Sensor Location Estimation Research Articles

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.

Exploiting Radio Frequency Characteristics With a Support Unmanned Aerial Vehicle to Improve Wireless Sensor Location Estimation Accuracy

Exploiting Radio Frequency Characteristics With a Support Unmanned Aerial Vehicle to Improve Wireless Sensor Location Estimation Accuracy

FPGA-Based neural network for accurate distance estimation of elderly falls using WSN in an indoor environment

In wireless sensor networks (WSNs), location or distance estimation information for the elderly is a critical issue. This study aims to improve the error in estimated distance between the anchor node and mobile node, which is carried by an elderly person while moving in an indoor environment. The distance between the mobile node and the anchor node was determined based on the measured received signal strength indicator (RSSI) of the anchor nodes and the artificial neural network (ANN). The ANN was implemented into a field programmable gate array (FPGA) so that it could be used in a real-world application. The hardware implementation of the FPGA was executed based on Xilinx (Virtex7). The results revealed that an accurate estimate of the distance error was obtained based on the ANN. The distance error in terms of mean absolute error was 0.019 m for training, 0.07 m for testing, and 0.19 m for validation. In addition, the estimated distances obtained from the FPGA were fully compatible with the actual distances. Moreover, the distance estimation error-based ANN outperformed other existing algorithms and soft computing techniques.

Energy-efficient localization and target tracking via underwater mobile sensor networks

Underwater mobile sensor networks (UMSNs) with free-floating sensors are more suitable for understanding the immense underwater environment. Target tracking, whose performance depends on sensor localization accuracy, is one of the broad applications of UMSNs. However, in UMSNs, sensors move with environmental forces, so their positions change continuously, which poses a challenge on the accuracy of sensor localization and target tracking. We propose a high-accuracy localization with mobility prediction (HLMP) algorithm to acquire relatively accurate sensor location estimates. The HLMP algorithm exploits sensor mobility characteristics and the multi-step Levinson-Durbin algorithm to predict future positions. Furthermore, we present a simultaneous localization and target tracking (SLAT) algorithm to update sensor locations based on measurements during the process of target tracking. Simulation results demonstrate that the HLMP algorithm can improve localization accuracy significantly with low energy consumption and that the SLAT algorithm can further decrease the sensor localization error. In addition, results prove that a better localization accuracy will synchronously improve the target tracking performance.

Time of Arrival range Based Wireless Sensor Localization in Precision Agriculture

Precision agriculture relies on information technology, whose precondition is providing real-time and accurate information. It depends on various kinds of advanced sensors, such as environmental temperature and humidity, wind speed, light intensity, and other types of sensors. Currently, it is a hot topic how to collect accurate information, the main raw data for agricultural experts, monitored by these sensors timely. Most existing work in WSNs addresses their fundamental challenges, including power supply, limited memory, processing power and communication bandwidth and focuses entirely on their operating system and networking protocol design and implementation. However, it is not easy to find the self-localization capability of wireless sensor networks. Because of constraints on the cost and size of sensors, energy consumption, implementation environment and the deployment of sensors, most sensors do not know their locations. This paper provides maximum likelihood estimators for sensor location estimation when observations are time-of arrival (TOA) range measurement.

Efficient Sensor Localization Method with Classifying Environmental Sensor Data

Sensor location estimation is important for many location-based systems in ubiquitous environments. Sensor location is usually determined using a global positioning system. For indoor localization, methods that use the received signal strength (RSS) of wireless sensors are used instead of a global positioning system because of the lack of availability of a global positioning system for indoor environments. However, there is a problem in determining sensor locations from the RSS: radio signal interference occurs because of the presence of indoor obstacles. To avoid this problem, we propose a novel localization method that uses environmental data recorded at each sensor location and a data classification technique to identify the location of sensor nodes. In this study, we used a wireless sensor node to collect data on various environmental parameters—temperature, humidity, sound, and light. We then extracted some features from the collected data and trained the location data classifier to identify the location of the wireless sensor node.

