IoT

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.

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The Internet of Things and edge computing are fostering a future of ecosystems

hosting complex decentralized computations, deeply integrated with our very dynamic

environments. Digitalized buildings, communities of people, and cities will be the

next-generation “hardware and platform”, counting myriads of interconnected devices, on top of

which intrinsically-distributed computational processes will run and self-organize. They will

spontaneously spawn, diffuse to pertinent logical/physical regions, cooperate and compete,

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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.

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The script "numericalExample.m" obtains per-source average AoI and per-source age violation probabilities for First Source First Serve (FSFS), Earliest Served First Serve (ESFS) and Single Buffer with Replacement (SBR) policies for N sources using analytical models based on Markov Fluid Queues (MFQ). It is also possible to obtain the exact per-source CDF using this script. To do so, only the input parameters of used functions, namely "FSFS.m", "ESFS.m" and "SBR.m", in this script should be modified accordingly.

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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.

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Artificial database that simulates COVID-19 patients and critical situations to be able to evaluate the BeCalm system performance (https://www.idatis.org/proyecto-becalm/). Generated with https://github.com/BOSCH-UCM/BeCalm

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Raspberry Pi benchmarking dataset monitoring CPU, GPU, memory and storage of the devices. Dataset associated with "LwHBench: A low-level hardware component benchmark and dataset for Single Board Computers" paper

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The number of private vehicles is still increasing from year to year. In order to limit environmental damage, a proper way of dealing with this trend is the introduction of intelligent automotive infrastructure. Besides traffic management solutions, smart parking guidance systems are important for reducing unnecessary traffic. For this, a key prerequisite are sensor networks that provide information about the occupancy state of every single parking spot in the parking infrastructure of high traffic targets e.g. nearby an airport or shopping mall.

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This data set was generated and used in determining the workability of a homemade Intelligent IoT Weather Station Using an Embedded System.

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ReSysTDepth collects trajectories of people in indoor spaces. Each trajectory is composed of a sequence of three-dimensional coordinates that capture the center of mass of people moving in the corridor of a building. It is composed of two datasets:

  • Real Dataset - real data captured by a depth camera (Microsoft Kinect v2).
  • Synthetic dataset - synthetic data generated artificially using splines.

 

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