scholarly journals Wireless Localization Method Based on AHP-WKNN and Amendatory AKF

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Su Pan ◽  
Sheng Hua ◽  
Duowei Pan ◽  
Xixia Sun
Keyword(s):  
Noise Reduction ◽  
Nearest Neighbor ◽  
Reduction Method ◽  
Indoor Localization ◽  
Signal Strength ◽  
Reference Points ◽  
Adaptive Kalman Filter ◽  
Wireless Localization ◽  
Analytic Hierarchy ◽  
Hierarchy Process

In this paper, we propose an AHP-WKNN method for indoor localization which combines the Analytic Hierarchy Process (AHP) technique and the Weighted K -nearest Neighbor (WKNN) algorithm. AHP serves to assign weights when WKNN is employed to select fingerprints for indoor positioning. The AHP technique can reasonably enlarge the influence that the received signal strength (RSS) gap between reference points has on the weights, achieving better performance in positioning. This paper also modifies the adaptive Kalman filter (AKF) noise reduction method by correcting the output based on the error between the RSS measurement and the expected output. The modified AKF can track the changes of RSS more effectively and achieve better performance of noise reduction. The simulation result shows that the proposed AHP-WKNN method and the modified AKF can improve positioning accuracy effectively.

scholarly journals BLE Fingerprint Indoor Localization Algorithm Based on Eight-Neighborhood Template Matching

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4859 ◽  
Author(s):  
Mingfeng Li ◽  
Lichen Zhao ◽  
Ding Tan ◽  
Xiaozhe Tong
Keyword(s):  
Template Matching ◽  
Indoor Environment ◽  
Nearest Neighbor ◽  
Indoor Localization ◽  
Signal Strength ◽  
Reference Points ◽  
Localization Algorithm ◽  
K Nearest Neighbor ◽  
Coverage Area ◽  
Fingerprint Database

Aiming at the problem of indoor environment, signal non-line-of-sight propagation and other factors affect the accuracy of indoor locating, an algorithm of indoor fingerprint localization based on the eight-neighborhood template is proposed. Based on the analysis of the signal strength of adjacent reference points in the fingerprint database, the methods for the eight-neighborhood template matching and generation were studied. In this study, the indoor environment was divided into four quadrants for each access point and the expected values of the received signal strength indication (RSSI) difference between the center points and their eight-neighborhoods in different quadrants were chosen as the generation parameters. Then different templates were generated for different access points, and the unknown point was located by the Euclidean distance for the correlation of RSSI between each template and its coverage area in the fingerprint database. With the spatial correlation of fingerprint data taken into account, the influence of abnormal fingerprint on locating accuracy is reduced. The experimental results show that the locating error is 1.0 m, which is about 0.2 m less than both K-nearest neighbor (KNN) and weighted K-nearest neighbor (WKNN) algorithms.

Case Retrieval Mechanism of the Intelligent RCM Analysis System

2010 ◽  
Vol 40-41 ◽  
pp. 686-691
Author(s):  
Zhong Hua Cheng ◽  
Lu Chao Wang ◽  
Li Bo Lv
Keyword(s):  
Nearest Neighbor ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
Analytic Hierarchy ◽  
Retrieval Method ◽  
Analysis Process ◽  
Reliability Centered Maintenance ◽  
Retrieval Mechanism ◽  
Analysis System ◽  
Hierarchy Process

To improve Reliability Centered Maintenance (RCM) analysis efficiency, the Artificial Intelligence (AI) technology, such as case-based reasoning (CBR) is successfully introduced into RCM analysis process and an intelligent RCM analysis (IRCMA) was studied, and an intelligent RCM analysis system (IRCMAS) was developed. The idea for IRCMAS is based on the fact that the historical records of RCM analysis on similar items can be referenced and used for the current RCM analysis of a new item. Case retrieval is the key part of the IRCMAS, of which mechanism has an importance effect on reasoning efficiency of system. In this paper, the IRCMAS is introduced, retrieval mechanism and process of cases are presented, and nearest neighbor retrieval method based on analytic hierarchy process (AHP) is in detail discussed by an example. Design of case retrieval mechanism lays steady foundation for development and realization of intelligent RCM analysis system.

