Optimal Number Of Sensors Research Articles

Information Maximizing Optimal Sensor Placement Robust Against Variations of Traffic Demand Based on Importance of Nodes

This paper defines a measure of importance for a node (intersection) in a transportation network, based on its topology and traffic demand. Consequentially, the measure is used in order to solve the sensor placement problem by maximizing the information gain in terms of users' routing choices by sensing the most uncertain areas in the system. It is demonstrated that utilizing the proposed strategy makes the performance robust against short- and long-term variations of traffic patterns. Finally, a method for finding the optimal number of sensors to be installed in a city is proposed. It models and maximizes the utility stemming from the tradeoff between cost, performance, robustness, and reliability of the sensor placement problem solution.

Wavelet-based algorithm for auto-detection of daily living activities of older adults captured by multiple inertial measurement units (IMUs)

A recent trend in human motion capture is the use of inertial measurement units (IMUs) for monitoring and performance evaluation of mobility in the natural living environment. Although the use of such systems have grown significantly, the development of methods and algorithms to process IMU data for clinical purposes is still limited. The aim of this work is to develop algorithms based on wavelet transform and discrete-time detection of events for the automatic segmentation of tasks related activities of daily living (ADL) from body worn IMUs.Seven healthy older adults (73 ± 4 years old) performed 10 ADL tasks in a simulated apartment during trials of different durations (3, 4, and 5 min). They wore a suit (Synertial UK Ltd IGS-180) comprised of 17 IMUs positioned strategically on body segments to capture full body motion. The proposed method automatically detected the number of template waveforms (representing each movement separately) using discrete wavelet transform (DWT) and discrete-time detection of events based on angular velocity, linear acceleration and 3D orientation data of pertinent IMUs. The sensitivity (Se.) and specificity (Sp.) of detection for the proposed method was established using time stamps of10tasks obtained from visual segmentation of each trial using the video records and the avatar provided by the system’s software.At first, we identified six pertinent sensors that were strongly associated to different activities (at most two sensors/task) that allowed detection of tasks with high accuracy. The proposed algorithm exhibited significant global accuracy (Nevents = 1999, Se. = 97.5%, Sp. = 94%), despite the variation in the occurrences of the performed tasks (free living). The Se. varied from 94% to 100% for all the detected ADL tasks and Sp. ranged from 90% to 100% with the worst Sp. = 85 and 87% for Release_mid (reaching for object held just beyond reach at chest height) and Turning_Left tasks, respectively.This study demonstrated that DWT in conjunction with a nonlinear transform and auto-adaptive thresholding process for decision rules are highly efficient in detecting and segmenting tasks performed during free-living activities. This study also helped to determine the optimal number of sensors, and their location to detect such activities. This work lays the foundation for the automatic assessment of mobility performance within the segmented signals, as well as potentially helps differentiate populations based on their mobility patterns and symptomatology.

A Method for Selecting Optimal Number of Sensors to Improve the Credibility

With the development of sensors, it is possible to embed many sensors within a certain space, which makes the monitor and alarm system with multisensor possible. There are two important parameters in a monitor and alarm system, namely, the false alarm rate and the missed alarm rate. In this work, a method for selecting optimal number of sensors in the sensor array is presented to improve the credibility. The influence factors of the weights and the false alarm rate and the missed alarm rate of one sensor and total number of sensors are discussed. An experimental setup was developed. The monitoring methods of common strategies and the proposed optimal number of sensors strategy are compared graphically by the receiver operating characteristic curves and the area under receiver operating characteristic curve values. The receiver operating characteristic curves graphically prove that the optimal number of sensors’ method presents the best performance, and it is shown that the optimal number of sensors’ method has the highest area under receiver operating characteristic value (0.9631). This method may aid future users of the monitor and alarm system by providing an optimal number of sensors strategy for high credibility.

