Optimal Sensor Location Research Articles

CFD Analysis to Optimize Air Quality in Aeroponic Tower Garden

Aeroponic tower gardens represent an innovative approach to urban agriculture, utilizing vertical structures to cultivate plants without soil. Research highlights the efficiency of this method in optimizing resource usage, such as water and space, compared to traditional farming. Studies emphasize increased crop yields, faster growth rates, and reduced environmental impact. Additionally, aeroponic systems enable precise nutrient delivery to plants, fostering optimal growth conditions. While the technology shows promise, further investigations are needed to address potential challenges and optimize its application in diverse agricultural settings. The study presents the simulation of air temperature and humidity distribution in a closed chamber of the vertical farm for optimization purposes. The environmental conditions inside the chamber are optimized for plant growth, to maintain the temperature at a range of 18-24 °C temperature and the relative humidity at a range of 75-85 %. The paper also discusses the challenges, limitations, and future recommendations for improving the system. Simulation of the system, on the other hand, is crucial not only for prototype fabrication but also for the optimization of sensor locations. The simulation of the tower garden system was performed using ANSYS-FLUENT to portray how the airflow inside the tower garden's enclosed environment was distributed. The system parameters such as humidity and temperature were set to meet the plants' growth requirements, and the system was treated as the steady state with an adiabatic boundary. Based on the simulation results, the best design recommended is to use two humidity and temperature sensors, one at the top and the other at the core of the prototype. Hence, a complete mapping of temperature and humidity distribution that is critical for plant growth would be occurred.

Analyzing Optimal Wearable Motion Sensor Placement for Accurate Classification of Fall Directions.

Falls represent a significant risk factor, necessitating accurate classification methods. This study aims to identify the optimal placement of wearable sensors-specifically accelerometers, gyroscopes, and magnetometers-for effective fall-direction classification. Although previous research identified optimal sensor locations for distinguishing falls from non-falls, limited attention has been given to the classification of fall direction across different body regions. This study assesses inertial measurement unit (IMU) sensors placed at 12 distinct body locations to determine the most effective positions for capturing fall-related data. The research was conducted in three phases: first, comparing classifiers across all sensor locations to identify the most effective; second, evaluating performance differences between sensors placed on the left and right sides of the body; and third, exploring the efficacy of combining sensors from the upper and lower body regions. Statistical analyses of the results for the most effective classifier model demonstrate that the support vector machine (SVM) is more effective than other classifiers across all sensor locations, with statistically significant differences in performance. At the same time, the comparison between the left and right sensor locations shows no significant performance differences within the same anatomical areas. Regarding optimal sensor placement, the findings indicate that sensors positioned on the pelvis and upper legs in the lower body, as well as on the shoulder and head in the upper body, were the most effective results for accurate fall-direction classification. The study concludes that the optimal sensor configuration for fall-direction classification involves strategically combining sensors placed on the pelvis, upper legs, and lower legs.

Pertinence of finite element method for impulse localization in urban environments

Although urban combat is nothing new, recent conflicts have shed light on Western armies' difficulties when operating in such environments. Thus, new surveillance and hazard detection challenges in complex environments are addressed. A new decision aid tool for acoustic sensor positioning has been developed to improve reconnaissance performance. The software's sensor location optimization kernel is based on a genetic algorithm developed by our partners from FKIE. It resolves successive localization problems using pre-computed Times Of Arrival (TOA) between an impulsive acoustic source and various surveillance microphones. The present study discusses the pertinence of the Time-Domain Finite-Element Method (TD-FEM) to simulate wave propagation within an urban area to obtain TOA and resolve localization problems. The approach is compared to experimental measurements in an urban area, and the pros and cons of the TD-FEM for this configuration are discussed.

Design and test of real-time monitoring system for maize entrainment loss based on piezoelectric signal classification

This study describes a novel entrainment loss monitoring system. The features of the collision signals of different maize materials were extracted in the time and frequency domains, and a kernel loss identification model was constructed using machine learning algorithms. The results show that the random forest model performed optimally, with classification accuracies of 96.83% and 94.85% on the training and test sets, respectively. An entrainment loss monitoring system was developed, and the optimal sensor location was determined. The accuracy of kernel classification was 91.09% and that of cob classification was 86.69% for the kernel/cob mixture. Field validation tests showed that the proposed entrainment loss monitoring system outperformed the traditional monitoring system. The method enables the precise monitoring of entrainment losses during maize harvesting, enabling intelligent maize production.

