NYC Wi-Fi Hotspot Locations Wi-Fi Providers: CityBridge, LLC (Free Beta): LinkNYC 1 gigabyte (GB), Free Wi-Fi Internet Kiosks Spot On Networks (Free) NYC HOUSING AUTHORITY (NYCHA) Properties Fiberless (Free): Wi-Fi access on Governors Island Free - up to 5 Mbps for users as the part of Governors Island Trust Governors Island Connectivity Challenge AT&T (Free): Wi-Fi access is free for all users at all times. Partners: In several parks, the NYC partner organizations provide publicly accessible Wi-Fi. Visit these parks to learn more information about their Wi-Fi service and how to connect. Cable (Limited-Free): In NYC Parks provided by NYC DoITT Cable television franchisees. ALTICEUSA previously known as “Cablevision” and SPECTRUM previously known as “Time Warner Cable” (Limited Free) Connect for 3 free 10 minute sessions every 30 days or purchase a 99 cent day pass through midnight. Wi-Fi service is free at all times to Cablevision’s Optimum Online and Time Warner Cable broadband subscribers. Wi-Fi Provider: Chelsea Wi-Fi (Free) Wi-Fi access is free for all users at all times. Chelsea Improvement Company has partnered with Google to provide Wi-Fi a free wireless Internet zone, a broadband region bounded by West 19th Street, Gansevoort Street, Eighth Avenue, and the High Line Park. Wi-Fi Provider: Downtown Brooklyn Wi-Fi (Free) The Downtown Brooklyn Partnership - the New York City Economic Development Corporation to provide Wi-Fi to the area bordered by Schermerhorn Street, Cadman Plaza West, Flatbush Avenue, and Tillary Street, along with select public spaces in the NYCHA Ingersoll and Whitman Houses. Wi-Fi Provider: Manhattan Downtown Alliance Wi-Fi (Free) Lower Manhattan Several public spaces all along Water Street, Front Street and the East River Esplanade south of Fulton Street and in several other locations throughout Lower Manhattan. Wi-Fi Provider: Harlem Wi-Fi (Free) The network will extend 95 city blocks, from 110th to 138th Streets between Frederick Douglass Boulevard and Madison Avenue is the free outdoor public wireless network. Wi-Fi Provider: Transit Wireless (Free) Wi-Fi Services in the New York City subway system is available in certain underground stations. For more information visit http://www.transitwireless.com/stations/. Wi-Fi Provider: Public Pay Telephone Franchisees (Free) Using existing payphone infrastructure, the City of New York has teamed up with private partners to provide free Wi-Fi service at public payphone kiosks across the five boroughs at no cost to taxpayers. Wi-Fi Provider: New York Public Library Using Wireless Internet Access (Wi-Fi): All Library locations offer free wireless access (Wi-Fi) in public areas at all times the libraries are open. Connecting to the Library's Wireless Network •You must have a computer or other device equipped with an 802.11b-compatible wireless card. •Using your computer's network utilities, look for the wireless network named "NYPL." •The "NYPL" wireless network does not require a password to connect. Limitations and Disclaimers Regarding Wireless Access •The Library's wireless network is not secure. Information sent from or to your laptop can be captured by anyone else with a wireless device and the appropriate software, within three hundred feet. •Library staff is not able to provide technical assistance and no guarantee can be provided that you will be able to make a wireless connection. •The Library assumes no responsibility for the safety of equipment or for laptop configurations, security, or data files resulting from connection to the Library's network

Wi-Fi dataset: the dataset may be downloaded from this link. If you use this dataset, please cite the following reference:

Anisa Allahdadi, Ricardo Morla, and Jaime S. Cardoso. "802.11 wireless simulation and anomaly detection using HMM and UBM". CoRR, abs/1707.02933, 2017. URL http://arxiv.org/abs/1707.02933.

Human3.6M dataset: preprocessed data can be downloaded from this link (third party provider). Please do not forget to check the dataset license agreement, available at the Human3.6M dataset website.

Wi-Fi Market size was valued at USD 14.6 Billion in 2024 and is projected to reach USD 40.5 Billion by 2031, growing at a CAGR of 22.6% from 2024 to 2031.

Global Wi-Fi Market Drivers

The market drivers for the Wi-Fi Market can be influenced by various factors. These may include:

Demand For Wireless Access Is Always Growing: As smart gadgets, Internet of Things (IoT) apps, and cloud-based services become more widely used, there is an ongoing need for wireless access that is quicker and more dependable.

5G network Expansion: Although 5G technology provides fast mobile internet, Wi-Fi is still essential for indoor connectivity, data traffic offloading from cellular networks, and connectivity in places with spotty cellular coverage.

Demand From Enterprises: Office networks, visitor Wi-Fi, and IoT deployments are just a few of the daily activities that largely depend on Wi-Fi in enterprises. The need for strong Wi-Fi infrastructure is increased by the move to remote work and digital transformation programmes.

