IoT

This data set contains data collected from an overhead crane (https://doi.org/10.1109/WF-IoT.2018.8355217) OPC UA server when driving an L-shaped path with different loads (0kg, 120kg, 500kg, and 1000kg). Each driving cycle was driven with an anti-sway system activated and deactivated. Each driving cycle consisted of repeating five times the process of lifting the weight, driving from point A to point B along with the path, lowering the weight, lifting the weight, driving back to point A, and lowering the weight.

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This dataset accurately models the internal behavior of an IoT spectrum sensor (belonging to the ElectroSense platform and consisting of a Raspberry Pi 3 with a software-defined radio kit) when it is functioning normally and under attack. To accomplish it, the system calls of the IoT sensor are monitored under normal behavior, gathered, cleaned, and stored in a centralized directory. Then, the device is infected with current malware affecting IoT devices, such as the Bashlite botnet, Thetick backdoor, Bdvl rootkit, and a Ransomware proof of concept.

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378 Views

The Development of an Internet of Things (IoT) Network Traffic Dataset with Simulated Attack Data.

Abstract— This research focuses on the requirements for and the creation of an intrusion detection system (IDS) dataset for an Internet of Things (IoT) network domain.

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606 Views

The Internet of Things (IoT) is reshaping our connected world, due to the prevalence of lightweight devices connected to the Internet and their communication technologies. Therefore, research towards intrusion detection in the IoT domain has a lot of significance. Network intrusion datasets are fundamental for this research, as many attack detection strategies have to be trained and evaluated using these datasets.

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1201 Views

This survey covers more than 150 published papers related to sub-6 GHz wideband LNAs from IEEE publications such as ISSCC, JSSC, TMTT, RFIC, MWCL, TCAS and NEWCAS published in the last 20 years. The considered LNAs are classified according to the technology node and its topology. The presented database is a useful tool for investigating technology trends and comparing the performance of common LNA design styles. 

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329 Views

This dataset is in support of my following Research papers  

Preprint  (Make sure you have read Caution) :

  • Novel ß Transtibial Prosthetic 9-DoF Artificial Leg Adaptive Controller - Part I* 

  https://doi.org/10.36227/techrxiv.19758517.v1

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1567 Views

Here we introduce so-far the largest subject-rated database of its kind, namely, "Effect of Sugarcane vegetation on path-loss between CC2650 and CC2538 SoC 32-bit Arm Cortex-M3 based sensor nodes operating at 2.4 GHz Radio Frequency (RF)".

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435 Views

Here we introduce so-far the largest subject-rated database of its kind, namely, "Effect of Paddy vegetation on path-loss between CC2650 SoC 32-bit Arm Cortex-M3 based sensor nodes operating at 2.4 GHz Radio Frequency (RF)". This database contains received signal strength measurements collected through campaigns in the IEEE 802.15.4 standard precision agricultural monitoring infrastructure developed for Paddy rice crop monitoring from period 03/07/2019 to 18/11/2019.

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420 Views

Here we introduce so-far the largest subject-rated database of its kind, namely, "Effect of Paddy Rice vegetation on received signal strength between CC2538 SoC 32-bit Arm Cortex-M3 based sensor nodes operating at 2.4 GHz Radio Frequency (RF)". This database contains received signal strength measurements collected through campaigns in the IEEE 802.15.4 standard precision agricultural monitoring infrastructure developed for Paddy Rice crop monitoring from the period 01/07/2020 to 03/11/2020.

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298 Views

Here we introduce so-far the largest subject-rated database of its kind, namely, "Effect of Millet vegetation on path-loss between CC2538 SoC 32-bit Arm Cortex-M3 based sensor nodes operating at 2.4 GHz Radio Frequency (RF)". This database contains received signal strength measurements collected through campaigns in the IEEE 802.15.4 standard precision agricultural monitoring infrastructure developed for millet crop monitoring from period 03/06/2020 to 04/10/2020.

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259 Views

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