A temperature-sensitive points selection method for machine tool based on rough set and multi-objective adaptive hybrid evolutionary algorithm

Reasonable deployment of temperature sensors is the key to accurately monitoring the temperature field of machine tools and improving the accuracy of thermal error prediction and compensation models. To determine the optimal deployment location of sensors, this paper proposes a temperature-sensitive points selection method tightly coupled with rough set and multi-objective optimization. Firstly, the importance of each temperature measurement point to the thermal error is calculated based on the rough set, and information entropy is introduced to amplify the importance difference among adjacent measurement points at the same heat source. Then, with the temperature measurement points groups as the variables, the number of temperature measurement points in the group, and the information importance of the group as the objectives, a multi-objective attribute reduction model is established, which transforms the temperature-sensitive points selection problem into a discrete multi-objective optimization problem. Finally, a multi-objective adaptive hybrid evolutionary algorithm is proposed, which designs a population initialization method based on mutual information and interval probability, and dynamic adaptive evolutionary parameters to achieve optimal temperature-sensitive points selection. Experiments on the high-speed dry hobbing machine verify the superiority and effectiveness of the proposed method.

Machine Learning Approach for Spatiotemporal Multivariate Optimization of Environmental Monitoring Sensor Locations

Abstract Long-term environmental monitoring is critical for managing the soil and groundwater at contaminated sites. Recent improvements in state-of-the-art sensor technology, communication networks, and artificial intelligence have created opportunities to modernize this monitoring activity for automated, fast, robust, and predictive monitoring. In such modernization, it is required that sensor locations be optimized to capture the spatiotemporal dynamics of all monitoring variables as well as to make it cost-effective. The legacy monitoring datasets of the target area are important to perform this optimization. In this study, we have developed a machine-learning approach to optimize sensor locations for soil and groundwater monitoring based on ensemble supervised learning and majority voting. For spatial optimization, Gaussian process regression (GPR) is used for spatial interpolation, while the majority voting is applied to accommodate the multivariate temporal dimension. Results show that the algorithms significantly outperform the random selection of the sensor locations for predictive spatiotemporal interpolation. While the method has been applied to a four-dimensional dataset (with two-dimensional space, time, and multiple contaminants), we anticipate that it can be generalizable to higher-dimensional datasets for environmental monitoring sensor location optimization.

Fisher Information–Based Optimal Sensor Locations for Structural Identification of Nonclassically Damped Systems

Fisher Information–Based Optimal Sensor Locations for Structural Identification of Nonclassically Damped Systems

Greedy selection of optimal location of sensors for uncertainty reduction in seismic moment tensor inversion

We address an optimal sensor placement problem through Bayesian experimental design for seismic full waveform inversion for the recovery of the associated moment tensor. The objective is that of optimally choosing the location of the sensors (stations) from which to collect the observed data. The Shannon expected information gain is used as the objective function to search for the optimal network of sensors. A closed form for such objective is available due to the linear structure of the forward problem, as well as the Gaussian modeling of the observational errors and prior distribution. The resulting problem being inherently combinatorial, a greedy algorithm is deployed to sequentially select the sensor locations that form the best network for learning the moment tensor. Numerical results are presented to display the optimal network of sensors and how the uncertainty in the inferred seismic moment tensor contracts. The scenario of full three-dimensional velocity models or unknown earthquake source locations is treated as nuisance uncertainty, contributing to the overall uncertainty without being the focus of the inversion. This is addressed using a consensus approach over a set of realizations of the nuisance parameter. We analyzed the resulting network of stations for the moment tensor inversion under model misspecification, which reflects realistic data-generating processes.

Sensor placement considering the observability of traffic dynamics: On the algebraic and graphical perspectives

In this paper, we present a new sensor location model that aims to maximize observability of link densities in a dynamic traffic network described using a piecewise linear system of ordinary differential equations. We develop an algebraic approach based on the eigenstructure to determine the sensor location for achieving full observability with a minimal number of sensors. Additionally, a graphical approach based on the concept of structural observability is developed. By exploiting the special property of flow conservation in traffic networks, we derive a simple analytical result that can be used to identify observable components in a partially observable system, which is the main contribution of this paper. The graphical and algebraic properties of observability are then integrated into a sensor location optimization model considering a wide range of traffic conditions. Through numerical experiments, we demonstrate the good performance of our sensor deployment strategies in terms of the average observability and estimation errors.