The Emergence Of Wi-Fi 6 And Wi-Fi 6E: The speed, capacity, and efficiency gains brought about by the launch of Wi-Fi 6 (802.11ax) and Wi-Fi 6E (which uses the 6 GHz spectrum) stimulate upgrades and investments in new Wi-Fi infrastructure.

Smart Entertainment And Home Appliances: High-performance Wi-Fi networks are becoming more and more necessary in homes as smart home devices, streaming services, and online gaming become more widely used.

Public Wi-Fi And Smart Cities: To provide internet access in urban areas, transit hubs, and public places, governments and municipalities are investing in public Wi-Fi infrastructure, which is fueling the Wi-Fi Market's expansion.

Wi-Fi As A Service (WAAS): WaaS models are becoming more and more popular, allowing companies to outsource the management of their Wi-Fi networks. This results in lower costs, easier deployments, and improved security.

Security And Privacy Concerns: With the increased use of Wi-Fi, there is a rising emphasis on resolving security flaws and safeguarding user privacy, which is motivating investments in standards compliance and Wi-Fi security solutions.

Technological Advancements: The performance and dependability of Wi-Fi networks are being enhanced by ongoing developments in Wi-Fi technology, such as mesh networking, beamforming, and MU-MIMO (Multi-User, Multiple Input, Multiple Output), which is driving market expansion.

Global Connectivity Initiatives: The Wi-Fi industry is growing as a result of initiatives like satellite-based internet services and community Wi-Fi projects that aim to close the digital divide and give underprivileged areas access to the internet.

A survey of global networking executives indicates that 5G is preferred for outdoor use cases, while Wi-Fi 6 is preferred for indoor and fixed-use cases. Both technologies offer higher speed, lower latency, and can support a greater number of devices.

Forecasts suggest that by 2019 there will be a total of 362 million public Wi-Fi hotspots available worldwide. This 2019 figure would represent a near quadrupling of public Wi-Fi hotspots since 2016, showing the rapid rise in these networks around the world. This trend towards growth is expected to continue into at least the early 2020s. Public Wi-Fi
Wi-fi wireless internet networks allow users to connect to the web using a range of mobile devices without the need to physically connect to ethernet ports. Public Wi-Fi offerings such as municipal wireless networks and those found in coffee shops or cafes allow users to connect without the need for a specific password. Although public Wi-Fi hotspots are a welcome service for many people, there are many concerns over the safety of information accessed over these networks. As of 2017, around 59 percent of people stated that they had logged in to a personal email account over public Wi-Fi, while 56 percent stated that they had logged in to their social media accounts. IT professionals tend to advise against using these public networks for tasks that require sensitive personal information as it may be accessible by other users of the network. Public opinion is relatively split about the safety of these public Wi-Fi networks: 61 percent of people state that they feel safe on public Wi-Fi, while 39 percent state that they feel unsafe.

Raw data used to investigate the use of Wi-Fi signals to monitor human respiratory motion. Data are from an anatomically correct breathing manikin. These data have been published in "Monitoring Respiratory Motion With Wi-Fi CSI: Characterizing Performance and the BreatheSmart Algorithm," DOI: 10.1109/ACCESS.2022.3230003Each figure from the publication is broken up into its own folder. Within each folder, the raw real/imaginary data from the Wi-Fi channel state information frames can be found. This can be used to recreate the figures from the publication, or to test different processing algorithms of interest to the user.A README file is available within the data set with additional guidance.

This dataset consists of baseband in-phase/quadrature (I/Q) radio frequency recordings of Wi-Fi and Bluetooth radiated emissions in the 2.4 GHz and 5 GHz unlicensed bands collected with low-cost software defined radios. A NIST technical note describing the data collection methods is pending publication. All I/Q captures are one second in duration, with a sampling rate of 30 mega samples per second (MS/s), and a center frequency of 2437 MHz for the 2.4 GHz band captures and 5825 MHz for the 5 GHz band captures. In total, the data consist of 900 one second captures, organized into five Hierarchical Data Format 5 (HDF5) files, where each HDF5 file has a size of 20.1 GB and consists of 180 one second captures. There is a metadata file associated with each data file in comma-separated values (CSV) format that contains relevant parameters such as center frequency, bandwidth, sampling rate, bit depth, receive gain, antenna and hardware information. There are two additional CSV files containing estimated gain calibration and noise floor values.

EyeFi Dataset

This dataset is collected as a part of the EyeFi project at Bosch Research and Technology Center, Pittsburgh, PA, USA. The dataset contains WiFi CSI values of human motion trajectories along with ground truth location information captured through a camera. This dataset is used in the following paper "EyeFi: Fast Human Identification Through Vision and WiFi-based Trajectory Matching" that is published in the IEEE International Conference on Distributed Computing in Sensor Systems 2020 (DCOSS '20). We also published a dataset paper titled as "Dataset: Person Tracking and Identification using Cameras and Wi-Fi Channel State Information (CSI) from Smartphones" in Data: Acquisition to Analysis 2020 (DATA '20) workshop describing details of data collection. Please check it out for more information on the dataset.

