Extensive use of unmanned aerial vehicles (UAVs) is expected to raise privacy and security concerns among individuals and
communities. In this context, detection and localization of UAVs will be critical for maintaining safe and secure airspace in the
future. In this work, Keysight N6854A radio frequency (RF) sensors are used to detect and locate a UAV by passively monitoring
the signals emitted from the UAV. First, the Keysight sensor detects the UAV by comparing the received RF signature with various

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# RSS data from smartwatch for Contact Tracing

 

This dataset was collected for the purpose to understand the proximity between any two smartwatches worn by human.

We used the Google's Wear OS based smartwatch, powered by a Qualcomm Snapdragon Wear 3100 processor, from Fossil sport to collect the data.

The smartwatch is powered by a Qualcomm Snapdragon Wear 3100 processor and has an internal memory of up to 1GB.

 

 

Two volunteers were required to wear the smartwatch on different hand and stand at a certain distance from each other.

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Any work using this dataset should cite the following paper:

Instructions: 

For details on instructions on how to use the dataset, the above mentioned paper may be studied.

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2867 Views

The dataset is divided into two parts. The measurement dataset and simulation dataset. The measurement dataset contains received power measurements at 28 GHz in an indoor corridor and outdoor open area. The received power and other channel statistics, e.g., root mean square delay spread, power and time of arrival of multipath components, and path loss were obtained using the PXI channel sounder system. Two different gain antennas 17 dBi and 23 dBi were used. The transmitter was fixed, whereas the receiver was moved in a straight line aligned to the boresight of the transmitter antenna.

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The presented data contain recordings of underwater acoustic transmissions collected from a field experiment whose goal was to evaluate the feasibility of in-band full-duplex acoustic communications in the underwater environment. The experiment was conducted in Lake Tuscaloosa in June 2021. Two boats, each equipped with an instrument line, were deployed for the experiment. For the local instrument line, the transducer was mounted 6 meters below the water surface. The receiving array was placed 7 meters below the transducer.

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The dataset is oriented on encrypted traffic classification problems. The dataset contains three classes of flows: web flows, YouTube flows, and Netflixflows. These classes are chosen because web and video traffic account for 90% of global traffic, while YouTube and Netflix are the largest video services. The structure of the dataset is as follows. It includes 100 download traces of the most popular web pages according to https://httparchive.org, 100 the most popular YouTube videos, and 50 Netflix series and movies.

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626 Views

This dataset includes the measured Downlink (DL) signal-to-noise ratios (SNRs) at the User Equipments (UEs), adopting one of the beams of the beamforming codebook employed at the Base Stations (BSs). First, we configured a system-level simulator that implements the most recent Third Generation Partnership Project (3GPP) 3D Indoor channel models and the geometric blockage Model-B to simulate an indoor network deployment of BSs and UEs adopting Uniform Planar Arrays (UPAs) and a codebook based transmission.

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The network attacks are increasing both in frequency and intensity with the rapid growth of internet of things (IoT) devices. Recently, denial of service (DoS) and distributed denial of service (DDoS) attacks are reported as the most frequent attacks in IoT networks. The traditional security solutions like firewalls, intrusion detection systems, etc., are unable to detect the complex DoS and DDoS attacks since most of them filter the normal and attack traffic based upon the static predefined rules.

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This code and related data is related to research work on quantified benchmarking of Reconfigurable Intelligent Surface (RIS). Related research article has been submitted to VTM titled "Reconfigurable Intelligent Surfaces: Tradeoff between Unit-Cell- and Surface-Level Design under Quantifiable Benchmarks". 'ReadMe.text' file in 'RIS_restricted_02.zip' explains how to use the code to generate RIS configurations for RIS of arbitrary size and unit cell design, which can accomodate restricting to certain size grouped control.

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