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Journal articles on the topic 'Assembly Line, Wireless Sensor Network, Localization'
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1
Gogolák, László, Igor Fürstner, and Szilveszter Pletl. "Wireless sensor network based localization in industrial environments." Analecta Technica Szegedinensia 8, no. 1 (January 11, 2014): 91–96. http://dx.doi.org/10.14232/analecta.2014.1.91-96.
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The use of wireless devices has greatly increased in the last decade, and it has been one of the most widely used medium of information transmission. Within the wireless devices the wireless sensor networks are the most contemporary and most commonly researched field. The work deals with the industrial use of wireless sensor networks and more precisely with monitoring and controlling industrial assembly lines. The focus of this study is localization by the use of wireless technology in the above mentioned environment. In the experiment wireless sensors are placed on the base elements of currently being assembled products. The developed system is able to specify the precise place of the product in the assembly line and record the time of localization. By the use these information the time of assembling the product can be monitored. For determining the place of the product the Received signal strength indication – RSSI has been used. The current position of the product is calculated by a neural network. The use of these sensors makes possible the measuring and recording of the influences on the product during the assembly, such as the effects of temperature, humidity, or if the product has been hit or damaged. By the use of these wireless sensor networks the quality of the assembled products can be improved and the process of assembly can be optimized.
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Gogolák, László, and Igor Fürstner. "Wireless Sensor Network Aided Assembly Line Monitoring According to Expectations of Industry 4.0." Applied Sciences 11, no. 1 (December 22, 2020): 25. http://dx.doi.org/10.3390/app11010025.
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Striving for excellence during the assembling process through incorporating the expectations of Industry 4.0 requires complex information management on issues of overall system status, especially the physical characteristics and position of the parts being assembled, as well as the assembling units and tools. This research introduces both an overall customized assembling system supervision model, which is based on a modified four-layer control system hierarchy that suits the specific requirements of such systems and the developed wireless sensor network technology for assembling process management with a particular focus on localization. The developed model highlights the localization problems of the system as well as other aspects required for overall system status determination. The localization of assembled parts is based on the fingerprint localization method by using the received signal strength indicator. The proposed localization algorithms are based either on artificial neural networks or on the weighted k-nearest neighbor method. The developed model has been tested both in laboratory conditions and in a simulated industrial environment. The research results offer a general solution to the problem of assembling system supervision, regardless of size and shape, with emphasis on the localization problem solution.
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Yan, Xiaoyong, Aiguo Song, Jimin Yu, and Zhong Yang. "Toward Collinearity-Avoidable Localization for Wireless Sensor Network." Journal of Sensors 2015 (2015): 1–16. http://dx.doi.org/10.1155/2015/908956.
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In accordance with the collinearity problem during computation caused by the beacon nodes used for location estimation which are close to be in the same line or same plane, two solutions are proposed in this paper: the geometric analytical localization algorithm based on positioning units and the localization algorithm based on the multivariate analysis method. The geometric analytical localization algorithm based on positioning units analyzes the topology quality of positioning units used to estimate location and provides quantitative criteria based on that; the localization algorithm based on the multivariate analysis method uses the multivariate analysis method to filter and integrate the beacon nodes coordinate matrixes during the process of location estimation. Both methods can avoid low estimation accuracy and instability caused by multicollinearity.
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Jiang, Pei, Xue Liang Pang, and Li Dong. "Survey on Mobile Target Localization in Wireless Sensor Networks." Applied Mechanics and Materials 738-739 (March 2015): 133–39. http://dx.doi.org/10.4028/www.scientific.net/amm.738-739.133.
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As a novel technology of information acquisition and processing, Wireless Sensor Network (WSN) has been widely used for complex large-scale localization tasks. With global distribution and sensing ability, wireless sensor network can provide valid optimal localization for mobile targets. According to the key problems of mobile target localization under wireless sensor network, this paper depicted current research status in both of line-of-sight and non-line-of-sight environments. Typical and representative algorithms are sorted and their ideas are evaluated. Finally, we discussed and anticipated the future research direction.
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Cheng, Long, Mingkun Xue, Ze Liu, and Yong Wang. "A Robust Tracking Algorithm Based on a Probability Data Association for a Wireless Sensor Network." Applied Sciences 10, no. 1 (December 18, 2019): 6. http://dx.doi.org/10.3390/app10010006.
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As one of the core technologies of the Internet of Things, wireless sensor network technology is widely used in indoor localization systems. Considering that sensors can be deployed to non-line-of-sight (NLOS) environments to collect information, wireless sensor network technology is used to locate positions in complex NLOS environments to meet the growing positioning needs of people. In this paper, we propose a novel time of arrival (TOA)-based localization scheme. We regard the line-of-sight (LOS) environment and non-line-of-sight environment in wireless positioning as a Markov process with two interactive models. In the NLOS model, we propose a modified probabilistic data association (MPDA) algorithm to reduce the NLOS errors in position estimation. After the NLOS recognition, if the number of correct positions is zero continuously, it will lead to inaccurate localization. In this paper, the NLOS tracer method is proposed to solve this problem to improve the robustness of the probabilistic data association algorithm. The simulation and experimental results show that the proposed algorithm can mitigate the influence of NLOS errors and achieve a higher localization accuracy when compared with the existing methods.