Optimum wireless sensor deployment scheme for structural health monitoring: a simulation study

With the rapid advancements in smart sensing technology and wireless communication technology, the wireless sensor network (WSN) offers an alternative solution to structural health monitoring (SHM). In WSNs, dense deployment of wireless nodes aids the identification of structural dynamic characteristics, while data transmission is a significant issue since wireless channels typically have a lower bandwidth and a limited power supply. This paper provides a wireless sensor deployment optimization scheme for SHM, in terms of both energy consumption and modal identification accuracy. A spherical energy model is established to formulate the energy consumption within a WSN. The optimal number of sensors and their locations are obtained through solving a multi-objective function with weighting factors on energy consumption and modal identification accuracy using a genetic algorithm (GA). Simulation and comparison results with traditional sensor deployment methods demonstrate the efficiency of the proposed optimization scheme.

Optimal number and location of Bluetooth sensors considering stochastic travel time prediction

Determining the optimal number and location of sensors is essential to effectively manage traffic on highways. Optimal solutions dealing with dynamic traffic patterns and relocation of sensors have received little attention. In this study, existing fixed sensors are used to estimate travel time prediction errors at candidate locations where we deploy portable sensors. Potential sampling error of each candidate location is also counted in selecting optimal locations. A two-stage stochastic formulation considers uncertainty of traffic conditions based on scenarios generated by principal component analysis and clustering analysis to uncover the underlying spatial correlations and temporal patterns. The first stage decision, determining the optimal number of sensors, is made before the deployment. The second stage, evaluating the expected travel time prediction errors, specifies sensor arrangements in each scenario. A dynamic model has predefined rearrangement stages. At each stage, sensor locations are modified as the pattern of travel time error changes over time, considering sensor acquisition and relocation expenses. The deterministic and stochastic solutions serve as a lower bound and an upper bound for the dynamic solution. Higher relocation expense leads to more sensors being used, while higher sensor costs leads to fewer sensors being used with more frequent relocations.

Application of model reduction technique and structural subsection technique on optimal sensor placement of truss structures

An optimal sensor placement (OSP) method based on structural subsection technique (SST) and model reduction technique was proposed for modal identification of truss structures, which was conducted using genetic algorithm (GA). The constraints of GA variables were determined by SST in advance. Subsequently, according to model reduction technique, the optimal group of master degrees of freedom and the optimal objective function value were obtained using GA in a case of the given number of sensors. Correspondingly, the optimal number of sensors was determined according to optimal objective function values in cases of the different number of sensors. The proposed method was applied on a scaled jacket offshore platform to get its optimal number of sensors and the corresponding optimal sensor layout. Then modal kinetic energy and modal assurance criterion were adopted to evaluate vibration energy and mode independence property. The experiment was also conducted to verify the effectiveness of the selected optimal sensor layout. The results showed that experimental modes agreed reasonably well with numerical results. Moreover the influence of the proposed method using different optimal algorithms and model reduction technique on optimal results was also compared. The results showed that the influence was very little.

WSN in Monitoring Oil Pipelines Using ACO and GA

Wireless Sensor Networks (WSNs) are one of the most important technologies in the fields of wireless networking today. WSNs have a vast amount of applications including sensors embedded in the outer surface of pipeline or mounted along the supporting structure of bridges, robotics and health care. In this paper, we study the issues of linear sensor placement to monitor oil pipelines. We address the problem of optimal number of sensors to be deployed given initial energy of each sensor node and message buffering limitations. The objectives of the deployment process are: 1) maximizing the coverage of the pipe, 2) producing a connected network, and 3) prolonging the overall network lifetime. The paper utilizes two of the evolutionary algorithms to solve the deployment problem which are Genetic Algorithms (GA) and Ant Colony Optimization (ACO). Extensive set of experiments are performed for performance evaluation.

