The work-cell is an essential industrial environment for testing wireless communication techniques in factory automation processes. A graph database approach to storing and analyzing network performance data from a manufacturing factory work-cell is introduced. A robotic testbed performs a pick-and-place task using two collaborative grade robot arms, machine emulators, and wireless communication devices. A graph database is implemented to capture network data and operational event data among the actors within the testbed. Using a proposed schema, the database is then populated with events from the testbed and the resulting graph is constructed. Query commands are then presented to examine and analyze network performance and relationships within the actors of the network. The resulting data from the experiments conducted are included in this dataset.

Wireless data traffic surged in the United States in 2023, with more than *** trillion megabytes of data transferred over mobile networks that year. This was almost ***** the volume consumed two years prior, with demand for data soaring amid the adoption of data intensive mobile activities.

Cyber-physical systems are systems governed by the laws of physics that are tightly controlled by computer-based algorithms and network-based sensing and actuation. Wireless communication technology is envisioned to play a primary role in conducting the information flows within such systems. A practical industrial wireless use case involving a robot manipulator control system, an integrated wireless force-torque sensor, and a remote vision-based observer is constructed and the performance of the cyber-physical system is examined. The resulting data from the experiments conducted are included in the dataset.

The number of wireless sessions, by branch, provided by the Montgomery County Public Library wireless service. These are annual numbers, regardless if the service is accessed within the walls of the Library or from the parking lot, etc. Updated annually.

USAID's Guest Wireless uses Xirrus equipment to provide a wireless network so that mobile device users can access a Dedicated Internet Network System (DINS). It serves as a follow-on to USAID wireless efforts implemented at AID/Washington (AID/W) and overseas missions, but with modifications to the design, function, and use. The solution maintains the business purpose, which is to allow authorized agency personnel and guests or partners to have wireless, controlled Internet access.

The information presented in this data set is based on records of dockets, petitions, tower share requests, and notices of exempt modifications received and processed by the Council. This database is not an exhaustive listing of all wireless telecommunications sites in the state in that it does not include all information about sites not under the jurisdiction of the Siting Council. The dataset includes a row for each Council action on any given facility. Although the Connecticut Siting Council makes every effort to keep this spreadsheet current and accurate, the Council makes no representation or warranty as to the accuracy of the data presented herein. The public is advised that the records upon which the information in this database is based are kept in the Siting Council’s offices at Ten Franklin Square, New Britain and are open for public inspection during normal working hours from 8:30 a.m. to 4:30 p.m. Monday through Friday. Note to Users: Over the years, some of the wireless companies have had several different corporate identities. In the database, they are identified by the name they had at the time of their application to the Siting Council. To help database users follow the name changes, the list below shows the different names by which the companies have been known. Recent mergers in the telecommunications industry have joined companies listed as separate entities. AT&T Wireless merged with Cingular to do business as New Cingular. Sprint and Nextel have merged to form Sprint/Nextel Corporation. Cingular: SNET, SCLP, and New Cingular after merger with AT&T T-Mobile: Omni (Omnipoint), VoiceStream Verizon: BAM, Cellco AT&T: AT&T Wireless, New Cingular after merger with Cingular, then Cingular rebranded as AT&T Nextel: Smart SMR

Wireless Data Communication Adapters Market Outlook

The global wireless data communication adapters market size was valued at USD 4.3 billion in 2023 and is anticipated to reach USD 8.9 billion by 2032, reflecting a steady compound annual growth rate (CAGR) of 8.3% during the forecast period. The growth in this market is primarily driven by the rising demand for seamless and high-speed internet connectivity across various sectors, including consumer electronics, automotive, and industrial applications, among others.

One of the primary factors contributing to the growth of the wireless data communication adapters market is the increasing penetration of internet-enabled devices. With the proliferation of smartphones, tablets, laptops, and other smart devices, the demand for efficient and reliable wireless communication solutions has surged. Consumers and businesses alike require robust and high-speed internet connections to support various online activities, from streaming media to conducting business operations, thereby driving the need for advanced wireless adapters.

