• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.1
• /
• pp.61-77
• /
• 2017

The most challenging issues in the multi-channel MAC of the IEEE 1609.4 standard is how to handle the dynamic vehicular traffic condition with a high mobility, dynamic topology, and a trajectory change. Therefore, dynamic channel coordination schemes between CCH and SCH are required to provide the proper bandwidth for CCH/SCH intervals and to improve the quality of service (QoS). In this paper, we use a Markov model to optimize the interval based on the dynamic vehicular traffic condition with high mobility nodes in the multi-channel MAC of the IEEE 1609.4 standard. We evaluate the performance of the three-dimensional Markov chain based on the Poisson distribution for the node distribution and velocity. We also evaluate the additive white Gaussian noise (AWGN) effect for the multi-channel MAC coordination scheme of the IEEE 1609.4 standard. The result of simulation proves that the performance of the dynamic channel coordination scheme is affected by the high node mobility and the AWGN. In this research, we evaluate the model analytically for the average delay on CCHs and SCHs and also the saturated throughput on SCHs.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.5
• /
• pp.2499-2522
• /
• 2017

Multi-hop networks are a low-setup-cost solution for enlarging an area of network coverage through multi-hop routing. Carrier sense multiple access with collision avoidance (CSMA/CA) is frequently used in multi-hop networks. Multi-hop networks face multiple problems, such as a rise in contention for the medium, and packet loss under heavy-load, saturated conditions, which consumes more bandwidth due to re-transmissions. The number of re-transmissions carried out in a multi-hop network plays a major role in the achievable quality of service (QoS). This paper presents a statistical, analytical model for the end-to-end delay of contention-based medium access control (MAC) strategies. These strategies schedule a packet before performing the back-off contention for both differentiated heterogeneous data and homogeneous data under saturation conditions. The analytical model is an application of Markov chain theory and queuing theory. The M/M/1 model is used to derive access queue waiting times, and an absorbing Markov chain is used to determine the expected number of re-transmissions in a multi-hop scenario. This is then used to calculate the expected end-to-end delay. The prediction by the proposed model is compared to the simulation results, and shows close correlation for the different test cases with different arrival rates.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.10
• /
• pp.4825-4847
• /
• 2016

In this paper, we focus on the per-flow throughput analysis of IEEE 802.11 multi-hop ad hoc networks. The importance of an accurate saturation throughput model lies in establishing the theoretical foundation for effective protocol performance improvements. We argue that the challenge in modeling the per-flow throughput in IEEE 802.11 multi-hop ad hoc networks lies in the analysis of the freezing process and probability of collisions. We first classify collisions occurring in the whole transmission process into instantaneous collisions and persistent collisions. Then we present a four-dimensional Markov chain model based on the notion of the fixed length channel slot to model the Binary Exponential Backoff (BEB) algorithm performed by a tagged node. We further adopt a continuous time Markov model to analyze the freezing process. Through an iterative way, we derive the per-flow throughput of the network. Finally, we validate the accuracy of our model by comparing the analytical results with that obtained by simulations.

• Journal of Applied Reliability
• /
• v.17 no.4
• /
• pp.332-342
• /
• 2017

Purpose: In this study, the lifetime distribution of a k-out-of-n system with heterogeneous components is suggested as Markov model, and the time-to-failure (TTF) distribution of each component is considered as phase-type distribution (PHD). Furthermore, based on the model, a redundancy allocation problem with a mix of components (RAPMC) is proposed. Methods: The lifetime distribution model for the system is formulated by the structured Markov chain. From the model, the various information on the system lifetime can be ascertained by the matrix-analytic (or-geometric) method. Conclusion: By the generalization of TTF distribution (PHD) and the consideration of heterogeneous components, the lifetime distribution model can delineate many real systems and be exploited for developing system operation policies such as preventive maintenance, warranty. Moreover, the effectiveness of the proposed RAPMC is verified by numerical experiments. That is, under the equivalent design conditions, it presented a system with higher reliability than RAP without component mixing (RAPCM).

