There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

With the development of the Industrial Internet of Things and the continuous expansion of application scenarios, many development bottlenecks have followed. Its data security issue has become an obstacle to its widespread application. It has attracted substantial attention from both academia and industry. Blockchain technology has the characteristics of decentralization, openness and transparency and non-tampering. It has natural advantages in solving the security of the Industrial Internet of Things. Accordingly, this paper first analyzes the security risks associated with data storage in the Industrial Internet of Things and proposes the use of blockchain technology to ensure the secure storage of data in the Industrial Internet of Things. In traditional blockchains, the data layer uses Merkle hash trees to store data; however, the Merkle hash tree not able to provide non-member proof, which makes it unable to resist attacks from malicious nodes in the network. To solve this problem, this paper replaces the Merkle hash tree with a password accumulator to provide member proof and non-member proof. Moreover, the existing accumulators have trapdoors and cannot be updated in batches, and unable to meet the blockchain’s expansion requirements. This paper presents an improved RSA accumulator and gives the definition of the accumulator. Finally, this paper uses RSA to construct a batch update accumulator scheme without trapdoor, and shows that the scheme is feasible through correctness and security

https://www.techscience.com/iasc/v26n5/40832/pdf

Blockchain-based data storage mechanism for industrial Internet of things

Industrial Internet of Things (IIoT) can be seen as an extension of the Internet of Things (IoT) services and applications to industry with the inclusion of Industry 4.0 that provides automation, reliability, and control in production and manufacturing. IIoT has tremendous potential to accelerate industry automation in many areas, including transportation, manufacturing, automobile, marketing, to name a few places. When the benefits of IIoT are visible, the development of large-scale IIoT systems faces various security challenges resulting in many large-scale cyber-attacks, including fraudulent transactions or damage to critical infrastructure. Moreover, a large number of connected devices over the Internet and resource limitations of the devices (e.g., battery, memory, and processing capability) further pose challenges to the system. The IIoT inherits the insecurities of the traditional communication and networking technologies; however, the IIoT requires further effort to customize the available security solutions with more focus on critical industrial control systems. Several proposals discuss the issue of security, privacy, and trust in IIoT systems, but comprehensive literature considering the several aspects (e.g., users, devices, applications, cascading services, or the emergence of resources) of an IIoT system is missing in the present state of the art IIoT research. In other words, the need for considering a vision for securing an IIoT system with broader security analysis and its potential countermeasures is missing in recent times. To address this issue, in this paper, we provide a comparative analysis of the available security issues present in an IIoT system. We identify a list of security issues comprising logical, technological, and architectural points of view and consider the different IIoT security requirements. We also discuss the available IIoT architectures to examine these security concerns in a systematic way. We show how the functioning of different layers of an IIoT architecture is affected by various security issues and report a list of potential countermeasures against them. This study also presents a list of future research directions towards the development of a large-scale, secure, and trustworthy IIoT system. The study helps understand the various security issues by indicating various threats and attacks present in an IIoT system.

Analysis of Security Issues and Countermeasures for the Industrial Internet of Things

Fifth generation (5G)-industrial Internet of things (IIoT) is the integration of IIoT and a private 5G network. The IIoT is a concept that involves incorporating smart objects, gadgets, and solutions into cutting-edge industrial operations to increase reliability, efficiency, and over-production costs. Furthermore, the integration of IIoT and 5G/beyond 5G (B5G) provides the potential for ubiquitous and instantaneous connectivity. The 5G architecture can handle the IIoT’s stringent ultra-low latency, realtime processing, high data rate, nearby storage, and reliability requirements. A new era of economic growth is predicted for IIoT-assisted 5G/B5G wireless networks. It should be noted that the majority of the work in IIoT is focused on the architecture, with reliability and throughput being largely ignored. This paper provides a comprehensive review of B5G assisted IIoT wireless networks, with a focus on enhanced mobile broadband (eMBB) and ultra-reliable low latency communication (URLLC) services. Furthermore, it provides insights into various applications and key enabling technologies from the perspective of URLLC, eMBB, and their tradeoff.