Using the dragonfly algorithm to find the optimal location of quality sensor of Lee and Deininger and Second and Third networks of EPANET software to reduce the contaminated water values

Accessing reliable quantitative and qualitative drinking water is one of the requirements of human society. Therefore, the water distribution network is designed to distribute reliable drinking water to consumers. In general, the entrance of any unintentional and intentional contamination reduces the reliability of this network. Therefore, it is necessary to monitor the water distribution network using quality sensors to minimize the amount of contaminated water. In this research, three benchmarks of the Lee and Denninger network and the second and third network examples of EPANET software are selected as case studies. Here, by defining different scenarios, the optimal locations of quality sensors are determined using the dragonfly algorithm (DA) and the results are compared with the genetic algorithm (GA). This algorithm is a binary (zero-one) algorithm that has not any hyperparameters leading to reliable results. In addition, this algorithm is not used in the fields of quality monitoring of water distribution networks and therefore it is used here. The results showed the efficiency of the DA to solve this problem. In other words, for the Lee and Deninger network, the amount of contaminated water is reduced by 4.98 %, considering one sensor and consumption pattern one, 26.69 %, considering at least two sensors and consumption pattern one, 27.52 %, considering one sensor and consumption pattern two, and 19.76 %, considering least two sensors and consumption pattern two, using the DA. In addition, for the second network example of EPANET, the amount of contaminated water is reduced by 32.09 %, considering the 24-h consumption pattern and one sensor, and 20.8 %, considering the 24-h consumption pattern and at least two sensors. Finally, for the third network example of EPANET, the amount of contaminated water is reduced by 0.122 %, considering the 24 h consumption pattern and one sensor, and 0.345 %, considering the 24-h consumption pattern and at least two sensors.

Optimal placement of light sensor for improving energy efficiency and visual comfort in smart buildings

The energy consumed by artificial lighting accounts for a significant part of the energy consumption in buildings. Thus, energy consumption can be significantly decreased using optimization techniques, such as the optimum placement of the lighting sensors. In this paper, an optimal placement method of the light sensors is presented to minimize the lighting system's equipment costs and energy consumption and also meet visual comfort under the requirements of the European standard EN12464–1 regarding illuminance-based criteria. In the beginning, based on the illumination measurement grid's mathematical model, the potential location of the sensors is determined. As an average of the dimming levels of the LED lights, the objective function has been introduced. Then, the illuminance and uniformity levels are defined as constraints for the optimization problem. The Battle Royale Optimization (BRO) algorithm is used to solve the optimization problem. Ultimately, based on the results of the BRO algorithm and the calculation of the illuminance deviation, the optimal location of the light sensors is determined. The proposed method is tested in an office room. A fuzzy logic controller is developed to regulate the lighting control system's dimming levels to evaluate the proposed method's performance with other approaches. The comparison results have shown that the proposed method is superior as regards the number of sensors and the optimal sensor position, significant savings in energy consumption of up to 30.8%, and satisfactory visual comfort per the requirements of the European standard EN12464–1.

Estimation of the effective range of PZT sensors for damage detection based on full wavefield measurements

Optimal location of sensors for enhanced damage detection capability is one of the key factors which needs to be considered when designing SHM system based on PZT sensors. However the sensitivity of a sensor to damage is not only dependent on its relative location with respect to elastic waves source and damage, but can also depend on structure geometry as well as particular signal features which are used for structure assessment. In the presentation, results of structure evaluation based on guided waves excited by PZT transducers and full wavefield measurements obtained by laser scanning vibrometer will be presented. The measurements were acquired for pristine state of test structure and with introduced damage, therefore it is possible to calculate spatial distribution of various signal characteristics and estimate effective range of PZT sensors for damage detection based on a given damage index. Various parameters affecting the damage detection range will be compared, in particular: type and the extent of damage, type of signal characteristic used, parameters of the excitation signal (e.g. frequency, duration), as well time interval used for damage index calculation. The obtained results may be useful for refining algorithms for sensor placement optimization as well as damage localization techniques, e.g. based on RAPID imaging algorithm.

Optimal sensor placement for ensemble-based data assimilation using gradient-weighted class activation mapping

We introduce an optimal sensor placement method using convolutional neural networks for ensemble-based data assimilation. The proposed method utilizes the gradient-weighted class activation mapping of the convolutional neural networks to identify important regions for assimilation processes. It is achieved by using the initial ensemble of samples for data assimilation as training data to construct a convolutional neural network-based surrogate model. In doing so, the method can estimate optimal sensor locations in an a priori manner, allowing for sensor placement before conducting data assimilation processing. Moreover, the gradient-weighted class activation mapping is used to alleviate the effect of error accumulation during the backpropagation process through global averaging. Further, these observation sensors are incorporated to reconstruct mean turbulent flow fields based on the ensemble Kalman method. The proposed optimal sensor placement method is applied to two flow applications with complex geometries, i.e., flows around periodic hills and an axisymmetric body of revolution. Both cases demonstrate that the proposed method can significantly reduce the number of sensors without sacrificing the accuracy of the reconstructed flow field.

