Long Short-Term Indoor Positioning System via Evolving Knowledge Transfer

Li, Lin; Guo, Xiansheng; Zhang, Yukun; Ansari, Nirwan; Li, Huiyong

doi:10.1109/twc.2022.3142064

Cited by 15 publications

(6 citation statements)

References 44 publications

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“…For instance, Transloc [20] combined source domain refinement and homogeneous feature space construction to solve the problem of heterogeneous feature dimensions caused by BS changes in long-term localization tasks and obtained acceptable results. Li et al [21] reduced data distribution divergences between different domains through multilevel knowledge transfer, including sample, feature, and model levels. Guo et al [22] calculated the similarity between source and target domain data using the Euclidean distance and maximized the likelihood function to obtain the most probable label estimation.…”

Section: A Transfer Learningmentioning

confidence: 99%

TRAIL: A Three-Step Robust Adversarial Indoor Localization Framework

Yang,

Guo,

Chen

et al. 2024

IEEE Sensors J.

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Indoor localization utilizing received signal strength (RSS) fingerprint has garnered significant attention over the past decade because it is readily captured from the MAC layer of ubiquitous hardware devices. However, the localization accuracy of RSS fingerprint-based methods is notably influenced by two primary factors: 1) disparities between offline and online data distributions induced by dynamic environmental changes and device heterogeneity and 2) inconsistencies among hetero-measure samples (different RSS samples collected at the same reference point (RP) during the online stage) stemming from unknown noise and interference. To address these issues, we propose a three-step robust adversarial indoor localization (TRAIL) framework. The model is pretrained in the first step (Step A), and an adversarial game is played between a regressor and a feature extractor within the model in the second step (Step B) and third step (Step C). Specifically, Step B trains the regressor to discover more "hard" samples, i.e., hetero-measure samples with notable positioning differences, and Step C trains the feature extractor to learn a suitable transformation that eliminates the disparities between offline and online data distributions and the "hard" samples. To harmonize the contributions of the two factors in model training, we integrate the multiple gradient descent algorithm (MGDA). Experimental results on both actual and simulated datasets demonstrate that TRAIL outperforms state-of-the-art methods and exhibits robustness in low signal-to-noise ratio (SNR) environments.

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Section: A Transfer Learningmentioning

confidence: 99%

TRAIL: A Three-Step Robust Adversarial Indoor Localization Framework

Yang,

Guo,

Chen

et al. 2024

IEEE Sensors J.

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“…For example, Chen et al [31] introduced Fidora, a WiFi-based system addressing fingerprint inconsistency challenges through domain adaptation and clustering. Li et al [32] proposed a dynamic fingerprint adaptation framework, requiring minimal human intervention for indoor localization. Chen et al [33] presented a few-sample migration learning system, reducing data collection and labeling costs while achieving accurate localization.…”

Section: B Dynamic Fingerprint Localizationmentioning

confidence: 99%

MFFALoc: CSI-Based Multifeatures Fusion Adaptive Device-Free Passive Indoor Fingerprinting Localization

Rao,

Luo,

Luo

et al. 2024

IEEE Internet Things J.

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In recent years, the rise of location-based service applications such as cashier-less shopping, mobile advertisement targeting, and geo-based augmented reality (AR) has been remarkable. These applications offer convenient and interactive experiences by utilizing indoor localization technology. One popular research area in indoor localization is passive fingerprinting localization based on Channel State Information (CSI), which uses general-purpose Wi-Fi platforms and "unconscious cooperative sensing" to achieve device-free localization. However, existing studies face challenges related to inadequate fingerprint richness, limited distinguishability, and inconsistent fingerprint features in real-world dynamic environments. To address these challenges, we prpose MFFLoc in this paper. MFFLoc extracts and processes amplitude and phase information from CSI in a 2D manner. It then fuses the amplitude and phase information using multimodal fusion representation, resulting in rich and distinguishable fused fingerprint features. This approach allows MFFLoc to achieve satisfactory accuracy with just one communication link, reducing deployment costs. To overcome the issue of inconsistent fingerprint features in dynamic environments, MFFLoc proposes an unsupervised domain adaptation method. It employs a dual-flow structure, with one flow operating in the source domain and the other in the target domain. The adaptation layer, with correlated weights, remains unshared between the two flows. Meta-learning is also used to automatically determine the most suitable adaptation layer. Through extensive 6-day experiments conducted in a dynamic indoor environment, MFFLoc showcases superior performance compared to state-ofthe-art systems. It demonstrates higher localization accuracy and robustness, making it a promising solution for indoor localization applications.

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“…Also, when the target domain has few data, domain adaptation can be difficult and can lead to over-fitting or poor generalization. Multi-level pose regress and feature discriminator - [197] Increases efficiency / Test on real scene [225] Long Short-Term indoor Positioning Improve Mean ACC: 18% [226] / Adjusting balance between target and environmental dynamics [236] Fingerprint-based heterogeneous knowledge Mean err: 1.58m and 2.23m [222] Robust in changing environment / Tested in other area [166] GNN-based few-shot TL For example [232] the authors suggest a novel framework is built on vision transformer neural networks to address the difficulty of the heterogeneity of wireless transceivers across various cellphones used by consumers reducing the dependability and accuracy. Moreover, the authors [244] propose a system based on multi-head attention NN that can be robust to device heterogeneity and offers a 35% accuracy improvement.…”

Section: B Il Based On Domain Adaptationmentioning

confidence: 99%

Uncovering the Potential of Indoor Localization: Role of Deep and Transfer Learning

Kerdjidj,

Himeur,

Sohail

et al. 2024

IEEE Access

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Indoor localization (IL) is a significant topic of study with several practical applications, particularly in the context of the Internet of Things (IoT) and smart cities. The area of IL has evolved greatly in recent years due to the introduction of numerous technologies such as WiFi, Bluetooth, cameras, and other sensors. Despite the growing interest in this field, there are numerous challenges and drawbacks that must be addressed to develop more accurate and sustainable systems for IL. This review study gives an in-depth look into IL, covering the most promising artificial intelligence-based and hybrid strategies that have shown excellent potential in overcoming some of the limitations of classic methods within IoT environments. In addition, the paper investigates the significance of high-quality datasets and evaluation metrics in the design and assessment of IL algorithms. Furthermore, this overview study emphasizes the crucial role that machine learning techniques, such as deep learning and transfer learning, play in the advancement of IL. A focus on the importance of IL and the various technologies, methods, and techniques that are being used to improve it. Finally, the survey highlights the need for continued research and development to create more accurate and scalable techniques that can be applied across a range of IoT-related industries, such as evacuationegress routes, hazard-crime detection, smart occupancy-driven energy reduction and asset tracking and management.

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Long Short-Term Indoor Positioning System via Evolving Knowledge Transfer

Cited by 15 publications

References 44 publications

TRAIL: A Three-Step Robust Adversarial Indoor Localization Framework

TRAIL: A Three-Step Robust Adversarial Indoor Localization Framework

MFFALoc: CSI-Based Multifeatures Fusion Adaptive Device-Free Passive Indoor Fingerprinting Localization

Uncovering the Potential of Indoor Localization: Role of Deep and Transfer Learning

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