scholarly journals Evaluation of Distance Error with Bluetooth Low Energy Transmission Model for Indoor Positioning

2021 ◽  
Author(s):  
Pichaya Supanakoon ◽  
Sathaporn Promwong
Keyword(s):  
Path Loss ◽  
Multipath Fading ◽  
Indoor Positioning ◽  
Low Energy ◽  
Location Based Services ◽  
Loss Model ◽  
Bluetooth Low Energy ◽  
Path Loss Model ◽  
Distance Error ◽  
Proposed Model

Currently, an indoor positioning is a challenge application for location-based services (LBS) and proximity-based services (PBS). However, the indoor channel has dense multipath fading, causing more distance error than outdoor positioning. In this paper, the distance error analysis model is proposed for indoor positioning. The indoor channel is modeled as the sum of path loss model and multipath fading model. The path loss model is a linear regression model (LRM) based on Friis’ transmission formula, used for estimating the distance from received signal strength (RSS). The multipath fading is a Gaussian statistical model with zero mean, used for characterizing the multipath fading effect. The normalized distance error is evaluated and defined. The indoor channel with Bluetooth low energy (BLE) beacons is measured and compared with the proposed model. From the results, the normalized distance error obtained from the proposed model corresponds very well to measurement. This proposed model can be used as a tool for designing an indoor positioning system to obtain the specific distance error.

Method of indoor navigation and collaborative semi-automatic Wi-Fi radiomap construction

2018 ◽  
pp. 95-104
Author(s):  
M. S. Shchekotov
Keyword(s):  
Model Calibration ◽  
Path Loss ◽  
Indoor Navigation ◽  
Low Energy ◽  
Loss Model ◽  
Bluetooth Low Energy ◽  
Path Loss Model ◽  
Automatic Construction ◽  
Map Construction ◽  
Radio Map

Introduction:An important and complicated problem related to the multilateration of Wi-Fi or Bluetooth Low Energy signals as well as Wi-Fi fingerprinting is the procedure of infrastructure adjustment which includes Wi-Fi radio map construction and Wi-Fi or Bluetooth Low Energy radio signal path loss model calibration.Purpose:Developing a method for navigation and Wi-Fi radio map construction, which would provide user’s indoor navigation, Bluetooth Low Energy path loss model calibration and Wi-Fi radio map collaborative semi-automatic construction.Results:The paper presents a collaborative semi-automatic Wi-Fi radio map construction method based on the combination of Bluetooth Low Energy multilateration, Wi-Fi fingerprinting, Wi-Fi radio map collaborative semiautomatic construction procedure and semi-automatic Bluetooth Low Energy path loss model calibration. For the semi-automatic calibration procedure of the Bluetooth Low Energy signal propagation model and for the method of collaborative semi-automatic construction of Wi-Fi radio map and indoor navigation, a calibration algorithm and an algorithm of collaborative semi-automatic construction of Wi-Fi radio map and indoor navigation were proposed, respectively. A mobile application has been developed which implements the proposed algorithms in order to test them and analyze the results.Practical relevance:The proposed method allows you to avoid time-consuming procedures of constructing a map of Wi-Fi radio signals and semi-automatic calibration of Bluetooth Low Energy signal propagation in the offline phase.

A new path loss model based on the volumetric occupancy rate for the pine forests at 5G frequency band

2020 ◽  
pp. 1-10
Author(s):  
Abdullah Genc
Keyword(s):  
Path Loss ◽  
Forest Area ◽  
Occupancy Rate ◽  
Loss Model ◽  
Path Loss Model ◽  
Model Based ◽  
Proposed Model ◽  
Pine Trees ◽  
Foliage Density ◽  
Reference Measurements

Abstract In this paper, a new empirical path loss model based on frequency, distance, and volumetric occupancy rate is generated at the 3.5 and 4.2 GHz in the scope of 5G frequency bands. This study aims to determine the effect of the volumetric occupancy rate on path loss depending on the foliage density of the trees in the pine forest area. Using 4.2 GHz and the effect of the volumetric occupancy rate contributes to the literature in terms of novelty. Both the reference measurements to generate a model and verification measurements to verify the proposed models are conducted in three different regions of the forest area with double ridged horn antennas. These regions of the artificial forest area consist of regularly sorted and identical pine trees. Root mean square error (RMSE) and R-squared values are calculated to evaluate the performance of the proposed model. For 3.5 and 4.2 GHz, while the RMSEs are 3.983 and 3.883, the values of R-squared are 0.967 and 0.963, respectively. Additionally, the results are compared with four path loss models which are commonly used in the forest area. The proposed one has the best performance among the other models with values 3.98 and 3.88 dB for 3.5 and 4.2 GHz.