scholarly journals Accurate indoor positioning system based on modify nearest point technique

2022 ◽  
Vol 12 (2) ◽  
pp. 1593
Author(s):  
Omar Ibrahim Mustafa ◽  
Hawraa Lateef Joey ◽  
Noor Abd AlSalam ◽  
Ibrahim Zeghaiton Chaloob
Keyword(s):  
Indoor Localization ◽  
Signal Strength ◽  
Real Data ◽  
Reference Points ◽  
Positioning System ◽  
Indoor Environments ◽  
Nearest Point ◽  
The Impact ◽  
Indoor Positioning System

Wireless fidelity (Wi-Fi) is common technology for indoor environments that use to estimate required distances, to be used for indoor localization. Due to multiple source of noise and interference with other signal, the receive signal strength (RSS) measurements unstable. The impression about targets environments should be available to estimate accurate targets location. The Wi-Fi fingerprint technique is widely implemented to build database matching with real data, but the challenges are the way of collect accurate data to be the reference and the impact of different environments on signals measurements. In this paper, optimum system proposed based on modify nearest point (MNP). To implement the proposal, 78 points measured to be the reference points recorded in each environment around the targets. Also, the case study building is separated to 7 areas, where the segmentation of environments leads to ability of dynamic parameters assignments. Moreover, database based on optimum data collected at each time using 63 samples in each point and the average will be final measurements. Then, the nearest point into specific environment has been determined by compared with at least four points. The results show that the errors of indoor localization were less than (0.102 m).

scholarly journals Received Signal Strength-Based Indoor Localization Using Hierarchical Classification

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1067 ◽  
Author(s):  
Chenbin Zhang ◽  
Ningning Qin ◽  
Yanbo Xue ◽  
Le Yang
Keyword(s):  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Indoor Localization ◽  
Hierarchical Classification ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Position Information ◽  
Area Of Interest

Commercial interests in indoor localization have been increasing in the past decade. The success of many applications relies at least partially on indoor localization that is expected to provide reliable indoor position information. Wi-Fi received signal strength (RSS)-based indoor localization techniques have attracted extensive attentions because Wi-Fi access points (APs) are widely deployed and we can obtain the Wi-Fi RSS measurements without extra hardware cost. In this paper, we propose a hierarchical classification-based method as a new solution to the indoor localization problem. Within the developed approach, we first adopt an improved K-Means clustering algorithm to divide the area of interest into several zones and they are allowed to overlap with one another to improve the generalization capability of the following indoor positioning process. To find the localization result, the K-Nearest Neighbor (KNN) algorithm and support vector machine (SVM) with the one-versus-one strategy are employed. The proposed method is implemented on a tablet, and its performance is evaluated in real-world environments. Experiment results reveal that the proposed method offers an improvement of 1.4% to 3.2% in terms of position classification accuracy and a reduction of 10% to 22% in terms of average positioning error compared with several benchmark methods.

scholarly journals Comprehensive Evaluation of Regional Logistics Competitiveness Considering Multiple Reference Points and Dynamic Index Improved Analytic Hierarchy Process

Symmetry ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 847
Author(s):  
Guodong Liu ◽  
Jianjun Zhu ◽  
Xiaodi Liu
Keyword(s):  
Analytic Hierarchy Process ◽  
Index System ◽  
Comprehensive Evaluation ◽  
Reference Value ◽  
Reference Points ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Analytic Hierarchy ◽  
Regional Logistics ◽  
Hierarchy Process

