Low Cost Easy-to-Install Indoor Positioning System

Karakaya, Suat; Ocak, Hasan

doi:10.1007/s10846-020-01193-1

Cited by 9 publications

(5 citation statements)

References 27 publications

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“…In the formula, D ⃗⃗ is the distance between the current whale and the leading whale, C ⃗ is the coefficient vector, t is the current number of iterations, X * ⃗⃗⃗⃗ is the position of the current leading whale, and X ⃗ ⃗ is the position of the current whale. In this case, the coefficient vector C ⃗ can be calculated by formula (7).…”

Section: Methodsmentioning

confidence: 99%

“…These practical application scenarios need to rely on highprecision location information, but these intelligent devices often because of the inaccurate positioning information and the relative aging of technology, which leads to the signal transmission blocked by obstacles, so that they cannot accurately obtain the required position information and fulfill their task requirements. Therefore, in the case that the original outdoor positioning system has lost its accuracy, we need to find a fast, stable, reliable and accurate indoor positioning method, so that intelligent equipment can be widely used and the use experience of intelligent equipment can be improved [6][7][8][9]. With the development of swarm intelligence optimization algorithm and the application of neural network, more and more researchers begin to combine the optimization algorithm and neural network to establish the corresponding data model, which can more accurately reflect the correlation between data and data, reduce the signal attenuation problem caused by the complex indoor environment, so as to reduce the positioning error and improve the indoor positioning accuracy The accuracy and stability of positioning can meet the actual needs of indoor positioning with high accuracy.…”

Section: Introductionmentioning

confidence: 99%

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A Node Localization Algorithm based on WoaBp Optimization

Lang¹,

Sun²,

Jun³

et al. 2021

IJWMT

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With the rapid development of 5G technology, the era of interconnection of all things has arrived. At the same time, a variety of hardware and software are getting more and more location information through sensors, and the accuracy of location information is increasingly important. Because traditional positioning relies on satellite signals, it achieves good results outdoors without obstruction, but indoors, due to the obstruction of various walls, such as Beidou satellite navigation system and U.S. Global Positioning System, it is difficult to meet the accuracy requirements for indoor positioning. Therefore, how to improve the positioning accuracy of indoor nodes has become a research hotspot in the field of wireless sensor. In order to improve the indoor positioning accuracy, this paper combines artificial neural network, intelligent optimization algorithm and node positioning to improve the accuracy of indoor positioning. One of the essences of the neural network is to solve the regression problem. Through the analysis of indoor node positioning, it can be concluded that the accuracy of distance-based positioning method lies in finding the relationship between signal strength and distance value. Therefore, the neural network can be used to regression analysis of signal strength and distance value and generate related models. In order to further improve the accuracy and stability of indoor node positioning, a method combining whale optimization algorithm with neural network is proposed. By using the whale optimization algorithm to find the optimal parameters of the neural network model, the training accuracy and speed of the neural network are improved. Then, using the excellent fitting ability of the neural network, the mapping relationship between RSSI value and distance value of indoor nodes is fitted, and the corresponding regression analysis model is generated, which can minimize the noise problem caused by abnormal signal attenuation and reduce the indoor positioning error. Finally, the data is processed by the neural network to get the parameters needed in the positioning algorithm. The experimental results show that the node positioning model based on the optimized neural network and the single optimization algorithm has significantly improved the positioning accuracy and stability.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Node Localization Algorithm based on WoaBp Optimization

Lang¹,

Sun²,

Jun³

et al. 2021

IJWMT

View full text Add to dashboard Cite

show abstract

“…Therefore, there is an urgent need for a convenient and fast method to achieve high-precision indoor positioning. In recent years, numerous indoor localization methods have been proposed: Bluetooth [2,3], Wi-Fi [4,5], infrared [6], ultrawideband (UWB) [7], etc. Among them, visual indoor positioning, with its characteristics of low cost, convenient data acquisition, and significant adaptability, has developed into one of the mainstream indoor positioning technologies.…”

Section: Introductionmentioning

confidence: 99%

Image Retrieval Method Based on Visual Map Pre-Sampling Construction in Indoor Positioning

Bai

Qin

Zheng

et al. 2023

IJGI

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In visual indoor positioning systems, the method of constructing a visual map by point-by-point sampling is widely used due to its characteristics of clear static images and simple coordinate calculation. However, too small a sampling interval will cause image redundancy, while too large a sampling interval will lead to the absence of any scene images, which will result in worse positioning efficiency and inferior positioning accuracy. As a result, this paper proposed a visual map construction method based on pre-sampled image features matching, according to the epipolar geometry of adjacent position images, to determine the optimal sampling spacing within the constraints and effectively control the database size while ensuring the integrity of the image information. In addition, in order to realize the rapid retrieval of the visual map and reduce the positioning error caused by the time overhead, an image retrieval method based on deep hashing was also designed in this paper. This method used a convolutional neural network to extract image features to construct the semantic similarity structure to guide the generation of hash code. Based on the log-cosh function, this paper proposed a loss function whose function curve was smooth and not affected by outliers, and then integrated it into the deep network to optimize parameters, for fast and accurate image retrieval. Experiments on the FLICKR25K dataset and the visual map proved that the method proposed in this paper could achieve sub-second image retrieval with guaranteed accuracy, thereby demonstrating its promising performance.

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“…The limitation situating must be sensible for the workplace, for example, wall structure,, lighting conditions, ceiling shape in indoor situations influence the exhibition of the positioning system. The level of these impacts may fluctuate contingent upon the utilized strategy (Karakaya et al 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Although the GPS based positioning system is the favored answer for outside situations (S. Karakaya et al 2020), however, it is not appropriate for indoor applications because of its inability to penetrate walls in buildings (Holm 2012). The IPSs are based mainly on radio frequency identification (RFID), ultrasound, and wireless-local area networks (WLANs) (Saab et al 2011), including Zigbee-based IoT, WiFi (Akram et al 2018), and Bluetooth (Li et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Dark light visible light communication positioning system with received signal strength technique

et al. 2021

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Visible Light Communication (VLC) can be utilized for Indoor Positioning Systems (IPSs). Most of VLC IPSs work when LED lights are ON. This causes energy less when lighting system is off, at least when sleeping. We face a fundamental challenge in VLC systems that require the light ON all time. Then how to communicate when the lights appears "OFF"? In this paper, we propose a positioning system of receiver that is used in dark light VLC based on the Received Signal Strength (RSS) positioning technique, showing its possibility to use in dark environment. Because of its simplicity, the RSS technique is used in indoor positioning. This proposition can ensure the IP even when light is "OFF", which can be actualized in VLC systems to save energy and give precise illumination control. The obtained results reveal that the average error in positioning with LED power of dark VLC is lower than that in the normal mode. The obtained simulation results reveal that the average positioning error is improved by 25% of traditional method when Dark VLC is used with 1.4 mW LED power without background noise. This ensures that the proposed system shows superiority when compared with other VLC IPS systems, in energy saving and high positioning accuracy.

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Low Cost Easy-to-Install Indoor Positioning System

Cited by 9 publications

References 27 publications

A Node Localization Algorithm based on WoaBp Optimization

A Node Localization Algorithm based on WoaBp Optimization

Image Retrieval Method Based on Visual Map Pre-Sampling Construction in Indoor Positioning

Dark light visible light communication positioning system with received signal strength technique

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