scholarly journals An Ensemble Filter for Indoor Positioning in a Retail Store Using Bluetooth Low Energy Beacons

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4550 ◽  
Author(s):  
Vasilis Stavrou ◽  
Cleopatra Bardaki ◽  
Dimitris Papakyriakopoulos ◽  
Katerina Pramatari
Keyword(s):  
Random Forest ◽  
Indoor Localization ◽  
Indoor Positioning ◽  
Retail Store ◽  
Low Energy ◽  
Location Based Services ◽  
Shopping Behavior ◽  
Multiple Sources ◽  
Bluetooth Low Energy ◽  
Product Categories

This paper has developed and deployed a Bluetooth Low Energy (BLE) beacon-based indoor positioning system in a two-floor retail store. The ultimate purpose of this study was to compare the different indoor positioning techniques towards achieving efficient position determination of moving customers in the retail store. The innovation of this research lies in its context (the retail store) and the fact that this is not a laboratory, controlled experiment. Retail stores are challenging environments with multiple sources of noise (e.g., shoppers’ moving) that impede indoor localization. To the best of the authors’ knowledge, this is the first work concerning indoor localization of consumers in a real retail store. This study proposes an ensemble filter with lower absolute mean and root mean squared errors than the random forest. Moreover, the localization error is approximately 2 m, while for the random forest, it is 2.5 m. In retail environments, even a 0.5 m deviation is significant because consumers may be positioned in front of different store shelves and, thus, different product categories. The more accurate the consumer localization, the more accurate and rich insights on the customers’ shopping behavior. Consequently, retailers can offer more effective customer location-based services (e.g., personalized offers) and, overall, better consumer localization can improve decision making in retailing.

Path Loss through Pine Forest around Beach for Wireless Sea Wave Energy Sensor Network

2012 ◽  
Vol 433-440 ◽  
pp. 3954-3958 ◽  
Author(s):  
Supachai Phaiboon ◽  
Supanuch Seesaiprai
Keyword(s):  
Sensor Network ◽  
Wave Energy ◽  
Path Loss ◽  
Low Noise ◽  
Loss Model ◽  
Path Loss Model ◽  
Proposed Model ◽  
Energy Sensor ◽  
Sea Wave ◽  
Good Agreement

This paper presents an empirical path loss model through forest for measuring sea wave energy using 2.4 GHz wireless sensor network (WSN). The empirical path loss model was determined from measurement campaign by using 18 dBm transmitter and the receivers with a low noise amplify. The conventional path loss models for forest environments were carried out such as Weissberger, ITU-R, COST 235 and Torrico models. From the results it is found that the proposed model provides a good agreement and is used for planning WSN.

scholarly journals Path Loss Model with Antenna Height Dependency under Indoor Stair Environment

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Yu Yu ◽  
Yang Liu ◽  
Wen-Jun Lu ◽  
Hong-Bo Zhu
Keyword(s):  
Standard Deviation ◽  
Maximum Likelihood ◽  
Path Loss ◽  
Likelihood Estimation ◽  
Least Square ◽  
Line Of Sight ◽  
Loss Model ◽  
Path Loss Model ◽  
Proposed Model ◽  
Receiving Antenna

A novel, receiving antenna-height-dependent path loss model under indoor stair environment is presented. The effect of a cross-beam in the stairs is also considered. The proposed model can be applied to describe both of the line-of-sight (LOS) and the non-LOS (NLOS) cases. By using least square criterion, the parameters of proposed model are extracted. Finally, using the maximum likelihood estimation, the precision of the proposed model is evaluated by the standard deviation of shadowing.

