考虑认知移动对等网络中对等点移动性的自适应超级对等点选择算法,International Journal of Communication Systems

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考虑认知移动对等网络中对等点移动性的自适应超级对等点选择算法
International Journal of Communication Systems ( IF 1.7 ) Pub Date : 2020-10-29 , DOI: 10.1002/dac.4661
Nahid Amirazodi ₁ , Ali Mohammad Saghiri ₂ , Mohammad Reza Meybodi ₂

Affiliation

认知对等网络是从认知网络概念和对等网络的组合中获得的。这些网络能够在动态和未知环境下运行时提高性能。认知对等网络会考虑对等点的未知物理属性，尝试为自身学习适当的配置。认知移动对等网络是指基于移动自组织网络构建的认知对等网络。在这些网络中，无线网络中对等方移动性的异构性和资源限制给网络管理算法带来了挑战。由于这些网络的动态性，应该以自适应方式指定管理算法。在这些网络的一种类型中，一些对等网络（称为超级对等网络）承担执行网络管理任务。对等方的移动性导致对等方之间的连接失败以及新超级对等方的重新选择。因此，由于选择超级对等体具有影响力，因此需要一种考虑对等体移动性的算法。到目前为止，还没有考虑到对等体以自适应方式的移动性而将自适应算法指定为超级对等体选择。本文提出了一种M-SSBLA，它是一种基于学习自动机的，考虑对等点移动性的自适应超级对等点选择算法。该算法是从基于学习自动机的认知引擎和MIS之间的协作中获得的。MIS是移动对等网络中众所周知的超级对等选择算法。我们将提出的算法与最近报道的算法进行了比较，特别是对于具有高移动性的网络。仿真结果表明，提出的算法可以覆盖最多的普通对等体，并具有少量的超级对等体，并提高了针对超级对等体故障的鲁棒性，同时减少了维护开销。

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

Cognitive 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.

更新日期：2020-12-03

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