An adaptive super-peer selection algorithm considering peers capacity utilizing asynchronous dynamic cellular learning automata

Saghiri, Ali Mohammad; Meybodi, Mohammad Reza

doi:10.1007/s10489-017-0946-8

Cited by 16 publications

(5 citation statements)

References 57 publications

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“…Then, the algorithm selects a new super peer using learning automata. Another work is proposed to improve the performance of peer selection using cellular learning automata . In addition, a reinforcement learning algorithm is aware for the delay between peers .…”

Section: Preliminaries and Proposed Methodsmentioning

confidence: 99%

“…Another work is proposed to improve the performance of peer selection using cellular learning automata. 23 In addition, a reinforcement learning algorithm is aware for the delay between peers. 25 Overall, in previous studies, 24,25 GT algorithm is improved and good results are achieved.…”

Section: Preliminaries and Proposed Methodsmentioning

confidence: 99%

“…Cellular learning automata has been utilized in Saghiri and Meybodi, and the authors designed management algorithms in peer‐to‐peer networks for super peer selection based on biologically inspired self‐organized models. In a previous study, LAGT.1 algorithm utilized learning automata, and designed management algorithms in peer‐to‐peer networks for super peer aware selection when a malicious peer frequently joins and leaves the network.…”

Section: Introductionmentioning

confidence: 99%

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Super peer selection strategy in peer‐to‐peer networks based on learning automata

Safara

Souri

Deiman

2020

Int J Communication

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Summary A super peer is a peer that has the highest capacity in comparison with other peers in the network. It is trying to reduce the load of the rest of the peers and improve network performance. Selecting a super peer in a peer‐to‐peer–based network is a very crucial challenge. As the ability of peers are very different, by considering capacity of each peer and selecting a proper role, we can use network components much more efficiently. Because of the dynamicity of these networks, comparative methods of selecting super peers is of special importance. Comparative selection is continuously trying to select proper super peer. In recent studies, learning automata was introduced as a powerful learning model to solve this issue. In most of the studies, learning automata with an S model is employed. In this article, another selection method of learning automata with a P model environment is presented and its capability for super peer selection is shown. Moreover, simulation results show that removing some of the super peers would result in better performance in terms of inversion time in the high level of super‐peer capacity, required time for selecting proper super peer, and super peer tolerance.

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Section: Preliminaries and Proposed Methodsmentioning

confidence: 99%

Section: Preliminaries and Proposed Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Super peer selection strategy in peer‐to‐peer networks based on learning automata

Safara

Souri

Deiman

2020

Int J Communication

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“…In recent years, LA has been used in different applications, such as cognitive networks [53], computer networks [54], and the shortest-path problem [55] to mention a few.…”

Section: Learning Automata Theorymentioning

confidence: 99%

A Framework for Component Selection Considering Dark Sides of Artificial Intelligence: A Case Study on Autonomous Vehicle

2021

Self Cite

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Nowadays, intelligent systems play an important role in a wide range of applications, including financial ones, smart cities, healthcare, and transportation. Most of the intelligent systems are composed of prefabricated components. Inappropriate composition of components may lead to unsafe, power-consuming, and vulnerable intelligent systems. Although artificial intelligence-based systems can provide various advantages for humanity, they have several dark sides that can affect our lives. Some terms, such as security, trust, privacy, safety, and fairness, relate to the dark sides of artificial intelligence, which may be inherent to the intelligent systems. Existing solutions either focus on solving a specific problem or consider the some other challenge without addressing the fundamental issues of artificial intelligence. In other words, there is no general framework to conduct a component selection process while considering the dark sides in the literature. Hence, in this paper, we proposed a new framework for the component selection of intelligent systems while considering the dark sides of artificial intelligence. This framework consists of four phases, namely, component analyzing, extracting criteria and weighting, formulating the problem as multiple knapsacks, and finding components. To the best of our knowledge, this is the first component selection framework to deal with the dark sides of artificial intelligence. We also developed a case study for the component selection issue in autonomous vehicles to demonstrate the application of the proposed framework. Six components along with four criteria (i.e., energy consumption, security, privacy, and complexity) were analyzed and weighted by experts via analytic hierarchy process (AHP) method. The results clearly show that the appropriate composition of components was selected through the proposed framework for the desired functions.

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“…e P2P topology in grid environments consists of the structured and unstructured models [16][17][18][19][20][21][22][23]. e super-peer mechanism is a category of P2P technology, which has been proposed in [24], for more details see [25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Resource Discovery Technique in the P2P Grid Systems

Wang

Pourqasem

Edalatpanah

2020

Wireless Communications and Mobile Computing

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Distributed discovery service is a main concept in the scalable and dynamic grid environments. In this paper, based on the super-peer technique, we propose a new topology for the grid discovery service. The model is designed in such a way that each super-peer within the cluster has the routing indices (RIs) based on cobweb and uses the hop-count routing index (HRI) to select the best neighbor. Besides, each super-peer includes a cache table, which stores the query and the query results. Furthermore, from the point of view of the response time and the number of submitted messages, we compare the new model with an existing method. An illustrative simulation is also presented to show the efficiency and validation of the new technique.

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An adaptive super-peer selection algorithm considering peers capacity utilizing asynchronous dynamic cellular learning automata

Cited by 16 publications

References 57 publications

Super peer selection strategy in peer‐to‐peer networks based on learning automata

Super peer selection strategy in peer‐to‐peer networks based on learning automata

A Framework for Component Selection Considering Dark Sides of Artificial Intelligence: A Case Study on Autonomous Vehicle

Optimizing Resource Discovery Technique in the P2P Grid Systems

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