An Enhanced Approach to Imaging the Indoor Environment Using WiFi RSSI Measurements

Dubey, Amartansh; Sood, Pranay; Santos, Jehiel; Ma, Dingfei; Chiu, Chi-Yuk; Murch, R.D.

doi:10.1109/tvt.2021.3101009

Cited by 20 publications

(3 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, indoor environments are still challenging due to the heterogeneous nature and the presence of various obstacles that cause variations in signal and noise levels, making high-precision localization a difficult task. To address this challenge, various technologies have been employed, including RFID, BLE, Wi-Fi and ZigBee [ 25 , 26 , 28 , 29 ]. These commercially available technologies can provide IPSs with an accuracy of the meter order, which may suffice for some applications.…”

Section: Introductionmentioning

confidence: 99%

Indoor Positioning System (IPS) Using Ultra-Wide Bandwidth (UWB)—For Industrial Internet of Things (IIoT)

Che

Ahmed

Lazaridis

et al. 2023

Sensors

View full text Add to dashboard Cite

The integration of the physical and digital world has become increasingly important, and location-based services have become the most sought-after application in the field of the Internet of Things (IoT). This paper delves into the current research on ultra-wideband (UWB) indoor positioning systems (IPS). It begins by examining the most common wireless communication-based technologies for IPSs followed by a detailed explanation of UWB. Then, it presents an overview of the unique characteristics of UWB technology and the challenges still faced by the IPS implementation. Finally, the paper evaluates the advantages and limitations of using machine learning algorithms for UWB IPS.

show abstract

Section: Introductionmentioning

confidence: 99%

Indoor Positioning System (IPS) Using Ultra-Wide Bandwidth (UWB)—For Industrial Internet of Things (IIoT)

Che

Ahmed

Lazaridis

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…They also developed two applications-material sensing, and device-free user localization-to demonstrate the versatility of CSI2Image [12]. In [13], Dubey et al developed an extended Rytov phaseless imaging (xRPI) technique that images object shapes and refractive indices in an indoor environment using the RSSI of WiFi signals. These advanced imaging techniques have motivated a novel ideas to improve image inpainting performance.…”

Section: B Imaging Using Rf Signalsmentioning

confidence: 99%

RF-Inpainter: Multimodal Image Inpainting Based on Vision and Radio Signals

et al. 2022

View full text Add to dashboard Cite

This study demonstrates the feasibility of image inpainting using both visual information and radio frequency (RF) signals. Recent developments in imaging and vision-based technologies using RF signals have revealed the potential of leveraging multimodal information to enhance image inpainting performance. In this context, we propose RF-Inpainter-a novel inpainting method that integrates visual and wireless information by fusing defective RGB images with the received signal strength indicator (RSSI) using a deep auto-encoder model. The inpainting performance of RF-Inpainter is evaluated using experimentally obtained images and RSSI datasets in an indoor environment. Image-only inpainting and RSSI-only inpainting models are used as baselines to illustrate the superiority of RF-Inpainter over inpainting methods based on a single modality. The results establish that RF-Inpainter generates satisfactory inpainted images in most experimental scenarios, achieving a maximum improvement of 36.4% and 14.6% in terms of mean peak signal-to-noise ratio (PSNR) and mean structural similarity index (SSIM), respectively.

show abstract

“…Human activity recognition (HAR) plays a pivotal role in emerging Internet of Things (IoT) technologies, encompassing domains such as smart healthcare, smart homes, and user identification [1,2]. Numerous HAR systems exist, including camera-based approaches [3], wearable sensor-based methods [4], radio frequency-based techniques [5,6], ultrasonicbased solutions [7], and FMCW-based methodologies [8,9].…”

Section: Introductionmentioning

confidence: 99%

Wi-CHAR: A WiFi Sensing Approach with Focus on Both Scenes and Restricted Data

Hao,

Han,

Zhang

et al. 2024

Sensors

View full text Add to dashboard Cite

Significant strides have been made in the field of WiFi-based human activity recognition, yet recent wireless sensing methodologies still grapple with the reliance on copious amounts of data. When assessed in unfamiliar domains, the majority of models experience a decline in accuracy. To address this challenge, this study introduces Wi-CHAR, a novel few-shot learning-based cross-domain activity recognition system. Wi-CHAR is meticulously designed to tackle both the intricacies of specific sensing environments and pertinent data-related issues. Initially, Wi-CHAR employs a dynamic selection methodology for sensing devices, tailored to mitigate the diminished sensing capabilities observed in specific regions within a multi-WiFi sensor device ecosystem, thereby augmenting the fidelity of sensing data. Subsequent refinement involves the utilization of the MF-DBSCAN clustering algorithm iteratively, enabling the rectification of anomalies and enhancing the quality of subsequent behavior recognition processes. Furthermore, the Re-PN module is consistently engaged, dynamically adjusting feature prototype weights to facilitate cross-domain activity sensing in scenarios with limited sample data, effectively distinguishing between accurate and noisy data samples, thus streamlining the identification of new users and environments. The experimental results show that the average accuracy is more than 93% (five-shot) in various scenarios. Even in cases where the target domain has fewer data samples, better cross-domain results can be achieved. Notably, evaluation on publicly available datasets, WiAR and Widar 3.0, corroborates Wi-CHAR’s robust performance, boasting accuracy rates of 89.7% and 92.5%, respectively. In summary, Wi-CHAR delivers recognition outcomes on par with state-of-the-art methodologies, meticulously tailored to accommodate specific sensing environments and data constraints.

show abstract

An Enhanced Approach to Imaging the Indoor Environment Using WiFi RSSI Measurements

Cited by 20 publications

References 56 publications

Indoor Positioning System (IPS) Using Ultra-Wide Bandwidth (UWB)—For Industrial Internet of Things (IIoT)

Indoor Positioning System (IPS) Using Ultra-Wide Bandwidth (UWB)—For Industrial Internet of Things (IIoT)

RF-Inpainter: Multimodal Image Inpainting Based on Vision and Radio Signals

Wi-CHAR: A WiFi Sensing Approach with Focus on Both Scenes and Restricted Data

Contact Info

Product

Resources

About