Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userGuardRider: Towards Sustainable Backscattering System over WiFi in the Wild
108
0
0.0
(
0
)
Added by
Xin He
Publication date
2019
fields
Electronic Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
The WiFi backscatter communications offer ultra-low power and ubiquitous connections for IoT systems. Caused by the intermittent-nature of the WiFi traffics, state-of-the-art WiFi backscatter communications are not reliable for backscatter link or simple for tag to do adaptive transmission. In order to build sustainable (reliable and simple) WiFi backscatter communications, we present GuardRider, a WiFi backscatter system that enables backscatter communications riding on WiFi signals in the wild. The key contribution of GuardRider is an optimization algorithm of designing RS codes to follow the statistical knowledge of WiFi traffics and adjust backscatter transmission. With GuardRider, the reliable baskscatter link is guaranteed and a backscatter tag is able to adaptively transmit information without heavily listening the excitation channel. We built a hardware prototype of GuardRider using a customized tag with FPGA implementation. Both the simulations and field experiments verify that GuardRider could achieve a notably gains in bit error rate and frame error rate, which are hundredfold reduction in simulations and around 99% in filed experiments.
rate research
Read More
Open-access telescopes of all apertures are needed to operate a competitive and efficient national science program. While larger facilities contribute light-gathering power and angular resolution, smaller ones dominate for field of view, time-resolution, and especially, total available observing time, thereby enabling our entire, diversely-expert community. Smaller aperture telescopes therefore play a critical and indispensable role in advancing science. Thus, the divestment of NSF support for modest-aperture (1 - 4 m) public telescopes poses a serious threat to U.S. scientific leadership, which is compounded by the unknown consequences of the shift from observations driven by individual investigators to survey-driven science. Given the much higher cost efficiency and dramatic science returns for investments in modest aperture telescopes, it is hard to justify funding only the most expensive facilities. We therefore urge the Astro2020 panel to explicitly make the case for modest aperture facilities, and to recommend enhancing this funding stream to support and grow this critical component of the OIR System. Further study is urgently needed to prioritize the numerous exciting potential capabilities of smaller facilities,and to establish sustainable, long-term planning for the System.
The accuracy of smartphone-based positioning methods using WiFi usually suffers from ranging errors caused by non-line-of-sight (NLOS) conditions. Previous research usually exploits several statistical features from a long time series (hundreds of samples) of WiFi received signal strength (RSS) or WiFi round-trip time (RTT) to achieve a high identification accuracy. However, the long time series or large sample size attributes to high power and time consumption in data collection for both training and testing. This will also undoubtedly be detrimental to user experience as the waiting time of getting enough samples is quite long. Therefore, this paper proposes a new real-time NLOS/LOS identification method for smartphone-based indoor positioning system using WiFi RTT and RSS. Based on our extensive analysis of RSS and RTT features, a machine learning-based method using random forest was chosen and developed to separate the samples for NLOS/LOS conditions. Experiments in different environments show that our method achieves a discrimination accuracy of about 94% with a sample size of 10. Considering the theoretically shortest WiFi ranging interval of 100ms of the RTT-enabled smartphones, our algorithm is able to provide the shortest latency of 1s to get the testing result among all of the state-of-art methods.
We present a new RF fingerprinting technique for wireless emitters that is based on a simple, easily and efficiently retrainable Ridge Regression (RR) classifier. The RR learns to identify devices using bursts of waveform samples, conveniently transformed and preprocessed by delay-loop reservoirs. Deep delay Loop Reservoir Computing (DLR) is our processing architecture that supports general machine learning algorithms on resource-constrained devices by leveraging delay-loop reservoir computing (RC) and innovative architectures of loop trees. In prior work, we trained and evaluated DLR using high SNR device emissions in clean channels. We here demonstrate how to use DLR for IoT authentication by performing RF-based Specific Emitter Identification (SEI), even in the presence of fading channels and heavy in-band jamming by leveraging a matched filter (MF) extension, dubbed MF-DLR. We show that the MF processing improves the SEI performance of RR without the RC transformation (MF-RR), but the MF-DLR is more robust and applicable for addressing signatures beyond waveform transients (e.g. turn-on).
Developing nations are particularly susceptible to the adverse effects of global warming. By 2040, 14 percent of global emissions will come from data centers. This paper presents early findings in the use AI and digital twins to model and optimize data center operations.
This paper presents a method that estimates the respiratory rate based on the frame capturing of wireless local area networks. The method uses beamforming feedback matrices (BFMs) contained in the captured frames, which is a rotation matrix of channel state information (CSI). BFMs are transmitted unencrypted and easily obtained using frame capturing, requiring no specific firmware or WiFi chipsets, unlike the methods that use CSI. Such properties of BFMs allow us to apply frame capturing to various sensing tasks, e.g., vital sensing. In the proposed method, principal component analysis is applied to BFMs to isolate the effect of the chest movement of the subject, and then, discrete Fourier transform is performed to extract respiratory rates in a frequency domain. Experimental evaluation results confirm that the frame-capture-based respiratory rate estimation can achieve estimation error lower than 3.2 breaths/minute.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
