• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.8
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• pp.3862-3888
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• 2017

In high diversity node situation, single-channel MAC protocols suffer from many collisions. To solve this problem, the research of multichannel MAC protocol has become a hotspot. And the cyclic quorum-based multichannel (CQM) MAC protocol outperformed others owing to its high frequency utilization. In addition, it can avoid the bottleneck that others suffered from and can be easily realized with only one transceiver. To obtain the accurate performance of CQM MAC protocol, a Markov chain model, which combines the channel hopping strategy of CQM protocol and IEEE 802.11 distributed coordination function (DCF), is proposed. The metrics (throughput and average packet transmission delay) are calculated in performance analysis, with respect to node number, packet rate, channel slot length and channel number. The results of numerical analysis show that the optimal performance of CQM protocol can be obtained in saturation bound situation. And then we obtain the saturation bound of CQM system by bird swarm algorithm (BSA). Finally, the Markov chain model and saturation bound are verified by Qualnet platform. And the simulation results show that the analytic and simulation results match very well.

• Journal of the Korea Society for Simulation
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• v.28 no.4
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• pp.21-32
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• 2019

Inspired by the social behavior models of a bird flock or fish school, particle swarm optimization (PSO) is a popular metaheuristic optimization algorithm and has been widely used from solving a complex optimization problem to learning a artificial neural network. However, PSO is difficult to apply to many real-life optimization problems involving stochastic noise, since it is originated in a deterministic environment. To resolve this problem, this paper incorporates a resampling method called the uncertainty evaluation (UE) method into PSO. The UE method allows the particles to converge on the accurate optimal solution quickly in a noisy environment by selecting the particles' global best position correctly, one of the significant factors in the performance of PSO. The results of comparative experiments on several benchmark problems demonstrated the improved performance of the propose algorithm compared to the existing studies. In addition, the results of the case study emphasize the necessity of this work. The proposed algorithm is expected to be effectively applied to optimize complex systems through digital twins in the fourth industrial revolution.