Performance analysis of cellular automata Monte Carlo Simulation for estimating network reliability

Yeh, Wei-Chang; Lin, Yu-Chiao; Chung, Yuk Ying

doi:10.1016/j.eswa.2009.09.070

Cited by 46 publications

(21 citation statements)

References 25 publications

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“…Owing to the simplicity of the stepwise-function update mechanism discussed above, it is easier to customize SSO by either replacing any item of its stepwise function with other algorithms, even hybrid algorithms in sequence or in parallel [3], to solve various problems than to customize other algorithms [3,24,27,28] Hence, after the invention of SSO, it became widely known as a swarm intelligence-based random optimization algorithm and has played as a very significant role in relevant studies of artificial intelligence. Furthermore, SSO has been applied in many studies to solve different types of problems in various fields [20,29,[33][34][35][36].…”

Section: A Sso and An Examplementioning

confidence: 99%

Improve Energy Consumption and Signal Transmission Quality of Routings in Wireless Sensor Networks

et al. 2020

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Wireless (smart) sensor networks (WSNs) comprise a myriad of embedded wireless smart sensors. They play a cardinal role in the functioning of many applications, such as the Internet of Things, smart grids, smart production systems, and smart homes, which ultimately render them paramount instruments in the modern age. Recent advances in WSNs have resulted in the rapid development of sensors. However, WSNs will only able to achieve better execution efficiencies if their energy consumption-owing to limited battery life and difficulty of recharging-can be better controlled. Moreover, signal transmission quality determines WSN performance. Hence, two main concerns-energy consumption and signal transmission quality-should be addressed to improve the performance of WSNs. Thus, a new bi-objective simplified swarm optimization algorithm (bSSO) is proposed by employing the concepts of simple routing, SSO, and crowd distance. The performance and applicability of the proposed bSSO using eight different parameter settings are demonstrated through an experiment involving ten WSN benchmarks ranging from 100 to 1000 sensors. The proposed algorithm is then compared with NSGA-II, which is an algorithm widely used to solve multi-objective problems. The results show that the proposed bSSO can successfully achieve the aim of this work.

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Section: A Sso and An Examplementioning

confidence: 99%

Improve Energy Consumption and Signal Transmission Quality of Routings in Wireless Sensor Networks

et al. 2020

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“…Some studies [4,13] have proposed Monte Carlo simulation approaches to estimate the reliability of network-structured systems using a state-space decomposition methodology. Significantly more research [12,22,[28][29][30][31] has been devoted to evaluating the system reliability of binary-state networks. Some studies [25,26] developed Monte Carlo simulation algorithms to estimate the reliability of a CFN.…”

Section: Related Workmentioning

confidence: 99%

“…In addition to numerical techniques, simulation tools have been widely applied for network modeling [4,12,13,22,[24][25][26][28][29][30][31]. Monte Carlo simulation is a common method to evaluate system reliability when exact numerical calculation is computationally complex.…”

Section: Related Workmentioning

confidence: 99%

Simulation approach to estimate the system reliability of a time-based capacitated flow network susceptible to correlated failures

Lin

Fiondella

Chang

2013

Simulation Modelling Practice and Theory

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“…This approach is easy to implement; its performance, however, degrades for large networks because of the exponential growth of minimal tie/cut sets. Approach 2 (Approximation): Approximation methods were proposed to reduce execution time and computational complexity. These approaches use lower and upper bounds of reliability or sampling techniques (Monte Carlo Simulation methods) to estimate the results. Approach 3 (Binary decision diagram (BDD) technique): To overcome the weakness of the first approach, BDD‐based methods have been proposed by various authors . A BDD is considered as an efficient data structure for storing large numbers of Boolean terms …”

Section: Introductionmentioning

confidence: 99%

“…These approaches use lower and upper bounds of reliability [16][17][18] or sampling techniques (Monte Carlo Simulation methods [19][20][21] to estimate the results. • Approach 3 (Binary decision diagram (BDD) technique): To overcome the weakness of the first approach, BDD-based methods have been proposed by various authors.…”

Section: Introductionmentioning

confidence: 99%

A Parallel Strategy for the Logical‐probabilistic Calculus‐based Method to Calculate Two‐terminal Reliability

Nguyen

2015

Quality & Reliability Eng

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The theory of network reliability has been applied to many complicated network structures, such as computer and communication networks, piping systems, electricity networks, and traffic networks. The theory is used to evaluate the operational performance of networks that can be modeled by probabilistic graphs. Although evaluating network reliability is an Non-deterministic Polynomial-time hard problem, numerous solutions have been proposed. However, most of them are based on sequential computing, which under-utilizes the benefits of multi-core processor architectures. This paper addresses this limitation by proposing an efficient strategy for calculating the two-terminal (terminal-pair) reliability of a binary-state network that uses parallel computing. Existing methods are analyzed. Then, an efficient method for calculating terminal-pair reliability based on logical-probabilistic calculus is proposed. Finally, a parallel version of the proposed algorithm is developed. This is the first study to implement an algorithm for estimating terminal-pair reliability in parallel on multicore processor architectures. The experimental results show that the proposed algorithm and its parallel version outperform an existing sequential algorithm in terms of execution time.

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Performance analysis of cellular automata Monte Carlo Simulation for estimating network reliability

Cited by 46 publications

References 25 publications

Improve Energy Consumption and Signal Transmission Quality of Routings in Wireless Sensor Networks

Improve Energy Consumption and Signal Transmission Quality of Routings in Wireless Sensor Networks

Simulation approach to estimate the system reliability of a time-based capacitated flow network susceptible to correlated failures

A Parallel Strategy for the Logical‐probabilistic Calculus‐based Method to Calculate Two‐terminal Reliability

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