internet of things

The Integrated Energy Management and Forecasting Dataset is a comprehensive data collection specifically designed for advanced algorithmic modeling in energy management. It combines two distinct yet complementary datasets - the Energy Forecasting Data and the Energy Grid Status Data - each tailored for different but related purposes in the energy sector.


This quantitative correlational research study aimed to investigate the factors affecting the implementation of zero-trust security and multifactor authentication (MFA) in a fog computing environment. Fog computing is an emerging decentralized technology that extends cloud computing capabilities near the user. A fog computing environment helps in faster communication with the internet of things (IoT) devices and reduces data transmission overheads.


This dataset is supplementary material for our paper "PUF for the Commons: Enhancing Embedded Security on the OS Level".


This dataset contains full details of the use case scenarios. Those can be used for effort elicitation using the adapted Use Case Points method. Despite the extensive use of UCP in software engineering, it has yet to be adapted for IoT systems, which is essential for project management and resource planning. Our proposed adaptation, UCP for IoT, is based on a four-layer IoT architecture and tailors the standard software UCP to the specifications of IoT systems.


This dataset consists of .csv files of 4 different routing attacks (Blackhole Attack, Flooding Attack, DODAG Version Number Attack and Decreased Rank Attack) targeting the RPL protocol and these files are taken from Cooja (Contiki network simulator). It gives researchers the opportunity to develop IDS for RPL-based IoT networks using Artificial Intelligence and Machine Learning methods without simulating attacks. Simulating these attacks is an important step towards developing and testing protection mechanisms against such attacks by mimicking real-world attack scenarios.


This dataset is hosted on IEEE DataPort(TM), a data repository created by IEEE to facilitate research reproducibility. A Systematic Literature Review SLR is presented on information retrieval for IoT and WoT scenarios, containing the definition of the research questions and the selection strategy. We specify the inclusion and exclusion criteria in conjunction with the quality assurance criteria. The data extraction procedure is then outlined.


Supplementary material for the article "A Sensor Network Utilizing Consumer Wearables for Telerehabilitation of Post-acute COVID-19 Patients": (1) TERESA (TEleREhabilitation Self-training Assistant) back-end application API documentation and (2) anonymous details of the Wristband protocols used in the study.


In this paper, we consider that the unmanned aerial vehicles (UAVs) with attached intelligent reflecting surfaces (IRSs) play the role of flying reflectors that reflect the signal of users to the destination, and utilize the power-domain non-orthogonal multiple access (PD-NOMA) scheme in the uplink. We investigate the benefits of the UAV-IRS on the internet of things (IoT) networks that improve the freshness of collected data of the IoT devices via optimizing power, sub-carrier, and trajectory variables, as well as, the phase shift matrix elements.


Abstract—In the 2021 and later we know that the technology

will have key participation of to help us in all kind of tasks

mainly using internet connection, due the new normality.

Industry 4.0 has been one of the most relevant field. IoT as part

of it. This Systematic Literature Review (SLR) we will cover

the South America countries and their development status,

addressing the development categories and the Hardware that

has been cited on papers on the last 5 years.


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.