Determinants of accuracy in a terrestrial microphone array

Acoustic sensor arrays can allow researchers to localize the position of vocalizing animals. During the course of research on a threatened bird species, the greater sage-grouse, we developed a 24-channel wired array to non-invasively monitor male courtship displays at traditional display grounds (i.e. leks). Here we describe a study in which we localized repeated playbacks of four local species while varying speaker position, the number and arrangement of microphones, and accuracy of speed of sound and sensor location estimates. As expected, localization accuracy was lowest when the speaker was outside the array and when using a linear microphone arrangements. We found no overall effect of species identity in spite of strong differences in time and frequency structure of the playbacks, although we did find significant interactions of species with other factors in our analysis. Simulated errors in speed-of-sound-in-air and estimation of sensor position revealed that while localization was most accurate when these errors were small, localization was still possible even with relatively large errors in these two factors. While we hope these results will help researchers to design effective sensor arrays, specific outcomes will depend on study-specific factors as well as the specific sound processing and localization algorithms employed.

Optimal location of measurements for parameter estimation of distributed parameter systems

Systematic methodologies for the optimal location of spatial measurements, for efficient estimation of parameters of distributed systems, are investigated. A review of relevant methods in the literature is presented, and a comparison between the results obtained with three distinctive existing techniques is given. In addition, a new approach based on the Proper Orthogonal Decomposition (POD), to address this important problem is introduced and discussed with the aid of illustrative benchmark case studies from the literature. Based on the results obtained here, it was observed that the method based on the Gram determinant evolution ( Vande Wouwer et al., 2000), does not always produce accurate results. It is strongly dependent on the behaviour of sensitivity coefficients and requires extensive calculations. The method based on max–min optimisation ( Alonso, Kevrekidis, Banga, & Frouzakis, 2004) assigns optimal sensor locations to the positions where system outputs reach their extrema values; however, in some cases it produces more than one optimal solution. The D-optimal design method, Uciński (2003, June 18–20), produces as results the optimal number and spatial positions of measurements based on the behaviour (rather than the magnitude) of the sensitivity functions. Here we show that the extrema values of POD modes can be used directly to compute optimal sensor locations (as opposed e.g. to Alonso, Kevrekidis, et al., 2004, where PODs are merely used to reduce the system and further calculations are needed to compute sensor locations). Furthermore, we demonstrate the equivalence between the extrema of POD modes and of sensitivity functions. The added value of directly using PODs for the computation of optimal sensor locations is the computational efficiency of the method, side-stepping the tedious computation of sensitivity coefficient Jacobian matrices and using only system responses and/or experimental results directly. Furthermore, the inherent combination of model reduction and sensor location estimation in this method becomes more important as the complexity of the original distributed parameter system increases.

Estimation of the Sensor Location and the Step for Personal Navigation System

This paper presents the sensor location and step estimation algorithm for personal navigation system (PNS). PNS has the disadvantage in that the position of the sensor must be fixed on a human body. Three-axis acceleration sensor is used to solve the disadvantage and to consider the real situation. We simplify the measurement data by using the band pass filter, witch It has the advantage in the detection of characteristic point. Through the detected characteristic points, it is possible to setup the parameter for the pattern detection. Depending on the sensor location, the parameters have the different type of noise covariance. Particularly, when the position of the sensor is changed, the impulse noise shows up. Considering the noise, we apply the recursive least square algorithm using the variable forgetting factors, which can classify the sensor location based on the estimated parameters. We performed the experiment for the verification of the proposed algorithm in the various environments. Through the experimental results, the effectiveness of the proposed method is verified.

Adaptive attenuation factor model for localization in wireless sensor networks

PurposeThe accuracy of sensor location estimation influences directly the quality and reliability of services provided by a wireless sensor network (WSN). However, current localization methods may require additional hardware, like global positioning system (GPS), or suffer from inaccuracy like detecting radio signals. It is not proper to add extra hardware in tiny sensors, so the aim is to improve the accuracy of localization algorithms.Design/methodology/approachThe original signal propagation‐based localization algorithm adopts a static attenuation factor model and cannot adjust its modeling parameters in accordance with the local environment. In this paper an adaptive localization algorithm for WSNs that can dynamically adjust ranging function to calculate the distance between two sensors is presented. By adjusting the ranging function dynamically, the location of a sensor node can be estimated more accurately.FindingsThe NCTUNs simulator is used to verify the accuracy and analyze the performance of the algorithm. Simulation results show that the algorithm can indeed achieve more accurate localization using just a small number of reference nodes in a WSN.Research limitations/implicationsThere is a need to have accurate location information of reference nodes.Practical implicationsThis is an effective low‐cost solution for the localization of sensor nodes.Originality/valueAn adaptive localization algorithm that can dynamically adjust ranging function to calculate the distance between two sensors for sensor network deployment and providing location services is described.