Data-driven traffic sensor location and path flow estimation using Wasserstein metric

This paper introduces link information value obtained by the traffic sensors and presents a traffic sensor location and flow estimation joint optimization model in an urban road network. In contrast to most previous studies, this paper adds new traffic sensors into the existing sensor network and proposes a data-driven path flow measurement method based on Wasserstein metric, which is utilized to measure the distance between the estimated traffic flow distribution and the actual distribution. Furthermore, this paper develops a customized greedy algorithm by combining a search strategy for the link information value to obtain the optimal sensor location scheme and perform traffic flow estimation under different budget conditions. Numerical experiments are conducted on Sioux-Falls test network and Eastern Massachusetts interstate highway subnetwork to verify the accuracy and effectiveness of the proposed model based on Wasserstein metric and the developed solution method. Computational results show that the sensor location scheme generated by the model based on Wasserstein metric can reduce the estimation error of the traffic flow compared with KL divergence model under the same deployment cost. Additionally, the customized greedy algorithm can achieve the better performance than the Brute force algorithm in terms of computing time and solution quality.

Identification of excessive contact pressures under hand orthosis based on finite element analysis.

Implicit magnitudes and distribution of excessive contact pressures under hand orthoses hinder clinicians from precisely adjusting them to relieve the pressures. To address this, contact pressure under a hand orthosis were analysed using finite element method. This paper proposed a method to numerically predict the relatively high magnitudes and critical distribution of contact pressures under hand orthosis through finite element analysis, to identify excessive contact pressure locations. The finite element model was established consisting of the hand, orthosis and bones. The hand and bones were assumed to be homogeneous and elastic bodies, and the orthosis was considered as an isotropic and elastic shell. Two predictions were conducted by assigning either low (fat) or high (skin) material stiffness to the hand model to attain the range of pressure magnitudes. An experiment was conducted to measure contact pressures at the predicted pressure locations. Identical pressure distributions were obtained from both predictions with relatively high pressure values disseminated at 12 anatomical locations. The highest magnitude was found at the thumb metacarpophalangeal joint with the maximum pressure range from 13 to 78 KPa. The measured values were within the predicted range of pressure magnitudes. Moreover, 6 excessive contact pressure locations were identified. The proposed method was verified by the measurement results. It renders understanding of interface conditions underneath the orthosis to inform clinicians regarding orthosis design and adjustment. It could also guide the development of 3D printed or sensorised orthosis by indicating optimal locations for perforations or pressure sensors.

Occupancy Prediction in IoT-Enabled Smart Buildings: Technologies, Methods, and Future Directions.

In today's world, a significant amount of global energy is used in buildings. Unfortunately, a lot of this energy is wasted, because electrical appliances are not used properly or efficiently. One way to reduce this waste is by detecting, learning, and predicting when people are present in buildings. To do this, buildings need to become "smart" and "cognitive" and use modern technologies to sense when and how people are occupying the buildings. By leveraging this information, buildings can make smart decisions based on recently developed methods. In this paper, we provide a comprehensive overview of recent advancements in Internet of Things (IoT) technologies that have been designed and used for the monitoring of indoor environmental conditions within buildings. Using these technologies is crucial to gathering data about the indoor environment and determining the number and presence of occupants. Furthermore, this paper critically examines both the strengths and limitations of each technology in predicting occupant behavior. In addition, it explores different methods for processing these data and making future occupancy predictions. Moreover, we highlight some challenges, such as determining the optimal number and location of sensors and radars, and provide a detailed explanation and insights into these challenges. Furthermore, the paper explores possible future directions, including the security of occupants' data and the promotion of energy-efficient practices such as localizing occupants and monitoring their activities within a building. With respect to other survey works on similar topics, our work aims to both cover recent sensory approaches and review methods used in the literature for estimating occupancy.