Clarification/Bug report: Please note that the order of antennas and subcarriers in .h5 files is not written clearly in the README.md file. The order of antennas and subcarriers are as follows for the 90 csi_real and csi_imag values : [subcarrier1-antenna1, subcarrier1-antenna2, subcarrier1-antenna3, subcarrier2-antenna1, subcarrier2-antenna2, subcarrier2-antenna3,… subcarrier30-antenna1, subcarrier30-antenna2, subcarrier30-antenna3]. Please see the description below. The newer version of the dataset contains this information in README.md. We are sorry for the inconvenience.

Data Collection Setup

In our experiments, we used Intel 5300 WiFi Network Interface Card (NIC) installed in an Intel NUC and Linux CSI tools [1] to extract the WiFi CSI packets. The (x,y) coordinates of the subjects are collected from Bosch Flexidome IP Panoramic 7000 panoramic camera mounted on the ceiling and Angle of Arrivals (AoAs) are derived from the (x,y) coordinates. Both the WiFi card and camera are located at the same origin coordinates but at different height, the camera is location around 2.85m from the ground and WiFi antennas are around 1.12m above the ground.

The data collection environment consists of two areas, first one is a rectangular space measured 11.8m x 8.74m, and the second space is an irregularly shaped kitchen area with maximum distances of 19.74m and 14.24m between two walls. The kitchen also has numerous obstacles and different materials that pose different RF reflection characteristics including strong reflectors such as metal refrigerators and dishwashers.

To collect the WiFi data, we used a Google Pixel 2 XL smartphone as an access point and connect the Intel 5300 NIC to it for WiFi communication. The transmission rate is about 20-25 packets per second. The same WiFi card and phone are used in both lab and kitchen area.

List of Files Here is a list of files included in the dataset:

|- 1_person |- 1_person_1.h5 |- 1_person_2.h5 |- 2_people |- 2_people_1.h5 |- 2_people_2.h5 |- 2_people_3.h5 |- 3_people |- 3_people_1.h5 |- 3_people_2.h5 |- 3_people_3.h5 |- 5_people |- 5_people_1.h5 |- 5_people_2.h5 |- 5_people_3.h5 |- 5_people_4.h5 |- 10_people |- 10_people_1.h5 |- 10_people_2.h5 |- 10_people_3.h5 |- Kitchen |- 1_person |- kitchen_1_person_1.h5 |- kitchen_1_person_2.h5 |- kitchen_1_person_3.h5 |- 3_people |- kitchen_3_people_1.h5 |- training |- shuffuled_train.h5 |- shuffuled_valid.h5 |- shuffuled_test.h5 View-Dataset-Example.ipynb README.md

In this dataset, folder 1_person/ , 2_people/ , 3_people/ , 5_people/, and 10_people/ contains data collected from the lab area whereas Kitchen/ folder contains data collected from the kitchen area. To see how the each file is structured, please see below in section Access the data.

The training folder contains the training dataset we used to train the neural network discussed in our paper. They are generated by shuffling all the data from 1_person/ folder collected in the lab area (1_person_1.h5 and 1_person_2.h5).

Why multiple files in one folder?

Each folder contains multiple files. For example, 1_person folder has two files: 1_person_1.h5 and 1_person_2.h5. Files in the same folder always have the same number of human subjects present simultaneously in the scene. However, the person who is holding the phone can be different. Also, the data could be collected through different days and/or the data collection system needs to be rebooted due to stability issue. As result, we provided different files (like 1_person_1.h5, 1_person_2.h5) to distinguish different person who is holding the phone and possible system reboot that introduces different phase offsets (see below) in the system.

Special note:

For 1_person_1.h5, this file is generated by the same person who is holding the phone, and 1_person_2.h5 contains different people holding the phone but only one person is present in the area at a time. Boths files are collected in different days as well.

Access the data To access the data, hdf5 library is needed to open the dataset. There are free HDF5 viewer available on the official website: https://www.hdfgroup.org/downloads/hdfview/. We also provide an example Python code View-Dataset-Example.ipynb to demonstrate how to access the data.