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Zhang, Hanqing, and Haitao Li. "Node Localization Technology of Wireless Sensor Network Based on RSSI Algorithm." International Journal of Online Engineering (iJOE) 12, no. 10 (October 31, 2016): 51. http://dx.doi.org/10.3991/ijoe.v12i10.6206.
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<p style="margin: 0in 0in 10pt;"><span style="-ms-layout-grid-mode: line;"><span style="font-family: Times New Roman; font-size: small;">Wireless sensor network, as one of the system compositions of the Internet of Things, is an important carrier of the efficient use of rural resources. An accurate localization algorithm is of great significance to the complete coverage of the farmland monitoring area and to ensure the connectivity of the whole network. This paper carries out an experiment based on the signal intensities of network nodes of wireless sensor in different farmland environments having crops or not at different placing heights and relative distances. In the research, an analysis is made on the attenuation relationship between the RSSI values and the distance and height between nodes. Besides, multiple linear regression methods are used to fit the propagation model of wireless signal. Then, a distance-measuring experiment is carried out according to the model, and finally the localization of unknown nodes is realized based on Gauss mixed algorithm. Average error of the experiment is 1.02m, indicating good experimental results.</span></span></p>
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Fan, Jianhua, Tao Liang, Tongxiang Wang, and Jianwei Liu. "Identification and Localization of the Jammer in Wireless Sensor Networks." Computer Journal 62, no. 10 (July 3, 2019): 1515–27. http://dx.doi.org/10.1093/comjnl/bxz055.
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Abstract Wireless sensor network can be easily attacked by jammers for its shared character and open access to the wireless channel. Jamming attack could produce a significant threat to the network by interrupting the normal transmission of nodes. To this end, several anti-jamming countermeasures have been proposed to improve the quality of service of the wireless sensor network. As an important building block for anti-jamming countermeasures, the estimation of jammer’s location could provide us a possible way to eliminate jammers artificially. However, existing localization algorithms mainly pay attention to locate the jammers that are equipped with omnidirectional antennas, which usually fail to cope with directional jammers. In order to bridge this gap, an algorithm of antenna identification and localization of the jammer (AILJ) based on the topology information of jamming scenarios is put forward in this paper. At first, a collection protocol is designed to collect the information of boundary nodes and jammed nodes. Then, an identification method based on the classification of the boundary nodes is proposed to derive the type of jammer’s antenna. At last, a range-free method is put forward to locate the jammer without relying on the propagation parameters. The proposed AILJ only depends on the localization of jammed nodes and boundary nodes and employs their geometry knowledge. For the omnidirectional jammer, the mean center of two circumcircles is considered as the jammer’s position. For the directional jammer, the mean value of the intersections between the straight line that represents jammer’s direction and the circumcircles is considered as the directional jammer’s position. Finally, a series of experiments have been conducted to evaluate the identification and localization performance of AILJ.
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Hua, Jingyu, Yejia Yin, Weidang Lu, Yu Zhang, and Feng Li. "NLOS Identification and Positioning Algorithm Based on Localization Residual in Wireless Sensor Networks." Sensors 18, no. 9 (September 7, 2018): 2991. http://dx.doi.org/10.3390/s18092991.
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The problem of target localization in WSN (wireless sensor network) has received much attention in recent years. However, the performance of traditional localization algorithms will drastically degrade in the non-line of sight (NLOS) environment. Moreover, variable methods have been presented to address this issue, such as the optimization-based method and the NLOS modeling method. The former produces a higher complexity and the latter is sensitive to the propagating environment. Therefore, this paper puts forward a simple NLOS identification and localization algorithm based on the residual analysis, where at least two line-of-sight (LOS) propagating anchor nodes (AN) are required. First, all ANs are grouped into several subgroups, and each subgroup can get intermediate position estimates of target node through traditional localization algorithms. Then, the AN with an NLOS propagation, namely NLOS-AN, can be identified by the threshold based hypothesis test, where the test variable, i.e., the localization residual, is computed according to the intermediate position estimations. Finally, the position of target node can be estimated by only using ANs under line of sight (LOS) propagations. Simulation results show that the proposed algorithm can successfully identify the NLOS-AN, by which the following localization produces high accuracy so long as there are no less than two LOS-ANs.
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., R. Divya. "LOCALIZATION BASED RANGE MAP STITCHING IN WIRELESS SENSOR NETWORK UNDER NON-LINE-OF-SIGHT ENVIRONMENTS." International Journal of Research in Engineering and Technology 03, no. 05 (May 25, 2014): 248–52. http://dx.doi.org/10.15623/ijret.2014.0305047.
