Deep Transfer Learning for WiFi Localization

Li, Peizheng; Cui, Han; Khan, Aftab; Raza, Usman; Piechocki, Robert J.; Doufexi, Angela; Farnham, Tim

doi:10.1109/radarconf2147009.2021.9455237

Cited by 25 publications

(13 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Zou et al (2019) adapt an ML model to detect gestures using Wi-Fi CSI data between two rooms in a building. Such applications involving transfer learning between domains has been explored in the application of occupancy and activity sensing in several other literature studies (e.g., Pan et al, 2008; Khalil et al, 2021; Li et al, 2021; Dridi et al, 2022; Omeragic et al, 2023). Pinto et al (2022) delve further into a review of transfer learning application in smart buildings.…”

Section: Applications Of ML In Smart Buildingsmentioning

confidence: 99%

Machine Learning for Smart and Energy-Efficient Buildings

Das,

Lin,

Agwan

et al. 2024

Environ. Data Science

View full text Add to dashboard Cite

Energy consumption in buildings, both residential and commercial, accounts for approximately 40% of all energy usage in the United States, and similar numbers are being reported from countries around the world. This significant amount of energy is used to maintain a comfortable, secure, and productive environment for the occupants. So, it is crucial that energy consumption in buildings must be optimized, all the while maintaining satisfactory levels of occupant comfort, health, and safety. Machine learning (ML) has been proven to be an invaluable tool in deriving important insights from data and optimizing various systems. In this work, we review some of the most promising ways in which ML has been leveraged to make buildings smart and energy-efficient. For the convenience of readers, we provide a brief introduction to the relevant ML paradigms and the components and functioning of each smart building system we cover. Finally, we discuss the challenges faced while implementing machine learning algorithms in smart buildings and provide future avenues for research in this field.

show abstract

Section: Applications Of ML In Smart Buildingsmentioning

confidence: 99%

Machine Learning for Smart and Energy-Efficient Buildings

Das,

Lin,

Agwan

et al. 2024

Environ. Data Science

View full text Add to dashboard Cite

show abstract

“…Local trust zones [408]- [414] Sustainable develop. [415]- [417] Mapping [418], [419] Indoor [409], [410], [412], [413], [415]- [419] Outdoor [408], [414] Sensors [410], [412]- [419] Radar [409] mmWave/THz [408] AoD, AoA, RSS…”

Section: Rmse Rss Transfer Learningmentioning

confidence: 99%

“…It is proven that with only 30% of the data in the second floor the TL enabled system can achieve the same level of performance. Finally, deep CNNs for pedestrian localization can be combined with TL approaches with great success (i.e., 45% increase of the training data and cut down on training time by half) [409]. 2) Conventional TL: Conventional TL methods augment the real-time measurements gathered from the target domain with labeled fingerprints from the source domain.…”

Section: F Transfer Learningmentioning

confidence: 99%

Localization as a key enabler of 6G wireless systems: A comprehensive survey and an outlook

Trevlakis¹,

Boulogeorgos²,

Pliatsios³

et al. 2023

Preprint

View full text Add to dashboard Cite

<p>When fully implemented, sixth generation (6G) wireless systems will constitute intelligent wireless networks that enable not only ubiquitous communication but also high-accuracy localization services. They will be the driving force behind this transformation by introducing a new set of characteristics and service capabilities in which location will coexist with communication while sharing available resources. To that purpose, this survey investigates the envisioned applications and use cases of localization in future 6G wireless systems, while analyzing the impact of the major technology enablers. Afterwards, system models for millimeter wave, terahertz and visible light positioning that take into account both line-of-sight (LOS) and non-LOS channels are presented, while localization key performance indicators are revisited alongside mathematical definitions. Moreover, a detailed review of the state of the art conventional and learningbased localization techniques is conducted. Furthermore, the localization problem is formulated, the wireless system design is considered and the optimization of both is investigated. Finally, insights that arise from the presented analysis are summarized and used to highlight the most important future directions for localization in 6G wireless systems.</p>

show abstract

“…In our proposed framework, network-based deep transfer learning (i.e., fine-tuning) is adopted to build personalized models for new users. As analyzed in [32], CSI data have some common hidden features even though the data packets are obtained from different environments with different sizes and layouts. For instance, the amplitude of adjacent subcarriers maintains a certain level of consistency, which holds within a certain range.…”

Section: B Federated Transfer Learningmentioning

confidence: 99%

FedPos: A Federated Transfer Learning Framework for CSI-Based Wi-Fi Indoor Positioning

Guo

Hou

et al. 2023

IEEE Systems Journal

View full text Add to dashboard Cite

This paper proposes FedPos, a federated transfer learning framework together with a novel position estimation method for Wi-Fi indoor positioning. Compared with traditional machine learning with privacy leakage problems and the cloud model trained through federated learning (FL) fails in personalization, the FedPos framework aggregates non-classification layer parameters of models trained from different environments to build a robust and versatile encoder on the cloud server while preserving user privacy. The global cloud encoder can aggregate different classifiers and then construct personalized models for new users through fine-tuning. The proposed framework can be updated incrementally and is highly extensible. Specifically, we exploit channel state information (CSI) as the positioning feature and assess the transferability of a lightweight convolutional neural network (CNN) in unfamiliar environments. We evaluate the performance of our proposed framework and position estimation method in different indoor environments. Our experimental results indicate that the proposed framework can achieve a mean localization error of 42.18 cm in a 64-position living room. They also confirm that FedPos can achieve a 5.22% average localization performance boost and reduce the average model training time by about 34.78% when compared with normal training. By reusing part of the feature extractor layers that are trained from other environments, at least 65% of training data can be saved to achieve a localization performance that is similar to the base model. Overall, the proposed position estimation method can effectively improve localization accuracy as compared with three other existing CSI-based methods.

show abstract

Deep Transfer Learning for WiFi Localization

Cited by 25 publications

References 15 publications

Machine Learning for Smart and Energy-Efficient Buildings

Machine Learning for Smart and Energy-Efficient Buildings

Localization as a key enabler of 6G wireless systems: A comprehensive survey and an outlook

FedPos: A Federated Transfer Learning Framework for CSI-Based Wi-Fi Indoor Positioning

Contact Info

Product

Resources

About