Each file is structured as (except the files under "training/" folder):

|- csi_imag |- csi_real |- nPaths_1 |- offset_00 |- spotfi_aoa |- offset_11 |- spotfi_aoa |- offset_12 |- spotfi_aoa |- offset_21 |- spotfi_aoa |- offset_22 |- spotfi_aoa |- nPaths_2 |- offset_00 |- spotfi_aoa |- offset_11 |- spotfi_aoa |- offset_12 |- spotfi_aoa |- offset_21 |- spotfi_aoa |- offset_22 |- spotfi_aoa |- nPaths_3 |- offset_00 |- spotfi_aoa |- offset_11 |- spotfi_aoa |- offset_12 |- spotfi_aoa |- offset_21 |- spotfi_aoa |- offset_22 |- spotfi_aoa |- nPaths_4 |- offset_00 |- spotfi_aoa |- offset_11 |- spotfi_aoa |- offset_12 |- spotfi_aoa |- offset_21 |- spotfi_aoa |- offset_22 |- spotfi_aoa |- num_obj |- obj_0 |- cam_aoa |- coordinates |- obj_1 |- cam_aoa |- coordinates ... |- timestamp

The csi_real and csi_imag are the real and imagenary part of the CSI measurements. The order of antennas and subcarriers are as follows for the 90 csi_real and csi_imag values : [subcarrier1-antenna1, subcarrier1-antenna2, subcarrier1-antenna3, subcarrier2-antenna1, subcarrier2-antenna2, subcarrier2-antenna3,… subcarrier30-antenna1, subcarrier30-antenna2, subcarrier30-antenna3]. nPaths_x group are SpotFi [2] calculated WiFi Angle of Arrival (AoA) with x number of multiple paths specified during calculation. Under the nPath_x group are offset_xx subgroup where xx stands for the offset combination used to correct the phase offset during the SpotFi calculation. We measured the offsets as:

The measurement is based on the work [3], where the authors state there are two possible offsets between two antennas which we measured by booting the device multiple times. The combination of the offset are used for the offset_xx naming. For example, offset_12 is offset 1 between antenna 1 & 2 and offset 2 between antenna 1 & 3 are used in the SpotFi calculation.

The num_obj field is used to store the number of human subjects present in the scene. The obj_0 is always the subject who is holding the phone. In each file, there are num_obj of obj_x. For each obj_x1, we have the coordinates reported from the camera and cam_aoa, which is estimated AoA from the camera reported coordinates. The (x,y) coordinates and AoA listed here are chronologically ordered (except the files in the training folder) . It reflects the way the person carried the phone moved in the space (for obj_0) and everyone else walked (for other obj_y, where y > 0).

The timestamp is provided here for time reference for each WiFi packets.

To access the data (Python):

import h5py

data = h5py.File('3_people_3.h5','r')

csi_real = data['csi_real'][()] csi_imag = data['csi_imag'][()]

cam_aoa = data['obj_0/cam_aoa'][()] cam_loc = data['obj_0/coordinates'][()]

For file inside training/ folder:

Files inside training folder has a different data structure:

|- nPath-1 |- aoa |- csi_imag |- csi_real |- spotfi |- nPath-2 |- aoa |- csi_imag |- csi_real |- spotfi |- nPath-3 |- aoa |- csi_imag |- csi_real |- spotfi |- nPath-4 |- aoa |- csi_imag |- csi_real |- spotfi

The group nPath-x is the number of multiple path specified during the SpotFi calculation. aoa is the camera generated angle of arrival (AoA) (can be considered as ground truth), csi_image and csi_real is the imaginary and real component of the CSI value. spotfi is the SpotFi calculated AoA values. The SpotFi values are chosen based on the lowest median and mean error from across 1_person_1.h5 and 1_person_2.h5. All the rows under the same nPath-x group are aligned (i.e., first row of aoa corresponds to the first row of csi_imag, csi_real, and spotfi. There is no timestamp recorded and the sequence of the data is not chronological as they are randomly shuffled from the 1_person_1.h5 and 1_person_2.h5 files.

Citation If you use the dataset, please cite our paper:

@inproceedings{eyefi2020, title={EyeFi: Fast Human Identification Through Vision and WiFi-based Trajectory Matching}, author={Fang, Shiwei and Islam, Tamzeed and Munir, Sirajum and Nirjon, Shahriar}, booktitle={2020 IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS)},

This is the second batch of WiFi RSS RTT datasets with LOS conditions we published. Please see https://doi.org/10.5281/zenodo.11558192 for the first release.

We provide three real-world datasets for indoor positioning model selection purpose. We divided the area of interest was divided into discrete grids and labelled them with correct ground truth coordinates and the LoS APs from the grid. The dataset contains WiFi RTT and RSS signal measures and is well separated so that training points and testing points will not overlap. Please find the datasets in the 'data' folder. The datasets contain both WiFi RSS and RTT signal measures with groud truth coordinates label and LOS condition label.

Lecture theatre: This is a entirely LOS scenario with 5 APs. 60 scans of WiFi RTT and RSS signal measures were collected at each reference point (RP).

Corridor: This is a entirely NLOS scenario with 4 APs. 60 scans of WiFi RTT and RSS signal measures were collected at each reference point (RP).

Office: This is a mixed LOS-NLOS scenario with 5 APs. At least one AP was NLOS for each RP. 60 scans of WiFi RTT and RSS signal measures were collected at each reference point (RP).

