As of 2029, the United States was the global leader by Internet of Things (IoT) spending, with 194 billion U.S. dollars spent. China and Japan followed, whereas Germany came in fourth worldwide and first in Europe that year.

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.

A minimal requirements Internet of Things (IoT) network system was built to produce a dataset according to IDS testing needs for IoT security. Testing was performed with 12 scenarios and resulted in 24 datasets which consisted of normal, attack and combined normal-attack traffic data. Testing focused on three denial of service (DoS) and distributed denial of service (DDoS) attacks—“finish” (FIN) flood, User Datagram Protocol (UDP) flood, and Zbassocflood/association flood—using two communication protocols, IEEE 802.11 (WiFi) and IEEE 802.15.4 (ZigBee). A preprocessing test result obtained 95 attributes for the WiFi datasets and 64 attributes for the Xbee datasets .

TCP FIN Flood Attack Pattern Recognition on Internet of Things with Rule Based Signature Analysis

Abstract-Focus of this research is TCP FIN flood attack pattern recognition in Internet of Things (IoT) network using rule based signature analysis method. Dataset is taken based on three scenarios normal, attack and normal-attack. The process of identification and recognition of TCP FIN flood attack pattern is done based on observation and analysis of packet attribute from raw data (pcap) using a feature extraction and feature selection method. Further testing was conducted using snort as an IDS. The results of the confusion matrix detection rate evaluation against the snort as IDS show the average percentage of the precision level.

Citing
Citation data : "TCP FIN Flood Attack Pattern Recognition on Internet of Things with Rule Based Signature Analysis" - https://online-journals.org/index.php/i-joe/article/view/9848

@article{article,

author = {Stiawan, Deris and Wahyudi, Dimas and Heryanto, Ahmad and Sahmin, Samsuryadi and Idris, Yazid and Muchtar, Farkhana and Alzahrani, Mohammed and Budiarto, Rahmat},

year = {2019},
month = {04},
pages = {124},
title = {TCP FIN Flood Attack Pattern Recognition on Internet of Things with Rule Based Signature Analysis},
volume = {15},
journal = {International Journal of Online and Biomedical Engineering (iJOE)},
doi = {10.3991/ijoe.v15i07.9848}
}

Features Extraction on IoT Intrusion Detection System Using Principal Components Analysis (PCA)

Feature extraction solves the problem of finding the most efficient and comprehensive set of features. A Principle Component Analysis (PCA) feature extraction algorithm is applied to optimize the effectiveness of feature extraction to build an effective intrusion detection method. This paper uses the Principal Components Analysis (PCA) for features extraction on intrusion detection system with the aim to improve the accuracy and precision of the detection. The impact of features extraction to attack detection was examined. Experiments on a network traffic dataset created from an Internet of Thing (IoT) testbed network topology were conducted and the results show that the accuracy of the detection reaches 100 percent.

Citing
Citation data : "Features Extraction on IoT Intrusion Detection System Using Principal Components Analysis (PCA)" - https://ieeexplore.ieee.org/document/9251292

@inproceedings{inproceedings,

author = {Sharipuddin, and Purnama, Benni and Kurniabudi, Kurniabudi and Winanto, Eko and Stiawan, Deris and Hanapi, Darmawiiovo and Idris, Mohd and Budiarto, Rahmat},

year = {2020},
month = {10},
pages = {114-118},
title = {Features Extraction on IoT Intrusion Detection System Using Principal Components Analysis (PCA)},
doi = {10.23919/EECSI50503.2020.9251292}
}

This deposit contains the processed data from the office scenario in the paper "Perils of Zero Interaction Security in the Internet of Things" by Mikhail Fomichev, Max Maass, Lars Almon, Alejandro Molina, Matthias Hollick, in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 3, Issue 1. See the index of all related datasets for more details on the paper, and see the included README for details on this dataset.

This work aims to investigate the potential fire hazard stemming from the overheating of power equipment. The advent of the artificial intelligence era has facilitated the fusion of blockchain and Internet of Things (IoT) technologies. This work delves into the technical standards for IoT equipment monitoring and smart grid communication, and the IoT environment of power grid equipment. This work introduces a temperature monitoring network tailored for IoT wireless power equipment suitable for the power environment, and conducts system debugging in the power laboratory. The findings affirm that the temperature out-of-limit alarm testing has met the required criteria, confirming the system’s ability to issue timely warnings when temperatures breach a predefined threshold, effectively avoiding high-temperature misfires. This work fully harnesses the secure and user-friendly operation of smart blockchain and the wireless sensing technology of the IoT to realize online monitoring and remote temperature measurement of the power system. It can effectively prevent equipment from overheating and damage, and promote the development of equipment condition monitoring technology in electric power engineering.

Poland was the Central and Eastern (CEE) country most prepared for the adoption and development of frontier technologies in 2022, with an index score of 0.77, followed by Slovenia, Estonia, and Czechia. The lowest scores were recorded in Albania and Moldova. None of the countries in the region were found to have a low level of readiness. The frontier technologies analyzed included industry 4.0 solutions, such as artificial intelligence (AI), Internet of Things (IoT), and blockchain; green technologies like solar photovoltaics and electric vehicles; nanotechnology; and gene editing.