An Efficient Sensor Deployment Scheme for Large-Scale Wireless Sensor Networks

Sensor deployment to achieve better system performance is one of the critical issues in wireless sensor networks. An effective sensor deployment scheme is proposed for large-area sensor networks, where the event occurrence rate varies over the sensor-deployed region. By utilizing the local event occurrence rate and applying the analytical sensor detection capability expression for the problem formulation, the proposed scheme determines the optimal number of sensors for a typical surveillance sensor network that should be deployed in each local region that minimizes the total number of sensors while satisfying the target probability of the overall detection. The optimality of the proposed scheme is examined, and it is also shown that the sensor deployment by the proposed scheme considerably reduces the total number of sensors in comparison with a conventional sensor deployment.

Data gathering architecture for temporary worksites based on a uniform deployment of wireless sensors

Data management is an important issue in wireless sensor networks. However, the data gathering process may be unsuccessful in disconnected or random networks: some data gathered cannot be delivered to the sink, coverage holes may occur and result in missing data. To cope with this problem, we propose two distributed redeployment algorithms (distributed virtual forces algorithm (DVFA) and adaptive distributed virtual forces algorithm (ADVFA)), based on virtual forces, that provide a uniform deployment of sensor nodes. Both ensure full area coverage and network connectivity.We define a three-tier architecture where after the execution of a redeployment algorithm, sensor nodes are uniformly deployed to monitor a temporary worksite. We compute the optimal number of sensors needed to cover an area. Using this result, we parameterise DVFA and evaluate its performances. We then propose ADVFA to cope with DVFA drawbacks (e.g., node oscillations). ADVFA outperforms DVFA, considerably reducing the distance travelled by nodes and then maximising network lifetime by saving energy.

Design of an intra-operative imaging system for the cochlear implant.

Research has shown that closer proximity of a cochlear implant electrode array to the inner modiolar wall can improve hearing performance in a recipient. It is understood that developing a real-time intra-operative imaging technique would bring the surgeon visual feedback that can correlate with tactile resistances during electrode insertion, reducing various risks of insertion traumas, while guiding the electrode for optimal placement. This paper presents the design of a 3-dimensional magnetic imaging system which provides a means for real-time imaging by reconstructing uniform segments of the electrode array's contours. In addition, the theoretical error of this reconstruction method is evaluated to select an optimal number of sensors within the electrode design. The theoretical error of the reconstruction method is evaluated for 7 to 22 inductive sensors within the electrode design. For 16 or more sensors, the error is improves to less than 0.5%. A working prototype was confirmed on a 10:1 scale with orthogonal Helmholtz coils at 85-115 kHz.

Reliability assessment of the rotation algorithm for earthquake damage estimation

A rotation algorithm was developed that computes the slope of permanently deformed columns subjected to earthquake ground motion using micro-electro-mechanical system accelerometers. Previous studies by the authors have shown that the algorithm can provide estimates of the residual displacement in the column, and thus the story drift can be evaluated. The algorithm was validated through applications to columns that deflect in single and double curvature. Information from tests on such columns conducted at various laboratories was used to conduct the study. The optimal number of sensors and their placement on the test columns were determined through a series of numerical analyses. The accuracy of the rotation and displacement estimates is assessed under the assumptions of zero-noise sensors and perfect measurements. In this paper, the effect of measurement error and sensor noise on the predicted rotations and corresponding deformations is studied in order to provide bounds on the accuracy of the rotation algorithm and on the allowable sensor noise. The same test columns are used for the purposes of these analyses.

