An approach for designing cognitive engines in cognitive peer-to-peer networks

Saghiri, Ali Mohammad; Meybodi, Mohammad Reza

doi:10.1016/j.jnca.2016.05.012

Cited by 34 publications

(8 citation statements)

References 39 publications

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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

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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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“…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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“…If a = b, the recurrence equations and are called linear reward penalty (L RP ) algorithm. More information can be found in Najim and Poznyak 11 :

\begin{matrix} centertrue \\ p_{i} (n + 1) = p_{i} (n) + a [1 - p_{i} (n)] \\ p_{j} (n + 1) = (1 - a) p_{j} (n) \forall j, j \neq i \end{matrix},

\begin{matrix} centertrue \\ p_{i} (n + 1) = (1 - b) p_{i} (n) \\ p_{j} (n + 1) = \frac{b}{r - 1} + (1 - b) p_{j} (n) \forall j, j \neq i \end{matrix} .

In the recent years, LA has been used in different applications such as cognitive networks, 1 ad hoc networks, 13 wireless sensor networks, 14 WiMAX networks, 15 network security, 16 wireless mesh networks, 17 mobile video network surveillance, 18 vehicular environment, 19,20 P2P networks, 9,21,22 wireless data broadcasting systems, 23–25 smart grid systems, 26 grid computing, 27 and cloud computing, 28 to mention a few.…”

Section: Preliminariesmentioning

confidence: 99%

“…These networks learn from previous experiences and improve their decisions about the configuration of the network in a self‐adaptive manner. In this section, we use the definition of CP2P networks given in Saghiri and Meybodi 1 to deploy them in CMP2P networks. CMP2P networks are defined as mobile networks with cognitive processes capable of learning from the results of their actions.…”

Section: Preliminariesmentioning

confidence: 99%

“…Cognitive peer‐to‐peer (CP2P) networks learn from past experiences and improve their performance while operating under unknown and dynamic environments 1 . The underlying network architecture of CP2P has a significant impact on the overall network performance 1 . CP2P networks may be constructed over mobile ad hoc networks as overlay networks.…”

Section: Introductionmentioning

confidence: 99%

“…Cognitive mobile peer‐to‐peer (CMP2P) networks try to adaptively find appropriate configuration for the networks in which the physical properties of the peers are unknown. In Saghiri and Meybodi, 1 it is studied that how learning algorithms may be used to solve network management problems of CP2P networks.…”

Section: Introductionmentioning

confidence: 99%

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A self‐adaptive algorithm for super‐peer selection considering the mobility of peers in cognitive mobile peer‐to‐peer networks

Amirazodi

Saghiri

Meybodi

2020

Int J Communication

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SummaryCognitive peer‐to‐peer networks are obtained from a combination of cognitive networking concepts and peer‐to‐peer networks. These networks are able to improve their performance while operating under dynamic and unknown environments. A cognitive peer‐to‐peer network tries to learn an appropriate configuration for itself considering the unknown physical properties of peers. Cognitive mobile peer‐to‐peer networks refer to cognitive peer‐to‐peer networks which are built over mobile ad hoc networks. In these networks, heterogeneity of the mobility of peers and resource limitation in wireless networks create challenges for network management algorithms. Because of the dynamicity of these networks, the management algorithms should be designated in self‐adaptive manner. In one type of these networks, some peers, called super‐peers, undertake to perform network managerial tasks. The mobility of peers leads to connection failure among peers and reselection of new super‐peers. Therefore, the selection of super‐peers, due to their influential role, requires an algorithm that considers the peers' mobility. Up to now, no self‐adaptive algorithm has been designated for super‐peer selection considering the mobility of peers in a self‐adaptive manner. This paper proposes M‐SSBLA, a self‐adaptive algorithm for super‐peer selection considering the mobility of peers based on learning automata. The proposed algorithm is obtained from cooperation between a learning automata‐based cognitive engine and MIS. MIS is a well‐known super‐peer selection algorithm in mobile peer‐to‐peer networks. We compared the proposed algorithm with recently reported algorithms, especially for a network with high mobility. Simulation results show that the proposed algorithm can cover maximum ordinary‐peer with a few super‐peer and improve robustness against super‐peer failures while decreasing maintenance overhead.

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A Trust‐Based Recommender System Built on IoT Blockchain Network With Cognitive Framework

Porkodi¹,

Kesavaraja²

2020

Recommender System With Machine Learning and Artificial Intelligence

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An approach for designing cognitive engines in cognitive peer-to-peer networks

Cited by 34 publications

References 39 publications

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

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

A self‐adaptive algorithm for super‐peer selection considering the mobility of peers in cognitive mobile peer‐to‐peer networks

A Trust‐Based Recommender System Built on IoT Blockchain Network With Cognitive Framework

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