Fingerprint Database Enhancement by Applying Interpolation and Regression Techniques for IoT-based Indoor Localization

Suroso, Dwi Joko; Adiyatma, Farid Yuli Martin; Cherntanomwong, Panarat; Sooraksa, Pitikhate

doi:10.28991/esj-2021-sp1-012

Cited by 15 publications

(6 citation statements)

References 35 publications

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“…[38] studied the use of linear interpolation for missing fingerprints to reduce the time required to create a fingerprint database, and compared the RSSI generated values with the actual measured values with an average error of up to 3.97dbm. [39] used a relatively sparse RSSI fingerprint dataset, applied a bilinear interpolation technique to construct a generative database, and built a fingerprint localization system based on the generative database with an average localization error of only 0.29m in a 5m × 5m localization environment.The basic idea of IDW is to provide weights for data points based on their distances to the estimation points. It is known that the closer the data point is to the estimation point, the larger its weight is [30].…”

Section: ) Gaussian Process Regressionmentioning

confidence: 99%

Low-Cost Indoor Wireless Fingerprint Location Database Construction Methods: A Review

et al. 2023

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The fingerprint positioning has achieved remarkable results in indoor localization tasks, but the method usually relies on a large amount of fingerprint data to build a fingerprint database, and the amount and diversity of fingerprint data will directly affect the effectiveness of fingerprint positioning. Since fingerprint acquisition is limited and disturbed by space and time, it consumes a lot of labor and time costs to collect fingerprint data in the localization environment, and wireless fingerprint data is time-sensitive and environment-dependent, and changes in the localization environment will reduce the usability of the existing fingerprint database. The complex and repetitive fingerprint acquisition work seriously affects the feasibility of practical deployment of fingerprint positioning systems in the positioning environment. Therefore, the study of low-cost wireless fingerprint database construction methods has become an inevitable part of promoting the widespread deployment of indoor fingerprint positioning systems. In this paper, we introduce the traditional data augmentation-based approach and the advanced machine learning model-based approach, systematically presenting the underlying models and algorithms of both. The former reviews the application of two traditional data enhancement methods, namely channel propagation models and interpolation or regression, to the construction of low-cost wireless fingerprint databases, while the latter taps into techniques for reducing the cost of fingerprint database construction by combining generative adversarial networks and small-sample learning models with the indoor localization domain. Finally, we discuss the current challenges and future research directions for achieving high-performance indoor localization based on low-cost wireless fingerprint databases, and suggest some useful research guidelines.

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Section: ) Gaussian Process Regressionmentioning

confidence: 99%

Low-Cost Indoor Wireless Fingerprint Location Database Construction Methods: A Review

et al. 2023

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“…Furthermore, the modified VAE and GAN to semisupervised method also proved promising for indoor Synthesis of a Small Fingerprint Database through a Deep Generative Model for Indoor Localisation localisation [15]- [19]. Semi-supervised, on the other hand, needs a source of unlabelled data.…”

Section: Introductionmentioning

confidence: 99%

Synthesis of a Small Fingerprint Database through a Deep Generative Model for Indoor Localisation

Suroso

Cherntanomwong

Sooraksa

2023

ELEKTRON ELEKTROTECH

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In deep learning (DL), the deep generative model is helpful for data augmentation objectives to tackle the lack of datasets that have a significant impact on learning performance. Data augmentation or synthesis is expected to solve the issue in a small/sparse database. The problem of databasing also exists in the fingerprint-based indoor localisation system. The dense offline fingerprint database must be constructed with the accuracy requirement. However, this will affect the high cost, massive laborious work, and increase the complexity of the system. Therefore, this paper proposes to address these issues by generating synthetic data via a deep generative model. The generative adversarial network (GAN) is selected to generate the synthetic fingerprint database for indoor localisation. Our database consideration consists of power-based parameters, i.e., the received signal strength indicator (RSSI) from Wi-Fi devices obtained from the actual measurement campaign. Some of the literature mainly discusses how GAN works in a vast and complex dataset. Here, we consider applying GAN in a relatively small dataset and for a simple setup. Our results show that by only using the 20 % fraction of actual RSSI data combined with the synthetic RSSI, the accuracy validation performance is slightly higher than when using all actual data usage. Moreover, in only 60 % of actual data usage and in combination with 625 samples of synthetic data, the accuracy performance is improved to 0.73 (1.37 times higher than the use of all actual data, 0.53). Thus, this result proves that the challenges of offline fingerprint databases can be alleviated by data synthesis through GAN by using only a small dataset.

