Adaptive Long-Term Wi-Fi Fingerprint-Based Indoor Localization: History
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Subjects: Engineering, Electrical & Electronic
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Tesfay Gidey Hailu

This research delves into the challenges of Wi-Fi fingerprint-based indoor localization in dynamic environments, addressing the evolving nature of signal patterns and feature spaces over time. The study focuses on improving adaptive long-term localization accuracy by examining temporal variations in signal strength across 25 months. The research employs key methodologies such as mean-based feature selection, principal component analysis (PCA), and functional discriminant analysis (FDA) to examine signal features and address multicollinearity. The paper introduces an innovative algorithm, Ada-LT IP, which integrates data reduction and transfer learning techniques to enhance accuracy. The proposed method effectively mitigates signal fluctuations and reduces computational complexity, resulting in superior performance compared to current state-of-the-art approaches, as measured by mean absolute error. This research provides critical insights into enhancing adaptive long-term Wi-Fi indoor localization systems, paving the way for more reliable applications in real-world settings.

  • indoor localization
  • Wi-Fi fingerprinting
  • functional discriminant analysis
  • transferlearning
  • features extraction
  • computational complexity

1. Introduction

With the advent of the Internet of Things (IoT), along with the rollout of 5G and emerging 6G technologies, the significance of location-based services (LBS) has markedly increased. Accurate indoor positioning information is essential for a range of applications, including business location services, data mining, security monitoring, and venue management [1][2][3][4]. While global positioning system (GPS) technology operates effectively in outdoor settings, it proves inadequate for indoor localization due to weak signal reception in complex environments. Key challenges include limited line of sight, insufficient satellite signal penetration, and interference from internal obstacles, such as shadows and multipath fading [5][6][7][8][9]. As urbanization intensifies and a majority of activities shift indoors, the demand for reliable indoor positioning systems (IPSs) has surged. A variety of wireless technologies have emerged to address this need, including radio frequency identification (RFID) [10], Bluetooth [11], ultra-wideband (UWB) [12], Zigbee [13], inertial navigation [14], and visible light communication (VLC) [15]. However, the implementation of these technologies often incurs significant infrastructure costs. Effective IPSs leverage diverse signal characteristics—such as received signal strength (RSS), channel state information (CSI), angle of arrival (AOA), and time of arrival (TOA)—to accurately locate objects or individuals in environments where GPS signals are compromised. To meet the demands of indoor settings, these systems must provide high accuracy, rapid estimation times, and low power consumption. Nevertheless, the dynamic nature of indoor environments introduces variability in signal patterns, which can adversely affect positioning performance [16][17][18]. To achieve a balance between computational costs and accuracy, IPSs must optimize available resources while accounting for environmental factors and maintaining an acceptable margin of error. The mission of the application and the overall system cost are also critical determinants of positioning performance [19][20][21]. Among the various indoor positioning technologies, Wi-Fi fingerprint-based IPS (FPBIPS) stands out as a particularly promising solution owing to its cost-effectiveness and ease of implementation. However, FPBIPS is susceptible to challenges posed by multipath effects, shadowing, and scattering, which are influenced by the dynamic nature of indoor environments [22][23][24]. Additionally, signal attenuation in wireless communication systems—primarily attributed to path loss, shadowing, and multipath effects—can significantly degrade location accuracy [25]. Figure 1 illustrates the impact of multipath on the received signal within an indoor setting.