Collection methodology

The APs utilised were Google WiFi Router AC-1304, the smartphone used to collect the data was Google Pixel 3 with Android 9.

The ground truth coordinates were collected using fixed tile size on the floor and manual post-it note markers.

Only RTT-enabled APs were included in the dataset.

The features of the dataset

The features of the lecture theatre dataset are as follows:

Testbed area: 15 × 14.5 m2 Grid size: 0.6 × 0.6 m2Number of AP: 5 Number of reference points: 120 Samples per reference point: 60 Number of all data samples: 7,200 Number of training samples: 5,400 Number of testing samples: 1,800 Signal measure: WiFi RTT, WiFi RSS Note: Entirely LOS

The features of the corricor dataset are as follows:

Testbed area: 35 × 6 m2 Grid size: 0.6 × 0.6 m2Number of AP: 4 Number of reference points: 114 Samples per reference point: 60 Number of all data samples: 6,840 Number of training samples: 5,130 Number of testing samples: 1,710 Signal measure: WiFi RTT, WiFi RSS Note: Miexed LOS-NLOS. At least one AP was NLOS for each RP.

The features of the office dataset are as follows:

Testbed area: 18 × 5.5 m2 Grid size: 0.6 × 0.6 m2Number of AP: 5 Number of reference points: 108 Samples per reference point: 60 Number of all data samples: 6,480 Number of training samples: 4,860 Number of testing samples: 1,620 Signal measure: WiFi RTT, WiFi RSS Note: Entirely NLOS

Dataset explanation

The columns of the dataset are as follows:

Column 'X': the X coordinates of the sample. Column 'Y': the Y coordinates of the sample. Column 'AP1 RTT(mm)', 'AP2 RTT(mm)', ..., 'AP5 RTT(mm)': the RTT measure from corresponding AP at a reference point. Column 'AP1 RSS(dBm)', 'AP2 RSS(dBm)', ..., 'AP5 RSS(dBm)': the RSS measure from corresponding AP at a reference point. Column 'LOS APs': indicating which AP has a LOS to this reference point.

Please note:

The RSS value -200 dBm indicates that the AP is too far away from the current reference point and no signals could be heard from it.

The RTT value 100,000 mm indicates that no signal is received from the specific AP.

Citation request

When using this dataset, please cite the following three items:

Feng, X., Nguyen, K. A., & Zhiyuan, L. (2024). WiFi RSS & RTT dataset with different LOS conditions for indoor positioning [Data set]. Zenodo. https://doi.org/10.5281/zenodo.11558792

@article{feng2024wifi, title={A WiFi RSS-RTT indoor positioning system using dynamic model switching algorithm}, author={Feng, Xu and Nguyen, Khuong An and Luo, Zhiyuan}, journal={IEEE Journal of Indoor and Seamless Positioning and Navigation}, year={2024}, publisher={IEEE} }@inproceedings{feng2023dynamic, title={A dynamic model switching algorithm for WiFi fingerprinting indoor positioning}, author={Feng, Xu and Nguyen, Khuong An and Luo, Zhiyuan}, booktitle={2023 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN)}, pages={1--6}, year={2023}, organization={IEEE} }

Description:

The Behavior-based WiFi User Dataset for user authentication. This dataset contains the physiological characteristics captured by WiFi from 10 participants for 10 different activities. Each participant performs 20 rounds for each activity. The experiments are conducted in two different environments, the campus office, and the home apartment. The dataset can be used by fellow researchers to reproduce the original work or to further explore other machine-learning problems in the domain of WiFi sense.

Format: .dat format

Section 1: Device Configuration

• Two commercial laptops, Dell E6430, as transmitter and receiver. Run with a Linux 14.04 operating system with 4.2.0 kernel. Equipped with 3 MINI PCI-E internal antennas.
• Intel 5300 network interface card (NIC) for CSI collection. The detail information regarding the CSI tool can be found at https://dhalperi.github.io/linux-80211n-csitool/faq.html.
• WiFi packet transmission is set to 1000 pkts/s

Section 2: Data Format

We provide raw data received by the CSI tool. The data files are saved in the dat format. The details are shown in the following:

1. 10 participants are included in two different experiments.
2. Each participant performed 20 rounds for each activity.
3. The dataset file name is presented as "User_Day_Action_Location". The detailed information as:
   • User: The participants that CSI was collected from.
   • Day: The date this data was collected.
   • Action: The specific activity performed.
   • Location: The specific location the experiment was conducted.

Section 3: Experimental Setups

There are two experiment setups for our data collection. An image of the experimental setup and the illustration of activities from two different environments is included in the dataset. Each activity was performed in a designated location. In each activity location, the specific activity was conducted in 4 different proximate locations at least one foot away from each other.