Identification of optimal accelerometer placement on trains for railway switch wear monitoring via multibody simulation

Accelerometers play a crucial role in the railway industry, especially in track monitoring. Traditionally, they are placed on the railway tracks or often on bridges to monitor the health and condition of the infrastructure. Recently, there has been an increased focus on using regular trains to monitor the condition of railway infrastructure. Often, the sensors are placed based on certain assumptions without much scientific evidence or support. This paper utilizes the multibody simulation software GENSYS to identify the optimal placement of accelerometers on a passenger train for monitoring railway switch wear. Switch wear profiles were generated systematically and used as input for the simulations, studying acceleration at a total of 93 locations distributed among the wheelsets, bogies, and carbody. Based on both time and frequency domain analyses, optimal sensor locations were identified, generally close to the first bogie or wheelset at the leading carbody. Accelerations generated by the wheelset passing the switch can also be captured in the carbody, but it is important to note that these are several orders lower in magnitude compared to the acceleration on the wheelset. If accelerometers are to be placed in the carbody, correct sensitivity must be chosen, and a high-pass filter should be applied to capture the acceleration signals associated with switch wear. The study confirms that there is a direct correlation between the depth of switch wear and the magnitude of the acceleration. It remains effective even under various curve radii and train speeds.

Nonlinear recurrence analysis of piezo sensor placement for unmanned aerial vehicle motor failure diagnosis

This paper is focused on the diagnostics of multicopter UAV propulsion system, in which the temporary transient states occur during operation in faulty conditions (eg. not all motor phases working properly). As a diagnostic sensor, the piezo strip has been used, which is very sensitive to any vibrations of the multi-rotor frame. The paper concerns the precise location of the sensor for more effective monitoring of the propulsion system state. For this purpose, a nonlinear analysis of the vibration times series was carefully presented. The obtained non-linear time series were studied with the recurrence analysis in short time windows, which were sensitive to changes in Unmanned Aerial Vehicle motor speeds. The tests were carried out with different percentage of the pulse width modulation signal used for the operation of the brushless motor and for different locations of the piezosensor (side and top planes of the multicopter arm). In the article, it was shown that the side location of the piezosensor is more sensitive to changes in the Unmanned Aerial Vehicle propulsion system, which was studied with the Principal Component Analysis method applied for four main recurrence quantifications. The research presented proves the possibility of using nonlinear recurrence analysis for propulsion system diagnostics and helps to determine the optimal sensor location for more effective health monitoring of multicopter motor.

Optimal Sensor Placement for Vibration-Based Damage Localization Using the Transmittance Function.

A methodology for optimal sensor placement is presented in the current work. This methodology incorporates a damage detection framework with simulated damage scenarios and can efficiently provide the optimal combination of sensor locations for vibration-based damage localization purposes. A classic approach in vibration-based methods is to decide the sensor locations based, either directly or indirectly, on the modal information of the structure. While these methodologies perform very well, they are designed to predict the optimal locations of single sensors. The presented methodology relies on the Transmittance Function. This metric requires only output information from the testing procedure and is calculated between two acceleration signals from the structure. As such, the outcome of the presented method is a list of optimal combinations of sensor locations. This is achieved by incorporating a damage detection framework that has been developed and tested in the past. On top of this framework, a new layer is added that evaluates the sensitivity and effectiveness of all possible sensor location combinations with simulated damage scenarios. The effectiveness of each sensor combination is evaluated by calling the damage detection framework and feeding as inputs only a specific combination of acceleration signals each time. The final output is a list of sensor combinations sorted by their sensitivity.

Optimising urban measurement networks for CO2 flux estimation: a high-resolution observing system simulation experiment using GRAMM/GRAL

Abstract. To design a monitoring network for estimating CO2 fluxes in an urban area, a high-resolution observing system simulation experiment (OSSE) is performed using the transport model Graz Mesoscale Model (GRAMMv19.1) coupled to the Graz Lagrangian Model (GRALv19.1). First, a high-resolution anthropogenic emission inventory which is considered as the truth serves as input to the model to simulate CO2 concentration in the urban atmosphere on 10 m horizontal resolution in a 12.3 km × 12.3 km domain centred in Heidelberg, Germany. By sampling the CO2 concentration at selected stations and feeding the measurements into a Bayesian inverse framework, CO2 fluxes on a neighbourhood scale are estimated. Different configurations of possible measurement networks are tested to assess the precision of posterior CO2 fluxes. We determine the trade-off between the quality and quantity of sensors by comparing the information content for different set-ups. Decisions on investing in a larger number or in more precise sensors can be based on this result. We further analyse optimal sensor locations for flux estimation using a Monte Carlo approach. We examine the benefit of additionally measuring carbon monoxide (CO). We find that including CO as tracer in the inversion enables the disaggregation of different emission sectors. Finally, we quantify the benefit of introducing a temporal correlation into the prior emissions. The results of this study have implications for an optimal measurement network design for a city like Heidelberg. The study showcases the general usefulness of the inverse framework developed using GRAMM/GRAL for planning and evaluating measurement networks in an urban area.