Cellular Goore Game and its application to quality-of-service control in wireless sensor networks

Ameri, Reyhaneh; Meybodi, Mohammad Reza; Khomami, Mohammad Mehdi Daliri

doi:10.1007/s11227-022-04435-1

Cited by 5 publications

(4 citation statements)

References 52 publications

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“…Higher H ( t ) values indicate higher rates of change in the actions chosen by the model's learning automata. The behavior of the CGG model can be analyzed in terms of the scaled average performance function ( G avg ) defined in Reference 48. The behavior quality of the GG model can also be evaluated by the value of the referee's performance criterion function in each round.…”

Section: Methodsmentioning

confidence: 99%

“…Each LA updates its action probability using the LR-I learning algorithm. 47,48 displays the pseudo-code for using GG with learning automata.…”

Section: 2mentioning

confidence: 99%

“…The Cellular Goore Game (CGG), described in Reference 48, can be used as a model for systems with many simple, identical parts that interact locally. Every node (cell) in a CGG network can act as a referee while playing a Goore Game with its neighboring nodes.…”

Section: Background and Related Workmentioning

confidence: 99%

“…A Learning Automata (LA) that represents each player performs actions based on the player's strategies. Each LA updates its action probability using the LR‐I learning algorithm 47,48 . displays the pseudo‐code for using GG with learning automata.…”

Section: Background and Related Workmentioning

confidence: 99%

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Cognitive blockchain and its application to optimize performance in blockchain systems

Ameri,

Meybodi

2024

Trans Emerging Tel Tech

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A distributed ledger called a blockchain is used for logging authenticated cryptographic transactions. The global ledger is updated with transactions using consensus techniques. Consensus algorithms are developed for networks with untrusted nodes to achieve reliability. Academics are paying attention to this technology because it has essential features like decentralization, stability, anonymity, and transparency. Even though the blockchain has some unique features, it has to deal with many challenges and restrictions, such as scalability, security, hidden centrality, and high cost. Artificial intelligence and blockchain are technologies that have been much discussed in the last decade and are developing rapidly. Combining the two to meet the existing challenges can have fascinating results. In this paper, we introduce the novel idea of cognitive blockchain by incorporating intelligent thought into the blockchain. With cognitive capabilities, blockchain technology can perceive the state of the network, analyze the data it has collected, make good decisions, and take appropriate action to improve network performance. We provide an operational framework for cognitive blockchain, which primarily refers to the connections between fundamental cognitive processes, including the perception‐action cycle, data analytics, knowledge discovery, intelligent decision‐making, and service provision. Then, using learning automata, we provide methods for creating cognitive engines for performance optimization by intelligently adjusting the block size, time interval, and validators in blockchain systems with BFT‐based consensus algorithms. Several experiments have been conducted to assess the suggested approaches' effectiveness. The findings demonstrate that the proposed performance optimization approach improves blockchain performance criteria.

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Section: Methodsmentioning

confidence: 99%

“…Each LA updates its action probability using the LR-I learning algorithm. 47,48 displays the pseudo-code for using GG with learning automata.…”

Section: 2mentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

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