The development of the regional economy cannot be separated from the support of regional logistics, as the scientific decisions of regional logistics are helpful to promote the healthy development of the regional economy. The comprehensive evaluation of regional logistics competitiveness is the premise and foundation of regional logistics scientific decision-making; the evaluation index system, evaluation data, and index weight are the key links affecting a comprehensive evaluation. In order to improve the quality of a comprehensive evaluation, the study aims at addressing problems such as the evaluation index system of regional logistics competitiveness being complex and scattered, the normalized distribution of the evaluation data being extremely asymmetric and seriously deviating from the normal distribution, and the logic of calculating index weights by the analytic hierarchy process (AHP) not being accurate. To do this, a triangle model of regional logistics competitiveness is constructed based on Porter’s diamond model, and the evaluation index system of regional logistics competitiveness is refined from the three dimensions of resource supply, logistics service, and market demand. Based on the concept of symmetry theory, a normalization method of segmental mapping with quartiles as multiple reference points is proposed, which improves the distribution rationality, symmetry, and distance discrimination of normalized data. The dynamic index scale is used to determine the scale of the analytic hierarchy process, and the evaluation matrix is constructed based on the importance level grading table; the index weights are directly solved without a consistency check, which improves the logical accuracy of a subjective evaluation. Based on the data of segment mapping, the comprehensive evaluation value of the evaluation object is calculated, and the competitiveness of regional logistics is compared and ranked, which improves the differentiation and consistency of the results. Through the comparative analysis of the calculation results, it was proven that the improvement of the data standardization method is necessary when the range is too large. The method in this paper can make the distribution of data standardization with a range too large closer to the normal distribution. It was found that the ranking of regional logistics competitiveness is highly consistent with the total social logistics, and that the total amount of regional logistics has an important reference value for the competitiveness of regional logistics. The ranking calculated by the indicators and methods in this paper has a certain reference value for regional logistics decision-making.

scholarly journals iBeacon indoor localization using trusted-ranges model

2019 ◽  
Vol 15 (1) ◽  
pp. 155014771882430 ◽  
Author(s):  
Tuan D Vy ◽  
Yoan Shin
Keyword(s):  
Nearest Neighbor ◽  
Indoor Localization ◽  
Radio Signal ◽  
Signal Strength ◽  
Signal Propagation ◽  
Received Signal Strength ◽  
Training Phase ◽  
Partition Scheme ◽  
Accurate Localization ◽  
Localization Performance

In this article, we propose an efficient approach to address mobile indoor localization using received signal strength from iBeacon combined with trusted-ranges model. In order to overcome the inconsistency of radio signal propagation, the trusted-ranges model supplies reliable ranges of received signal strength values from a certain number of nearest neighbor iBeacon nodes by classifying received signal strength values into various levels of range. By observing the signal propagation, the trusted-ranges model is built to provide important information for the training phase. Based on this, a partition scheme is applied to effectively determine the position of mobile devices. The experimental results show fast, robust, and accurate localization performance in the proposed method.

scholarly journals Logistic Modeling of the Last Mile: Case Study Santiago, Chile

2020 ◽  
Vol 12 (2) ◽  
pp. 648 ◽  
Author(s):  
Juan Guillermo Urzúa-Morales ◽  
Juan Pedro Sepulveda-Rojas ◽  
Miguel Alfaro ◽  
Guillermo Fuertes ◽  
Rodrigo Ternero ◽  
...  
Keyword(s):  
Distribution System ◽  
Nearest Neighbor ◽  
Analytic Hierarchy ◽  
Location Selection ◽  
Cross Docking ◽  
Last Mile ◽  
Distribution Process ◽  
Hierarchy Process ◽  
The City

