Radio-frequency noise mapping data collected from Downtown, Back Bay and North End neighborhoods within Boston, MA, USA in 2018 and 2019.
Data consist of :
* distance, in meters, along the measurement path. This field is likely not useful for anyone other than the authors, but is included here for completeness.
* geographic location of the measurement, in decimal degrees, WGS84
* median external radio-frequency noise power, measured in a 1 MHz bandwidth about a center frequency of 142.0 MHz, in dBm
* peak external radio-frequency noise power, also measured in a 1 MHz bandwidth about a center frequency of 142.0 MHz, in dBm. Here, peak power is defined as the threshold where 99.99% of the data lie below this value.
* for North End and Back Bay datasets, the official zoning district containing the measurement location is included. Measurements in the Downtown data were all collected within Business and Mixed Use zoning districts, and thus are not listed.
Wide area control (WAC) in smart grid requires low-latency communications across the grid to transfer various control commands and Phasor Measurement Unit (PMU) measurements. Smart grid communications are generally implemented between multiple control centers, transformer substations and control centers, and power plants and control centers.
The diversity of video delivery pipeline poses a grand challenge to the evaluation of adaptive bitrate (ABR) streaming algorithms and objective quality-of-experience (QoE) models.
Here we introduce so-far the largest subject-rated database of its kind, namely WaterlooSQoE-IV, consisting of 1350 adaptive streaming videos created from diverse source contents, video encoders, network traces, ABR algorithms, and viewing devices.
We collect human opinions for each video with a series of carefully designed subjective experiments.
The Waterloo Quality-of-Experience IV database consists of 1,350 streaming videos (generated from 5 source videos x 2 encoders x 9 network traces x 5 ABR algorithms x 3 viewing devices). The 5 ABR algorithms include RB, BB, FastMPC, Pensieve, and RDOS.
The waterloo_sqoe4_feature.zip contains all meta data such as chunk level bitrate, rebuffering duration, spatial resolution, and MOS.
The waterloo_sqoe4_server_video.zip contains the dash videos on the server.
The waterloo_sqoe4_full.zip contains all the streaming videos in mp4.
This Dataset includes the lsit of articles indexed by Google Scholar from 2003-2019, realted to: Edge Computing, Fog Computing, Multi-Access Edge Computing, Mobile Cloud Computing.
The data has been cleaned by removing duplicates, non-english articles, un-realted articles (i.e. computing edges in a picture is not about Edge Computing) and articles that weren't directly or easily accessable.
This is a simple CSV file. Import it into your favorite spreasdsheet or text editor.
A practical alternative to the solution of the spectrum scarcity problem in wireless communication is the use of Cognitive Radio. The Primary users of which can be protected from secondary user interference by accurate prediction of TV White Spaces (TVWS) by using appropriate propagation modelling. In implementing any mobile communication system, the essential chore is to envisage the coverage of the projected system in a wide range. Also, the accurate determination of the propagation path loss leads to the development of efficient design and operation of quality networks.
Hardware tools used for drive test include:
1.Spectrum Analyzer (RF Explorer 3G combo model)
2.Personal Computer (HP Laptop)
3.Global Positioning System (GPS) receiver set
7.vehicle for mobility purposes.
The software tool used was the Touchstone-Pro software.
During the first half of 2020, the COVID-19 pandemic changed the social gathering lifestyle to online business and social interaction. The worldwide imposed travel bans and national lockdown prevented social gatherings, making learning institutions and businesses to adopt an online platform for learning and business transactions. This development led to the incorporation of video conferencing into daily activities. This data article presents broadband data usage measurement collected using Glasswire software on various conference calls made between July and August.
This data presents the Reference Signal Received power for LTE, GSM and HSPA. The Mobile signal measurement were taken around Covenant University, Nigeria. The experiment setup was on an indoor scenario. The data was collected to investigate the best network for Mobile subscribers on roaming services and local subscriber's high performance and data rates.
· The data contain screenshots of the raw data measured
· The data contain Excel files in CSV format of the raw data and CSV file of processed and classified data
· The read me file (.txt) is attached with the password for the raw data Excel workbook
The tool used to analyse the data is python juypter notebook. the scripts are uploaded.
The dataset contains:
1. We conducted a A 24-hour recording of ADS-B signals at DAB on 1090 MHz with USRP B210 (8 MHz sample rate). In total, we got the signals from more than 130 aircraft.
2. An enhanced gr-adsb, in which each message's digital baseband (I/Q) signals and metadata (flight information) are recorded simultaneously. The output file path can be specified in the property panel of the ADS-B decoder submodule.
3. Our GnuRadio flow for signal reception.
4. Matlab code of the paper, wireless device identification using the zero-bias neural network.
1. The "main.m" in Matlab code is the entry of simulation.
2. The "csv2mat" is a CPP program to convert raw records (adsb_records1.zip) of our gr-adsb into matlab manipulatable format. Matio library (https://github.com/tbeu/matio) is required.
3. The Gnuradio flowgraph is also provided with the enhanced version of gr-adsb, in which you are supposed to replace the original one (https://github.com/mhostetter/gr-adsb). And, you can specify an output file path in the property panel of the ADS-B decoder submodule.
4. Related publication: Zero-Bias Deep Learning for Accurate Identification of Internet of Things (IoT) Devices, IEEE IoTJ (accepted for publication on 21 August 2020), DOI: 10.1109/JIOT.2020.3018677