/pdf/urllc-and-embb-in-5g-industrial-iot-a-survey-1q4vfxss.pdf

URLLC and eMBB in 5G Industrial IoT: A Survey

In this paper, a novel deep learning-based robust automatic modulation classification (AMC) method is proposed for cognitive radio networks. Generally, as network input of AMC convolutional neural networks (CNNs) images or complex signals are utilized in time domain or frequency domain. In terms of the image that contains RGB(Red, Green, Blue) levels the input size may be larger than the complex signal, which represents the increase of computational complexity. In terms of the complex signal it is normally used as $2 \times N$ size for the input, which is divided into in-phase and quadrature-phase (IQ) components. In this paper, the input size is extended as $4 \times N$ size by copying IQ components and concatenating in reverse order to improve the classification accuracy. Since the increase in the amount of computation complexity due to the extended input size, the proposed CNN archiecture is designed to reduce the size from $4 \times N$ to $2 \times N$ by an average pooling layer, which can enhence the classification accuracy as well. The simulation results show that the classification accuracy of the proposed model is higher than the conventional models in the almost signal-to-noise ratio (SNR) range.

Deep Learning-Based Robust Automatic Modulation Classification for Cognitive Radio Networks

This paper proposes an impulse radio ultrawideband (IR-UWB) wireless network channel model for next-generation wireless avionic system (NGWAS). Complexities such as poor network localization, redundant wiring, network security flaws, suitable physical proximity to the aircraft system's controller and need to guarantee scalable wireless network access have necessitated the adoption of wing system units of a passenger aircraft for this scheme's deployment. The proposed IR-UWB approach aims at alleviating the attendant drawbacks by selecting optimal channel path from available paths on the basis of bit error rate (BER), based on the numerical results obtained from the Saleh–Valenzuela (S-V) principle. Cooperative relay transmission schemes factored upon transmission power, overall outage probability and spectral-energy efficiency tradeoffs for the relay network communication were also investigated and compared with the noncooperative schemes. Simulation results validate maximum data packet delivery while a strong network signal strength is attained. Comparative approach of the performances of the IR-UWB standardized channel models are also evaluated.

Ultrawideband Network Channel Models for Next-Generation Wireless Avionic System

Real-time optimizations in energy profiles and end-to-end delay in WSN using two-hop information

This letter proposes dual fieldbus communication for Industrial Internet of Things using an edge server. Dual fieldbus communication implements a redundant fieldbus to prevent bus faults and increase the system’s availability. The primary communication link uses Modbus while the redundant fieldbus is a Controller Area Network (CAN). When an error occurs, the protocol changes automatically to use the CAN bus because of its faster error recovery. It outperforms the conventional Modbus for both detected data error and data success rates. Meanwhile, the edge server implements a long short-term memory (LSTM) and performs 95 percent faster than processing in the cloud.

Dual fieldbus industrial IoT networks using edge server architecture

The various works on the corresponding and the associated need for a wireless industrial sensor network connectivity, applicable in the Industrial Internet of things (IIoT) domain have been exhaustive quite recently. This study has also clearly shown how they are grouped differently to that which is required by the consumer industries, with much emphasis on energy consumption and control, throughput reliability, stability of the sensors and their embedded security characteristics. To accomplish a deserving Quality of Service (QoS) of IIoT to which is expected, the data centers must have a guaranteed performance as a backbone of computation resources. This paper implements and investigates energy efficient sensors in the computing resources of data center for Industrial Internet of Things (IIoT). The simulation works investigates the resulting throughput performance and energy consumption profiles and control of the sensor nodes with respect to the three-tier network architecture as one of candidate backbone in IIoT.

Williams-Paul Nwadiugwu

Papers

Deep Learning-Based Robust Automatic Modulation Classification for Cognitive Radio Networks

Ultrawideband Network Channel Models for Next-Generation Wireless Avionic System

Real-time optimizations in energy profiles and end-to-end delay in WSN using two-hop information

Dual fieldbus industrial IoT networks using edge server architecture

Energy-efficient Sensors in Data Centers for Industrial Internet of Things (IIoT)