Person identification plays a vital role in enabling intelligent, personalized, and secure human-computer interaction. Recent research has demonstrated the feasibility of leveraging Wi-Fi signals for passive person identification using a person's unique gait pattern. Although most existing work focuses on sub-6 GHz frequencies, the emergence of mmWave offers new opportunities through its finer spatial resolution, though its comparative advantages for person identification remain unexplored. This work presents the first comparative study between sub-6 GHz and mmWave Wi-Fi signals for person identification with commercial off-the-shelf (COTS) Wi-Fi, using a novel dataset of synchronized measurements from the two frequency bands in an indoor environment. To ensure a fair comparison, we apply identical training pipelines and model configurations across both frequency bands. Leveraging end-to-end deep learning, we show that even at low sampling rates (10 Hz), mmWave Wi-Fi signals can achieve high identification accuracy (91.2% on 20 individuals) when combined with effective background subtraction.

Many of these challenges are cross-cutting across various fields. While current RGB devices remain the most suitable for spatial resolution, image quality, data richness, and noise management, they are inherently prone to the reported limitations. Consequently, recent efforts have been intensified to develop technologies capable of replacing or complementing current visual technologies, aiming to overcome, at least in part, these issues in visual tasks. Wi-Fi sensing is an evolving technology that has already proven to be highly effective in a wide range of applications over the past two decades 30-33, from smart home automation and security to healthcare monitoring and human-computer interaction. In recent years, Wi-Fi devices have been used not only for current monitoring activities but also as a type of "vision" system capable of capturing and shaping significant information to accomplish computer vision tasks through deep analysis of propagated signal spectra. For example, in Avola et al. 34, the authors use Wi-Fi signal information that has passed through various subjects to develop a person re-identification system capable of providing more robust and reliable biometric signatures than visual ones, which are dependent on factors like changes in lighting or clothing. Meanwhile, Wang et al. 35 utilize Wi-Fi signals to reconstruct the skeletons of individuals, on which classical methods can then be applied to determine human body poses and movements. While there is potential to lose valuable information, such as the texture and colors that define the surface of objects, Wi-Fi sensing offers extraordinary capabilities, including analyzing the interior of solid objects, overcoming obstacles to mitigate occlusion issues, and providing stricter privacy constraints. The promising future of Wi-Fi applications in various domains is further supported by the recent establishment of the IEEE 802.11bf standard 36, which formalizes and standardizes Wi-Fi sensing capabilities within the existing IEEE 802.11

Recent advancements in Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse tasks. However, their potential to integrate physical model knowledge for real-world signal interpretation remains largely unexplored. In this work, we introduce Wi-Chat, the first LLM-powered Wi-Fi-based human activity recognition system. We demonstrate that LLMs can process raw Wi-Fi signals and infer human activities by incorporating Wi-Fi sensing principles into prompts. Our approach leverages physical model insights to guide LLMs in interpreting Channel State Information (CSI) data without traditional signal processing techniques. Through experiments on real-world Wi-Fi datasets, we show that LLMs exhibit strong reasoning capabilities, achieving zero-shot activity recognition. These findings highlight a new paradigm for Wi-Fi sensing, expanding LLM applications beyond conventional language tasks and enhancing the accessibility of wireless sensing for real-world deployments.

With the emergence of new technologies and a growing number of wireless networks, we face the problem of radio spectrum shortages. As a result, identifying the wireless channel spectrum to exploit the channel's idle state while also boosting network security is a pivotal issue. Detecting and classifying protocols in the MAC sublayer enables Cognitive Radio users to improve spectrum utilization and minimize potential interference. In this paper, we classify the Wi-Fi and Bluetooth protocols, which are the most widely used MAC sublayer protocols in the ISM radio band. With the advent of various wireless technologies, especially in the 2.4 GHz frequency band, the ISM frequency spectrum has become crowded and high-traffic, which faces a lack of spectrum resources and user interference. Therefore, identifying and classifying protocols is an effective and useful method. Leveraging machine learning and deep learning techniques, known for their advanced classification capabilities, we apply Support Vector Machine and K-Nearest Neighbors algorithms, which are machine learning algorithms, to classify protocols into three classes: Wi-Fi, Wi-Fi Beacon, and Bluetooth. To capture the signals, we use the USRP N210 Software Defined Radio device and sample the real data in the indoor environment in different conditions of the presence and absence of transmitters and receivers for these two protocols. By assembling this dataset and studying the time and frequency features of the protocols, we extract the frame width and the silence gap between the two frames as time features and the PAPR of each frame as a power feature. By comparing the output of the protocols classification in different conditions and also adding Gaussian noise, it was found that the samples in the nonlinear SVM method with RBF and KNN functions have the best performance, with 97.83% and 98.12% classification accuracy, respectively.