• Journal of Communications and Networks
• /
• v.11 no.2
• /
• pp.175-186
• /
• 2009

A new medium access control (MAC) scheme embedding physical channels into multiband carrier sense multiple access/collision avoidance (CSMA/CA) networks is proposed to provide strict quality of service (QoS) guarantee to high priority users. In the proposed scheme, two priority classes of users, primary and secondary users, are supported. For primary users physical channels are provided to ensure strict QoS, whereas secondary users are provided with best-effort service using CSMA/CA modified for multiband operation. The performance of the proposed MAC scheme is investigated using a new multiband CSMA/CA Markov chain model capturing the primary user activity and the operation of secondary users in multiple bands. The throughput of secondary users is obtained as a function of the primary user activity and other CSMA/CA parameters. It is shown that the new MAC scheme yields larger throughput than the conventional single-band CSMA/CA when both schemes use the same bandwidth.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.7 no.5
• /
• pp.1108-1130
• /
• 2013

Wireless Body Area Networks (WBANs) are becoming increasingly important to solve the issue of health care. IEEE 802.15.6 is a wireless communication standard for WBANs, aiming to provide a real-time and continuous monitoring. In this paper, we present our development of a modified Markov Chain model and a backoff model, in which most features such as user priorities, contention windows, modulation and coding schemes (MCSs), and frozen states are taken into account. Then we calculate the normalized throughput and average access delay of IEEE 802.15.6 networks under saturation and ideal channel conditions. We make an evaluation of network performances by comparing with IEEE 802.15.4 and the results validate that IEEE 802.15.6 networks can provide high quality of service (QoS) for nodes with high priorities.

• Journal of Korean Society for Quality Management
• /
• v.35 no.4
• /
• pp.159-165
• /
• 2007

Using statistical methods a change-point analysis of oil supply disruption is conducted. The statistical distribution of oil supply disruption is a weibull distribution. The detection of the change-point is applied to Bayesian method and weibull parameters are estimated through Markov chain monte carlo and parameter approach. The statistical approaches to the estimation for the change-point and weibull parameters is implemented with the sets of simulated and real data with small sizes of samples.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.4 no.1
• /
• pp.119-124
• /
• 2004

Real data as web log file tend to be incomplete. But we have to find useful knowledge from these for optimal decision. In web log data, many useful things which are hyperlink information and web usages of connected users may be found. The size of web data is too huge to use for effective knowledge discovery. To make matters worse, they are very sparse. We overcome this sparse problem using Markov Chain Monte Carlo method as multiple imputations. This missing value imputation changes spare web data to complete. Our study may be a useful tool for discovering knowledge from data set with sparseness. The more sparseness of data in increased, the better performance of MCMC imputation is good. We verified our work by experiments using UCI machine learning repository data.

• Journal of the Korean Data and Information Science Society
• /
• v.27 no.6
• /
• pp.1621-1629
• /
• 2016

This paper studies Bayesian pooling for analysis of categorical data from small areas. Many surveys consist of categorical data collected on a contingency table in each area. Statistical inference for small areas requires considerable care because the subpopulation sample sizes are usually very small. Typically we use the hierarchical Bayesian model for pooling subpopulation data. However, the customary hierarchical Bayesian models may specify more exchangeability than warranted. We, therefore, investigate the effects of pooling in hierarchical Bayesian modeling for the contingency table from small areas. In specific, this paper focuses on the methods of direct or indirect pooling of categorical data collected on a contingency table in each area through Dirichlet priors. We compare the pooling effects of hierarchical Bayesian models by fitting the simulated data. The analysis is carried out using Markov chain Monte Carlo methods.

• Journal of the Korea Institute of Building Construction
• /
• v.11 no.1
• /
• pp.91-99
• /
• 2011

Bayesian network is a form of probabilistic graphical model. It incorporates human reasoning to deal with sparse data availability and to determine the probabilities of uncertain cases. In this research, bayesian network is adopted to model the problem of construction project cost. General information, time, cost, and material, the four main factors dominating the characteristic of construction costs, are incorporated into the model. This research presents verify a model that were conducted to illustrate the functionality and application of a decision support system for predicting the costs. The Markov Chain Monte Carlo (MCMC) method is applied to estimate parameter distributions. Furthermore, it is shown that not all the parameters are normally distributed. In addition, cost estimates based on the Gibbs output is performed. It can enhance the decision the decision-making process.