The variability of fingerprint values in indoor environments, influenced by factors such as device heterogeneity, measurement timing, user orientation, and channel conditions, significantly impacts positioning performance. This dynamic variability often leads to mismatches between stored and real-time fingerprints, posing a critical challenge for accurate indoor positioning. To address these issues, various fingerprint-matching strategies have been developed [26][27][28], broadly categorized into deterministic [29][30][31] and stochastic approaches [32][33][34]. To mitigate the challenges posed by complex indoor signal fluctuations, several FPBIPS methods have been proposed. One approach involves modeling signal jitter using the path loss model; however, this method is constrained by its dependence on map information and the assumption of a fixed receiver position [35][36][37]. In addition, machine learning (ML) algorithms have also been applied to RSS fingerprint-based indoor positioning problems, yet these techniques often fail to consider critical factors, such as leveraging related source domains, which could enhance the overall positioning accuracy and reduce the labor-intensive costs associated with offline fingerprint data collection [38][39][40]. In addition to that, recent advancements in addressing the inherent challenges associated with FPBIPS have been extensively documented in the literature. Various studies have proposed innovative algorithms and methodologies aimed at enhancing the resilience of these systems against signal fluctuations and the deterioration of fingerprints over time due to the dynamic nature of indoor environments [41][42][43][44][45]. For instance, advanced techniques and machine learning approaches have been demonstrated to significantly improve accuracy and robustness in environments with fluctuating signals and evolving conditions [44][45]. A novel multi-modal indoor localization method that integrates visual information, Wi-Fi signals, and lidar data, achieving high precision with an average 3D localization accuracy of 0.62 m and a mean square error of 1.24 m in two-dimensional tracking [44]. The study highlights the potential of hybrid techniques in enhancing location-based services within complex environments. Nevertheless, the performance relies on the accuracy and compatibility of the multimodal sensors used. In addition, the joint processing of multiple data sources might introduce additional overhead costs, which could limit deployment on low-power devices.
Furthermore, achieving the desired accuracy with RSS-based fingerprinting requires a large number of labeled samples, which is expensive and time-consuming. Crowdsourcing approaches have been studied to create and update radio maps, aiming to eliminate the need for site surveying [46][47][48]. Algorithms are being developed to generate radio maps using user traces collected from the crowd. However, trace-matching algorithms based on inertial sensors often face issues with unstable posture and high-power consumption of smartphones [49][50][51]. While our work focuses on single-signal metrics, hybrid methods combining Bluetooth, Wi-Fi, UWB, and ZigBee [52] have been proposed to enhance indoor positioning. Other examples include the integration of Wi-Fi with Visual Light Positioning (VLP) [53] and Bluetooth Low Energy (BLE) [54]. A novel localization framework has been developed that integrates GNSS, Wi-Fi Fine Time Measurement (FTM), and built-in sensors to achieve precise meter-level accuracy [41]. The framework utilizes advanced techniques, including pedestrian dead reckoning and an adaptive multi-model extended Kalman filter, to ensure seamless indoor and outdoor positioning. Experimental results demonstrate substantial improvements in localization reliability, making it highly suitable for complex environments [41]. However, the framework’s reliance on multiple data sources and algorithms can introduce complexity, requiring significant computational resources and careful calibration. Moreover, although these hybrid approaches can achieve meter-level localization accuracy, they may introduce complexities in system integration and increase overall costs. These contributions underscore the ongoing efforts to refine IPS performance in complex indoor settings while acknowledging the inherent limitations. In addition, a recent study in [55] has also proposed an innovative indoor localization system named iToLoc, which combines adversarial learning and semi-supervised techniques to address the limitations of existing FPBIPS methods. By utilizing a domain adversarial neural network, iToLoc effectively mitigates issues related to signal variability and device differences, achieving a localization accuracy of 1.92 m with over 90% success rate even after several months of operation. However, the impact of signal sampling fluctuations, the application of various data reduction techniques to extract significant predictors, and the use of positive knowledge transfer, which are critical aspects, have been overlooked in addressing the major challenges of indoor localization. Thus, in this paper, we propose a functional discriminant analysis method for feature extraction in Wi-Fi indoor localization systems. This paper employs advanced data reduction techniques to mitigate the overhead of fingerprint calibration by transforming Wi-Fi RSS values into a novel vector using linear transformation. The goal of this research paper is to enhance indoor localization performance for adaptive long-term Wi-Fi indoor positioning (adaptive LT Wi-Fi IP) by maximizing variance in a lower dimension while reducing computational complexity.
This study examines the temporal fluctuations in signal strength and proposes the implementation of transfer learning methodologies to enhance model performance in indoor positioning, even in scenarios with limited training data [56]. However, a key limitation of this approach lies in the presumption of similar data distributions between the training and testing datasets; discrepancies in these distributions can significantly impact model accuracy and reliability [57]. The dynamic nature of indoor environments is underscored by substantial variations in signal distributions observed between the training and testing datasets, as confirmed by the Mann–Whitney U test (see Figure 2). To mitigate this challenge, the study highlights the necessity for developing adaptable models capable of accommodating these environmental variations. Thus, the contributions of this paper include:
(1)
We propose the application of functional discriminant analysis (FDA) in combination with transfer learning techniques to tackle the challenge of high offline fingerprint calibration overhead. To achieve this, we generate new feature spaces that focus on the most significant predictors. These predictors enhance the separability of the model, leading to improved accuracy in indoor positioning estimates.
(2)
We examined the impact of sampling signal fluctuations on different algorithms in indoor localization scenarios. Multiple training samples were used to assess the influence of sampling fluctuations, while all collected testing samples for each month were used to evaluate algorithm robustness.
(3)
We applied covariance analysis (CA) to reduce the multicollinearity problem of the various RSS values collected at a reference point (RP), aiming to minimize computational complexity.
(4)
We compare the performance of different feature extraction methods, namely mean signal values, principal component analysis (PCA), and linear discriminant analysis (LDA/FDA), for adaptive LT Wi-Fi IP. We evaluate the effectiveness of these methods based on the achieved metrics and also investigate the hybrid effect of combining features extracted from multiple methods.
The rest of the paper is organized as follows: Related works are presented in Section 2. Section 3 describes the fingerprinting localization framework and its problem formulation. Experimental results, discussions, and evaluation metrics are presented in Section 4. Conclusions and recommendations are provided in Section 5.
Details can be found here:
https://www.mdpi.com/1424-8220/24/17/5665

This entry is adapted from the peer-reviewed paper 10.3390/s24175665

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