SVM Based Event Detection and Identification: Exploiting Temporal Attribute Correlations Using SensGru

In the context of anomaly detection in cyber physical systems (CPS), spatiotemporal correlations are crucial for high detection rate. This work presents a new quarter sphere support vector machine (QS-SVM) formulation based on the novel concept ofattribute correlations. Our event detection approach, SensGru, groups multiple sensors on a single node and thus eliminates communication between sensor nodes without compromising the advantages of spatial correlation. It makes use of temporal-attribute (TA) correlations and is thus a TA-QS-SVM formulation. We show analytically that SensGru (or interchangeably TA-QS-SVM) results in a reduced node density and gives the sameevent detectionperformance as more denseSpatiotemporal-Attribute Quarter-Sphere SVM(STA-QS-SVM) formulation which exploits both spatiotemporal and attribute correlations. Moreover, this paper develops theoretical bounds on the internode distance, the optimal number of sensors, and the sensing range with SensGru so that the performance difference with SensGru and STA-QS-SVM is negligibly small. Both schemes achieve event detection rates as high as 100% and an extremely low false positive rate.

The importance of fire simulation in fire prediction

The appearance of fire in objects with lot of humans inside represents very possible real situation that could be very danger and could cause destructive consequences on human lives and material properties. Very important influence in fire prediction, fire protection, human and material properties safety could be a fire simulation in object. This simulation could give many useful information of possible fire propagation; possible and existed evacuation routes; possible and exited placing of fire, smoke, temperature conditions in object and many other information of crucial importance for human lives and material properties, such as the best places for sensors position, optimal number of sensors, projection of possible evacuation routes etc. There are many different programs for fire simulation. This paper presents complete fire simulation in Electrotechnical school Nikola Tesla in Niš in FDS.

Sensors for Improved Efficiency of Irrigation in Greenhouse and Nursery Production

The use of sensors can provide quantitative information to help guide and automate the decision-making process for irrigation. This article provides an overview of the most common sensors that can be used for this purpose. Such sensors include those that are commonly used for weather stations as well as sensors to monitor the water status of the soil or substrate, and sensors that can be used to monitor and troubleshoot irrigation systems. Although collecting data with sensors is relatively easy, data are only useful if the sensors are used correctly and the limitations of sensors are understood. Optimizing the value of the collected data requires selecting the best sensor(s) for a particular purpose, determining the optimal number of sensors to be deployed, and assuring that collected data are as accurate and precise as possible. We describe general sensing principles and how these principles can be applied to a variety of sensors. Based on our experience, proper use of sensors can result in large increases in irrigation efficiency and improve the profitability of ornamental production in greenhouses and nurseries.

Modelling water quality in drinking water distribution networks from real-time direction data

Abstract. Modelling of contamination spread and location of a contamination source in a water distribution network is an important task. There are several simulation tools developed, however the significant part of them is based on hydraulic models that need node demands as input data that sometimes may result in false negative results and put users at risk. The paper considers applicability of a real-time flow direction data based model for contaminant transport in a distribution network of a city and evaluates the optimal number of flow direction sensors. Simulation data suggest that the model is applicable for the distribution network of the city of Riga and that the optimal number of sensors in this case is around 200.

Optimal Network Size and Encoding Rate for Wireless Sensor Network-Based Decentralized Estimation under Power and Bandwidth Constraints

In this paper, we address the problem of decentralized parameter estimation via Wireless Sensor Networks (WSNs). We consider two different encoding strategies, namely, Quantize-and-Estimate (Q&E) and Compress-and-Estimate (C&E) and assume that sensor observations are conveyed to the Fusion Center (FC) over a number of orthogonal Gaussian or Rayleigh-fading channels. We constrain both power and bandwidth to be constant irrespectively of the network size and find approximate closed-form expressions of the optimal number of sensors for a number of cases of interest. Besides, we derive the optimal encoding rate for the Q&E scheme when, in the absence of Transmit Channel State Information (CSIT), sensors must encode their observations at a common and constant rate. For the (successive) C&E strategy, we also determine the encoding order that minimizes the resulting distortion in the FC estimates. We complement the analysis by deriving an expression of the asymptotic distortion when the number of sensors grows without bound, and the rate at which distortion decreases in the high-SNR regime. Finally, we close the paper by presenting some computer simulation results.

