This data set contains measurements on reading and writing data to OPC UA servers directly and via REST and GraphQL interfaces. Each measurement is conducted 1000 times. Measurements include reading a single value and reading 50 values. Measurements using cache server were also performed. Measurement data is collected with Wireshark and the .csv files are exported from it. in addition, .txt files contain request execution times recorded by the client.


A shortage of beds and cross-infection in hospitals due to patient crowding and overloading during the COVID-19 pandemic necessitate the use of telemedicine over face-to-face treatment. This study used statistical analysis to evaluate the impact of treatment choice among hospitals, patients, and the government to encourage them to employ telemedicine to avoid overload risk in the IoT environment during the pandemic by analyzing data from Tongji Hospital of Wuhan, China from January to September 2020.


We consider a

large location with M number of grid points, each grid point is labeled with a unique fingerprint consisting of the received signal

strength (RSS) values measured from N number of Bluetooth Low Energy (BLE) beacons. Given the fingerprint observed by the

smartphone, the user’s current location can be estimated by finding the top-k similar fingerprints from the list of fingerprints registered

in the database.


Shannon’s Index of Difficulty (ID), reputable for quantifying the perceived difficulty of pointing tasks as a logarithmic relationship between movement-amplitude (A) and target-width (W), is used for modelling the corresponding observed movement-times (MT_O) in such tasks in controlled experimental setup.


The measurement and diagnosis of the severity of failures in rotating machines allow the execution of predictive maintenance actions on equipment. These actions make it possible to monitor the operating parameters of the machine and to perform the prediction of failures, thus avoiding production losses, severe damage to the equipment, and safeguarding the integrity of the equipment operators. This paper describes the construction of a dataset composed of vibration signals of a rotating machine.


The following data is accumulated zonal mean statistic for Pune city representing the time series of satellite data for Landsat, EBBI, NDVI, Wind Speed and Direction, MODIS day and night time LST observations.


This dataset consists of measurements from a foot-mounted inertial measurement unit (IMU). In total, we provide data from five different test subjects travelling over more than 7.6 km. The data are combined with various forms of ground truth positioning information that can be used to evaluate the accuracy of a zero-velocity-aided, foot-mounted inertial navigation system (INS).


Herein, we provide inertial data (produced from an inertial measurement unit) collected in three different test environments. Each environment contains multiple trajectories, and we provide the raw, foot-mounted inertial measurements for each trajectory. Additionally, we include forms of ground truth for each trajectory (the form of ground truth varies within each test environment). For each trajectory, we include processed results that can be used for benchmarking other systems. Our processed results (six degree-of-freedom trajectory estimates) are generated from a baseline zero-velocity-aided inertial navigation system (INS).

Once the dataset has been downloaded, it should be unpacked into the PyShoe repository in order to use the available tools with the provided dataset: To do so, unzip the results and data folders into the main directory. The correct folder structure is as follows:

| - - pyshoe

|        | - - data

|                | - - hallway

|                | - - stairs

|                | - - vicon

|         | - - results

Following this, we refer you to the readme instructions in the Github repository for detailed instructions on how the data can be used with our open-source INS. See the individual readme files within the various data subdirectories to understand how the dataset is formatted.


Extensive experimental measurement campaigns of more than 30,000 data points of end-to-end latency measurements for the following network architecture schemes is available:

  • Unlicensed IoT (standalone LoRa)
  • Cellular IoT (standalone LTE-M)
  • Concatenated IoT (LoRa interfaced with LTE-M)

Download to access all relevant files for the open data measurements.

Related Paper:


Driving behavior plays a vital role in maintaining safe and sustainable transport, and specifically, in the area of traffic management and control, driving behavior is of great importance since specific driving behaviors are significantly related with traffic congestion levels. Beyond that, it affects fuel consumption, air pollution, public health as well as personal mental health and psychology. Use of Smartphone sensors for data acquisition has emerged as a means to understand and model driving behavior. Our aim is to analyze driving behavior using on Smartphone sensors’ data streams.


The datasets folder include .csv files of sensor data like Accelerometer, Gyroscope, etc. This data was recorded in live traffic while driver was executing certain driving events. The travel time for each one way trip was approximately 5kms - 20kms. The smartphone position was fixed horizontally in the vehicles utility box. Vehicle type used for data recording was LMV.


High-voltage batteries in battery electric vehicles face significant load fluctuations due to driving behavior. This dynamic performance of the powertrain is contrasted by the almost constant load of the auxiliary consumers. The highest auxiliary consumption is generated by the heating and air conditioning system, which decreases the vehicles range significantly. 72 real driving trips with a BMW i3 (60 Ah) were recorded, serving for model validation of a full vehicle model consisting of the powertrain and the heating circuit.


Plase see the attached readme.txt.