ABC-ANN Based Indoor Position Estimation Using Preprocessed RSSI

Ünlerşen, Muhammed Fahri

doi:10.3390/electronics11234054

Cited by 5 publications

(1 citation statement)

References 46 publications

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“…Although GPS has been served for years to provide location-based services in outdoor scenarios, it cannot provide highly reliable positioning accuracy in indoor environments due to signal attenuation and multi-path effects. Therefore, researchers have developed a variety of positioning techniques, such as using magnetic field [1][2][3], radio frequency identification (RFID) [4][5][6], and WiFi [7][8][9][10][11] to compensate for the shortage of GPS in indoor positioning.…”

Section: Introductionmentioning

confidence: 99%

Crowdsourced Indoor Positioning with Scalable WiFi Augmentation

Dong

Arslan

et al. 2023

Sensors

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In recent years, crowdsourcing approaches have been proposed to record the WiFi signals annotated with the location of the reference points (RPs) extracted from the trajectories of common users to reduce the burden of constructing a fingerprint (FP) database for indoor positioning. However, crowdsourced data is usually sensitive to crowd density. The positioning accuracy degrades in some areas due to a lack of FPs or visitors. To improve the positioning performance, this paper proposes a scalable WiFi FP augmentation method with two major modules: virtual reference point generation (VRPG) and spatial WiFi signal modeling (SWSM). A globally self-adaptive (GS) and a locally self-adaptive (LS) approach are proposed in VRPG to determine the potential unsurveyed RPs. A multivariate Gaussian process regression (MGPR) model is designed to estimate the joint distribution of all WiFi signals and predicts the signals on unsurveyed RPs to generate more FPs. Evaluations are conducted on an open-source crowdsourced WiFi FP dataset based on a multi-floor building. The results show that combining GS and MGPR can improve the positioning accuracy by 5% to 20% from the benchmark, but with halved computation complexity compared to the conventional augmentation approach. Moreover, combining LS and MGPR can sharply reduce 90% of the computation complexity against the conventional approach while still providing moderate improvement in positioning accuracy from the benchmark.

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Section: Introductionmentioning

confidence: 99%

Crowdsourced Indoor Positioning with Scalable WiFi Augmentation

Dong

Arslan

et al. 2023

Sensors

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An Effective Tag Estimation Method Based upon Artificial Neural Networks and Signal Strength for Anticollision in Radio Frequency Identification Systems

Alhuthali,

Murad,

Tasadduq

et al. 2024

Int J Comput Intell Syst

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Radio frequency identification (RFID) technology has been widely used in applications such as access control, inventory management, spatial positioning, and object identification. Accurate tag estimation is one of the major challenges in RFID reader systems particularly in areas where large tag populations are to be identified such as shopping carts, warehouse inventory monitoring, and small ruminant farms. This paper proposes a new tag estimation technique employing artificial neural networks (ANNs) and signal strength to read large tag populations. The technique estimates the number of tags through the signal strength of the backscatter channel for efficient implementation of dynamic framed slotted Aloha (DFSA) protocol by analyzing the RN16 and the received signal strength indicator (RSSI). The ANN model is trained using the signal strength of various tag populations and can identify the number of tags with minimal errors. The proposed technique does not require any modification in the tags and is implemented as a minimal software script to be added to the tag estimation module of the reader. The proposed signal strength-ANN model is able to estimate the accurate number of tags thereby improving the performance of the employed DFSA model.

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