Adaptive search in unstructured peer-to-peer networks based on ant colony and Learning Automata

An unstructured peer-to-peer network is an overlay network where all nodes play equal roles, and the topology and data location do not follow restrictive rules. So, in a traditional file search mechanism, such as flooding, a peer broadcasts a query to its neighbors through an unstructured peer-to-peer (P2P) network until the time-to-live decreases to zero. A major disadvantage of flooding is that, in a large-scale network, this blind-choice strategy usually incurs an enormous traffic overhead. AntP2PR is a protocol to search based on the ant colony in unstructured peer to peer networks, which faces with some problems such as high communication overhead and low success rate due to the lack of an appropriate decision-making mechanism. In this paper, two adaptive improved version of AntP2PR named DLAntP2P and LAntP2P to improve the search problem in unstructured peer-to-peer networks are proposed. The DLAntP2P and LAntP2P protocols utilize the Distributed Learning Automata (DLA) and Learning Automata (LA), respectively, as an adaptive decision-making mechanism to determine number of ant and enhance the way of selecting the neighbors to forward the query messages. Simulations show the effectiveness of proposed protocols in terms of communication overhead and success rate, compared to AntP2PR and K-walker random walk.