scholarly journals IPSCL: An Accurate Indoor Positioning Algorithm Using Sensors and Crowdsourced Landmarks

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2891 ◽  
Author(s):  
Beakcheol Jang ◽  
Hyunjung Kim ◽  
Jong Wook Kim
Keyword(s):  
Signal Strength ◽  
Indoor Positioning ◽  
Reference Points ◽  
Sensor Technology ◽  
Indicator Values ◽  
Received Signal Strength Indicator ◽  
Surrounding Environment ◽  
Heading Direction ◽  
Positioning Algorithm ◽  
Additional Device

Indoor positioning technology has attracted the attention of researchers due to the increasing pervasiveness of smartphones and the development of sensor technology, along with the increase of indoor time. Sensor technology, which is one of the most commonly used data sources for indoor positioning, has the advantage that sensors can receive data from a smartphone without installing any additional device. However, the readings of built-in sensors are easily affected by the surrounding environment and are even occasionally different from each other which adversely influence the accuracy of indoor positioning. Moreover, once an error occurs, it can accumulate because there is not any reference point in the sensor, only in indoor positioning. In this paper, we present an accurate indoor positioning technology, which uses smartphone built-in sensors and Bluetooth beacon-based landmarks. Our proposed algorithm chooses proper one between values of sensors alternately based on their characteristics. It exploits landmarks as the reference points of indoor positioning. It also allows individuals to add the location where they repeatedly detect the same and special beacon received signal strength indicator values as a crowdsourced landmark. Extensive experimental results show that our proposed algorithm facilitates the acquisition of accurate heading direction and coordinates of the user.

A spatial model of received signal strength indicator values for automated collision notification technology

2005 ◽  
Vol 13 (5-6) ◽  
pp. 432-447 ◽  
Author(s):  
Eric M. Delmelle ◽  
Peter A. Rogerson ◽  
Mohan R. Akella ◽  
Rajan Batta ◽  
Alan Blatt ◽  
...  
Keyword(s):  
Spatial Model ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Indicator Values ◽  
Received Signal Strength Indicator

scholarly journals Indoor Positioning System (IPS) Berdasarkan Kekuatan Received Signal Strength Indicator (RSSI)

2018 ◽  
Vol 2 (01) ◽  
pp. 27-33
Author(s):  
Budi Rahmadya Rahmadya
Keyword(s):  
Signal Strength ◽  
Indoor Positioning ◽  
Received Signal Strength ◽  
Shopping Mall ◽  
Positioning System ◽  
Received Signal Strength Indicator ◽  
Indoor Positioning System

Shopping Mall merupakan area pusat perbelanjaan yang besar dan memiliki sistem keamanan seperti sistem layanan informasi yang dapat dimanfaatkan oleh konsumen untuk mendapatkan informasi yang dibutuhkan. Penggunaan sistem layanan informasi pada area Shopping Mall bagi konsumen terkadang sangat tidak efektif. Hal ini dikarenakan konsumen membutuhkan waktu yang lama dalam mendapatkan informasi, dimana konsumen terlebih dahulu harus mencari lokasi tempat sistem layanan informasi tersebut. Hal ini menjadikan sistem keamanan pada Shopping Mall menjadi lemah. Indoor Positioning System (IPS) merupakan sistem yang dapat digunakan untuk mengetahui posisi pengguna melalui kekuatan sinyal Wi-Fi yang didapat dalam gedung. Pada penelitian ini, penulis membuat suatu aplikasi android yang dapat digunakan untuk mengetahui posisi konsumen pada area Shopping Mall tersebut.

scholarly journals Accurate Indoor-Positioning Model Based on People Effect and Ray-Tracing Propagation

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5546
Author(s):  
Firdaus Firdaus ◽  
Noor Azurati Ahmad ◽  
Shamsul Sahibuddin
Keyword(s):  
Ray Tracing ◽  
Nearest Neighbor ◽  
Signal Strength ◽  
Signal Propagation ◽  
Local Area ◽  
Indoor Positioning ◽  
K Nearest Neighbor ◽  
Received Signal Strength Indicator ◽  
Multipath Effects ◽  
Level 3

