Vehicular Network Simulation Environment via Discrete Event System Modeling

Wang, Le; Iida, Renato F.; Wyglinski, Alexander M.

doi:10.1109/access.2019.2922766

Cited by 22 publications

(16 citation statements)

References 21 publications

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“…Traffic flows information from SUMO sent to the network simulator through the interface, and conversely, the directions from the OMNeT++ are sent to SUMO. [21]. Table 4 summarizes the parameters used for performance evaluation.…”

Section: Performance Evaluation a Simulation Setupmentioning

confidence: 99%

Channel-Aware Congestion Control in Vehicular Cyber-Physical Systems

2020

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In vehicular cyber-physical systems, cars are connected to create a mobile network called a vehicular ad hoc network (VANET) to perform various functions, including improved awareness of the surrounding environment. Moving vehicles continually broadcast beacon signals containing information such as position, heading, acceleration, steering angle, vehicle size, and accident notification. However, channel congestion in dense traffic conditions adversely affects network performance. To resolve congestion in VANETs, several works in the literature have studied congestion control. However, they have considered packet loss only as an indication of channel congestion regardless of channel condition. In this paper, we present a channel-aware congestion control algorithm (CACC) that controls the transmission power and data rate. We take into account the received signal strength (RSS) when diagnosing packet loss to determine channel conditions, such as severe fading or channel congestion. In the case of severe fading, we decrease the data rate for a more robust modulation and coding scheme. Additionally, we adjust the transmission power to maintain a desirable packet error rate. Our simulation results show that CACC significantly outperforms other distributed congestion control algorithms by reducing the packet loss rate and increasing the packet delivery ratio. INDEX TERMS Congestion control, channel-aware protocol, mobile cyber-physical systems, packet loss, vehicular ad hoc networks.

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Section: Performance Evaluation a Simulation Setupmentioning

confidence: 99%

Channel-Aware Congestion Control in Vehicular Cyber-Physical Systems

2020

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“…For the selected best design x b , each of the k −1 relationships in (4) can be verified by the hypothesis tests, and the p-value from each test is denoted by p b,i , where i = b. Each value of p b,i can be used to assess the degree to which the current simulation results of x b can be significant evidence for each relationship in (4). If the results of x b are not evidence for verifying even one relationship in (4), the results cannot be evidence for the correct selection of x b , and the selection becomes uncertain; thus, the uncertainty of x b , δ b can be defined as the maximum value among p b,i s, as follows:…”

Section: Proposed Procedures a Uncertainty Evaluationmentioning

confidence: 99%

“…Discrete-event simulation is widely used to analyze modern industrial complex systems, such as manufacturing [1], military [2], smart grid [3], telecommunications [4], and transportation [5]. The most significant advantage of the simulation is that, with just a few assumptions, it can accurately analyze complex systems that cannot be described using closed-form analytic models [6].…”

Section: Introductionmentioning

confidence: 99%

A Simulation Budget Allocation Procedure for Finding Both Extreme Designs Simultaneously in Discrete-Event Simulation

Choi

2020

IEEE Access

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Although discrete-event simulation has been widely used in various engineering fields, its efficiency remains an issue. Ranking and selection (R&S) procedures can solve this efficiency problem by allocating a limited simulation budget intelligently. While the existing R&S procedures mostly aim to find only the best simulation input design, practitioners sometimes require the worst design as well to analyze systems requiring high reliability, such as military systems, municipal waste management, etc. Motivated by these practical needs, we propose a simulation budget allocation procedure for selecting both extreme designs simultaneously in the presence of large stochastic noise. To maximize the accuracy of the selections under a limited budget, the proposed procedure sequentially allocates a small budget and updates the simulation results such that they can be used as significant evidence for the correct selections. Our experimental results on benchmark and practical problems demonstrate improved efficiency compared to previous works. It is expected that the proposed procedure will be effectively utilized in the fields of the fourth industrial revolution, such as digital twins that demand quickly finding both extreme designs to maintain synchronization with the corresponding real systems.

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“…Discrete-event system simulation is a powerful tool for analyzing modern industrial systems such as telecommunication [1], manufacturing [2], microgrid [3], transportation [4], healthcare [5], and military [6] systems that cannot be described as a closed-form analytic model [7]. As one of the essential applications of simulation, simulation-based optimization (SBO) finds the optimal configurations of the systems' decision variables (i.e., the optimal designs) that satisfy the given optimal requirements of the system performance (e.g., maximize the performance) using simulations [8].…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Noise Robustness of the Optimal Computing Budget Allocation Approach

Kim

2020

IEEE Access

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Since an optimal computing budget allocation (OCBA) approach maximizes the efficiency of the simulation budget allocation to correctly find the optimal solutions, various OCBA-based procedures, such as OCBA, OCBAm+, and MOCBA+, have been widely applied to solve simulation-based optimization problems. Recently, it has been found that the stochastic noise in a simulation model increases due to the increasing complexity of modern industrial systems. However, the OCBA approach may be inefficient for these practical problems. That is, it is very likely to waste a lot of budget on other candidates that are not truly optimal due to the abnormal simulation results, which occurs frequently in noisy environments. In this paper, we intuitively analyze the causes of this efficiency deterioration of the OCBA approach, and then a simple heuristic adjustment is proposed to enhance the noise robustness of the OCBA approach based on our analysis results. The proposed adjustment allows the OCBA approach to further consider the precision of the simulation results, thereby significantly reducing the wasted budget and increasing the efficiently. In addition, it can be applied to the existing allocation rules without modification and does not require additional computational costs. Many experimental results for the eight OCBA-based procedures clearly demonstrate the effectiveness of this adjustment. In particular, the results of practical problems emphasize its necessity.

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Vehicular Network Simulation Environment via Discrete Event System Modeling

Cited by 22 publications

References 21 publications

Channel-Aware Congestion Control in Vehicular Cyber-Physical Systems

Channel-Aware Congestion Control in Vehicular Cyber-Physical Systems

A Simulation Budget Allocation Procedure for Finding Both Extreme Designs Simultaneously in Discrete-Event Simulation

Enhancing the Noise Robustness of the Optimal Computing Budget Allocation Approach

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