Introduction to Learning Automata Models

Rezvanian, Alireza; Moradabadi, Behnaz; Ghavipour, Mina; Khomami, Mohammad Mehdi Daliri; Meybodi, Mohammad Reza

doi:10.1007/978-3-030-10767-3_1

Cited by 19 publications

(8 citation statements)

References 127 publications

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“…A similar dynamic mechanism for the aggregation of packets in IoT and Low power and Lossy Networks (LLNs) to decrease energy consumption and increase battery life was proposed in [37]. In the proposed scheme, each node is equipped with a learning automaton [38], which grants permission to the node to aggregate small packets that need to be aggregated into a larger one, and denies aggregation permission for some packets that need to be transmitted immediately.…”

Section: Packet Aggregation In Iot and Wireless Sensor Networkmentioning

confidence: 99%

Performance Analysis of Packet Aggregation Mechanisms and Their Applications in Access (e.g., IoT, 4G/5G), Core, and Data Centre Networks

Kuaban

Atmaca

Kamli

et al. 2021

Sensors

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The transmission of massive amounts of small packets generated by access networks through high-speed Internet core networks to other access networks or cloud computing data centres has introduced several challenges such as poor throughput, underutilisation of network resources, and higher energy consumption. Therefore, it is essential to develop strategies to deal with these challenges. One of them is to aggregate smaller packets into a larger payload packet, and these groups of aggregated packets will share the same header, hence increasing throughput, improved resource utilisation, and reduction in energy consumption. This paper presents a review of packet aggregation applications in access networks (e.g., IoT and 4G/5G mobile networks), optical core networks, and cloud computing data centre networks. Then we propose new analytical models based on diffusion approximation for the evaluation of the performance of packet aggregation mechanisms. We demonstrate the use of measured traffic from real networks to evaluate the performance of packet aggregation mechanisms analytically. The use of diffusion approximation allows us to consider time-dependent queueing models with general interarrival and service time distributions. Therefore these models are more general than others presented till now.

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Section: Packet Aggregation In Iot and Wireless Sensor Networkmentioning

confidence: 99%

Performance Analysis of Packet Aggregation Mechanisms and Their Applications in Access (e.g., IoT, 4G/5G), Core, and Data Centre Networks

Kuaban

Atmaca

Kamli

et al. 2021

Sensors

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“…In this method, the LP‐based algorithms require heavy computational overhead and long running time. Han et.al [28] proposed two algorithms, a Greedy algorithm and a genetic algorithm. The Greedy‐based algorithm can find a faster solution than other exploratory algorithms; however, it may fail to find an optimal solution due to the local search of this algorithm. Some of the features of a learning automaton that make it suitable to many applications include the following [29]: It can be used without any priori information about the underlying application. It is useful for applications with large amounts of uncertainty. It has simple structure and easy software and hardware implementation. It requires a very low and simple feedback from the environment. The action set of LA can be a set of symbolic or numeric values. Optimization of learning automata algorithms does not require an objective function to analyse customizable parameter functions. A learning automaton requires little mathematical operation, which makes it useful to real‐time applications. It has flexibility and analytical transfer tractability, which are required for most applications. One of the restrictions of LA appears when it is to optimize a continuous function. LA needs to discrete the parameter space so that LA actions can obtain the possible values of the relevant parameters.…”

Section: Continuous Action‐set Learning Automatamentioning

confidence: 99%

“…Some of the features of a learning automaton that make it suitable to many applications include the following [29]: It can be used without any priori information about the underlying application. It is useful for applications with large amounts of uncertainty. It has simple structure and easy software and hardware implementation. It requires a very low and simple feedback from the environment. The action set of LA can be a set of symbolic or numeric values. Optimization of learning automata algorithms does not require an objective function to analyse customizable parameter functions. A learning automaton requires little mathematical operation, which makes it useful to real‐time applications. It has flexibility and analytical transfer tractability, which are required for most applications. …”

Section: Continuous Action‐set Learning Automatamentioning

confidence: 99%

Solving the target coverage problem in multilevel wireless networks capable of adjusting the sensing angle using continuous learning automata

et al. 2021

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Today, a directional sensor network is a popular environment for solving the target coverage problem. Monitoring all targets in a DSN is a crucial challenge to scholars working in this field of study. Adjusting the angle and range of the sensors can be an efficient technique for improving the network performance. In this way, the network has the most extended lifespan and, at the same time, spends the least time to find the best cover set. In this method, each sensor dynamically adjusts its own sensing angle in order to find the targets by choosing the best range. The present study proposed a continuous learning automata‐based method to choose the optimum sensing angle for the sensors in a DSN. Then, to evaluate the proposed algorithm performance, its results were compared to those of a conventional automata‐based method whose algorithm worked based on continuous automata. The comparative analysis confirmed the superiority of the proposed method over the conventional automata‐based method regarding the extension of the network lifespan.

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“…Availed erroneousness and duplication of data are major IoT organization-based underprovided to automate its processes [107], [115]. RL set of rules and extrapolative modeling algorithms can expressively improve this situation.…”

Section: A Physical Data Entrymentioning

confidence: 99%

Reinforcement Learning Rebirth, Techniques, Challenges, and Resolutions

Shafik

Matinkhah

Etemadinejad

et al. 2020

JOIV : Int. J. Inform. Visualization

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Reinforcement learning (RL) is a new propitious research space that is well-known nowadays on the internet of things (IoT), media and social sensing computing are addressing a broad and pertinent task through making decisions sequentially by deterministic and stochastic evolutions. The IoTs extend world connectivity to physical devices like electronic devices network by use interconnect with others over the Internet with the possibility of remotely being supervised and meticulous. In this paper, we comprehensively survey an in-depth assessment of RL techniques in IoT systems focusing on the main known RL techniques like artificial neural network (ANN), Q-learning, Markov Decision Process (MDP), Learning Automata (LA). This study examines and analyses learning technique with focusing on challenges, models performance, similarities and the differences in IoTs accomplish with most correlated proposed state of the art models. The results obtained can be used as a foundation for designing, a model implementation based on the bottlenecks currently assessed with an evaluation of the most fashionable hands-on utility of current methods for reinforcement learning.

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Introduction to Learning Automata Models

Cited by 19 publications

References 127 publications

Performance Analysis of Packet Aggregation Mechanisms and Their Applications in Access (e.g., IoT, 4G/5G), Core, and Data Centre Networks

Performance Analysis of Packet Aggregation Mechanisms and Their Applications in Access (e.g., IoT, 4G/5G), Core, and Data Centre Networks

Solving the target coverage problem in multilevel wireless networks capable of adjusting the sensing angle using continuous learning automata

Reinforcement Learning Rebirth, Techniques, Challenges, and Resolutions

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