Time-Dependence in Multi-Agent MDP Applied to Gate Assignment Problem

Aoun, Oussama; Afia, Abdellatif El

doi:10.14569/ijacsa.2018.090247

Cited by 6 publications

(3 citation statements)

References 30 publications

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“…In the case of Covid-19, Liu et al (2020) showed that mathematical methods provided more reliable results and more accurate estimates than those of stochastic and statistical models 11 . Although, stochastic modeling can be useful in numerous other study cases like in Aoun and El Afia (2014a, 2014b, 2018 and El Afia and Aoun (2017).…”

Section: Modelingmentioning

confidence: 99%

Modeling the spread of Covid-19 pandemic: case of Morocco

Lotfi

Aoun

2020

Epidemiologic Methods

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AbstractObjectiveThis paper is establishing the relationship between the spreading dynamics of the Covid-19 pandemic in Morocco and the efficiency of the measures and actions taken by public authorities to contain it. The main objective is to predict the evolution of the COVID-19 pandemic in Morocco and to estimate the time needed for its disappearance.MethodsFor these reasons, we have highlighted the role of mathematical models in understanding the transmission chain of this virus as well as its future evolution. Then we used the SIR epidemiological model, which proves to be well suited to address this issue. It shows that identification of the key parameters of this pandemic, such as the probability of transmission, should help to adequately explain its behaviour and make it easier to predict its progress.ResultsAs a result, the measures and actions taken by the public authorities in Morocco allowed to record lower number of virus reproduction than many countries.ConclusionSo, in the case of Morocco, we were able to predict that the Covid-19 pandemic should disappear in a shorter time and without registering a larger number of infected individuals compared to other countries.

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Section: Modelingmentioning

confidence: 99%

Modeling the spread of Covid-19 pandemic: case of Morocco

Lotfi

Aoun

2020

Epidemiologic Methods

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“…This extensive production of keys served to simulate a real-world application scenario and to stress test the algorithm's scalability and adaptability across devices with varying workloads and operating conditions. Subsequently, the generated keys from each device were subjected to an empirical study [18] using the NIST Statistical Test Suite (STS).…”

Section: A Robustness Of the Algorithmmentioning

confidence: 99%

Secure IoT Seed-based Matrix Key Generator

AINE,

LEGHRIS

2024

IJACSA

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The rapid evolution of the Internet of Things (IoT) has significantly transformed various aspects of both personal and professional spheres, offering innovative solutions in fields from home automation to industrial manufacturing. This progression is driven by the integration of physical devices with digital networks, facilitating efficient communication and data processing. However, such advancements bring forth critical security challenges, especially regarding data privacy and network integrity. Conventional cryptographic methods often fall short in addressing the unique requirements of IoT environments, such as limited device computational power and the need for efficient energy consumption. This paper introduces a novel approach to IoT security, inspired by the principles of steganographythe art of concealing information within other non-secret data. This method enhances security by embedding secret information within the payload or communication protocols, aligning with the low-power and minimal processing capabilities of IoT devices. We propose a steganographic key generation algorithm, adapted from the Diffie-Hellman key exchange model, tailored for IoT. This approach eliminates the need for explicit parameter exchange, thereby reducing vulnerability to key interception and unauthorized access, prevalent in IoT networks. The algorithm utilizes a pre-shared 2D matrix and a synchronized seed-based approach for covert communication without explicit data exchange. Furthermore, we have rigorously tested our algorithm using the NIST Statistical Test Suite (STS), comparing its execution time with other algorithms. The results underscore our algorithm's superior performance and suitability for IoT applications, highlighting its potential to secure IoT networks effectively without compromising on efficiency and device resource constraints. This paper presents the design, implementation, and potential implications of this algorithm for enhancing IoT security, ensuring the full realization of IoT benefits without compromising user security and privacy.

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“…Another paper by these authors [ 29 ] took into account that aircraft could be delayed, established an allocation model based on a multi-agent Markov decision-making process, and expressed the stand as a collaborative agent trying to complete a series of flight assignment tasks, finally providing the staff with a robust priority solution. In 2018, they [ 30 ] also designed a time-varying multi-agent Markov decision process model for other random situations in aircraft slot allocation, which provides allocation schemes in each time series.…”

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Genetic Algorithm Based on Imitation Learning for the Airport Gate Assignment Problem

Ding

Wang

2023

Entropy

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Airport gates are the main places for aircraft to receive ground services. With the increased number of flights, limited gate resources near to the terminal make the gate assignment work more complex. Traditional solution methods based on mathematical programming models and iterative algorithms are usually used to solve these static situations, lacking learning and real-time decision-making abilities. In this paper, a two-stage hybrid algorithm based on imitation learning and genetic algorithm (IL-GA) is proposed to solve the gate assignment problem. First of all, the problem is defined from a mathematical model to a Markov decision process (MDP), with the goal of maximizing the number of flights assigned to contact gates and the total gate preferences. In the first stage of the algorithm, a deep policy network is created to obtain the gate selection probability of each flight. This policy network is trained by imitating and learning the assignment trajectory data of human experts, and this process is offline. In the second stage of the algorithm, the policy network is used to generate a good initial population for the genetic algorithm to calculate the optimal solution for an online instance. The experimental results show that the genetic algorithm combined with imitation learning can greatly shorten the iterations and improve the population convergence speed. The flight rate allocated to the contact gates is 14.9% higher than the manual allocation result and 4% higher than the traditional genetic algorithm. Learning the expert assignment data also makes the allocation scheme more consistent with the preference of the airport, which is helpful for the practical application of the algorithm.

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Time-Dependence in Multi-Agent MDP Applied to Gate Assignment Problem

Cited by 6 publications

References 30 publications

Modeling the spread of Covid-19 pandemic: case of Morocco

Modeling the spread of Covid-19 pandemic: case of Morocco

Secure IoT Seed-based Matrix Key Generator

A Hybrid Genetic Algorithm Based on Imitation Learning for the Airport Gate Assignment Problem

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