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

Abstract

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.