How to silence group chats and emails without missing important notifications on your iPhone. If you work from home, you know how important it is to have a fast and reliable Wi-Fi connection. But sometimes, your Wi-Fi can get slow or spotty, especially if you have a large house or a lot of devices using the network at the same time. You might think that the only solution is to move closer to your router or buy a more expensive one. But what if we told you that you can extend your Wi-Fi coverage by using your Amazon Echo device and an Eero mesh system?

Abstract: Water is a necessary fluid to the human body and automatic checking of its quality and cleanness is an ongoing area of research. One such approach is to present the liquid to various types of signals and make the amount of signal attenuation an indication of the liquid category. In this article, we have utilized the Wi-Fi signal to distinguish clean water from poisoned water via training different machine learning algorithms. The Wi-Fi access points (WAPs) signal is acquired via equivalent smartphone-embedded Wi-Fi chipsets, and then Channel-State-Information CSI measures are extracted and converted into feature vectors to be used as input for machine learning classification algorithms. The measured amplitude and phase of the CSI data are selected as input features into four classifiers k-NN, SVM, LSTM, and Ensemble. The experimental results show that the model is adequate to differentiate poison water from clean water with a classification accuracy of 89% when LSTM is applied, while 92% classification accuracy is achieved when the AdaBoost-Ensemble classifier is applied.

Sign up for internet service with Comcast's Xfinity, and the company will get you in for $19.95 for a relatively slow 25 megabits per second, or $49.99 for "faster speeds" like 200 Mbps. But if you're having trouble with your video calls dropping out, buffering when watching Netflix or waiting for websites to load on your computer, getting faster internet speed may not be the answer. That's the admittedly biased opinion of Nick Weaver, founder of Eero, a device that connects to your home internet and spreads Wi-Fi signals more evenly throughout the various rooms. "You're welcome to pay Comcast pay more money monthly if you like, but it won't solve the problem," Weaver says. You will get faster internet if using a wired connection, "but not in the places of the home where you need it," as in devices that depend upon Wi-Fi like laptops, smart TVs, connected speakers like Amazon Echo and more.

Sign up for internet service with Comcast's Xfinity, and the company will get you in for $19.95 for a relatively slow 25 megabits per second, or $49.99 for "faster speeds" like 200 Mbps. But if you're having trouble with your video calls dropping out, buffering when watching Netflix or waiting for websites to load on your computer, getting faster internet speed may not be the answer. That's the admittedly biased opinion of Nick Weaver, the founder of Eero, a device that connects to your home internet and spreads Wi-Fi signals more evenly throughout the various rooms. "You're welcome to pay Comcast pay more money monthly if you like, but it won't solve the problem," says Weaver. You will get faster internet if using a wired connection, "but not in the places of the home where you need it," as in devices that depend upon Wi-Fi like laptops, smart TVs, connected speakers like Amazon Echo and more.

He and his colleagues have built a device, about the size of a thin laptop, that emits sound at frequencies 10 times higher than the shrillest note a piccolo can sustain. The pitches it produces are inaudible to the human ear. When Guo's team aims the device at a person and fires an ultrasonic pitch, the gadget listens for the echo using its hundreds of embedded microphones. Then, employing artificial intelligence techniques, his team tries to decipher what the person is doing from the reflected sound alone. The technology is still in its infancy, but they've achieved some promising initial results.

You can learn a lot about people from the gadgets on their person, history has taught us, and that includes their movements. In a paper published on the preprint server Arxiv.org Wi-Fi has a number of advantages over commonly used modality classification schemes, the researchers point out. It's ubiquitous, for one, and it works reliably indoors even in "challenging" environments like urban highrises. "Due to their … pervasive nature, Wi-Fi networks have the potential to collect large-scale, low-cost, and disaggregate data on multimodal transportation," the paper's authors explained. "In this study, we develop a … framework to utilize Wi-Fi communications obtained from smartphones for the purpose of transportation mode detection."