An Adaptive Location-Based Tracking Algorithm Using Wireless Sensor Network for Smart Factory Environment

In recent years, how to improve the performance of smart factories and reduce the cost of operation has been the focus of industry attention. This study proposes a new type of location-based service (LBS) to improve the accuracy of location information delivered by self-propelled robots. Traditional localization algorithms based on signal strength cannot produce accurate localization results because of the multipath effect. This study proposes a localization algorithm that combines the Kalman filter (KF) and the adaptive-network-based fuzzy inference system (ANFIS). Specifically, the KF was adopted to eliminate noise during the signal transmission process. Through the learning of the ANFIS, the environment parameter suitable for the target was generated, to overcome the deficiency of traditional localization algorithms that cannot obtain real signal strength. In this study, an experiment was conducted in a real environment to compare the proposed localization algorithm with other commonly used algorithms. The experimental results show that the proposed localization algorithm produces minimal errors and stable localization results.

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Accuracy analysis of BLE beacon-based localization in smart buildings

Journal of Ambient Intelligence and Smart Environments ◽

10.3233/ais-210607 ◽

2021 ◽

pp. 1-20

Author(s):

Rosen Ivanov

Keyword(s):

Use Case ◽

Accuracy Analysis ◽

Localization Algorithm ◽

Computing Power ◽

Smart Buildings ◽

Localization Accuracy ◽

Positioning Systems ◽

Deployment Strategy ◽

Accurate Localization ◽

Localization Algorithms

The majority of services that deliver personalized content in smart buildings require accurate localization of their clients. This article presents an analysis of the localization accuracy using Bluetooth Low Energy (BLE) beacons. The aim is to present an approach to create accurate Indoor Positioning Systems (IPS) using algorithms that can be implemented in real time on platforms with low computing power. Parameters on which the localization accuracy mostly depends are analyzed: localization algorithm, beacons’ density, deployment strategy, and noise in the BLE channels. An adaptive algorithm for pre-processing the signals from the beacons is proposed, which aims to reduce noise in beacon’s data and to capture visitor’s dynamics. The accuracy of five range-based localization algorithms in different use case scenarios is analyzed. Three of these algorithms are specially designed to be less sensitive to noise in radio channels and require little computing power. Experiments conducted in a simulated and real environment show that using proposed algorithms the localization accuracy less than 1 m can be obtained.

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A Localization Algorithm for Wireless Sensor Network Based on the Weighted Correction in Geometric Measurement

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.740.823 ◽

2015 ◽

Vol 740 ◽

pp. 823-829

Author(s):

Meng Long Cao ◽

Chong Xin Yang

Keyword(s):

Wireless Sensor Network ◽

Sensor Network ◽

Signal Strength ◽

Received Signal Strength ◽

Wireless Sensor ◽

Localization Algorithm ◽

Received Signal Strength Indicator ◽

Localization Algorithms ◽

Weighted Centroid Localization ◽

Geometric Measurement

Firstly, the characteristics of regular Zigbee localization algorithms-the received signal strength indicator algorithm (RSSI) and the weighted centroid localization algorithm are introduced. Then, the factors of the errors existing in the aforementioned algorithms are analyzed. Based on these above, the improved RSSI algorithm-correction geometric measurement based on weighted is proposed. Finally, utilizing this algorithm to design and implement the localization nodes, which have the CC2431 wireless microcontroller on them. The simulation and experimental results show that the accuracy of this localization algorithm improved about 2%, comparing with the regular algorithms.

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A novel vertical handover algorithm based on Adap-tive Neuro-Fuzzy Inference System (ANFIS)

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i1.8592 ◽

2018 ◽

Vol 7 (1) ◽

pp. 74 ◽

Cited By ~ 4

Author(s):

Eman Zakaria ◽

Amr A.Awamry ◽

Abdelkerim Taman ◽

Abdelhalim Zekry

Keyword(s):

Fuzzy Inference ◽

Heterogeneous Wireless Networks ◽

Access Network ◽

Signal Strength ◽

Vertical Handover ◽

Inference System ◽

Internet Connection ◽

Neuro Fuzzy ◽

Always Best Connected

Nowadays, there is an increased demand on an Internet connection anywhere at any time. Therefore, one has to exploit all available heterogeneous wireless networks where the target is achieving the Always Best Connected (ABC) among the different networks like UMTS, WiMAX, and WLAN. So, vertical handover techniques are used to ensure the best connectivity anywhere at any time. In this paper, novel ANFIS-based vertical handover is presented and compared with TOPSIS algorithm and other algorithms as a representative of Multi-criteria decision making (MCDM) algorithm's family. The simulation results show that the proposed handover technique provided better performance in terms of minimizing the time delay and improving the quality of service (QOS). This is because ANFIS requires iterations only in training phase otherwise, it has a much faster response. Our simulations considered the effect of many practical parameters on handover, such as subscriber speed, jitter, initial delay, bandwidth and received signal strength (RSS).According to these parameters, output values produced, which is utilized to choose the best candidate access network.