Fundamental guided wave attenuations and sensor location estimation in fluid-filled steel pipes

The sound intensities required to sustain a certain interval between two ultrasonic sensors that are installed outside a steep pipe for structural health monitoring are numerically investigated. Two fundamental guided wave modes, longitudinal and flexural, are considered for the calculation of their attenuations via the concept of parametric density and a sink inside steel pipes, and these attenuations are used for the sound-intensity calculations. The results, attenuations, and sound intensities are useful as a guideline when guided-ultrasonic-wave-based inspection is planned for pipeline maintenance and safety, especially for estimation of the axial location between ultrasonic sensors.

The Sectoral Sweeper Scheme for Wireless Sensor Networks: Adaptive Antenna Array Based Sensor Node Management and Location Estimation

We introduce a novel sensor node management and location estimation method referred as sectoral sweeper (SS) scheme that uses an adaptive antenna array (AAA) at a central node in wireless sensor networks (WSNs). With the SS scheme, the central node can activate or deactivate the nodes in a desired region which is specified by beam direction and beam width of the transmit beam and also by minimum and maximum thresholds (R min and R max) for the received signal strength indicator (RSSI) of signals received by the nodes. In order to perform a specified task that is associated with a Task_id, two different beams are transmitted, which are task region beam and routing region beam to switch the nodes into active or routing modes. Since our scheme does not require any additional software or hardware for node management and location estimation in sensor nodes, the deficiencies of tiny sensors are effectively eliminated. The proposed scheme is shown to reduce the number of sensing nodes and the amount of data traffic in the network, thus leading to considerable savings in energy consumption and prolonged sensor lifetime.

Relative location estimation in wireless sensor networks

Self-configuration in wireless sensor networks is a general class of estimation problems that we study via the Cramer-Rao bound (CRB). Specifically, we consider sensor location estimation when sensors measure received signal strength (RSS) or time-of-arrival (TOA) between themselves and neighboring sensors. A small fraction of sensors in the network have a known location, whereas the remaining locations must be estimated. We derive CRBs and maximum-likelihood estimators (MLEs) under Gaussian and log-normal models for the TOA and RSS measurements, respectively. An extensive TOA and RSS measurement campaign in an indoor office area illustrates MLE performance. Finally, relative location estimation algorithms are implemented in a wireless sensor network testbed and deployed in indoor and outdoor environments. The measurements and testbed experiments demonstrate 1-m RMS location errors using TOA, and 1- to 2-m RMS location errors using RSS.

Maximum-likelihood source localization and unknown sensor location estimation for wideband signals in the near-field

In this paper, we derive the maximum-likelihood (ML) location estimator for wideband sources in the near field of the sensor array. The ML estimator is optimized in a single step, as opposed to other estimators that are optimized separately in relative time-delay and source location estimations. For the multisource case, we propose and demonstrate an efficient alternating projection procedure based on sequential iterative search on single-source parameters. The proposed algorithm is shown to yield superior performance over other suboptimal techniques, including the wideband MUSIC and the two-step least-squares methods, and is efficient with respect to the derived Cramer-Rao bound (CRB). From the CRB analysis, we find that better source location estimates can be obtained for high-frequency signals than low-frequency signals. In addition, large range estimation error results when the source signal is unknown, but such unknown parameter does not have much impact on angle estimation. In some applications, the locations of some sensors may be unknown and must be estimated. The proposed method is extended to estimate the range from a source to an unknown sensor location. After a number of source-location frames, the location of the uncalibrated sensor can be determined based on a least-squares unknown sensor location estimator.