of-the-art methods with regards to wind field reconstruction error.

The Development of a Data-Based Leakage Pinpoint Detection Technique for Water Distribution Systems

Leakage is one of the abnormal conditions in water distribution systems (WDSs). Real-time monitoring can be used to prevent or recover quickly from leakage. However, this is not enough: for improved leakage detection, a status diagnosis of the WDS must be performed together with this real-time monitoring, and numerous studies have been conducted on this. Furthermore, the existing proposed methodology only provides optimal sensor location and fast recognition. This paper proposes a technique that can quantitatively evaluate the volume of leakage along with leakage detection using deep learning technology. The hydraulic data (e.g., pressure, velocity, and flow) from the calibrated hydraulic model were used as training data and deep learning techniques were applied to conduct a simultaneous detection of leakage volume and location. We examined various scenarios regarding leakage volume and location for the data configuration of a simulated leakage accident. Furthermore, for optimal leakage detection performance, the detection performance according to the size of the network, the meter types of meters, the number of meters, and the locations of the meters were analyzed. This study is expected to be helpful in various aspects such as recovery and restoration decision making after leakage, because it simultaneously identifies the amount and location of the leakage.

Effective sensor location for detection of change in structural dynamic response

This paper investigates the sensitivity of structural system response to the sensor location by investigating consequences of small changes in the location to the structural system response. The paper discusses how maximum observability (based on mode shape and the participation of that mode in the input provided) drives optimal location. The structural responses were investigated in terms of the g-rms response for various low-frequency inputs (pure sinusoids and real-life inputs such as an earthquake and trains). Results were then analyzed in the context of Modal Contributions Factors (MCF) and changes to the Force-to-response Transfer Functions (TRFs). A modal-matching process is first presented using a MatlabTM-based Finite Element Method (FEM) model of a cantilever beam and instrumentation to determine the location of a small mass based on three different criteria. Subsequently, the structural response is investigated using experiments and the FEM model. The accelerometer of small mass (at 1/3 height) was moved up or down to obtain changes in the structural response (TRF) to various realistic low-frequency inputs. Modal Contribution Factor (MCF) and derivative (slope) of the associated mode-shapes were correlated to the observed changes in TRFs. Results show how optimal sensor locations for detecting change in structural response can be based on the MCFs and the associated mode-shapes.

Selecting the best location of water quality sensors in water distribution networks by considering the importance of nodes and contaminations using NSGA-III (case study: Zahedan water distribution network, Iran).

One of the most effective ways to minimize polluted water consumption is to arrange quality sensors properly in the water distribution networks (WDNs). In this study, the NSGA-III algorithm is developed to improve the optimal locations of sensors by balancing four conflicting objectives: (1) detection likelihood, (2) expected detection time, (3) detection redundancy, and (4) the affected nodes before detection. The research procedure proposed the dynamic variations of chlorine between defined upper and lower bounds, which were determined utilizing the Monte Carlo simulation model. For selecting a contamination matrix with the same characteristics and effects of all possible events, a heuristic method was applied. The coefficients of importance are introduced in this study for the assessment of contamination events and network nodes. The Pareto fronts for each of the two sets of conflicting objectives were computed for benchmark and real water distribution networks using the proposed simulation-optimization approach. Results indicated that sensors should be installed downstream of the network to maximize sensor detection likelihood; however, this increases detection time. For the benchmark network, maximum and minimum detection likelihoods were calculated as 92.8% and 61.1%, respectively, which corresponded to the worst detection time of 11.58min and the best detection time of 5.06min. So, the position of sensors regarding the two objective functions conflicts with each other. Also, the sensitivity analysis related to the number of sensors illustrated that the Pareto fronts became a more efficient tool when the number of sensors increased. The best pollution detection likelihood in the real water network increased by 18.93% and 24.66% by incrementing the number of sensors from 5 to 10 and 5 to 15, respectively. Moreover, adding more than 10 sensors to the benchmark network and more than 15 to the real system will provide little additional detection likelihood.

Optimal sensor placement and estimator-based temperature control for a deep drawing process

Deep drawing is one of the most important forming processes for the forming of flat sheet blanks, where the formation of wrinkles and the appearance of cracks can be a problem, especially in areas of high geometric complexity. The local increase of the temperature in these critical areas can help to improve the formability of the material and thus reduce defects. The present paper aims at a targeted temperature control of the die of a deep-drawing mold. For this sensors are placed systematically to develop an estimator for the spatial–temporal temperature evolution to subsequently realize tracking control using the embedded actuation devices. A continuum representation of the temperature distribution in the die is derived and transferred to a high order finite element (FE) approximation to take the complex-shaped geometry of the tool into account. Parameter identification is performed based on measurement data to improve the accuracy of the FE approximation and model order reduction (MOR) techniques are applied to determine a sufficiently low order system representation. A mixed-integer optimization problem is formulated and solved making use of different formulations of the observability Gramian to determine the optimal sensor locations and a Kalman filter is designed as an estimator based on a reduced order model. Moreover, a linear-quadratic regulator with integral part combined with the Kalman filter is developed to react efficiently towards disturbances. Finally this theoretical framework is tested in a real experiment.