WiFi and Bluetooth Low Energy Beacons for Indoor Positioning System

2019 ◽  
Vol 1 (2) ◽  
pp. 1-5
Author(s):  
Nurul Fatehah Zulkpli ◽  
Nor Azlina Ab. Aziz ◽  
Noor Ziela Abd Rahman ◽  
Rosli Besar
Keyword(s):  
Indoor Positioning ◽  
Low Energy ◽  
Location Based Services ◽  
Positioning System ◽  
Indoor Environments ◽  
Wireless Technologies ◽  
Bluetooth Low Energy ◽  
Advantages And Disadvantages ◽  
Performance Accuracy ◽  
Indoor Positioning System

Indoor Positioning System (IPS) is used to locate a person, an object or a location inside a building. IPS is important in providing location-based services, which has recently gain much popularity. The services ease visitors’ navigation at unfamiliar premises. Location-based services depend on the capability of IPS to accurately determine the location of the user, which is a challenging issue in indoor environments. Several wireless technologies are available. In this paper, two of the most widely used IPS technologies are reviewed which are, WiFi and Bluetooth low energy (BLE). Their advantages and disadvantages are reviewed and reported here. Comparison of the systems based on their performance, accuracy and limitations are presented as well.

scholarly journals Indoor Positioning Based on Bluetooth Low-Energy Beacons Adopting Graph Optimization

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3736 ◽  
Author(s):  
Zheng Zuo ◽  
Liang Liu ◽  
Lei Zhang ◽  
Yong Fang
Keyword(s):  
Cost Function ◽  
Path Loss ◽  
Dead Reckoning ◽  
Indoor Positioning ◽  
Low Energy ◽  
Least Square ◽  
Bluetooth Low Energy ◽  
Practical Applications ◽  
Wide Range ◽  
Graph Optimization

Bluetooth Low-Energy (BLE) beacons-based indoor positioning is a promising method for indoor positioning, especially in applications of position-based services (PbS). It has low deployment cost and it is suitable for a wide range of mobile devices. Existing BLE beacon-based positioning methods can be categorized as range-based methods and fingerprinting-based methods. For range-based methods, the positions of the beacons should be known before positioning. For fingerprinting-based methods, a pre-requisite is the reference fingerprinting map (RFM). Many existing methods focus on how to perform the positioning assuming the beacon positions or RFM are known. However, in practical applications, determining the beacon positions or RFM in the indoor environment is normally a difficult task. This paper proposed an efficient and graph optimization-based way for estimating the beacon positions and the RFM, which combines the range-based method and the fingerprinting-based method. The method exists without need for any dedicated surveying instruments. A user equipped with a BLE-enabled mobile device walks in the region collecting inertial readings and BLE received signal strength indication (RSSI) readings. The inertial measurements are processed through the pedestrian dead reckoning (PDR) method to generate the constraints at adjacent poses. In addition, the BLE fingerprints are adopted to generate constraints between poses (with similar fingerprints) and the RSSIs are adopted to generate distance constraints between the poses and the beacon positions (according to a pre-defined path-loss model). The constraints are then adopted to form a cost function with a least square structure. By minimizing the cost function, the optimal user poses at different times and the beacon positions are estimated. In addition, the RFM can be generated through the pose estimations. Experiments are carried out, which validates that the proposed method for estimating the pre-requisites (including beacon positions and the RFM). These estimated pre-requisites are of sufficient quality for both range-based and fingerprinting-based positioning.

scholarly journals Quantitative Measurement of Path Loss Model Adaptation Using the Least Squares Method in an Urban DVB-T2 System

2018 ◽  
Vol 2018 ◽  
pp. 1-8
Author(s):  
Pitak Keawbunsong ◽  
Sarun Duangsuwan ◽  
Pichaya Supanakoon ◽  
Sathaporn Promwong
Keyword(s):  
Least Squares ◽  
Quantitative Measurement ◽  
Least Squares Method ◽  
Measurement Data ◽  
Path Loss ◽  
Model Adaptation ◽  
Loss Model ◽  
Path Loss Model ◽  
Proposed Model ◽  
Using Data

The aim of this paper was to propose quantitative measurement of path loss model adaptation in urban radio propagation for a second-generation, terrestrial digital video broadcasting standard (DVB-T2) system. The measurement data was analyzed using data processing based on the least squares (LS) method to verify the probabilistic quantitation of realistic data measurement such as mean error (ME), root mean square error (RMSE), and standard deviation of error (SD), as well as relative error (RE). To distinguish the experimental evaluation, the researchers compared between the conventional Hata path loss model and the proposed model. The result showed that path loss based on the proposed model was more accurate in predicting the quantitative measurement of propagation data properly.

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