Another significant growth driver is the expansion of the Internet of Things (IoT) ecosystem. IoT devices, which range from smart home appliances to industrial sensors, rely on wireless communication adapters to connect to networks and the internet. As IoT technology continues to evolve and find applications in diverse fields such as healthcare, transportation, and manufacturing, the demand for wireless data communication adapters is expected to grow exponentially. This trend is further supported by advancements in wireless communication technologies such as 5G, which promise faster data transmission speeds and more reliable connections.

The trend towards remote work and online education has also contributed to the market's expansion. The COVID-19 pandemic accelerated the adoption of remote working and online learning, leading to an increased demand for reliable internet connectivity solutions. Wireless data communication adapters have become essential for ensuring stable and high-quality internet access in home environments, supporting productivity and learning. This shift in work and education paradigms is expected to have a lasting impact on the market, sustaining demand in the long term.

In the realm of consumer electronics, the Notebook Wireless Network Card has emerged as a crucial component for ensuring seamless connectivity. As laptops continue to dominate the market for portable computing devices, the demand for integrated wireless solutions has grown significantly. The Notebook Wireless Network Card provides users with the flexibility to connect to Wi-Fi networks effortlessly, supporting a range of activities from browsing the internet to streaming high-definition content. This component is particularly valued for its ability to maintain stable connections in various environments, making it indispensable for both personal and professional use. As technology advances, manufacturers are focusing on enhancing the capabilities of these network cards, incorporating features such as improved data transfer rates and energy efficiency to meet the evolving needs of consumers.

Regionally, the market for wireless data communication adapters is witnessing significant growth across several key areas. North America, with its advanced technological infrastructure and high penetration of internet-enabled devices, remains a dominant player in the market. However, the Asia Pacific region is emerging as a vital growth hub, driven by rapid urbanization, increasing internet penetration, and the growing adoption of smart devices. Europe also presents substantial opportunities, supported by technological advancements and a strong industrial base. The Middle East & Africa and Latin America regions are also expected to contribute to market growth, albeit at a comparatively slower pace.

Product Type Analysis

Wireless data communication adapters are available in various forms, including USB adapters, PCI adapters, PCMCIA adapters, and others. Each type has its own unique advantages and applications. USB adapters, for instance, are highly popular due to their ease of use and compatibility with a wide range of devices. They are typically plug-and-play, making them an ideal choice for consumers who require quick and effortless wireless connectivity solutions. The demand for USB adapters is also driven by their affordability and widespread availability in both online and offline

This is a point data set representing wireless access points within the City of Perth. Some errors and/or duplicate data may exist. Show full description

The global demand for mobile data is set to skyrocket in the coming years, with monthly data traffic forecast to exceed *** exabytes by 2029. A range of factors are set to drive this explosion in data consumption, not least the widespread adoption of data intensive technologies such as artificial intelligence and the cloud. FWA bridges connectivity gaps Around a ***** of total mobile traffic at the end of the decade is set to come from fixed wireless access (FWA) connections. FWA involves the use of mobile networks to provide broadband internet to a fixed location, and has gained traction in areas underserved by traditional fixed infrastructure. When using 5G mobile networks, FWA services can rival traditional fixed broadband in both reliability and connection quality. A lack of 5G investment Although FWA has been posed as a means of bridging the global digital divide, a lack of 5G investment in several regions prevents it from being an effective solution in the near future. For example, 5G adoption in Sub-Saharan Africa remained below **** percent in 2023, reflecting ongoing challenges related to 5G affordability and availability.

This statistic shows the average monthly wireless data usage per Android user in the United States in *************, broken down by plan type. In *************, Android users on the service plan with monthly allowance consumed an average of ****** megabytes of Wifi data per month in the United States.