Optimal sensor location and direction to accurately classify people with early-stage multiple sclerosis using gait stability

BackgroundThe local divergence exponent (LDE) has been used to assess gait stability in people with multiple sclerosis (pwMS). Although previous studies have consistently found that stability is lower in pwMS, inconsistent methodologies have been used to assess patients with a broad range of disability levels. QuestionsWhat sensor location and movement direction(s) are better able to classify pwMS at early stages of the disease? Methods49 pwMS with EDSS ≤ 2.5 and 24 healthy controls walked overground for 5 min while 3D acceleration data was obtained from sensors placed at the sternum (STR) and lumbar (LUM) areas. Unidirectional (vertical [VT], mediolateral [ML], and anteroposterior [AP]) and 3-dimensional (3D) LDEs were calculated using STR and LUM data over 150 strides. ROC analyses were performed to assess classification models using single and combined LDEs, with and without velocity per lap (VELLAP) as a covariate. ResultsFour models performed equally well by using combinations of VELLAP, LUM3D, LUMVT, LUMML, LUMAP, STRML, and STRAP (AUC = 0.879). The best model using single sensor LDEs included VELLAP, STR3D, STRML, and STRAP (AUC = 0.878), whereas using VELLAP + STRVT (AUC = 0.869) or VELLAP + STR3D (AUC=0.858) performed best using a single LDE. SignificanceThe LDE offers an alternative to currently insensitive tests of gait impairment in pwMS at early stages, when deterioration is not clinically evident. For clinical purposes, the implementation of this measure can be simplified using a single sensor at the sternum and a single LDE measure, but speed should be considered. Longitudinal studies to determine the predictive power and responsiveness of the LDE to MS progression are still needed.

Concrete Autoencoder for the Reconstruction of Sea Temperature Field from Sparse Measurements

This paper presents a new method for finding the optimal positions for sensors used to reconstruct geophysical fields from sparse measurements. The method is composed of two stages. In the first stage, we estimate the spatial variability of the physical field by approximating its information entropy using the Conditional Pixel CNN network. In the second stage, the entropy is used to initialize the distribution of optimal sensor locations, which is then optimized using the Concrete Autoencoder architecture with the straight-through gradient estimator for the binary mask and with adversarial loss. This allows us to simultaneously minimize the number of sensors and maximize reconstruction accuracy. We apply our method to the global ocean under-surface temperature field and demonstrate its effectiveness on fields with up to a million grid cells. Additionally, we find that the information entropy field has a clear physical interpretation related to the mixing between cold and warm currents.

Fusion-Based Body-Worn IoT Sensor Platform for Gesture Recognition of Autism Spectrum Disorder Children.

The last decade's developments in sensor technologies and artificial intelligence applications have received extensive attention for daily life activity recognition. Autism spectrum disorder (ASD) in children is a neurological development disorder that causes significant impairments in social interaction, communication, and sensory action deficiency. Children with ASD have deficits in memory, emotion, cognition, and social skills. ASD affects children's communication skills and speaking abilities. ASD children have restricted interests and repetitive behavior. They can communicate in sign language but have difficulties communicating with others as not everyone knows sign language. This paper proposes a body-worn multi-sensor-based Internet of Things (IoT) platform using machine learning to recognize the complex sign language of speech-impaired children. Optimal sensor location is essential in extracting the features, as variations in placement result in an interpretation of recognition accuracy. We acquire the time-series data of sensors, extract various time-domain and frequency-domain features, and evaluate different classifiers for recognizing ASD children's gestures. We compare in terms of accuracy the decision tree (DT), random forest, artificial neural network (ANN), and k-nearest neighbour (KNN) classifiers to recognize ASD children's gestures, and the results showed more than 96% recognition accuracy.

Computational Tools for Supporting the Operation and Management of Water Distribution Systems towards Digital Transformation

This paper presents a set of computational tools specially developed for supporting the operation and management of water distribution systems towards digital transformation of water services. These tools were developed in the scope of two R&D projects carried out in Portugal, DECIdE and WISDom, during 2018–2022. The DECIdE project focused on the development of tools for importing cadastral and operational data, as well as on the three operational tools for supporting the performance assessment: the first allows the calculation of different key performance indicators, both at a global and sectorial level, which is an annual requirement of the water regulator, and the other two allow the calculation of the water and the energy balances and a set of complementary indices. The WISDom project aimed at the implementation of applications that directly address specific water utility needs, namely, the flow rate data processing, the optimal location of pressure sensors, the identification of critical areas in the distribution network for pipe burst location, and the prioritization of pipes for rehabilitation. Implemented tools are useful to support water utilities in the daily operation and management of their systems, being a step forward towards digital transformation of the water sector.

Optimal Transducer Placement for Deep Learning-Based Non-Destructive Evaluation.

