Peer-to-peer (P2P) networks are gaining increased attention from both the scientific community and the larger Internet user community. Data retrieval algorithms lie at the center of P2P networks, and this paper addresses the problem of efficiently searching for files in unstructured P2P systems. We propose an Improved Adaptive Probabilistic Search (IAPS) algorithm that is fully distributed and bandwidth efficient. IAPS uses ant-colony optimization and takes file types into consideration in order to search for file container nodes with a high probability of success. We have performed extensive simulations to study the performance of IAPS, and we compare it with the Random Walk and Adaptive Probabilistic Search algorithms. Our experimental results show that IAPS achieves high success rates, high response rates, and significant message reduction

Abstract---O ne of the important and challenging matters in sensor network is energy of life span of nodes in the network.Node's movement, specifically movement of central node (sink) in these networks cause to increase routing updating overhead and consequently to increase power consumption and to decrease network life span. Directed Diffusion algorithm is one of methods in sensor network which is a data-oriented algorithm. One of the important definitions of basic algorithm is not supporting central node's movement. In case of movement of central node, data packs pass on unreliable rout toward central node. In fact, they pass a rout on which the central node is not present at the time being and it has moved to another place. Therefore, route of data is out of order and there is the need to build new routes. This problem causes to create lots of overhead and waste energy.In this article, it is tried to solve mentioned problem of central node's movement by learning automata. In suggested algorithm by learning automata a route amendment tree is built which prevents from creation of the whole route and its overhead.