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Localization of isotropic and anisotropic wireless sensor networks in 2D and 3D fields

Telecommunication Systems ◽

10.1007/s11235-021-00862-2 ◽

2021 ◽

Author(s):

Soumya J. Bhat ◽

K. V. Santhosh

Keyword(s):

Least Squares Method ◽

Sensor Nodes ◽

Wireless Sensor ◽

Localization Algorithm ◽

Localization Accuracy ◽

Smart Systems ◽

Reference Node ◽

Accurate Localization ◽

Localization Algorithms ◽

Node Ratio

AbstractInternet of Things (IoT) has changed the way people live by transforming everything into smart systems. Wireless Sensor Network (WSN) forms an important part of IoT. This is a network of sensor nodes that is used in a vast range of applications. WSN is formed by the random deployment of sensor nodes in various fields of interest. The practical fields of deployment can be 2D or 3D, isotropic or anisotropic depending on the application. The localization algorithms must provide accurate localization irrespective of the type of field. In this paper, we have reported a localization algorithm called Range Reduction Based Localization (RRBL). This algorithm utilizes the properties of hop-based and centroid methods to improve the localization accuracy in various types of fields. In this algorithm, the location unknown nodes identify the close-by neighboring nodes within a predefined threshold and localize themselves by identifying and reducing the probable range of existence from these neighboring nodes. The nodes which do not have enough neighbors are localized using the least squares method. The algorithm is tested in various irregular and heterogeneous conditions. The results are compared with a few state-of-the-art hop-based and centroid-based localization techniques. RRBL has shown an improvement in localization accuracy of 28% at 10% reference node ratio and 26% at 20% reference node ratio when compared with other localization algorithms.

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A Bronchoscope Localization Method Using an Augmented Reality Co-Display of Real Bronchoscopy Images with a Virtual 3D Bronchial Tree Model

Sensors ◽

10.3390/s20236997 ◽

2020 ◽

Vol 20 (23) ◽

pp. 6997

Author(s):

Jong-Chih Chien ◽

Jiann-Der Lee ◽

Ellen Su ◽

Shih-Hong Li

Keyword(s):

Fuzzy Inference ◽

Bronchial Tree ◽

Navigation Systems ◽

Tree Model ◽

Flexible Bronchoscope ◽

Inference System ◽

Registration Method ◽

Image Guide ◽

Accurate Localization ◽

Current Location

In recent years, Image-Guide Navigation Systems (IGNS) have become an important tool for various surgical operations. In the preparations for planning a surgical path, verifying the location of a lesion, etc., it is an essential tool; in operations such as bronchoscopy, which is the procedure for the inspection and retrieval of diagnostic samples for lung-related surgeries, it is even more so. The IGNS for bronchoscopy uses 2D-based images from a flexible bronchoscope to navigate through the bronchial airways in order to reach the targeted location. In this procedure, the accurate localization of the scope becomes very important, because incorrect information could potentially cause a surgeon to mistakenly direct the scope down the wrong passage. It would be a great aid for the surgeon to be able to visualize the bronchoscope images alongside the current location of the bronchoscope. For this purpose, in this paper, we propose a novel registration method to match real bronchoscopy images with virtual bronchoscope images from a 3D bronchial tree model built using computed tomography (CT) image stacks in order to obtain the current 3D position of the bronchoscope in the airways. This method is a combination of a novel position-tracking method using the current frames from the bronchoscope and the verification of the position of the real bronchoscope image against an image extracted from the 3D model using an adaptive-network-based fuzzy inference system (ANFIS)-based image matching method. Experimental results show that the proposed method performs better than the other methods used in the comparison.

