Wyniki wyszukiwania - BazTech

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EEULA: an energy-aware event-driven unicast algorithm for wireless sensor network by learning automata

Kashani A. A. S., Noori A. M. M.

Advances in Science and Technology. Research Journal

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2016

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Vol. 10, nr 30

9--18

EN

Energy consumption is one of the major challenges in wireless sensor networks, thus necessitating an approach for its minimization and for load balancing data. The network lifetime ends with the death of one of its nodes, which, in turn, causes energy depletion in and partition of the network. Furthermore, the total energy consumption of nodes depends on their location; that is, because of the loaded data, energy discharge in the nodes close to the base station occurs faster than other nodes, the model presented here, through using learning automata, selects the path appropriate for data transferring; the selected path is rewarded or penalized taking the reaction of surrounding paths into account. We have used learning automata for energy management in finding the path; the routing protocol was simulated by NS2 simulator; the lifetime, energy consumption and balance in an event-driven network in our proposed method were compared with other algorithms.

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Stochastic Bounded Diameter Minimum Spanning Tree Problem

Torkestani J. A.

Fundamenta Informaticae

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2015

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Vol. 140, nr 2

205--219

EN

In this paper, a learning automata-based algorithm is proposed for approximating a near optimal solution to the bounded diameter minimum spanning tree (BDMST) problem in stochastic graphs. A stochastic graph is a graph in which the weight associated with each edge is a random variable. Stochastic BDMST problem seeks for finding the BDMST in a stochastic graph. To the best of our knowledge, no work has been done on solving the stochastic BDMST problem, where the weight associated with the graph edge is random variable. In this study, we assume that the probability distribution of the edges random weight is unknown a priori. This makes the stochastic BDMST problem incredibly hard-to-solve. To show the efficiency of the proposed algorithm, its results are compared with those of the standard sampling method (SSM). Numerical results show the superiority of the proposed sampling algorithm over the SSM both in terms of the sampling rate and convergence rate.

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Learning Automata based Algorithms for Finding Optimal Policies in Fully Cooperative Markov Games

Masoumi B., Meybodi M.R., Abtahi F.

Przegląd Elektrotechniczny

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2012

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R. 88, nr 6

280-289

EN

Markov games, as the generalization of Markov decision processes to the multi agent case, have long been used for modeling multi-agent systems. In this paper, several learning automata based multi-agent system algorithms for finding optimal policies in fully-cooperative Markov Games are proposed. In the proposed algorithms, Markov problem is described as a directed graph in which the nodes are the states of the problem, and the directed edges represent the actions that result in transition from one state to another. Each state of the environment is equipped with a variable structure learning automata whose actions are moving to different adjacent states of that state. Each agent moves from one state to another and tries to reach the goal state. In each state, the agent chooses its next transition with help of the learning automaton in that state. The actions taken by learning automata along the path traveled by the agent is then rewarded or penalized based on the value of the traveled path according to a learning algorithm. In the second group of the proposed algorithms, the concept of entropy has been imported into learning automata based multi-agent systems to drive the magnitude of the reinforcement signal given to the LA and improve the performance of the algorithms. The results of experiments have shown that the proposed algorithms perform better than the existing learning automata based algorithms in terms of speed and the accuracy of reaching the optimal policy.

PL

Zaprezentowano szereg automatów uczących bazujących na algorytmach systemów typu multi-agent w celu poszukiwania optymalnej polityki w kooperatywnej grze Markova. Proces Markova jest opisany w postaci grafów których węzły opisują stan problemu, a krawędzie reprezentują akcje.