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Li, Qiyue, Baoyu Chu, Zhong Wu, Wei Sun, Liangfeng Chen, Jie Li, and Zhi Liu. "RMDS: Ranging and multidimensional scaling–based anchor-free localization in large-scale wireless sensor networks with coverage holes." International Journal of Distributed Sensor Networks 13, no. 8 (August 2017): 155014771772465. http://dx.doi.org/10.1177/1550147717724659.
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Sensor node localization is a crucial aspect of many location-related applications that utilize wireless sensor networks. Among the many studies in the literature, multidimensional scaling-based localization techniques have been proven to be efficient, obtaining high accuracy with lower information requirements. However, when applied to large-scale wireless sensor networks with coverage holes, which are common in many scenarios, such as underground mines, the transmission path can become deviated, degrading the localization performance of this type of connectivity-based technique. Furthermore, in such complex wireless environments, non-line-of-sight reference objects, the presence of obstacles and signal fluctuations change the communication range and make it difficult to obtain an accurate position. In this article, we present a anchor-free localization scheme for large-scale wireless sensor networks called the ranging and multidimensional scaling–based localization scheme. We use ranging and non-line-of-sight error mitigation techniques to estimate accurate distances between each node pair and attempt to find inflection nodes using a novel flooding protocol to correct transmission paths that have become deviated by a coverage hole. Moreover, we replace the singular value decomposition with an iterative maximum gradient descent method to reduce the computational complexity. The results of the simulations and experiments show that our scheme performs well on wireless sensor networks with different coverage holes and is robust to varying network densities.
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Chu, Hao, and Cheng-dong Wu. "A Non-Parametric Propagation Condition Identification Method and Non-Line of Sight Mitigation Algorithm for Wireless Sensor Network." Open Electrical & Electronic Engineering Journal 10, no. 1 (August 31, 2016): 80–87. http://dx.doi.org/10.2174/1874129001610010080.
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The wireless sensor network (WSN) has received increasing attention since it has many potential applications such as the internet of things and smart city. The localization technology is critical for the application of the WSN. The obstacles induce the larger non-line of sight (NLOS) error and it may decrease the localization accuracy. In this paper, we mainly investigate the non-line of sight localization problem for WSN. Firstly, the Pearson's chi-squared testing is employed to identify the propagation condition. Secondly, the particle swarm optimization based localization method is proposed to estimate the position of unknown node. Finally the simulation experiments are implemented. The simulation results show that the proposed method owns higher localization accuracy when compared with other two methods.
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Mr. Rahul Sharma. "Monitoring of Drainage System in Urban Using Device Free Localization Neural Networks and Cloud computing." International Journal of New Practices in Management and Engineering 7, no. 04 (December 31, 2018): 08–14. http://dx.doi.org/10.17762/ijnpme.v7i04.69.
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Wireless Sensor Network is a Wi-Fi community consisting of spatially propagated and self-sufficient devices using sensors to detect physical or environmental conditions. During heavy rainfall, the urban drainage system cannot drain the water. A wireless sensor with many interconnected wireless sensor nodes captures real-time data from the network environment and transmits this data to a base station for analysis and operation. With wireless sensor nodes, it is possible to capture and monitor the amount of water in drainages and the difference in water flow between the two points in the drainage system. Nevertheless, the majority localization techniques aims on device based localization, which can find target with festinated devices. It is not suitable for applications such as terrain, drainage flow and flooding. Here device free wireless localization system using artificial neural networks and a cluster based wireless sensor network system to monitor urban drainage is proposed. There are two stages in the system. During the off-line preparation stage, Acceptable Signal Strength (RSS) differential metrics are calculated between the RSS metrics together while the monitor area is empty and calculated by a specialized in the region. Some RSS dissimilarity values are selected in the RSS Difference Matrix. The RSS dissimilarity standards and associated matrix indices are taken as the inputs of the ANN representation in addition to the identified position coordinate are in its outputs. The real-time data collected from the wireless sensor network is used to detect overflow and provide alarms before disturbances arise.
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Bhat, Soumya J., and K. V. Santhosh. "Is Localization of Wireless Sensor Networks in Irregular Fields a Challenge?" Wireless Personal Communications 114, no. 3 (May 23, 2020): 2017–42. http://dx.doi.org/10.1007/s11277-020-07460-6.
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Abstract Wireless sensor networks have been considered as an emerging technology for numerous applications of cyber-physical systems. These applications often require the deployment of sensor nodes in various anisotropic fields. Localization in anisotropic fields is a challenge because of the factors such as non-line of sight communications, irregularities of terrains, and network holes. Traditional localization techniques, when applied to anisotropic or irregular fields, result in colossal location estimation errors. To improve location estimations, this paper presents a comparative analysis of available localization techniques based on taxonomy framework. A detailed discussion on the importance of localization of sensor nodes in irregular fields from the reported real-life applications is presented along with challenges faced by existing localization techniques. Further, taxonomy based on techniques adopted by localization methods to address the effects of irregular fields on location estimations is reported. Finally, using the designed taxonomy framework, a comparative analysis of different localization techniques addressing irregularities and the directions towards the development of an optimal localization technique is addressed.
