جستجوی مقالات مرتبط با کلیدواژه « learning automata » در نشریات گروه « برق »

The use of wireless sensor networks is becoming more and more important due to the COVID - 19 pandemic and the living conditions of human beings today. The three main goals in designing this type of network are to reduce energy consumption, choose the shortest route and choose a reliable route for data transmission. In this paper, these th ree goals are considered in routing. Due to the fact that this type of network is exposed to many attacks, identifying malicious nodes and removing them creates security in this type of network. This paper presents an energy - aware and trusted - based routing method using learning automata and an evaluation function. Learning automata identifies trusted nodes (to send data) and malicious nodes using the corresponding evaluation function. The evaluation function considers the residual energy, the node's trust a nd the number of hops to the sink parameters. Thus, the data reaches its destination in a safe and reliable way. The evaluation results of the proposed method show an improvement in the performance of this method compared to other relevant methods.

Wireless Sensor Networks (WSNs) are a special class of wireless ad-hoc networks where their performance is affected by different factors.Congestion is of paramount importance in WSNs. It badly affects channel quality, loss rate, link utilization, throughput, network life time, traffic flow, the number of retransmissions, energy, and delay. In this paper, congestion control schemes are classified as classic or softcomputing-based schemes. The soft computing-based congestion control schemes are classified as fuzzy logic-based, game theory-based, swarm intelligence-based, learning automata-based, and neural network-based congestion control schemes. Thereafter, a comprehensive review of different soft computing-based congestion control schemes in wireless sensor networks is presented. Furthermore, these schemes are compared using different performance metrics. Finally, specific directives are used to design and develop novel soft computing-based congestion control schemes in wireless sensor networks

The Transactions in web data often consist of quantitative data, suggesting that fuzzy set theory can be used to represent such data. The time spent by users on each web page is one type of web data, was regarded as a trapezoidal membership function (TMF) and can be used to evaluate user browsing behavior. The quality of mining fuzzy association rules depends on membership functions and since the membership functions of each web page are different from those of other web pages, so automatic finding the number and position of TMF is significant. In this paper, a different reinforcement-based optimization approach called LA-OMF was proposed to find both the number and positions of TMFs for fuzzy association rules. In the proposed algorithm, the centers and spreads of TMFs were considered as parameters of the search space, and a new representation using learning automata (LA) was proposed to optimize these parameters. The performance of the proposed approach was evaluated and the results were compared with the results of other algorithms on a real dataset. Experiments on datasets with different sizes confirmed that the proposed LA-OMF improved the efficiency of mining fuzzy association rules by extracting optimized membership functions.

Task scheduling is one of the main and important challenges in the cloud environment. The dynamic nature and changing conditions of the cloud generally leads to problems for the task scheduling. Hence resource management and scheduling are among the important cases to improve throughput of cloud computing. This paper presents an online, a non-preemptive scheduling solution using two learning automata for the task scheduling problem on virtual machines in the cloud environment that is called LABTS. This algorithm consists three phases: in the first one, the priority of tasks sent by a learning automaton is predicted. In the second phase, the existing virtual machines are clustered according to the predictions in the previous phase. Finally, using another learning automaton, tasks are assigned to the virtual machines in the third phase. The simulation results show that the proposed algorithm in the cloud environment reduces the value of two parameters makespan and degree of imbalance.