This research proposes a new distribution system of goods in the historical center of the city of Santiago, Chile. For the design of the urban logistic system, the methodology city logistics and last mile are used. This design incorporates to the freight transport flexible solutions that improve the efficiency of the distribution process and trade supply, minimizing the environmental impact of the atmospheric pollution (AP). The proposal was made through the data collection, the characterization of the sector and the diagnosis of the urban logistics processes. The analysis of the factors allowed to evaluate the costs of the AP negative externalities. The causes were used as design criteria for the proposals, with the aim of improving the quality of life of the city users. The physical location selection of the Cross-Docking was made through an optimization model of maximum coverage. The optimization algorithm of the nearest neighbor was proposed for vehicle routing. The analytic hierarchy process (AHP) was used to generate a ranking of the best non-polluting vehicles to be used in the zone. Finally, the results obtained allowed a 53 ton decrease in carbon dioxide in the square kilometer and reduced 1103 h of interruptions per year in the vehicular congestion of the sector.

scholarly journals A three-dimensional pattern recognition localization system based on a Bayesian graphical model

2020 ◽  
Vol 16 (9) ◽  
pp. 155014771988489 ◽  
Author(s):  
Abdulraqeb Alhammadi ◽  
Fazirulhisyam Hashim ◽  
Mohd. Fadlee A Rasid ◽  
Saddam Alraih
Keyword(s):  
Graphical Model ◽  
Indoor Localization ◽  
Three Dimensional ◽  
Signal Strength ◽  
Local Area ◽  
Received Signal Strength ◽  
Reference Points ◽  
Indoor Environments ◽  
Localization System ◽  
Proposed Model

Access points in wireless local area networks are deployed in many indoor environments. Device-free wireless localization systems based on available received signal strength indicators have gained considerable attention recently because they can localize the people using commercial off-the-shelf equipment. Majority of localization algorithms consider two-dimensional models that cause low positioning accuracy. Although three-dimensional localization models are available, they possess high computational and localization errors, given their use of numerous reference points. In this work, we propose a three-dimensional indoor localization system based on a Bayesian graphical model. The proposed model has been tested through experiments based on fingerprinting technique which collects received signal strength indicators from each access point in an offline training phase and then estimates the user location in an online localization phase. Results indicate that the proposed model achieves a high localization accuracy of more than 25% using reference points fewer than that of benchmarked algorithms.

scholarly journals An Accurate Visual-Inertial Integrated Geo-Tagging Method for Crowdsourcing-Based Indoor Localization

2019 ◽  
Vol 11 (16) ◽  
pp. 1912 ◽  
Author(s):  
Tao Liu ◽  
Xing Zhang ◽  
Qingquan Li ◽  
Zhixiang Fang ◽  
Nadeem Tahir
Keyword(s):  
Indoor Localization ◽  
Signal Strength ◽  
Indoor Positioning ◽  
Received Signal Strength ◽  
Reference Points ◽  
External Information ◽  
Calibration Error ◽  
K Nearest Neighbor ◽  
Initial Location ◽  
Radio Map

One of the unavoidable bottlenecks in the public application of passive signal (e.g., received signal strength, magnetic) fingerprinting-based indoor localization technologies is the extensive human effort that is required to construct and update database for indoor positioning. In this paper, we propose an accurate visual-inertial integrated geo-tagging method that can be used to collect fingerprints and construct the radio map by exploiting the crowdsourced trajectory of smartphone users. By integrating multisource information from the smartphone sensors (e.g., camera, accelerometer, and gyroscope), this system can accurately reconstruct the geometry of trajectories. An algorithm is proposed to estimate the spatial location of trajectories in the reference coordinate system and construct the radio map and geo-tagged image database for indoor positioning. With the help of several initial reference points, this algorithm can be implemented in an unknown indoor environment without any prior knowledge of the floorplan or the initial location of crowdsourced trajectories. The experimental results show that the average calibration error of the fingerprints is 0.67 m. A weighted k-nearest neighbor method (without any optimization) and the image matching method are used to evaluate the performance of constructed multisource database. The average localization error of received signal strength (RSS) based indoor positioning and image based positioning are 3.2 m and 1.2 m, respectively, showing that the quality of the constructed indoor radio map is at the same level as those that were constructed by site surveying. Compared with the traditional site survey based positioning cost, this system can greatly reduce the human labor cost, with the least external information.

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