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Gharghan, Sadik, Saleem Mohammed, Ali Al-Naji, Mahmood Abu-AlShaeer, Haider Jawad, Aqeel Jawad, and Javaan Chahl. "Accurate Fall Detection and Localization for Elderly People Based on Neural Network and Energy-Efficient Wireless Sensor Network." Energies 11, no. 11 (October 23, 2018): 2866. http://dx.doi.org/10.3390/en11112866.
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Falls are the main source of injury for elderly patients with epilepsy and Parkinson’s disease. Elderly people who carry battery powered health monitoring systems can move unhindered from one place to another according to their activities, thus improving their quality of life. This paper aims to detect when an elderly individual falls and to provide accurate location of the incident while the individual is moving in indoor environments such as in houses, medical health care centers, and hospitals. Fall detection is accurately determined based on a proposed sensor-based fall detection algorithm, whereas the localization of the elderly person is determined based on an artificial neural network (ANN). In addition, the power consumption of the fall detection system (FDS) is minimized based on a data-driven algorithm. Results show that an elderly fall can be detected with accuracy levels of 100% and 92.5% for line-of-sight (LOS) and non-line-of-sight (NLOS) environments, respectively. In addition, elderly indoor localization error is improved with a mean absolute error of 0.0094 and 0.0454 m for LOS and NLOS, respectively, after the application of the ANN optimization technique. Moreover, the battery life of the FDS is improved relative to conventional implementation due to reduced computational effort. The proposed FDS outperforms existing systems in terms of fall detection accuracy, localization errors, and power consumption.
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Arbula, Damir, and Sandi Ljubic. "Indoor Localization Based on Infrared Angle of Arrival Sensor Network." Sensors 20, no. 21 (November 4, 2020): 6278. http://dx.doi.org/10.3390/s20216278.
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Accurate, inexpensive, and reliable real-time indoor localization holds the key to the full potential of the context-aware applications and location-based Internet of Things (IoT) services. State-of-the-art indoor localization systems are coping with the complex non-line-of-sight (NLOS) signal propagation which hinders the use of proven multiangulation and multilateration methods, as well as with prohibitive installation costs, computational demands, and energy requirements. In this paper, we present a novel sensor utilizing low-range infrared (IR) signal in the line-of-sight (LOS) context providing high precision angle-of-arrival (AoA) estimation. The proposed sensor is used in the pragmatic solution to the localization problem that avoids NLOS propagation issues by exploiting the powerful concept of the wireless sensor network (WSN). To demonstrate the proposed solution, we applied it in the challenging context of the supermarket cart navigation. In this specific use case, a proof-of-concept navigation system was implemented with the following components: IR-AoA sensor prototype and the corresponding WSN used for cart localization, server-side application programming interface (API), and client application suite consisting of smartphone and smartwatch applications. The localization performance of the proposed solution was assessed in, altogether, four evaluation procedures, including both empirical and simulation settings. The evaluation outcomes are ranging from centimeter-level accuracy achieved in static-1D context up to 1 m mean localization error obtained for a mobile cart moving at 140 cm/s in a 2D setup. These results show that, for the supermarket context, appropriate localization accuracy can be achieved, along with the real-time navigation support, using readily available IR technology with inexpensive hardware components.
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Yu, Xiaosheng, Peng Ji, Ying Wang, and Hao Chu. "Mean Shift-Based Mobile Localization Method in Mixed LOS/NLOS Environments for Wireless Sensor Network." Journal of Sensors 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/5325174.
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Mobile localization estimation is a significant research topic in the fields of wireless sensor network (WSN), which is of concern greatly in the past decades. Non-line-of-sight (NLOS) propagation seriously decreases the positioning accuracy if it is not considered when the mobile localization algorithm is designed. NLOS propagation has been a serious challenge. This paper presents a novel mobile localization method in order to overcome the effects of NLOS errors by utilizing the mean shift-based Kalman filter. The binary hypothesis is firstly carried out to detect the measurements which contain the NLOS errors. For NLOS propagation condition, mean shift algorithm is utilized to evaluate the means of the NLOS measurements and the data association method is proposed to mitigate the NLOS errors. Simulation results show that the proposed method can provide higher location accuracy in comparison with some traditional methods.
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Cheng, Long, Sihang Huang, Mingkun Xue, and Yangyang Bi. "A Robust Localization Algorithm Based on NLOS Identification and Classification Filtering for Wireless Sensor Network." Sensors 20, no. 22 (November 19, 2020): 6634. http://dx.doi.org/10.3390/s20226634.
