Robot-Assisted Backscatter Localization for IoT Applications

Zhang, Shengkai; Wang, Wei; Tang, Sheyang; Jin, Shi; Jiang, Tao

doi:10.1109/twc.2020.2997393

Cited by 24 publications

(12 citation statements)

References 38 publications

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“…The AoA of a signal propagating in multiple paths can be derived by Eqn. (3). MUSIC analyzes the eigenstructure of a signal's correlation matrix R to estimate the AoA.…”

Section: B Aoa Estimationmentioning

confidence: 99%

“…Wang et al [27] enables human activity recognition but needs to wear an RFID reader on the user's body. WiCapture [11], Rover [3] and WINS [8] require the user to hold an active WiFi radio to enable the WiFi sensing. Such instrumentations of users hinder these approaches from widely deployments.…”

Section: A Device-based Sensing Systemmentioning

confidence: 99%

“…Radio frequency (RF) sensing is an emerging research field and has attracted considerable attentions. It impels many remarkable applications, such as indoor localization [1], [2], [3], [4], gesture recognition [5], [6], fall detection [7], robot navigation [8], [9], [10] and human motion tracking [11], [12]. Despite the success in diverse applications, recent years have witnessed the rapid proliferation of RF-based human imaging technologies, which provide privacy-preserving services for applications like surveillance, security inspection, and health monitoring.…”

Section: Introductionmentioning

confidence: 99%

“…e.g., localization [3], gait recognition [15], and breath monitoring [16], holds the potential to meet the above requirements due to that 1) WiFi is ubiquitously available in modern cities, 2) WiFi devices are very cheap (costs 10-30 US dollars), and 3) multi-antenna transceivers and WiFi OFDM signals with multiple subcarriers encode rich spatial and temporal information of signal propagation, and thus can improve the accuracy. Existing WiFi-based imaging systems [17] suffers from lackluster performance, leaving the design space to improve the imaging accuracy.…”

Section: Introductionmentioning

confidence: 99%

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WiImage: Fine-grained Human Imaging from Commodity WiFi

Zhang

Liu

et al. 2021

Preprint

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Privacy-preserving wireless-based human imaging technologies attract much attention in academia and industry. They can serve in surveillance, security inspection, and health monitoring, while preventing the privacy leakage from current camera-based surveillance system. However, previous solutions either requires dedicated hardware or costly infrastructure deployment, which hurts their practicality. In this paper, we propose WiImage, a low-cost, instantly-deployed sensing system that can capture a fine-grained human image and infer his contour using ubiquitous WiFi. WiImage is free of pre-training or placing any marker on a user’s body. WiImage consists of two technical components. First, a lightweight multipath resolution algorithm exploit the spatial and temporal correlation of WiFi packets to address the multipath and extract reflected signals from a human body. Second, an imaging algorithm infers the contour of a human from WiFi reflections. We prototype WiImage using offthe-shelf WiFi chips and conduct experiments in several typical indoor settings. The results show that WiImage can recover a representative human figure and use it to identify the user precisely.

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“…The AoA of a signal propagating in multiple paths can be derived by Eqn. (3). MUSIC analyzes the eigenstructure of a signal's correlation matrix R to estimate the AoA.…”

Section: B Aoa Estimationmentioning

confidence: 99%

Section: A Device-based Sensing Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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WiImage: Fine-grained Human Imaging from Commodity WiFi

Zhang

Liu

et al. 2021

Preprint

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“…In addition, the proliferation of backscatter technology has enabled the realization of ubiquitous and long‐term connectivity which has empowered smart systems. In Zhang et al, 2 the researchers have demonstrated the implementation of the backscatter technology for Internet‐of‐Things (IoT) communications. The IoT technology has extended its opportunistic wings for the developments of intelligent vehicles in smart cities.…”

Section: Introductionmentioning

confidence: 99%

Metamaterial‐loaded multiband antenna for embedded automotive Internet‐of‐Things communications

Malathy

Thanikachalam

Ruby

et al. 2021

Int J Communication

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Summary This study presents the design of a multiband monopole antenna using the metamaterial (MTM) loading technique for embedded automotive Internet‐of‐Things (IoT) communications. The proposed antenna consists of a simple microstrip line monopole loaded with a novel E‐slotted MTM. The unit cell MTM consists of a central transmission line loaded with parasitic patches that are deployed with “E”‐shaped slots. The MTM is symmetrically loaded to either side of the monopole antenna to achieve miniaturization through frequency reduction and to realize multiple resonances. The proposed monopole antenna has a footprint of 10 × 30 mm2 and operates at 2.45, 3.3, and 3.8 GHz suitable for Industrial Scientific and Medical applications (ISM), Wireless Fidelity (WiFi), and Worldwide Interoperability for Microwave Access (WiMAX) applications that are exploited in IoT communications. The prototype antenna is fabricated, and the simulation results are validated using experimental measurements. The prototype antenna offers the measured impedance bandwidth of 6%, 7%, and 10% at the said frequencies. The measured gain of the antenna is greater than 3.2 dBi, and the simulated efficiency is close to 86% throughout the operating bandwidth. The performance of the proposed antenna in the vicinity of the vehicle is tested, and the results are presented.

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