scholarly journals Resilience Quantification for Probabilistic Design of Cyber-Physical System Networks

2018 ◽  
Vol 4 (3) ◽  
Author(s):  
Yan Wang
Keyword(s):  
Communication Networks ◽  
System Performance ◽  
Network Architecture ◽  
Performance Metrics ◽  
Discrete Event ◽  
Systems Design ◽  
Conditional Entropy ◽  
Probabilistic Design ◽  
Resilience Engineering ◽  
Physical Systems

Cyber-physical systems (CPS) are the physical systems of which individual components have functional identities in both physical and cyber spaces. Given the vastly diversified CPS components in dynamically evolving networks, designing an open and resilient architecture with flexibility and adaptability thus is important. To enable a resilience engineering approach for systems design, quantitative measures of resilience have been proposed by researchers. Yet, domain-dependent system performance metrics are required to quantify resilience. In this paper, generic system performance metrics for CPS are proposed, which are entropy, conditional entropy, and mutual information associated with the probabilities of successful prediction and communication. A new probabilistic design framework for CPS network architecture is also proposed for resilience engineering, where several information fusion rules can be applied for data processing at the nodes. Sensitivities of metrics with respect to the probabilistic measurements are studied. Fine-grained discrete-event simulation models of communication networks are used to demonstrate the applicability of the proposed metrics.

scholarly journals Sixth Generation (6G) Cognitive Radio Network (CRN) Application, Requirements, Security Issues, and Key Challenges

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Muhammad Muzamil Aslam ◽  
Liping Du ◽  
Xiaoyan Zhang ◽  
Yueyun Chen ◽  
Zahoor Ahmed ◽  
...  
Keyword(s):  
Communication Networks ◽  
Channel Coding ◽  
Network Architecture ◽  
Satellite Communication ◽  
Performance Metrics ◽  
Spectral Energy ◽  
Future Technology ◽  
Security Issues ◽  
Sixth Generation ◽  
And Performance

Recently, 5G installation has been started globally. Different capabilities are in the consistent procedure, like ultrareliability, mass connectivity, and specific low latency. Though, 5G is insufficient to meet all the necessities of the future technology in 2030 and so on. Next generation information and communication technology is playing an important role in attraction of researchers, industries, and technical people. With respect to 5G networks, sixth-generation (6G) CR networks are anticipated to familiarize innovative use cases and performance metrics, such as to offer worldwide coverage, cost efficiency, enhanced spectral, energy improved intelligence, and safety. To reach such requirements, upcoming 6G CRNs will trust novel empowering technologies. Innovative network architecture and transmission technologies and air interface are of excessive position, like multiple accesses, waveform design, multiantenna technologies, and channel coding schemes. (1) To content, the condition should be of worldwide coverage, there will be no limit on 6G to global CR communication networks that may require to be completed with broadcast networks, like satellite communication networks, therefore, attaining a sea integrated communication network. (2) The spectrums overall will be entirely travelled to the supplementary rise connection density data rates in optical frequency bands, millimeter wave (mmWave), sub-6 GHz, and terahertz (THz). (3) To see big datasets created because of tremendously varied CR communication networks, antenna rush, diverse communication scenarios, new provision necessities, wide bandwidth, and 6G CRNs will allow an innovative variety of intelligent applications with the assistance of big data and AI technologies. (4) Need to improve network security when deploying 6G technology in CR networks. 6G is decentralized, intended, intelligent innovative, and distributed network. In this article, we studied a survey of current developments and upcoming trends. We studied the predicted applications, possible technologies, and security issues for 6G CR network communication. We also discussed predicted future key challenges in 6G.

scholarly journals Reconfigurable intelligent surfaces assisted wireless communication networks: ergodic capacity and symbol error rate

2022 ◽  
Vol 25 (1) ◽  
pp. 358
Author(s):  
Dinh-Thuan Do ◽  
Chi-Bao Le
Keyword(s):  
Monte Carlo ◽  
Communication Networks ◽  
Error Rate ◽  
System Performance ◽  
Performance Metrics ◽  
Ergodic Capacity ◽  
Symbol Error Rate ◽  
Base Station ◽  
Wireless Communication Networks ◽  
Relaying System

By enabling reconfigurable intelligent surfaces (RIS), we can deploy intelligent reflecting signals from the base station to destinations. Different from traditional relaying system, RIS relies on programmable metasurfaces and mirrors to improve system performance of destinations. We derive the formulas of main system performance metrics such as ergodic capacity and symbol error rate (SER). Based on types of modulation, we need to demonstrate other parameters which make influence to system performance. We show analytically that the number of reflecting elements along with the transmit power at the source can improve system performance. Moreover, we check the exactness of derived expressions by matching Monte-Carlo with analytical simulations. Finally, we find the best performance can be achieved at specific parameters and results are verified by explicit simulations.