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With the rapid development of information and communication technology, the wireless sensor network (WSN) has shown broad application prospects in a growing number of fields. The non-line-of-sight (NLOS) problem is the main challenge to WSN localization, which seriously reduces the positioning accuracy. In this paper, a robust localization algorithm based on NLOS identification and classification filtering for WSN is proposed to solve this problem. It is difficult to use a single filter to filter out NLOS noise in all cases since NLOS cases are extremely complicated in real scenarios. Therefore, in order to improve the robustness, we first propose a NLOS identification strategy to detect the severity of NLOS, and then NLOS situations are divided into two categories according to the severity: mild NLOS and severe NLOS. Secondly, classification filtering is performed to obtain respective position estimates. An extended Kalman filter is applied to filter line-of-sight (LOS) noise. For mild NLOS, the large outliers are clipped by the redescending score function in the robust extended Kalman filter, yielding superior performance. For severe NLOS, a severe NLOS mitigation algorithm based on LOS reconstruction is proposed, in which the average value of NLOS error is estimated and the measurements are reconstructed and corrected for subsequent positioning. Finally, an interactive multiple model algorithm is employed to obtain the final positioning result by weighting the position estimation of LOS and NLOS. Simulation and experimental results show that the proposed algorithm can effectively suppress NLOS error and obtain higher positioning accuracy when compared with existing algorithms.
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Hu, Nan, Chuan Lin, Fangjun Luan, Chengdong Wu, Qi Song, and Li Chen. "A mobile localization method based on a robust extend Kalman filter and improved M-estimation in Internet of things." International Journal of Distributed Sensor Networks 16, no. 9 (September 2020): 155014772096123. http://dx.doi.org/10.1177/1550147720961235.
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As the key technology for Internet of things, wireless sensor networks have received more attentions in recent years. Mobile localization is one of the significant topics in wireless sensor networks. In wireless sensor network, non-line-of-sight propagation is a common phenomenon leading to the growing non-line-of-sight error. It is a fatal impact for the localization accuracy of the mobile target. In this article, a novel method based on the nearest neighbor variable estimation is proposed to mitigate the non-line-of-sight error. First, the linear regression model of the extended Kalman filter is used to obtain the residual of the distance measurement value. After that, the residual analysis is used to complete the identification of the measurement value state. Then, by analyzing the statistical characteristics of the non-line-of-sight residual, the nearest neighbor variable estimation is proposed to estimate the probability density function of residual. Finally, the improved M-estimation is proposed to locate the mobile robot. Experiment results prove that the accuracy and robustness of the proposed algorithm are better than other methods in the mixed line-of-sight/non-line-of-sight environment. The proposed algorithm effectively inhibits the non-line-of-sight error.
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Galajda, Pavol, Alena Galajdova, Stanislav Slovak, Martin Pecovsky, Milos Drutarovsky, Marek Sukop, and Ihab BA Samaneh. "Robot vision ultra-wideband wireless sensor in non-cooperative industrial environments." International Journal of Advanced Robotic Systems 15, no. 4 (July 1, 2018): 172988141879576. http://dx.doi.org/10.1177/1729881418795767.
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In this article, the ultra-wideband technology for localization and tracking of the robot gripper (behind the obstacles) in industrial environments is presented. We explore the possibilities of ultra-wideband radar sensor network employing the centralized data fusion method that can significantly improve tracking capabilities in a complex environment. In this article, we present ultra-wideband radar sensor network hardware demonstrator that uses a new wireless ultra-wideband sensor with an embedded controller to detect and track online or off-line movement of the robot gripper. This sensor uses M-sequence ultra-wideband radars front-end and low-cost powerful processors on a system on chip with the advanced RISC machines (ARM) architecture as a main signal processing block. The ARM-based single board computer ODROID-XU4 platform used in our ultra-wideband sensor can provide processing power for the preprocessing of received raw radar signals, algorithms for detection and estimation of target’s coordinates, and finally, compression of data sent to the data fusion center. Data streams of compressed target coordinates are sent from each sensor node to the data fusion center in the central node using standard the wireless local area network (WLAN) interface that is the feature of the ODROID-XU4 platform. The article contains experimental results from measurements where sensors and antennas are located behind the wall or opaque material. Experimental testing confirmed capability of real-time performance of developed ultra-wideband radar sensor network hardware and acceptable precision of software. The introduced modular architecture of ultra-wideband radar sensor network can be used for fast development and testing of new real-time localization and tracking applications in industrial environments.
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Wang, Yan, Jinquan Hang, Long Cheng, Chen Li, and Xin Song. "A Hierarchical Voting Based Mixed Filter Localization Method for Wireless Sensor Network in Mixed LOS/NLOS Environments." Sensors 18, no. 7 (July 19, 2018): 2348. http://dx.doi.org/10.3390/s18072348.
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In recent years, the rapid development of microelectronics, wireless communications, and electro-mechanical systems has occurred. The wireless sensor network (WSN) has been widely used in many applications. The localization of a mobile node is one of the key technologies for WSN. Among the factors that would affect the accuracy of mobile localization, non-line of sight (NLOS) propagation caused by a complicated environment plays a vital role. In this paper, we present a hierarchical voting based mixed filter (HVMF) localization method for a mobile node in a mixed line of sight (LOS) and NLOS environment. We firstly propose a condition detection and distance correction algorithm based on hierarchical voting. Then, a mixed square root unscented Kalman filter (SRUKF) and a particle filter (PF) are used to filter the larger measurement error. Finally, the filtered results are subjected to convex optimization and the maximum likelihood estimation to estimate the position of the mobile node. The proposed method does not require prior information about the statistical properties of the NLOS errors and operates in a 2D scenario. It can be applied to time of arrival (TOA), time difference of arrival (TDOA), received signal (RSS), and other measurement methods. The simulation results show that the HVMF algorithm can efficiently reduce the effect of NLOS errors and can achieve higher localization accuracy than the Kalman filter and PF. The proposed algorithm is robust to the NLOS errors.