This dataset contains wireless communication signal data collected from an unmanned aerial vehicle (UAV) at different altitudes (40 m, 70 m, and 100 m). For the 40 m altitude, data was collected at varying sampling rates (5 MHz, 10 MHz, and 20 MHz), corresponding to bandwidths of 1.25 MHz, 2.5 MHz, and 5 MHz, respectively. The IQ recordings were made using USRP B210 devices at five fixed nodes (LW1-LW5). The dataset includes IQ samples, GPS coordinates, and received signal strength (RSS) values stored in SigMF format files. A Python script (example.py) is provided for data processing and visualization. Methods Trajectory and Altitudes: The UAV followed the same trajectory path at three different altitudes: 40 m, 70 m, and 100 m. Sampling Rates: For the 40 m altitude, data was collected using three different sampling rates: 5 MHz, 10 MHz, and 20 MHz, corresponding to bandwidths of 1.25 MHz, 2.5 MHz, and 5 MHz, respectively. Data Collection Intervals: To reduce data volume, the system collected data for 20 ms intervals every 100 ms. USRP B210 Devices: The IQ recordings were made using USRP B210 devices placed at the five fixed nodes (LW1-LW5). Each SigMF file contains IQ samples for two channels corresponding to the USRP's dual-channel configuration. GPS and Radio Measurements:

GPS coordinates (GPSx, GPSy, GPSz) were measured once per second, independent of the time at which the radio measurements were made. Radio measurements may have occurred more than once per second or not at exactly the same time as the GPS measurements. GPS and radio measurements are both time-stamped, and interpolation was used to calculate mX, mY, mZ values, which align the GPS data with the radio measurements based on their timestamps.

Folder Structure:

Each altitude (40 m, 70 m, 100 m) is represented by a folder. Within the 40 m folder, subfolders represent the three different sampling rates (5 MHz, 10 MHz, and 20 MHz). For each altitude and sampling rate combination, the dataset contains subfolders for five fixed nodes (LW1, LW2, LW3, LW4, and LW5). The locations of these nodes are detailed in the "LW1-5_locations.txt" file.

File Descriptions: SigMF Files:

results_3320000000_5000000_2024_07_15_12_26_01_248.sigmf-data: Contains IQ samples recorded at a frequency of 3320000000 Hz and a sampling rate of 5 MHz. The file contains two channels, one for each of the USRP B210 device's channels.

GPS Data:

gps_data.sigmf-data: Contains timestamped GPS coordinates of the UAV (GPSx, GPSy, GPSz), measured once per second.

Measurement Data:

measurement_rss_data.sigmf-data: Contains RSS data and interpolated position measurements (mX, mY, mZ) for each timestamp, with RSS1 for channel 1 and RSS2 for channel 2.

Python Script: example.py: This Python script demonstrates how to load and process the data from the SigMF files.

It uses libraries such as numpy, json, and matplotlib for loading data and plotting results. The script loads GPS metadata from gps_data.sigmf-meta and extracts position (GPSx, GPSy, GPSz) and timestamps. It also loads radio measurement data from measurement_rss_data.sigmf-meta and interpolates GPS data to align with radio measurements to compute positions (mX, mY, mZ) for further analysis. The script includes plotting functionalities to visualize the GPS trajectory and the corresponding RSS data over time.