Recently, The Egyptian health sector whether it is public or private; utilizes emerging technologies such as data mining, business intelligence, Internet of Things (IoT), among many others to enhance the service and to deal with increasing costs and growing pressures. However, process mining has not yet been used in the Egyptian organizations, whereas the process mining can enable the domain experts in many fields to achieve a realistic view of the problems that are currently happening in the undertaken field, and thus solve it. This paper presents application of the process mining techniques in the healthcare field to obtain meaningful insights about its careflows, e.g., to discover typical paths followed by certain patient groups. Also, to analyze careflows that have a high degree of dynamic and complexity. The proposed methodology starts by the preprocess step on the event logs to eliminate outliers and clean the event log. And then apply a set of the popular discovery miner algorithms to discover the process model. Then careflows processes are analyzed from three main perspectives: the control-flow perspective, the performance perspective and, the organizational perspective. That contributes with many insights for the domain experts to improve the existing careflows. Through evaluating the simplicity metric of extracted models; the paper suggested a method to quantify the simplicity metric. The paper used a dataset from a cardiac surgery unit in an Egyptian hospital. The results of the applied process mining techniques provide the hospital managers a real analysis and insights to make the patient journey easier.

According to a study that evaluates the level of preparedness for the adoption and development of cutting-edge technologies around the world, Brazil was the only country in the Latin American and Caribbean region that achieved a high level of readiness in this area. The term "frontier technologies" encompasses artificial intelligence, the Internet of Things (IoT), big data, blockchain, robotics, 3D printing, drones, gene editing, 5G, nanotechnology, and solar photovoltaics. In 2022, the highest scoring nation in the region was thus Brazil, with 0.71 index points. A score of one indicates the highest level of readiness and zero, the lowest. This analysis also reveals that states such as Haiti, Nicaragua, and Honduras are some of the least prepared for these disruptive technologies in the Americas.

As an equipment failure that often occurs in coal production and transportation, belt conveyor failure usually requires many human and material resources to be identified and diagnosed. Therefore, it is urgent to improve the efficiency of fault identification, and this paper combines the internet of things (IoT) platform and the Light Gradient Boosting Machine (LGBM) model to establish a fault diagnosis system for the belt conveyor. Firstly, selecting and installing sensors for the belt conveyor to collect the running data. Secondly, connecting the sensor and the Aprus adapter and configuring the script language on the client side of the IoT platform. This step enables the collected data to be uploaded to the client side of the IoT platform, where the data can be counted and visualized. Finally, the LGBM model is built to diagnose the conveyor faults, and the evaluation index and K-fold cross-validation prove the model’s effectiveness. In addition, after the system was established and debugged, it was applied in practical mine engineering for three months. The field test results show: (1) The client of the IoT can well receive the data uploaded by the sensor and present the data in the form of a graph. (2) The LGBM model has a high accuracy. In the test, the model accurately detected faults, including belt deviation, belt slipping, and belt tearing, which happened twice, two times, one time and one time, respectively, as well as timely gaving warnings to the client and effectively avoiding subsequent accidents. This application shows that the fault diagnosis system of belt conveyors can accurately diagnose and identify belt conveyor failure in the coal production process and improve the intelligent management of coal mines.

As an equipment failure that often occurs in coal production and transportation, belt conveyor failure usually requires many human and material resources to be identified and diagnosed. Therefore, it is urgent to improve the efficiency of fault identification, and this paper combines the internet of things (IoT) platform and the Light Gradient Boosting Machine (LGBM) model to establish a fault diagnosis system for the belt conveyor. Firstly, selecting and installing sensors for the belt conveyor to collect the running data. Secondly, connecting the sensor and the Aprus adapter and configuring the script language on the client side of the IoT platform. This step enables the collected data to be uploaded to the client side of the IoT platform, where the data can be counted and visualized. Finally, the LGBM model is built to diagnose the conveyor faults, and the evaluation index and K-fold cross-validation prove the model’s effectiveness. In addition, after the system was established and debugged, it was applied in practical mine engineering for three months. The field test results show: (1) The client of the IoT can well receive the data uploaded by the sensor and present the data in the form of a graph. (2) The LGBM model has a high accuracy. In the test, the model accurately detected faults, including belt deviation, belt slipping, and belt tearing, which happened twice, two times, one time and one time, respectively, as well as timely gaving warnings to the client and effectively avoiding subsequent accidents. This application shows that the fault diagnosis system of belt conveyors can accurately diagnose and identify belt conveyor failure in the coal production process and improve the intelligent management of coal mines.

Technology spending on smart city initiatives worldwide is forecast to more than double between 2018 and 2023, increasing from 81 billion U.S. dollars in 2018 to 189.5 billion in 2023. Smart cities use data collected through sensors to automate a range of services in order to bring about better performance, lower costs or reduced environmental impacts. A smart city is a vertical of Internet of Things (IoT) - a term used to define a network that not only connects people, but also the objects around them.

The ‘smartest’ of ‘smart’ cities    

As of 2019, Oslo, Norway was ranked as one of the smartest cities to live in with an index score of 7.63. This index was calculated by looking into different categories such as transport and mobility, sustainability, innovation economy, living standard and expert perception. The index included the provision of smart parking and mobility, recycling rates, blockchain ecosystem and other factors that improve the standard of living to give a top index score of 10. Next to Oslo is another Norwegian city Bergen, as well as Amsterdam, with index scores of 7.57 and 7.55 respectively.