Wi-Fi is among the most successful wireless technologies ever invented. As Wi-Fi becomes more and more present in public and private spaces, it becomes natural to leverage its ubiquitousness to implement ground-breaking wireless sensing applications such as human presence detection, activity recognition, and object tracking, just to name a few. This paper reports ongoing efforts by the IEEE 802.11bf Task Group (TGbf), which is defining the appropriate modifications to existing Wi-Fi standards to enhance sensing capabilities through 802.11-compliant waveforms. We summarize objectives and timeline of TGbf, and discuss some of the most interesting proposed technical features discussed so far. We also introduce a roadmap of research challenges pertaining to Wi-Fi sensing and its integration with future Wi-Fi technologies and emerging spectrum bands, hoping to elicit further activities by both the research community and TGbf. Read More
Tag Archives: WiFi
Quality of Service Optimization in Mobile Edge Computing Networks via Deep Reinforcement Learning
Mobile edge computing (MEC) is an emerging paradigm that integrates computing resources in wireless access networks to process computational tasks in close proximity to mobile users with low latency. In this paper, we propose an online double deep Q networks ( DDQN) based learning scheme for task assignment in dynamic MEC networks, which enables multiple distributed edge nodes and a cloud data center to jointly process user tasks to achieve optimal long-term quality of service (QoS). The proposed scheme captures a wide range of dynamic network parameters including non-stationary node computing capabilities, network delay statistics, and task arrivals. It learns the optimal task assignment policy with no assumption on the knowledge of the underlying dynamics.In addition, the proposed algorithm accounts for both performance and complexity, and addresses the state and action space explosion problem in conventional Q learning.The evaluation results show that the proposed DDQN-based task assignment scheme significantly improves the QoS performance, compared to the existing schemes that do not consider the effects of network dynamics on the expected long-term rewards,while scaling reasonably well as the network size increases. Read More
#iot, #wifiTwelve Million Phones, One Dataset, Zero Privacy
Every minute of every day, everywhere on the planet, dozens of companies — largely unregulated, little scrutinized — are logging the movements of tens of millions of people with mobile phones and storing the information in gigantic data files. The Times Privacy Project obtained one such file, by far the largest and most sensitive ever to be reviewed by journalists. It holds more than 50 billion location pings from the phones of more than 12 million Americans as they moved through several major cities, including Washington, New York, San Francisco and Los Angeles.
Each piece of information in this file represents the precise location of a single smartphone over a period of several months in 2016 and 2017. Read More
DARPA is betting on AI to bring the next generation of wireless devices online
It’s so seamless you almost never notice it, but wireless communication is the foundation upon which much of modern life is built: it powers our ability to text and make calls, hail an Uber, and stream Netflix shows. With the introduction of 5G, it also promises to lower the barrier to safer self-driving cars and kick off a revolution in the internet of things. But this next leap in wireless technology will not be possible without a key ingredient: artificial intelligence. Read More
With a Second Repeating Radio Burst, Astronomers Close In on an Explanation of Fast Radio Bursts (FRBs)
Between this past Christmas and New Year’s Day, Brian Metzger realized he had his home to himself — no emails coming in, no classes to teach — and maybe, just maybe, the glimmer of an answer to one of astronomy’s most stubborn mysteries.
He chased hard after the lead, worried a little error could unravel everything or that someone else would put together the same pieces first. “You’re racing a little bit against the clock, because other people probably see this as well,” said Metzger, an astrophysicist at Columbia University. “It can kind of become all-consuming.”
Along with scores of other researchers around the world, Metzger has spent the last few years brainstorming ways to understand fast radio bursts (FRBs). Read More
If DARPA Has Its Way, AI Will Rule the Wireless Spectrum
In the early 2000s, Bluetooth almost met an untimely end. The first Bluetooth devices struggled to avoid interfering with Wi-Fi routers, a higher-powered, more-established cohort on the radio spectrum, with which Bluetooth devices shared frequencies. Bluetooth engineers eventually modified their standard—and saved their wireless tech from early extinction—by developing frequency-hopping techniques for Bluetooth devices, which shifted operation to unoccupied bands upon detecting Wi-Fi signals.
Frequency hopping is just one way to avoid interference, a problem that has plagued radio since its beginning. Long ago, regulators learned to manage spectrum so that in the emerging wireless ecosystem, different radio users were allocated different frequencies for their exclusive use. While this practice avoids the challenges of detecting transmissions and shifting frequencies on the fly, it makes very inefficient use of spectrum, as portions lay fallow. Read More
DeepCount: Crowd Counting with WiFi via Deep Learning
Recently, the research of wireless sensing has achieved more intelligent results, and the intelligent sensing of human location and activity can be realized by means of WiFi devices. However, most of the current human environment perception work is limited to a single person’s environment, because the environment in which multiple people exist is more complicated than the environment in which a single person exists. In order to solve the problem of human behavior perception in a multi-human environment, we first proposed a solution to achieve crowd counting (inferred population) using deep learning in a closed environment with WIFI signals – DeepCount, which is the first in a multi-human environment. step. Since the use of WiFi to directly count the crowd is too complicated, we use deep learning to solve this problem, use Convolutional Neural Network(CNN) to automatically extract the relationship between the number of people and the channel, and use Long Short Term Memory(LSTM) to resolve the dependencies of number of people and Channel State Information(CSI) . To overcome the massive labelled data required by deep learning method, we add an online learning mechanism to determine whether or not someone is entering/leaving the room by activity recognition model, so as to correct the deep learning model in the fine-tune stage, which, in turn, reduces the required training data and make our method evolving over time. The system of DeepCount is performed and evaluated on the commercial WiFi devices. By massive training samples, our end-to-end learning approach can achieve an average of 86.4% prediction accuracy in an environment of up to 5 people. Meanwhile, by the amendment mechanism of the activity recognition model to judge door switch to get the variance of crowd to amend deep learning predicted results, the accuracy is up to 90%. Read More
Artificial intelligence senses people through walls
X-ray vision has long seemed like a far-fetched sci-fi fantasy, but over the last decade a team led by Professor Dina Katabi from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) has continually gotten us closer to seeing through walls.
Their latest project, “RF-Pose,” uses artificial intelligence (AI) to teach wireless devices to sense people’s postures and movement, even from the other side of a wall. Read More
AI uses Wi-Fi data to estimate how many people are in a room
You can tell a lot about people from their Wi-Fi connections — including, as it turns out, how many of them are standing near an access point. In a newly published research paper (“DeepCount: Crowd Counting with WiFi via Deep Learning“) on the preprint server Arxiv.org, scientists describe an AI activity recognition model — DeepCount — that infers the population size of rooms from wireless data.
Their work comes not long after researchers at Ryerson University in Torontodemonstrated a neural network that can determine whether smartphone owners are walking, biking, or driving around a few city blocks by using Wi-Fi data, and after Purdue University researchers developed a system that uses Wi-Fi access logs to suss out relationships among users, locations, and activities. Read More
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