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Ye, Tingcong, Michael Walsh, Peter Haigh, John Barton, Alan Mathewson, and Brendan O’Flynn. "An Experimental Evaluation of IEEE 802.15.4a Ultra Wide Band Technology for Precision Indoor Ranging." International Journal of Ambient Computing and Intelligence 4, no. 2 (April 2012): 48–63. http://dx.doi.org/10.4018/jaci.2012040104.
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Ultra Wide Band (UWB) wireless transmission has recently been the object of considerable attention in the field of next generation location aware wireless sensor networks (WSNs). This is due to its fine time resolution, energy efficiency and robustness to interference in harsh environments. This paper presents a thorough applied examination of prototype IEEE 802.15.4a impulse UWB transceiver technology to quantify the effect of line of sight (LOS) and non line of sight (NLOS) ranging in real indoor and outdoor environments. The results included draw on an extensive array of experiments that fully characterize the 802.15.4a UWB transceiver technology, its reliability and ranging capabilities for the first time. The goal of this work is to validate the technology as a dependable wireless communication mechanism for the subset of sensor network localization applications where reliability and precision positions are key concerns.
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Jing, Nan, Yu Sun, Lin Wang, and Jinxin Shan. "Fine-grained wireless propagation ambience sensing." International Journal of Distributed Sensor Networks 14, no. 10 (October 2018): 155014771880469. http://dx.doi.org/10.1177/1550147718804699.
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The ubiquitous wireless network infrastructure and the need of people’s indoor sensing inspire the work leveraging wireless signal into broad spectrum for indoor applications, including indoor localization, human–computer interaction, and activity recognition. To provide an accurate model selection or feature template, these applications take the system reliability of the signal in line-of-sight and non-line-of-sight propagation into account. Unfortunately, these two types of signal propagation are analyzed in static or mobile scenario separately. Our question is how to use the wireless signal to estimate the signal propagation ambience to facilitate the adaptive complex environment? In this paper, we exploit the Fresnel zone theory and channel state information (CSI) to model the static and mobile ambience detectors. Considering the spatiotemporal correlation of indoor activities, the propagation ambience can be divided into three categories: line-of-sight (LOS), non-line-of-sight (NLOS), and semi-line-of-sight (SLOS), which is used to represent the intermediate state between the LOS and NLOS propagation ambience during user movement. Leveraging the hidden Markov model to estimate the dynamic propagation ambience in the mobile environment, a novel propagation ambience identification method, named Ambience Sensor (Asor), is proposed to improve the real-time performance for the upper applications. Furthermore, Asor is integrated into a localization algorithm, Asor-based localization system (Aloc), to confirm the effectiveness. We prototype Asor and Aloc based on commodity WiFi infrastructure without any hardware modification. In addition, the real-time performance of Asor is evaluated by conducting tracking experiments. The experimental results show that the median detection rate of propagation ambience is superior to the existing methods in absence of any a priori hypothesis of static or mobile scenarios.
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Wen, Wu Song, and Lu Wang. "Path Planning of Mobile Beacon for Localization Based on Distribution of Unknown Nodes." Advanced Materials Research 712-715 (June 2013): 1933–37. http://dx.doi.org/10.4028/www.scientific.net/amr.712-715.1933.
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For wireless sensor network (WSN), without full consideration of the influences of unknown nodes distribution and density when planning beacons moving path, most of existing localization methods have lower efficiency. In this paper, beacon model is presented according to the theory of equal distance 3-optimal-coverage, a new heuristic path planning method is proposed for the ROI in which unknown nodes distribute randomly and the node density is limited, this proposed method can make on-line decision for the moving direction and distance over every step. Simulations show that the proposed scheme is efficient.
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Cheng, Long, Yifan Li, Yan Wang, Yangyang Bi, Liang Feng, and Mingkun Xue. "A Triple-Filter NLOS Localization Algorithm Based on Fuzzy C-means for Wireless Sensor Networks." Sensors 19, no. 5 (March 10, 2019): 1215. http://dx.doi.org/10.3390/s19051215.