1. Residential Apartment
   • Environment: The experiments are conducted in a residential apartment with a size 33ft × 17ft.
   • Participant: 10 users are students from Rutgers University (aged from 20 to 30).
   • Activity: 7 activities were performed.

     Detailed Activities Performed in Apartment

     Code	Activity
     A→B	Walking (trajectory 1)
     B→C	Walking (trajectory 2)
     B	Picking up a remote control
     C	Sitting in a chair
     D	Exercising
     E	Operating on the oven
     F	Using the stove

2. Office
   • Environment: The experiments are conducted in an office with a size 21ft × 12ft.
   • Participant: 5 users are students from Rutgers University (aged from 20 to 30).
   • Activity: 3 activities were performed.

     Detailed Activities Performed in Office

     Code	Activity
     G	Sitting in a seat
     H	Stretching the body
     I	Typing on a keyboard

Section 4: Data Description

We separate our raw data into different folders based on different environment types. In each environment type, data are further distributed in terms of date. Each file includes all data from three internal antennas. All data files are in .dat format. We also provide Matlab scripts for CSI analysis and visualization. The following variables can be revealed from the codes:

1. CSI: This is the Channel State Information (CSI) received from one receiver antenna. It describes the signal propagation from the transmitter to the receiver, and it is very sensitive to the impact of environmental changes. Each data reveals CSI from 30 subcarriers.
2. Relative Phase: Relative Phase is a measurement to describe the degree of synchronization between data received from different antennas. It can be used to determine the phase offset for further signal preprocessing.
3. Time: This is the time interval in which the data file contains. It measures time by the number of seconds. It can be used to determine how long the signal has been received.

Section 5: Codes

• analysis_spectrogram.m: load a .dat file and extract all data by Data description(I.e, CSI, and Relative Phase).

Section 6: Citations

If your paper is related to our works, please cite our papers as follows.

https://ieeexplore.ieee.org/document/9356038

C. Shi, J. Liu, N. Borodinov, B. Leao and Y. Chen, "Towards Environment-independent Behavior-based User Authentication Using WiFi," 2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS), Delhi, India, 2020, pp. 666-674, doi: 10.1109/MASS50613.2020.00086

Bibtex:

@INPROCEEDINGS{9356038,
author={Shi, Cong and Liu, Jian and Borodinov, Nick and Leao, Bruno and Chen, Yingying},
booktitle={2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)},
title={Towards Environment-independent Behavior-based User Authentication Using WiFi},
year={2020},
volume={},
number={},
pages={666-674},
doi={10.1109/MASS50613.2020.00086}}

Home Use WiFi Router Market Outlook

The global market size for home use WiFi routers was valued at approximately USD 10 billion in 2023 and is projected to reach around USD 20 billion by 2032, growing at a Compound Annual Growth Rate (CAGR) of about 7.5% during the forecast period. This growth can be attributed to the increasing demand for high-speed internet in residential settings, the proliferation of smart home devices, and the rising trend of remote working and online education.

A primary growth driver for the home use WiFi router market is the increasing adoption of smart home devices. Smart home ecosystems comprising devices like thermostats, security cameras, smart lighting, and voice-activated assistants require robust and reliable internet connectivity. This demand drives the need for advanced WiFi routers capable of supporting multiple devices simultaneously without compromising on speed and performance. Additionally, the surge in streaming services for entertainment purposes further amplifies the need for high-capacity routers.

Another significant growth factor is the growing trend of remote work and online education, accelerated by the COVID-19 pandemic. With more people working from home and students attending virtual classes, the home network's reliability and speed have become critical. This shift has led to a surge in demand for modern WiFi routers that can handle increased bandwidth requirements and provide seamless connectivity across multiple devices in a household. As a result, consumers are increasingly upgrading from basic single band routers to more advanced dual and tri-band routers.

Technological advancements in WiFi technology, such as the introduction of WiFi 6 and the emerging WiFi 7 standards, are also propelling market growth. WiFi 6 offers significant improvements over its predecessors, including faster speeds, lower latency, and better performance in congested environments. These technologies are particularly beneficial in densely populated residential areas where multiple WiFi signals can interfere with each other. The ongoing development and adoption of these advanced technologies are expected to fuel the demand for new and upgraded WiFi routers.

The introduction of the Commercial WI-FI 6 Router has been a game-changer in the industry, offering enhanced connectivity and efficiency for businesses and large-scale deployments. These routers are designed to handle a higher number of simultaneous connections, making them ideal for environments with dense device usage. With features like improved data transfer rates, reduced latency, and better performance in congested areas, Commercial WI-FI 6 Routers are becoming increasingly popular among enterprises seeking reliable and fast internet solutions. Their ability to support advanced applications and provide seamless connectivity across large spaces makes them a preferred choice for commercial settings, where uninterrupted internet access is crucial for operations.

