A Learning Automata-Based Version of SG-1 Protocol for Super-Peer Selection in Peer-to-Peer Networks

Gholami, Shahrbanoo; Meybodi, Mohammad Reza; Saghiri, Ali Mohammad

doi:10.1007/978-3-319-06538-0_19

Cited by 10 publications

(5 citation statements)

References 15 publications

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“…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][24][25] smart grid systems, 26 grid computing, 27 and cloud computing, 28 to mention a few.…”

Section: Learning Automatamentioning

confidence: 99%

“…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%

“…Learning automata have been found to be useful in dynamic environments such as mobile peer-to-peer (MP2P) and peer-to-peer (P2P) networks. 2,9 The proposed algorithm is obtained from fusing a learning automata-based cognitive engine to MIS 10 algorithm that is a well-known super-peer selection algorithm in MP2P networks. The rest of the paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we propose a self‐adaptive algorithm for super‐peer selection considering the mobility of peers that uses learning automata as an adaptive decision‐making mechanism. Learning automata have been found to be useful in dynamic environments such as mobile peer‐to‐peer (MP2P) and peer‐to‐peer (P2P) networks 2,9 . The proposed algorithm is obtained from fusing a learning automata‐based cognitive engine to MIS 10 algorithm that is a well‐known super‐peer selection algorithm in MP2P 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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Section: Learning Automatamentioning

confidence: 99%

“…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} .

Section: Preliminariesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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“…Learning automata have found applications in many areas, such as sensor networks [27][28][29][30], wireless data broadcasting systems [31][32][33], cognitive networks [34][35][36], mesh networks [37], peer-to-peer networks [38][39][40][41][42], channel assignment [43], image processing [44], neural networks engineering [45,46] and evolutionary computing [47,48], to mention a few.…”

Section: Learning Automatamentioning

confidence: 99%

A distributed adaptive landmark clustering algorithm based on mOverlay and learning automata for topology mismatch problem in unstructured peer‐to‐peer networks

Saghiri

Meybodi

2015

Int J Communication

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Peer-to-peer networks are overlay networks that are built on top of communication networks that are called underlay networks. In these networks, peers are unaware of the underlying networks, so the peers choose their neighbors without considering the underlay positions, and therefore, the resultant overlay network may have mismatches with its underlying network, causing redundant end-to-end delay. Landmark clustering algorithms, such as mOverlay, are used to solve topology mismatch problem. In the mOverlay algorithm, the overlay network is formed by clusters in which each cluster has a landmark peer. One of the drawbacks of mOverlay is that the selected landmark peer for each cluster is fixed during the operation of the network. Because of the dynamic nature of peer-to-peer networks, using a non-adaptive landmark selection algorithm may not be appropriate. In this paper, an adaptive landmark clustering algorithm obtained from the combination of mOverlay and learning automata is proposed. Learning automata are used to adaptively select appropriate landmark peers for the clusters in such a way that the total communication delay will be minimized. Simulation results have shown that the proposed algorithm outperforms the existing algorithms with respect to communication delay and average round-trip time between peers within clusters. peers in the overlay network. This graph is called as overlay graph. The topology of the underlay network can be represented by a graph G′ = (V′, E′) in which V′ = {position 1 , position 2 , …, position n } is a set of positions in the underlay network and E′ ⊆ V′ × V′ is a set of links connecting the positions in the underlay network. In peer-to-peer networks, each peer in V is mapped to a position in V′ according to a one-to-one function H : V → V′. In landmark clustering algorithms, the overlay graph is formed by clusters in which each cluster has a landmark peer. The peers that are not selected as landmark peers are called ordinary peers. The communication between ordinary peers should be handled by landmark peers. Let C i (where i ∈ {1, 2, 3, 4, …, p}) represent the set of p clusters in the network and L i ∈ V denote the landmark peer of cluster C i . The topology of the landmark peers network can be represented by a graph G l = (V l , E l ) in which V l ⊆ V is a set of landmark peers and E l ⊆ V × V is a set of links connecting the landmark peers in the overlay network. This graph is called landmark graph. In the peer-to-peer network that is constructed based on the landmark clustering algorithms, each landmark peer in V l is mapped to several ordinary peers in V according to a one-to-many function H′ : V l → V. Each landmark peer in V l is mapped to several positions in V′ using H′ and H. Figure 1 shows an example of a peer-to-peer network that uses two clusters to manage the overlay topology. In this example, peer m and peer j are two landmark peers.A primary goal of landmark clustering algorithms is to improve the performance of the overlay network in terms of communication dela...

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Blockchain Architecture

Saghiri

2019

Studies in Big Data

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A Learning Automata-Based Version of SG-1 Protocol for Super-Peer Selection in Peer-to-Peer Networks

Cited by 10 publications

References 15 publications

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

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

A distributed adaptive landmark clustering algorithm based on mOverlay and learning automata for topology mismatch problem in unstructured peer‐to‐peer networks

Blockchain Architecture

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