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“…For localization purposes, Wi-Fi empowers various parameters, such as Received Signal Strength Indicator (RSSI) [11], Angle of Arrival (AoA) [12], Time of Arrival (ToA) [13], Time Difference of Arrival (TDoA) [14] and Channel State Information (CSI) [15]. Compared to others, RSSI has a valuable advantage due to its ease of extraction, which involves straightforward procedures and does not require additional hardware installation [16].…”

Section: Introductionmentioning

confidence: 99%

“…This technique is highly favored since it is resistant to multipath fading. The fingerprint technique involves two phases: the offline phase, dedicated to constructing a radio map or fingerprint database, and the online phase, where the localization process occurs, in which target signal parameters are compared to fingerprint databases to infer their location [16]. During the online phase, various pattern matching algorithms are commonly employed, including Euclidean distance [16], nearest neighbor [18], machine and deep learning-based such as K-Nearest Neighbors (KNN) [19], [20], Support Vector Machine (SVM) [21], [22], Convolutional Neural Networks (CNN) [23], [24], [25].…”

Section: Introductionmentioning

confidence: 99%

TLB & WC-TLB-MM: The Improved Min-Max Algorithms for Multi Targets Indoor Localization

Adiyatma,

Suroso,

Cherntanomwong

2023

IEEE Access

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Internet of Things (IoT)-based Indoor localization is the most commonly used system to determine target locations indoors. It applies to various purposes, e.g., indoor navigation, asset tracking in warehouse management, and tracking people in hospitals. Distance-based techniques using the Received Signal Strength Indicator (RSSI), e.g., Min-Max, are widely applied because they can be directly implemented without prerequisite work such as site surveys. However, a challenging indoor environment with high numbers of interiors and people can obstruct signal propagation. This obstruction can reduce the accuracy of translating RSSI to distance using the path loss model, which will degrade the localization accuracy. In this paper, we introduce two improved Min-Max (MM) algorithms, i.e., Three Layer Bounding Box Min-Max (TLB-MM) and Weighted Centroid TLB-MM (WC-TLB-MM), to alleviate the issue and achieve higher localization accuracy. The novelty of the proposed TLB-MM is incorporating RSSI error functions to generate three-layer bounding boxes: the inner, middle, and outer in the Min-Max algorithm. Meanwhile, WC-TLB-MM enhanced the TLB-MM algorithm by integrating the Weighted Centroid Localization Algorithm (WCLA) in the calculation process. We validate our proposal by conducting various experiments using Wi-Fi at 2.4 GHz deployed in a laboratory room of 10.17 m × 9.12 m. Experimental results demonstrate that TLB-MM improved the accuracy performance to 55.78% and 30.86%, while WC-TLB-MM gave 40.93% and 7.65% compared to Min-Max and WCLA, respectively. From these results, our proposed methods are proven simple yet applicable to RSSI-based indoor localization systems.

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Fingerprint Database Enhancement by Applying Interpolation and Regression Techniques for IoT-based Indoor Localization

Cited by 15 publications

References 35 publications

Low-Cost Indoor Wireless Fingerprint Location Database Construction Methods: A Review

Low-Cost Indoor Wireless Fingerprint Location Database Construction Methods: A Review

Synthesis of a Small Fingerprint Database through a Deep Generative Model for Indoor Localisation

TLB & WC-TLB-MM: The Improved Min-Max Algorithms for Multi Targets Indoor Localization

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