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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

Routing is a very important matter for ad hoc networks which means choosing the suitable and correct path for transferring data from a source node to a destination node. Today, the methods using several paths for routing are attended very much. In this paper, a new plan in multiple paths On-Demand Distance Vector (AODV) routing is presented which its routing is based on the distance of nodes from the center of the network. In this new routing algorithm, the number of dropped packets will be decreased and indeed this will be happened with more balancing in the network. This claim will be proved by some simulations using ns2 (network simulator).

one of the important and challenging matters in sensor network is energy of life span of nodes in the network.Directed routing algorithm is one of propounded methods in sensor networks which are a data-oriented algorithm. This algorithm focuses on saving energy within life span of network nodes. One of problems of directed diffusion method is existence of multiple routes. Now, consider that some sinks from the same origin request the same data who's Data Volume is very much. Directed routing algorithm establishes one route toward targeted route for each query. The problem of this algorithm is multiplicity of routes for the same data.Therefore, if we can establish a route which has the most common feature with regards to nodes which forms the route, we have prevented wasting energy.In this paper, it is tried to remove problem of multiplicity of routes for the same data by learning automata. We named this algorithm RDDLA. RDDLA decrease overhead and energy in the network Considerable against with some others methods.

This paper explores how fuzzy features' number and reasoning rules can influence the rate of emotional speech recognition. The speech emotion signal is one of the most effective and neutral methods in individuals' relationships that facilitate communication between man and machine. This paper introduces a novel method based on mind inference and recognition of speech emotion recognition. The foundation of the proposed method is the inference of rules in Fuzzy Petri-net (FPN) and the learning automata. FPN is a new method of classification which is introduced for the first time on emotion speech recognition. This method helps to analyze different rules in a dynamic environment like human's mind. The input of FPN is computed by learning automata. Therefore learning automata has been used to adjust the membership functions for each feature vector in the dynamic environment. The proposed algorithm is divided into different parts: preprocessing; feature extraction; learning automata; fuzzification; inference engine and defuzzification. The proposed model has been compared with different models of classification. Experimental results show that the proposed algorithm outperforms other models.