From a regional perspective, North America holds the largest market share, driven by high internet penetration rates, the prevalence of smart homes, and the early adoption of advanced technologies. However, the Asia Pacific region is anticipated to witness the highest growth rate during the forecast period. This growth is attributed to the increasing internet user base, rapid urbanization, and rising disposable incomes in countries like China and India. The market dynamics in these regions highlight the diverse factors contributing to the overall expansion of the home use WiFi router market.

Product Type Analysis

The home use WiFi router market is segmented by product type into single band, dual band, and tri-band routers. Single band routers operate on a single frequency band, typically the 2.4 GHz band, and are suitable for basic internet usage such as browsing and emailing. They are often the most affordable option, making them popular among budget-conscious consumers. However, their limited bandwidth can be a drawback when multiple devices are connected simultaneously, leading to potential congestion and slower speeds.

Dual band routers operate on both the 2.4 GHz and 5 GHz bands, providing better performance and flexibility. The 2.4 GHz band is useful for longer-range connectivity, while the 5 GHz band offers higher speeds and is less prone to interference. This makes dual band routers ideal for households with moderate to high internet usage, in

WiFi CSI-Based Long-Range Through-Wall Human Activity Recognition with the ESP32

This repository contains the WiFi CSI human presence detection and activity recognition datasets proposed in [1].

Datasets

• DP_LOS - Line-of-sight (LOS) presence detection dataset, comprised of 392 CSI amplitude spectrograms.
• DP_NLOS - Non-line-of-sight (NLOS) presence detection dataset, comprised of 384 CSI amplitude spectrograms.
• DA_LOS - LOS activity recognition dataset, comprised of 392 CSI amplitude spectrograms.
• DA_NLOS - NLOS activity recognition dataset, comprised of 384 CSI amplitude spectrograms.

Table 1: Characteristics of presence detection and activity recognition datasets.

Data Format

Each dataset employs an 8:1:1 training-validation-test split, defined in the provided label files trainLabels.csv, validationLabels.csv, and testLabels.csv. Label files use the sample format [i c], with i corresponding to the spectrogram index (i.png) and c corresponding to the class. For presence detection datasets (DP_LOS , DP_NLOS), c in {0 = "no presence", 1 = "presence in room 1", ..., 5 = "presence in room 5"}. For activity recognition datasets (DA_LOS , DA_NLOS), c in {0="no activity", 1="walking", and 2="walking + arm-waving"}. Furthermore, the mean and standard deviation of a given dataset are provided in meanStd.csv.

Download and Use
This data may be used for non-commercial research purposes only. If you publish material based on this data, we request that you include a reference to our paper [1].

[1] Strohmayer, Julian, and Martin Kampel. "WiFi CSI-Based Long-Range Through-Wall Human Activity Recognition with the ESP32" International Conference on Computer Vision Systems. Cham: Springer Nature Switzerland, 2023.

BibTeX citation:

@inproceedings{strohmayer2023wifi,
 title={WiFi CSI-Based Long-Range Through-Wall Human Activity Recognition with the ESP32},
 author={Strohmayer, Julian and Kampel, Martin},
 booktitle={International Conference on Computer Vision Systems},
 pages={41--50},
 year={2023},
 organization={Springer}
}

Wi-Fi in Public Space (Open Space) is an analysis of the ‘NYC Wi-Fi Hotspot Locations’ OpenData records focused on the use of Wi-Fi in open spaces (in public parks and on Governors Island).

A reliable and comprehensive public WiFi fingerprinting database for researchers to implement and compare the indoor localization’s methods.The database contains RSSI information from 6 APs conducted in different days with the support of autonomous robot.

Information about stations that are equipped with SBB public WLAN.

Global Wi-Fi 6 market size is expected to reach $102.27 billion by 2029 at 34.4%, rising deployment of public wi-fi fuels the demand for wi-fi 6 technology

This is the final contract with the vendor. It shows what they will provide to Louisville Metro.This is the transparency page for the Russell Public WiFi project that is part of the Smart Russell initiative from the Office of Civic Innovation & Technology. The purpose of this page is to provide documentation related to the project so the public understand and verify the intentions of the project and the outcomes.Every month, the project manager will put up the usage numbers for the network and the access logs for the data. This is to show who is accessing the data and to share the data that is accessed to provide transparency to the public about what information is being collected by the network.Data Dictionary:Location = Text description of the location, will not help with mapping itx = latitudey = longitudeNotes = context and information about the access pointsHomepage URL

802.11be (Wi-Fi 7) Wi-Fi Chipset Market Outlook

The global 802.11be (Wi-Fi 7) Wi-Fi chipset market size is projected to reach approximately USD 12.7 billion by 2032, up from USD 3.5 billion in 2023, growing at a compound annual growth rate (CAGR) of 15.2% during the forecast period. This remarkable growth is driven by the increasing adoption of high-speed internet solutions across various industries and the growing demand for enhanced networking capabilities in residential and commercial settings. The rising penetration of smart devices and the need for faster and more reliable internet connections are significant factors contributing to the market's expansion.