This work provides an in-depth analysis of theListen-Before-Talk (LBT) procedures that cellular technologiesmust adhere to for operation in the unlicensed spectrum. In par-ticular

The wireless telecommunication carrier industry has witnessed significant shifts recently, driven by evolving consumer demands and technological advancements. The popularity of smartphones and rising data consumption habits have mainly driven growth. Households have chosen to disconnect their landlines to cut costs and receive network access away from home. Industry revenue was bolstered during the current period by a surge in mobile internet demand. The revival of unlimited data and call plans prompted industry-wide adjustments to pricing and data offerings. While competition has intensified, leading to price wars and slender margins, carriers have embraced bundled offerings of value-added services, like streaming subscriptions, to distinguish themselves. Despite these efforts, revenue growth remains sluggish amid high operational costs and a saturated market. Overall, Wireless Telecommunications Carriers' revenue has modestly grown at an annualized rate of 0.1% to total $340.3 billion in 2025, when revenue will climb an estimated 6.0%, as the early shift to fifth-generation (5G) enables businesses to renegotiate the current product-price paradigm with consumers. The industry is defined by a transition from primarily providing voice services to focusing on providing data services. Technological change, namely the shift from fourth-generation (4G) wireless data services to 5G, continues to shape the industry. Companies expand scope through mergers and acquisitions, acquiring spectrum and niche customer bases. The battle for wireless spectrum intensified as 5G technology became a focal point, requiring carriers to secure valuable frequency bands through hefty investments. For instance, Verizon's $45 billion expenditure in the C-band spectrum auction highlights the critical importance of spectrum acquisition. While Federal Communications Commission (FCC) regulations have curtailed large-scale consolidations, strategic alliances and mergers have been common to share infrastructure and expand market reach. Also, unlimited data plans have shaken up cost structures and shifted consumers to new providers. Following the expansion of unlimited data and calls, profit is poised to inch downward as the cost of acquiring new customers begins to mount. Profitability is additionally hindered by supply chain disruptions, which still loom large, as equipment delays and price hikes impact rollout timeliness. Industry revenue is forecast to incline at an annualized 5.4% through 2030, totaling an estimated $443.5 billion, driven by the expansion of mobile devices using data services and increasing average revenue per user. As the rollout of 5G networks increases the speed of wireless data services, more consumers will view on-the-go internet access as an essential function of mobile phones. Moving forward, the industry landscape will be characterized by the heightened competition among carriers for wireless spectrum, an already scarce resource and efforts to connect more Americans in remote parts of the country to fast and reliable internet. Subscriber saturation presents a formidable challenge, compelling carriers to focus on existing customers and innovative service packages. Companies like AT&T and Verizon are pioneering flexible infrastructure projects, which could redefine the industry’s operational efficiency. Despite facing spectrum supply limitations, the industry is poised to benefit from seamless connectivity solutions for various sectors, potentially redefining wireless carriers’ roles in an increasingly interconnected world.

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

This statistic shows the average monthly wireless data usage per user in the United States by age in the first two quarters of 2018. In the first half of 2018, users 25 years and younger used *** GB of cellular and **** GB of Wi-Fi wireless data.

The statistic shows the volume of wireless data traffic in the United States from 2010 to 2018. In 2014, the wireless data traffic in the United States amounted to around * billion gigabytes.

CSV dataset generated gathering data from a production wireless mesh community network. Data is gathered every 5 minutes during the interval 2021-04-13 00:00:00 to 2021-04-16 00:00:00. During the interval 2021-04-14 02:00:00 2021-04-14 17:50:00 (both included) there is the failure of a gateway in the mesh (nodeid 24).

Live mesh network monitoring link: http://dsg.ac.upc.edu/qmpsu

The dataset consists of single gzip compressed CSV file. The first line of the file is a header describing the features. The first column is a GMT timestamp of the sample in the format as "2021-03-16 00:00:00". The rest of the columns provide the comma-separated values of the features collected from each node in the corresponding capture.

A suffix with the nodeid is added to each feature. For instance, the feature having the number of processes of node with nodeid 24 is named as "processes-24". In total, 63 different nodes showed up during the samples, each being assigned a different nodeid.

