DataSet used in learning process of the traditional technique's operation, considering different devices and scenarios, the proposed approach can adapt its response to the device in use, identifying the MAC layer protocol, perform the commutation through the protocol in use, and make the device to operate with the best possible configuration.

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The rise of the Internet of Things (IoT) has opened new research lines that focus on applying IoT applications to domains further beyond basic user-grade applications, such as Industry or Healthcare. These domains demand a very high Quality of Service (QoS), mainly a very short response time. In order to meet these demands, some works are evaluating how to modularize and deploy IoT applications in different nodes of the infrastructure (edge, fog, cloud), as well as how to place the network controllers, since these decisions affect the response time of the application.

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The data set contains electrical and mechanical signals from experiments on three-phase induction motors. The experimental tests were carried out for different mechanical loads on the induction motor axis and different severities of broken bar defects in the motor rotor, including data regarding the rotor without defects. Ten repetitions were performed for each experimental condition.

Instructions: 

Experimental Setup:

The experimental workbench consists of a three-phase induction motor coupled with a direct-current machine, which works as a generator simulating the load torque, connected by a shaft containing a rotary torque wrench.

- Induction motor: 1hp, 220V/380V, 3.02A/1.75A, 4 poles, 60 Hz, with a nominal torque of 4.1 Nm and a rated speed of 1715 rpm. The rotor is of the squirrel cage type composed of 34 bars.

- Load torque: is adjusted by varying the field winding voltage of direct current generator. A single-phase voltage variator with a filtered full-bridge rectifier is used for the purpose. An induction motor was tested under 12.5, 25, 37.5, 50, 62.5, 75, 87.5 and 100% of full load.

- Broken rotor bar: to simulate the failure on the three-phase induction motor's rotor, it was necessary to drill the rotor. The rupture rotor bars are generally adjacent to the first rotor bar, 4 rotors have been tested, the first with a break bar, the second with two adjacent broken bars, and so on rotor containing four bars adjacent broken.

Monitoring condition:

All signals were sampled at the same time for 18 seconds for each loading condition and ten repetitions were performed from transient to steady state of the induction motor.

- mechanical signals: five axial accelerometers were used simultaneously, with a sensitivity of 10 mV/mm/s, frequency range from 5 to 2000Hz and stainless steel housing, allowing vibration measurements in both drive end (DE) and non-drive end (NDE) sides of the motor, axially or radially, in the horizontal or vertical directions.

- electrical signals: the currents were measured by alternating current probes, which correspond to precision meters, with a capacity of up to 50ARMS, with an output voltage of 10 mV/A, corresponding to the Yokogawa 96033 model. The voltages were measured directly at the induction terminals using voltage points of the oscilloscope and the manufacturer Yokogawa.

Data Set Overview:

-          Three-phase Voltage

-          Three-phase Current

-          Five Vibration Signals

 

Acknowledgements:

            The database was acquired in the Laboratory of Intelligent Automation of Processes and Systems and Laboratory of Intelligent Control of Electrical Machines, School of Engineering of São Carlos of the University of São Paulo (USP), Brazil.

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The global system for mobile communications (GSM) supports mobile operators for cellular networks. Huge devices are connected to obtain services through the internet. To avoid failures when connecting IoT devices to mobile networks, GSM has provided two datasets: IoT device connection efficiency and Mobile IoT (MIoT) common test cases (TCs) and guidelines as per the IoT systems specifications. GSM produces TCs at least each year since 2015 till present.

Instructions: 

 

This dataset is based on the GSM 2014, GSM 2015 and GSM 2017 PDFs found as:

GSM, 2014. IoT Device Connection Efficiency Guidelines 1–73.

GSM, 2015. IoT Device Connection Efficiency Common Test Cases 30 January 2015 1–51.

GSM, 2017. MIoT Test Requirements 1–24. TC, G., 2017. MIoT Test Cases 1–40.

 

The dataset consists of 13 files as follows:

1.IoT-system-requirements.xlsx: it contains the IoT system specifications of GSM 2014 and 2015 in excel format.

2.IoT-system-requirements-MIoT.xlsx: it contains the IoT system specifications of GSM 2017 in excel format.

3.IoT-system-test-cases.xlsx: it contains the IoT test cases of GSM 2014 and 2015 in excel format.

4.IoT-system-test-cases-MIoT.xlsx: it contains the IoT test cases of GSM 2017 in excel format.

5. IoT-system-traceability-matrix.xlsx: it contains the traceability matrix we created for GSM 2014 and 2015 system requirements with their related test cases in excel format.

6. IoT-system-traceability-matrix-MIoT.xlsx: it contains the traceability matrix we created for GSM 2017 system requirements with their related test cases in excel format.

7. IoT-system-test-cases-attributes-extraction.xlsx: it contains the test cases attributes we extracted from GSM 2014 and 2015 test cases using our developed IoT-CIRTF in excel format, in terms of coverage rate, fault detection rate and execution time.

8. IoT-system-test-cases-attributes-extraction-MIoT.xlsx: it contains the test cases attributes we extracted from GSM 2017 test cases using our developed IoT-CIRTF in excel format, in terms of coverage rate, fault detection rate and execution time.

9. IoT-system-selected-prioritized-integration-test-cases.xlsx: it contains the test cases selected and prioritized for IoT integration testing we generated for GSM 2014 and 2015, using our developed IoT-CIRTF in excel format.

10. IoT-system-selected-prioritized-integration-test-cases-MIoT.xlsx: it contains the test cases selected and prioritized for IoT integration testing we generated for GSM 2017, using our developed IoT-CIRTF in excel format.

11. IoT-system-selected-prioritized-regression-test-cases.xlsx: it contains the test cases selected and prioritized for IoT regression testing we generated GSM 2014 and 2015, using our developed IoT-CIRTF in excel format.

12. IoT-system-selected-prioritized-regression-test-cases-MIoT.xlsx: it contains the test cases selected and prioritized for IoT regression testing we generated GSM 2017, using our developed IoT-CIRTF in excel format.

13. IoT-CIRTF Demo.mp4: A demoenstration video for the runtime execution of our developed IoT-CIRTF.

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The data are four Xilinx ISE projects for Montgomery modualr multiplication and modular exponentiation.

Instructions: 

There are 4 directions in the data, the first 2 of which are Montgomery modular multiplications, and the last 2 of which are modular exponentiations.

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This dataset includes the additional material attached to paper entitled ”SNSR: Robot-Sensor Network Security Architecture”, submitted for publication in IEEE Internet of Things Journal.

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test

Instructions: 

self test

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This dataset is captured from a Mirai type botnet attack on an emulated IoT network in OpenStack. Detailed information on the dataset is depicted in the following work. Please cite it when you use this dataset for your research.

  • Kalupahana Liyanage Kushan Sudheera, Dinil Mon Divakaran, Rhishi Pratap Singh, and Mohan Gurusamy, "ADEPT: Detection and Identification of Correlated Attack-Stages in IoT Networks," in IEEE Internet of Things Journal.

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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.

Instructions: 

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

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