Automated Graph-Based Methodology for Fault Detection and Location in Power Systems

This study investigates how the model-based fault detection and location approach of structural analysis can be adapted to meet the needs of power systems, where challenges associated with increased system complexity make conventional protection schemes impractical. With a global view of the protected system and the systematic and automated use of the system's analytical redundancy, faults are detected and located by more than one means. This redundancy can be used as a confirmation mechanism within a wide-area protection scheme to avoid unnecessary or false tripping due to protection component failure or disturbance. Furthermore, this redundancy turns the sensor configuration problem into an optimization problem with regard to fault detection and location. The effectiveness of different system topologies can then be compared on the basis of the optimal number of sensors they require. The principle of structural analysis is described in detail and illustrated on a simple power system model. Pertinence of the approach is demonstrated through simulation.

Entropy-based optimal sensor networks for structural health monitoring of a cable-stayed bridge

The sudden collapse of Interstate 35 Bridge in Minneapolis gave a wake-up call to US municipalities to re-evaluate aging bridges. In this situation, structural health monitoring (SHM) technology can provide the essential help needed for monitoring and maintaining the nation`s infrastructure. Monitoring long span bridges such as cable-stayed bridges effectively requires the use of a large number of sensors. In this article, we introduce a probabilistic approach to identify optimal locations of sensors to enhance damage detection. Probability distribution functions are established using an artificial neural network trained using a priori knowledge of damage locations. The optimal number of sensors is identified using multi-objective optimization that simultaneously considers information entropy and sensor cost-objective functions. Luling Bridge, a cable-stayed bridge over the Mississippi River, is selected as a case study to demonstrate the efficiency of the proposed approach.

A Bayesian machine learning method for sensor selection and fusion with application to on-board fault diagnostics

In applications like feature-level sensor fusion, the problem of selecting an optimal number of sensors can lead to reduced maintenance costs and the creation of compact online databases for future use. This problem of sensor selection can be reduced to the problem of selecting an optimal set of groups of features during model selection. This is a more complex problem than the problem of feature selection, which has been recognized as a key aspect of statistical model identification. This work proposes a new algorithm based on the use of a Bayesian framework for the purpose of selecting groups of features during regression and classification. The hierarchical Bayesian formulation introduces grouping for the parameters of a generalized linear model and the model hyper-parameters are estimated using an empirical Bayes procedure. A novel aspect of the algorithm is its ability to simultaneously perform feature selection within groups to reduce over-fitting of the data. Further, the parameters obtained from this algorithm can be used to obtain a rank order among the selected sensors. The performance of the algorithm is first tested on a synthetic regression example. Finally, it is applied to the problem of fault detection in diesel engines (30,000 data records from 43 sensors, 8 classes) and used to compare the misclassification rates with a varying number of sensors.

Optimal sensor distribution for multi-station assembly process using chaos-embedded fast-simulated annealing

This paper presents a novel methodology for the allocation of sensors in multi-station assembly processes. It resolves two core issues pertaining to the determination of an optimal number of sensors to be employed and their best locations. To make the traditional approach more effective, the effect of noise on sensor placement is minimized by maximizing the determinant of the Fischer information matrix. A state-space approach is adopted to model the variation propagation pertaining to the transfer of parts in a given multi-station assembly process. Further, the objective function conceived is significant over other contributions with respect to adding the effect of noise coupled with the sensors. Moreover, a new algorithm is developed to optimize the newly formulated objective function. The proposed algorithm combines chaotic sequences with traditional evolutionary fast simulated annealing (EFSA), hence it is termed chaos-embedded fast-simulated annealing (CEFSA). It can find the optimal sensor distribution with the minimum effect of noise in the sensor data. This paper reports on conceptual work, which underpins the research, and also presents details of a numerical example carried out in an industrial context to test the efficacy of the proposed algorithm. Further analysis reveals that the proposed approach obtains optimal distribution of sensors and offers more generic results compared with previously concluded analysis.