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With the rapid development of communication technology in recent years, Wireless Sensor Network (WSN) has become a promising research project. WSN is widely applied in a number of fields such as military, environmental monitoring, space exploration and so on. The non-line-of-sight (NLOS) localization is one of the most essential techniques for WSN. However, the NLOS propagation of WSN is largely influenced by many factors. Hence, a triple filters mixed Kalman Filter (KF) and Unscented Kalman Filter (UKF) voting algorithm based on Fuzzy-C-Means (FCM) and residual analysis (TF-FCM) has been proposed to cope with this problem. Firstly, an NLOS identification algorithm based on residual analysis is used to identify NLOS errors. Then, an NLOS correction algorithm based on voting and NLOS errors classification algorithm based on FCM are used to process the NLOS measurements. Hard NLOS measurements and soft NLOS measurements are classified by FCM classification. Secondly, KF and UKF are applied to filter two categories of NLOS measurements. Thirdly, maximum likelihood localization (ML) is employed to estimate the position of mobile nodes. The simulation result confirms that the accuracy and robustness of TF-FCM are better than IMM, UKF and KF. Finally, an experiment is conducted to test and verify our algorithm which obtains higher localization accuracy.
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T.I, Onyeyili, Alumona T.L, and Nwizu C.U. "Improving the Tracking Performance of a Wireless Sensor Network on a Water Pipe Line Using Leak Detection and Localization Technique." IJARCCE 8, no. 11 (November 30, 2019): 61–67. http://dx.doi.org/10.17148/ijarcce.2019.81112.
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Wang, Yubo, Weimin Yang, Zheng Wang, Wenjun Zhou, Liang Li, and Hongsen Zou. "Location of Moving Targets in Substation Non-Line-of-Sight Environment." Sensors 19, no. 23 (December 3, 2019): 5321. http://dx.doi.org/10.3390/s19235321.
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In substations, a localization system based on a wireless sensor network (WSN) is a challenge, because the propagation of the measured signal could be blocked by various devices. In other words, non-line-of-sight (NLOS) propagation, where the signal propagation path is occluded, will affect measurement accuracy. A novel localization method based on a two-step weighted least squares and a probability distribution function is proposed to reduce the influence of NLOS error on the localization result. In this method, the initial multi-group localization result is obtained by the two-step weight weighted least-squares method, and the probability distribution function of the target is constructed by using the initial localization results, which can effectively reduce the influence of the NLOS error on the localization result. The simulation and test results show that the proposed method can keep the coordinate error within 30 cm in the substation. Compared with the localization result of two-step weighted least-squares (TSWLS) method, the average localization error is reduced by more than 1 m. Compared with the other two similar algorithms, the localization accuracy is improved by more than 50%. The tested results show that the localization performance of the method is robustness in the NLOS environment of the substation. While ensuring stability, the proposed algorithm is less efficient than some existing ones. However, under the calculation conditions of ordinary computers, the average single-point calculation time is less than 0.1 s, which can meet the needs of applications in substations.
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Cheng, Long, Yong Wang, Mingkun Xue, and Yangyang Bi. "An Indoor Robust Localization Algorithm Based on Data Association Technique." Sensors 20, no. 22 (November 18, 2020): 6598. http://dx.doi.org/10.3390/s20226598.
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As a key technology of the Internet of Things, wireless sensor network (WSN) has been used widely in indoor localization systems. However, when the sensor is transmitting signals, it is affected by the non-line-of-sight (NLOS) transmission, and the accuracy of the positioning result is decreased. Therefore, solving the problem of NLOS positioning has become a major focus for indoor positioning. This paper focuses on solving the problem of NLOS transmission that reduces positioning accuracy in indoor positioning. We divided the anchor nodes into several groups and obtained the position information of the target node for each group through the maximum likelihood estimation (MLE). By identifying the NLOS method, a part of the position estimates polluted by NLOS transmission was discarded. For the position estimates that passed the hypothesis testing, a corresponding poly-probability matrix was established, and the probability of each position estimate from line-of-sight (LOS) and NLOS was calculated. The position of the target was obtained by combining the probability with the position estimate. In addition, we also considered the case where there was no continuous position estimation through hypothesis testing and through the NLOS tracking method to avoid positioning errors. Simulation and experimental results show that the algorithm proposed has higher positioning accuracy and higher robustness than other algorithms.
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Popescu, Dan, Cristian Dragana, Florin Stoican, Loretta Ichim, and Grigore Stamatescu. "A Collaborative UAV-WSN Network for Monitoring Large Areas." Sensors 18, no. 12 (November 30, 2018): 4202. http://dx.doi.org/10.3390/s18124202.
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Large-scale monitoring systems have seen rapid development in recent years. Wireless sensor networks (WSN), composed of thousands of sensing, computing and communication nodes, form the backbone of such systems. Integration with unmanned aerial vehicles (UAVs) leads to increased monitoring area and to better overall performance. This paper presents a hybrid UAV-WSN network which is self-configured to improve the acquisition of environmental data across large areas. A prime objective and novelty of the heterogeneous multi-agent scheme proposed here is the optimal generation of reference trajectories, parameterized after inter- and intra-line distances. The main contribution is the trajectory design, optimized to avoid interdicted regions, to pass near predefined way-points, with guaranteed communication time, and to minimize total path length. Mixed-integer description is employed into the associated constrained optimization problem. The second novelty is the sensor localization and clustering method for optimal ground coverage taking into account the communication information between UAV and a subset of ground sensors (i.e., the cluster heads). Results show improvements in both network and data collection efficiency metrics by implementing the proposed algorithms. These are initially evaluated by means of simulation and then validated on a realistic WSN-UAV test-bed, thus bringing significant practical value.