Features are of two types: (i) absolute values, for instance, the CPU 1-minute load average, and (ii) counters that are monotonically increased, for instance the number of transmitted packets. We have converted counter-type kernel variables to rates, by dividing the difference between two consecutive samples, over the difference of the corresponding timestamps in seconds, as shown in the following pseudo-code:
feature.rate are columns computed from feature as
feature.rate <- (feature[2:n]-feature[1:(n-1)])/(epoch[2:n]-epoch[1:(n-1)])
feature.rate <- feature.rate[feature.rate >= 0] # discard samples where the counter is restarted
where n is the number of samples

features
- processes number of processes
- loadavg.m1 1 minute load average
- softirq.rate servicing softirqs
- iowait.rate waiting for I/O to complete
- intr.rate
- system.rate processes executing in kernel mode
- idle.rate twiddling thumbs
- user.rate normal processes executing in user mode
- irq.rate servicing interrupts
- ctxt.rate total number of context switches across all CPUs
- nice.rate niced processes executing in user mode
- nr_slab_unreclaimable The part of the Slab that can't be reclaimed under memory pressure
- nr_anon_pages anonymous memory pages
- swap_cache Memory that once was swapped out, is swapped back in but still also is in the swapfile
- page_tables Memory used to map between virtual and physical memory addresses
- swap
- eth.txe.rate tx errors over all ethernet interfaces
- eth.rxe.rate rx errors over all ethernet interfaces
- eth.txb.rate tx bytes over all ethernet interfaces
- eth.rxb.rate rx bytes over all ethernet interfaces
- eth.txp.rate tx packets over all ethernet interfaces
- eth.rxp.rate rx packets over all ethernet interfaces
- wifi.txe.rate tx errors over all wireless interfaces
- wifi.rxe.rate rx errors over all wireless interfaces
- wifi.txb.rate tx bytes over all wireless interfaces
- wifi.rxb.rate rx bytes over all wireless interfaces
- wifi.txp.rate tx packets over all wireless interfaces
- wifi.rxp.rate rx packets over all wireless interfaces
- txb.rate tx bytes over all ethernet and wifi interfaces
- txp.rate tx packets over all ethernet and wifi interfaces
- rxb.rate rx bytes over all ethernet and wifi interfaces
- rxp.rate rx packets over all ethernet and wifi interfaces
- sum.xb.rate tx+rx bytes over all ethernet and wifi interfaces
- sum.xp.rate tx+rx packets over all ethernet and wifi interfaces
- diff.xb.rate tx-rx bytes over all ethernet and wifi interfaces
- diff.xp.rate tx-rx packets over all ethernet and wifi interfaces

Gain in-depth insights into Wireless Data Loggers Market Report from Market Research Intellect, valued at USD 1.5 billion in 2024, and projected to grow to USD 3.2 billion by 2033 with a CAGR of 9.5% from 2026 to 2033.

The telecom wireless data market was estimated at US$ 24.27 billion in 2022 and is anticipated to grow at a CAGR of 6.16% to reach US$ 41.51 billion by 2032.

This dataset includes the position data of a two-dimensional gantry system experiment in which the G-code commands for the gantry were transmitted through a wireless communications link. The testbed is composed of four main components related to the operation of the gantry system. These components are the gantry system, the Wi-Fi network, the RF channel emulator, and the supervisory computer. In the experimental study, we run a scenario in which the gantry tool moves sequentially between four positions and has a preset dwell at each of the positions. The wireless channel impact is produced through the RF channel emulator. First, we consider the benchmark channel with free-space log-distance path loss and ideal channel impulse response (CIR) which has no multi-path. Second, we consider a measured delay profile of an industrial environment where the CIR is experimentally measured and processed to be deployed using the channel emulator and to reflect the industrial environment impact. Moreover, time-varying log-normal shadowing is introduced due to the fluctuations in the signal level because of obstructions. The variance of zero-mean log-normal shadowing is set through the emulator. In order to collect the position information of the gantry system tool, we used a vision tracking system. In this dataset, we attached a meta_data.csv file to map various files to their corresponding parameters. A README.doc file is included to describe the measurement apparatus.