Wireless local area networks (WLAN)-fingerprinting has been highlighted as the preferred technology for indoor positioning due to its accurate positioning and minimal infrastructure cost. However, its accuracy is highly influenced by obstacles that cause fluctuation in the signal strength. Many researchers have modeled static obstacles such as walls and ceilings, but few studies have modeled the people’s presence effect (PPE), although the human body has a great impact on signal strength. Therefore, PPE must be addressed to obtain accurate positioning results. Previous research has proposed a model to address this issue, but these studies only considered the direct path signal between the transmitter and the receiver whereas multipath effects such as reflection also have a significant influence on indoor signal propagation. This research proposes an accurate indoor-positioning model by considering people’s presence and multipath using ray-tracing, we call it (AIRY). This study proposed two solutions to construct AIRY: an automatic radio map using ray tracing and a constant of people’s effect for the received signal strength indicator (RSSI) adaptation. The proposed model was simulated using MATLAB software and tested at Level 3, Menara Razak, Universiti Teknologi Malaysia. A K-nearest-neighbor (KNN) algorithm was used to define a position. The initial accuracy was 2.04 m, which then reduced to 0.57 m after people’s presence and multipath effects were considered.

scholarly journals A quadratic weighted centroid algorithm for tunnel personnel positioning

2020 ◽  
Vol 16 (4) ◽  
pp. 155014772091702
Author(s):  
Haiying Wang ◽  
Linhao Liang ◽  
Jian Xu ◽  
Hui She ◽  
Wuxiang Li
Keyword(s):  
Signal Strength ◽  
Received Signal Strength ◽  
Weighting Factor ◽  
Gaussian Filter ◽  
Received Signal Strength Indicator ◽  
Positioning Accuracy ◽  
Filter Model ◽  
Positioning Systems ◽  
Centroid Algorithm ◽  
Positioning Algorithm

To improve the accuracy and generalization of tunnel personnel positioning systems, this article proposes a quadratic weighted centroid algorithm. By adopting a Gaussian filter model to improve the range accuracy of the received signal strength indicator algorithm and combining the centroid algorithm and weighting factor with a trilateration positioning model, a quadratic weighted centroid algorithm is proposed to improve the positioning accuracy of unknown positioning nodes. The key ideas behind the quadratic weighted centroid algorithm include an optimization of the received signal strength indicator range value scheme, a centroid algorithm based on trilateral measurement positioning, and a weighting factor to improve the positioning accuracy of the trilateral centroid positioning algorithm. Compared with the centroid algorithm, the Min-Max multilateration algorithm, and the weighted centroid based on distance algorithm, the simulation results showed that (1) the positioning performance of the quadratic weighted centroid algorithm was superior to the other three algorithms; (2) when the reference nodes were symmetrically arranged, the positioning accuracy was higher than a fold line layout; and (3) when the lateral reference node spacing was extended from 20 to 30 m, the average positioning error met positioning accuracy requirements, which could reduce overall system costs.

scholarly journals An Enhanced Indoor Positioning Technique Based on a Novel Received Signal Strength Indicator Distance Prediction and Correction Model

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 719
Author(s):  
Mohammed Nagah Amr ◽  
Hussein M. ELAttar ◽  
Mohamed H. Abd El Azeem ◽  
Hesham El Badawy
Keyword(s):  
Signal Strength ◽  
Indoor Positioning ◽  
Low Energy ◽  
Indoor Environments ◽  
Positioning Accuracy ◽  
Correction Model ◽  
Positioning Technique ◽  
Unknown Node ◽  
Positioning Algorithm ◽  
Distance Prediction

Indoor positioning has become a very promising research topic due to the growing demand for accurate node location information for indoor environments. Nonetheless, current positioning algorithms typically present the issue of inaccurate positioning due to communication noise and interferences. In addition, most of the indoor positioning techniques require additional hardware equipment and complex algorithms to achieve high positioning accuracy. This leads to higher energy consumption and communication cost. Therefore, this paper proposes an enhanced indoor positioning technique based on a novel received signal strength indication (RSSI) distance prediction and correction model to improve the positioning accuracy of target nodes in indoor environments, with contributions including a new distance correction formula based on RSSI log-distance model, a correction factor (Beta) with a correction exponent (Sigma) for each distance between unknown node and beacon (anchor nodes) which are driven from the correction formula, and by utilizing the previous factors in the unknown node, enhanced centroid positioning algorithm is applied to calculate the final node positioning coordinates. Moreover, in this study, we used Bluetooth Low Energy (BLE) beacons to meet the principle of low energy consumption. The experimental results of the proposed enhanced centroid positioning algorithm have a significantly lower average localization error (ALE) than the currently existing algorithms. Also, the proposed technique achieves higher positioning stability than conventional methods. The proposed technique was experimentally tested for different received RSSI samples’ number to verify its feasibility in real-time. The proposed technique’s positioning accuracy is promoted by 80.97% and 67.51% at the office room and the corridor, respectively, compared with the conventional RSSI trilateration positioning technique. The proposed technique also improves localization stability by 1.64 and 2.3-fold at the office room and the corridor, respectively, compared to the traditional RSSI localization method. Finally, the proposed correction model is totally possible in real-time when the RSSI sample number is 50 or more.

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