System Resilience Quantification for Probabilistic Design of Internet-of-Things Architecture

2016 ◽  
Author(s):  
Yan Wang
Keyword(s):  
Internet Of Things ◽  
Information Fusion ◽  
System Architecture ◽  
Systems Design ◽  
Virtual Space ◽  
Probabilistic Design ◽  
Resilience Engineering ◽  
Evolving Networks ◽  
System Resilience ◽  
The Internet Of Things

The objects in the Internet of Things (IoT) form a virtual space of information gathering and sharing through the networks. Designing IoT-compatible products that have the capabilities of data collection, processing, and communication requires open and resilient architecture with flexibility and adapability for dynamically evolving networks. Design for connectivity becomes an important subject in designing such products. To enable a resilience engineering approach for IoT systems design, quantitative measures of resilience are needed for analysis and optimization. In this paper, an approach for probabilistic design of IoT system architecture is proposed, where resilience is quantified with entropy and mutual information associated with the probabilities of detection, prediction, and communication among IoT-compatible products. Information fusion rules and sensitivities are also studied.

Effect of CSI Imperfection on Cognitive Underlay Transmission over Nakagami-m Fading Channel

2015 ◽  
Vol 1 ◽  
pp. 59
Author(s):  
Nguyen Hong Giang ◽  
Vo Nguyen Quoc Bao ◽  
Hung Nguyen-Le
Keyword(s):  
Fading Channels ◽  
System Performance ◽  
Performance Metrics ◽  
Maximal Ratio Combining ◽  
Relay Nodes ◽  
Channel State ◽  
Primary Network ◽  
Secondary Network ◽  
System Designs ◽  
Approximate Expressions

This paper analyzes the performance of a cognitive underlay system over Nakagami-m fading channels, where maximal ratio combining (MRC) is employed at secondary destination and relay nodes. Under the condition of imperfect channel state information (CSI) of interfering channels, system performance metrics for the primary network and for the secondary network are formulated into exact and approximate expressions, which can be served as theoretical guidelines for system designs. To verify the performance analysis, several analytical and simulated results of the system performance are provided under various system and channel settings.

On-Demand Public Transit: A Markovian Continuous Approximation Model

2021 ◽  
Author(s):  
Daniel F. Silva ◽  
Alexander Vinel ◽  
Bekircan Kirkici
Keyword(s):  
System Performance ◽  
Performance Metrics ◽  
Public Transit ◽  
Continuous Approximation ◽  
Efficient Operation ◽  
Fleet Sizing ◽  
On Demand ◽  
Single Vehicle ◽  
Transit Agencies ◽  
Using Data

With recent advances in mobile technology, public transit agencies around the world have started actively experimenting with new transportation modes, many of which can be characterized as on-demand public transit. Design and efficient operation of such systems can be particularly challenging, because they often need to carefully balance demand volume with resource availability. We propose a family of models for on-demand public transit that combine a continuous approximation methodology with a Markov process. Our goal is to develop a tractable method to evaluate and predict system performance, specifically focusing on obtaining the probability distribution of performance metrics. This information can then be used in capital planning, such as fleet sizing, contracting, and driver scheduling, among other things. We present the analytical solution for a stylized single-vehicle model of first-mile operation. Then, we describe several extensions to the base model, including two approaches for the multivehicle case. We use computational experiments to illustrate the effects of the inputs on the performance metrics and to compare different modes of transit. Finally, we include a case study, using data collected from a real-world pilot on-demand public transit project in a major U.S. metropolitan area, to showcase how the proposed model can be used to predict system performance and support decision making.

An artificial neural network approach to discrete-event simulation

1995 ◽  
Vol 9 (1) ◽  
pp. 37-49 ◽  
Author(s):  
Ian Flood ◽  
Kenneth Worley
Keyword(s):  
Neural Network ◽  
Discrete Event Simulation ◽  
Network Architecture ◽  
Discrete Event ◽  
Network Approach ◽  
Neural Network Approach ◽  
Event Simulation ◽  
The Neural Network ◽  
Component Processes ◽  
And Performance

AbstractThis paper proposes and evaluates a neural network-based method for simulating manufacturing processes that exhibit both noncontinuous and stochastic behavior processes more conventionally modeled, using discrete-event simulation algorithms. The incentive for developing the technique is its potential for rapid execution of a simulation through parallel processing, and facilitation of the development and improvement of models particularly where there is limited theory describing the dependence between component processes. A brief introduction is provided to a radial-Gaussian neural network architecture and training process, the system adopted for the work presented in this paper. A description of the basic approach proposed for applying this technology to simulation is then described. This involves the use of a modularized neural network approach to model construction and the prediction of the occurrence of events using information retained from several previous states of the simulation. A class of earth-moving systems, comprising a push-dozer and a fleet of scrapers, is used as the basis for assessing the viability and performance of the proposed approach. A series of experiments show the neural network to be capable of both capturing the characteristic behavior and making an accurate prediction of production rates of scraper-based earth-moving systems. The paper concludes with an indication of some areas for further development and evaluation of the technique.

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