In this study, the Convolution Neural Network (CNN) algorithm is applied for non-destructive evaluation of aluminum panels. A method of classifying the locations of defects is proposed by exciting an aluminum panel to generate ultrasonic Lamb waves, measuring data with a sensor array, and then deep learning the characteristics of 2D imaged, reflected waves from defects. For the purpose of a better performance, the optimal excitation location and sensor locations are investigated. To ensure the robustness of the training model and extract the feature effectively, experimental data are collected by slightly changing the excitation frequency and shifting the location of the defect. The high classification accuracy for each defect location can be achieved. It is found that the proposed algorithm is also successfully applied even when a bar is attached to the panel.

Impact of sensor placement in soil water estimation: a real-case study

One important problem in implementing a closed-loop irrigation system is the determination of the optimal locations to install the sensors, both vertically and horizontally, such that improved pressure head estimation can be obtained. In this work, we find the best locations of the sensors in terms of degree of observability in an actual field and investigate whether the optimal sensor placement can significantly improve the pressure head estimation performance in actual applications. We investigate the impact of sensor placement in pressure head estimation for an actual agricultural field in Lethbridge, Alberta, Canada. In an experiment on the studied field, 42 watermark sensors were installed at different depths to collect the soil water tension measurements. A three-dimensional agro-hydrological model with heterogeneous soil parameters of the studied field is set up. The modal degree of observability is applied to the three-dimensional system to determine the optimal sensor locations. The extended Kalman filter (EKF) is chosen as the data assimilation tool to estimate the pressure head of the studied field. Pressure head estimation results for different scenarios are obtained and analyzed to investigate the effects of sensor placement on the performance of pressure head estimation in the actual applications. Estimation results indicate that optimally placed sensors reduce the average root mean square error by about 30% compared to the case the sensor positions are selected randomly.

Subsurface Radar Imaging by Optimizing Sensor Locations in Spatio-Spectral Domains

Subsurface Radar Imaging by Optimizing Sensor Locations in Spatio-Spectral Domains

A Robust Framework to Design Optimal Sensor Locations for TOA or RSS Source Localization Techniques

We focus on the problem of finding optimal sensor locations for source localization techniques based on either time of arrival (TOA) or received signal strength (RSS) measurements. Without any specific assumption on the actual source position, we propose a design framework that directly establishes the optimal sensor locations by minimizing suitable Cramer-Rao bound (CRB)-related cost functions. Specifically, we just consider a region where the source is likely to be present and focus on two cost functions: the former relies on the trace/determinant of CRB averaged over a grid of points resulting from the sampling of the surveillance area (shortly average CRB), whereas, the latter leverages the maximum trace/determinant of CRB over the mentioned grid (shortly worst-case CRB). Moreover, each sensor position is constrained to lie within a pre-specified set (deployment constraint set). Hence, we propose an optimization framework based on block majorization-minimization to deal with both the design paradigms. The iterative steps of the technique monotonically decrease the corresponding figure of merit and eventually converge to a stationary point of the design problem. The proposed methodology can also handle the case of nonuniform noise variances. Finally, through various numerical simulations, we show the effectiveness of the developed resource allocation policies.

A Turbidity Module to Measure Spray Mixture Concentration for Premixing In-Line Injection System

Highlights A turbidity sensor module was investigated to monitor concentrations of pesticide mixtures in real time. A flow-through measurement platform was built to calibrate the turbidity sensor and determine its accuracy. Optimal location of the turbidity sensor was determined for monitoring in-line mixture concentrations. Spray mixture uniformity reached acceptable level for the premixing in-line injection system. Abstract. Monitoring mixture concentrations for precision pesticide spray systems in real time can assure the desired amount of chemicals distributed uniformly to target areas. An in-line turbidity sensor module was investigated to monitor concentrations of spray mixtures produced with a premixing in-line injection system developed for precision variable-rate orchard sprayers. The turbidity sensor was calibrated with simulated pesticides at concentrations ranging from 0% to 30.0%. A cubic polynomial regression model was established for the relationship between sensor output voltages and mixture concentrations. Sensors were mounted at three in-line locations to detect the mixture uniformity differences in the premixing in-line injection system. The module was found to have adequate precision and accuracy to measure concentrations of spray mixtures with simulated pesticides. Relative errors of the sensor were less than 4.70% and the sensor accuracy did not vary with mixture flow rates. Mounting the turbidity sensor downstream of the buffer tank in the premixing in-line injection system would be the optimal location to monitor spray mixture uniformity for variable-rate spray applications. At this location, the relative errors of measured mixture concentrations were between 0.12% and 3.70% which agreed with previous manual measurements. Therefore, there would be a great potential to integrate the in-line turbidity sensor into the variable-rate and even conventional constant-rate sprayers to achieve uniform spray applications in the target field. Keywords: Agitation, Mixture uniformity, Pesticide concentration, Sensor calibration, Variable-rate sprayer.