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Wang, Yan, Huihui Jie, and Long Cheng. "A Fusion Localization Method based on a Robust Extended Kalman Filter and Track-Quality for Wireless Sensor Networks." Sensors 19, no. 17 (August 21, 2019): 3638. http://dx.doi.org/10.3390/s19173638.
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As one of the most essential technologies, wireless sensor networks (WSNs) integrate sensor technology, embedded computing technology, and modern network and communication technology, which have become research hotspots in recent years. The localization technique, one of the key techniques for WSN research, determines the application prospects of WSNs to a great extent. The positioning errors of wireless sensor networks are mainly caused by the non-line of sight (NLOS) propagation, occurring in complicated channel environments such as the indoor conditions. Traditional techniques such as the extended Kalman filter (EKF) perform unsatisfactorily in the case of NLOS. In contrast, the robust extended Kalman filter (REKF) acquires accurate position estimates by applying the robust techniques to the EKF in NLOS environments while losing efficiency in LOS. Therefore it is very hard to achieve high performance with a single filter in both LOS and NLOS environments. In this paper, a localization method using a robust extended Kalman filter and track-quality-based (REKF-TQ) fusion algorithm is proposed to mitigate the effect of NLOS errors. Firstly, the EKF and REKF are used in parallel to obtain the location estimates of mobile nodes. After that, we regard the position estimates as observation vectors, which can be implemented to calculate the residuals in the Kalman filter (KF) process. Then two KFs with a new observation vector and equation are used to further filter the estimates, respectively. At last, the acquired position estimates are combined by the fusion algorithm based on the track quality to get the final position vector of mobile node, which will serve as the state vector of both KFs at the next time step. Simulation results illustrate that the TQ-REKF algorithm yields better positioning accuracy than the EKF and REKF in the NLOS environment. Moreover, the proposed algorithm achieves higher accuracy than interacting multiple model algorithm (IMM) with EKF and REKF.
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Zhu, Quan Zheng, Le Yang, and Wei Li. "Simple and Robust RSSI Estimation Using M-Estimator." Advanced Materials Research 756-759 (September 2013): 3946–51. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.3946.
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Accurate estimation of the received signal strength indicator (RSSI) from a set of sequentially measured ones is essential for a number of practical applications including link quality evaluation for sensor network routing, indoor wireless localization and more recently, handover in health monitoring systems. This paper develops a simple and robust RSSI estimation algorithm that can effectively mitigate the magnitude variation in the RSSI measurements due to the combined effects of fast fading and non-line-of-sight (NLOS) signal propagation. The new method is based on the robust M-estimator and we propose a simple approach that requires bisection search only to obtain the robust RSSI estimate. Computer simulations corroborate the validity of the theoretical developments and demonstrate the superior performance of the proposed technique over commonly adopted RSSI estimation methods including the simple moving average, the discrete Kalman filter and the exponential smoothing.
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Jatlaoui, Mohamed Mehdi, Daniela Dragomirescu, Mariano Ercoli, Michael Krämer, Samuel Charlot, Patrick Pons, Hervé Aubert, and Robert Plana. "Wireless communicating nodes at 60 GHz integrated on flexible substrate for short-distance instrumentation in aeronautics and space." International Journal of Microwave and Wireless Technologies 4, no. 1 (November 17, 2011): 109–17. http://dx.doi.org/10.1017/s1759078711000961.
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This paper presents the research done at LAAS-CNRS and in the context of “NANOCOMM” project. This project aims to demonstrate the potential of nanotechnology for the development of reconfigurable, ultra-sensitive, low consumption, and easy installation sensor networks with high performance in terms of reliability in line with the requirements of aeronautics and space. Each node of the sensor network is composed of nano-sensors, transceiver, and planar antenna. In this project, three-dimensional (3D) heterogeneous integration of these different components, on flexible polyimide substrate, is planned. Two types of sensors are selected for this project: strain gauges are used for the structure health monitoring (SHM) application and electrochemical cells are used to demonstrate the ability to detect frost phenomenon. After processing, sensors data are processed and transmitted to the reader unit using an ultra-wide band (UWB) transceiver. (digital baseband and radiofrequency (RF) head). The design and implementation of reconfigurable wireless communication architectures are provided according to the application requirements using nanoscale 65 nm CMOS technology. It is proposed to integrate on flexible substrate the transceiver using the flip-chip technique. A 60 GHz planar antenna is connected to the transceiver for the wireless data transmission. This paper is focused on the 3D integration techniques and the technological process used for the realization of such communicating nano-objects on polyimide substrate. The first assembly tests were carried out. Tests of interconnections quality and electrical contacts (Daisy Chain, calibration kit, etc.) were also performed with good results. A bumps contact resistance of 15 mΩ is measured.