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The Additive Manufacturing Process Setting Feasible Space Exploration and Association With Variable Product Platform Design

Volume 1A: 35th Computers and Information in Engineering Conference ◽

10.1115/detc2015-46665 ◽

2015 ◽

Author(s):

Xiling Yao ◽

Seung Ki Moon ◽

Guijun Bi

Keyword(s):

Additive Manufacturing ◽

Production Cost ◽

Fuzzy Inference ◽

Design Parameters ◽

Product Platform ◽

Activity Based Costing ◽

Inference System ◽

Wide Range ◽

The Cost ◽

Feasible Space

Additive manufacturing (AM) has evolved from prototyping to functional part fabrication for a wide range of applications. AM process settings have significant impact to both part quality and production cost, which makes the process setting adjustment a key consideration during product development and manufacturing. This research aims to investigate the relationship among process setting adjustments, costs, and component design parameters. Platform-based product family design and process family planning are used in this research as the strategy to provide product diversity while controlling cost. In this paper, the concept of a variable product platform and its corresponding AM process setting variants are proposed to describe the characteristics of additive manufactured platform modules. AM production cost drivers are identified. A Fuzzy Time-Driven Activity-Based Costing (FTDABC) approach is proposed to estimate the cost increment due to process setting adjustments. Time equations in the FTDABC are computed in a trained Adaptive Neuro-Fuzzy Inference System (ANFIS). The process setting adjustment’s feasible space boundary searching is formulated as an optimization problem, with minimizing the cost increment and maximizing the design parameters’ variability as objective functions. The upper and lower limits of variable platform module’s design parameters are mapped from process setting adjustments in a Mamdani-type expert system. The proposed methodology is illustrated in the analysis of a honeycomb-shaped bumper, which is taken as a variable platform module for a family of R/C racing cars. The result provides boundaries for design parameters, which confines the AM-enabled design space for product platform modules.

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Fuzzy control based on new type of Takagi-Sugeno fuzzy inference system

2015 International Siberian Conference on Control and Communications (SIBCON) ◽

10.1109/sibcon.2015.7146977 ◽

2015 ◽

Cited By ~ 6

Author(s):

Igor V. Anikin ◽

Igor P. Zinoviev

Keyword(s):

Fuzzy Control ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Inference System ◽

New Type ◽

Takagi Sugeno

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Compensation of Voltage Sags with Phase-Jumps through DVR with Minimum VA Rating Using PSO based ANFIS Controller

International Journal of Swarm Intelligence Research ◽

10.4018/jsir.2010070102 ◽

2010 ◽

Vol 1 (3) ◽

pp. 19-33

Author(s):

Anil Kumar Ramakuru ◽

Siva G. Kumar ◽

Kalyan B. Kumar ◽

Mahesh K. Mishra

Keyword(s):

Distribution System ◽

Fuzzy Inference ◽

Voltage Sags ◽

Sinusoidal Voltage ◽

Dynamic Voltage Restorer ◽

Inference System ◽

Detailed Simulation ◽

Dynamic Voltage ◽

In Series ◽

The Cost

Dynamic Voltage Restorer (DVR) restores the distribution system load voltage to a nominal balanced sinusoidal voltage, when the source voltage has distortions, sag/swell and unbalances. DVR has to inject a required amount of Volt-Amperes (VA) into the system to maintain a nominal balanced sinusoidal voltage at the load. Keeping the cost effectiveness of DVR, it is desirable to have a minimum VA rating of the DVR, for a given system without compromising compensation capability. In this regard, a methodology has been proposed in this work to minimize VA rating of DVR. The optimal angle at which DVR voltage has to be injected in series to the line impedance so as to have minimum VA loading on DVR as well as the removal of phase jumps in the three-phases is computed by the Particle Swarm Optimization (PSO) technique. The proposed method is able to compensate voltage sags with phase jumps by keeping the DVR voltage and power ratings minimum, effectively. The proposed PSO methodology together with adaptive neuro–fuzzy inference system used to make the DVR work online with minimum VA loading. The proposed method has been validated through detailed simulation studies.

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New Type of Takagi-Sugeno Fuzzy Inference System as Universal Approximator

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.598.453 ◽

2014 ◽

Vol 598 ◽

pp. 453-458 ◽

Cited By ~ 8

Author(s):

lgor Anikin ◽

lgor Zinoviev

Keyword(s):

Fuzzy Inference System ◽

Fuzzy Inference ◽

Fuzzy Regression ◽

Fuzzy Inference Systems ◽

Inference System ◽

Fuzzy Approximation ◽

Learning Procedure ◽

New Type ◽

Inference Systems ◽

Takagi Sugeno

A new type of fuzzy inference systems (FIS) is presenting. It is based on Takagi-Sugeno fuzzy inference system. New FIS has been called the enhanced fuzzy regression (EFR). In opposition to the Takagi-Sugeno, new type of FIS has fuzzy coefficients in right parts of the fuzzy rules. Fuzzy approximation theorem has been proved for the EFR. We have suggested learning procedure for EFR inference system.

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