One of the primary growth factors for the 802.11be (Wi-Fi 7) Wi-Fi chipset market is the proliferation of smart devices and the Internet of Things (IoT). As households and businesses continue to adopt an increasing number of connected devices, there is a corresponding need for more robust and higher-capacity wireless networks. Wi-Fi 7, with its enhanced speed, capacity, and efficiency, is well-positioned to meet these demands, making it an attractive option for consumers and businesses alike. Additionally, technological advancements in Wi-Fi chipset design and manufacturing are further propelling market growth.

Another significant growth factor is the increasing demand for high-definition video streaming and other bandwidth-intensive applications. With the growing popularity of 4K and 8K video content, online gaming, and virtual reality experiences, consumers require faster and more stable internet connections. Wi-Fi 7 chipsets offer significant improvements in data transfer rates and latency over previous generations, making them ideal for these applications. The continuous improvement in Wi-Fi technology, including better spectrum utilization and enhanced multi-user capabilities, supports the market's upward trajectory.

The enterprise sector is also a major driver of the 802.11be (Wi-Fi 7) Wi-Fi chipset market. Businesses are increasingly adopting advanced wireless networks to support their operations, improve productivity, and enhance customer experiences. The adoption of Wi-Fi 7 in corporate environments can lead to more efficient communication, seamless collaboration, and improved network security. Furthermore, the growing trend of remote work and the need for robust virtual communication tools are accelerating the demand for advanced Wi-Fi solutions, further contributing to market growth.

The introduction of SU-MIMO Wi-Fi Chipset technology is another pivotal advancement in the Wi-Fi industry. SU-MIMO, or Single User Multiple Input Multiple Output, enhances the data transmission capabilities by allowing multiple data streams to be sent to a single device simultaneously. This technology significantly improves the efficiency and speed of wireless networks, making it particularly beneficial for environments with high data demands. As the number of smart devices continues to rise, the need for efficient data handling becomes more critical. SU-MIMO Wi-Fi Chipsets are designed to address these challenges by optimizing the use of available bandwidth, thus ensuring a smoother and faster internet experience for users. This innovation is expected to play a crucial role in the evolution of Wi-Fi technology, supporting the growing demand for high-speed internet connectivity in both residential and commercial settings.

Regionally, North America and Asia Pacific are expected to dominate the 802.11be (Wi-Fi 7) Wi-Fi chipset market. North America, with its advanced technological infrastructure and high adoption of smart devices, is anticipated to maintain a significant market share. The region's established IT and telecommunications sector plays a crucial role in the widespread adoption of Wi-Fi 7 technology. Meanwhile, Asia Pacific is projected to register the highest growth rate during the forecast period, driven by rapid urbanization, increasing internet penetration, and substantial investments in smart city projects. The rising demand for high-speed internet in emerging economies like China and India further boosts the market growth in this region.

Product Type Analysis

The 802.11be (Wi-Fi 7) Wi-Fi chipset market is segmented by product type into standalone chipsets and integrated chipsets. Standalone chipsets, which are dedicated components used solely for Wi-Fi connectivity, are projected to witness substantial growth. These chipsets are often preferred in high-performan

WiFi measurements database for UJI's library and supporting material.

The measurements were collected by one person using mainly one Android smartphone during 25 months at two floor of the library building from Universitat Jaume I, in Spain. It contains 103,584 WiFi fingerprints, which are organized into datasets. Each dataset is the result of a collection campaign.

The supporting material includes Matlab® scripts to load and filter the desired data, and provides examples on possible studies that the database may enable. The supporting material also includes the bookshelves local coordinates.

Citation request:

Mendoza-Silva, G.M.; Richter, P.; Torres-Sospedra, J.; Lohan, E.S.; Huerta, J. Long-Term WiFi Fingerprinting Dataset for Research on Robust Indoor Positioning. Data 2018, 3, 3.

G.M. Mendoza-Silva, P. Richter, J. Torres-Sospedra, E.S. Lohan, J. Huerta, "Long-Term Wi-Fi fingerprinting dataset and supporting material", Zenodo repository, DOI 10.5281/zenodo.1066041.

This dataset contains WiFi Channel State Information (CSI) data intended for device-to-device localisation. The experimental setup consists of a single transmitter and a single receiver, each equipped with an Intel 5300 network interface card.

The raw complex CSI data was collected at the receiver over 3 receiving antennas and 30 subcarriers and the packet rate was fixed at 2500 Hz.

The CSI data was collected over the 5 GHz WiFi band (Channel 64 with 40 MHz channel bandwidth).

The transmitter was placed at two different angles with respect to the receiver: 30 degrees and -60 degrees and at 5 different distances: 1m, 2m, 3m, 4m, 5m.

Please refer to the "readme.txt" and "Setup_and_Parameters.pdf" files for more details.