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24
Cellular Learning Automata with Multiple Learning Automata in Each Cell and its Applications
"... The cellular learning automata, which is a combination of cellular automata and learning automata, is introduced recently. This model is superior to cellular automata because of its ability to learn and also is superior to single learning automaton because it is a collection of learning automata w ..."
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Cited by 16 (11 self)
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The cellular learning automata, which is a combination of cellular automata and learning automata, is introduced recently. This model is superior to cellular automata because of its ability to learn and also is superior to single learning automaton because it is a collection of learning automata which can interact with each other. The basic idea of cellular learning automata is to use the learning automata to adjust the state transition probability of stochastic cellular automata. Recently, various types of cellular learning automata such as synchronous, asynchronous, and open cellular learning automata have been introduced. In some applications such as cellular networks we need to have a model of cellular learning automata for which multiple learning automata resides in each cell. In this paper, we study a cellular learning automata model for which each cell has several learning automata. It is shown that for a class of rules, called commutative rules, the cellular learning automata converges to a stable and compatible configuration. Two applications of this new model such as channel assignment in cellular mobile networks and function optimization are also given. For both applications, it has been shown through computer simulations that the cellular learning automata based solutions produce better results.
Dynamic point coverage problem in wireless sensor networks: a cellular learning automata approach
 Journal of Ad hoc and Sensors Wireless Networks
, 2010
"... One way to prolong the lifetime of a wireless sensor network is to schedule the active times of sensor nodes, so that a node is active only when it is really needed. In the dynamic point coverage problem, which is to detect some moving target points in the area of the sensor network, a node is need ..."
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Cited by 15 (9 self)
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One way to prolong the lifetime of a wireless sensor network is to schedule the active times of sensor nodes, so that a node is active only when it is really needed. In the dynamic point coverage problem, which is to detect some moving target points in the area of the sensor network, a node is needed to be active only when a target point is in its sensing region. A node can be aware of such times using a predicting mechanism. In this paper, we propose a solution to the problem of dynamic point coverage using irregular cellular learning automata. In this method, learning automaton residing in each cell in cooperation with the learning automata residing in its neighboring cells predicts the existence of any target point in the vicinity of its corresponding node in the network. This prediction is then used to schedule the active times of that node. In order to show the performance of the proposed method, computer experimentations have been conducted. The results show that the proposed method outperforms the existing methods such as LEACH, GAF, PEAS and PW in terms of energy consumption.
A learning automata based scheduling solution to the dynamic point coverage problem in wireless sensor networks. Computer Networks doi:10.1016/j
, 2010
"... The dynamic point coverage problem in wireless sensor networks is to detect some moving target points in the area of the network using as little sensor nodes as possible. One way to deal with this problem is to schedule sensor nodes in such a way that a node is activated only at the times a target p ..."
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Cited by 12 (6 self)
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The dynamic point coverage problem in wireless sensor networks is to detect some moving target points in the area of the network using as little sensor nodes as possible. One way to deal with this problem is to schedule sensor nodes in such a way that a node is activated only at the times a target point is in its sensing region. In this paper we propose SALA, a scheduling algorithm based on learning automata, to deal with the problem of dynamic point coverage. In SALA each node in the network is equipped with a set of learning automata. The learning automata residing in each node try to learn the maximum sleep duration for the node in such a way that the detection rate of target points by the node does not degrade dramatically. This is done using the information obtained about the movement patterns of target points while passing throughout the sensing region of the nodes. We consider two types of target points; events and moving objects. Events are assumed to occur periodically or based on a Poisson distribution and moving objects are assumed to have a static movement path which is repeated periodically with a randomly selected velocity. In order to show the performance of SALA, some experiments have been conducted. The experimental results show that SALA outperforms the existing methods such as LEACH, GAF, PEAS and PW in terms of energy consumption.
CELLULAR LEARNING AUTOMATA BASED DYNAMIC CHANNEL ASSIGNMENT ALGORITHMS
, 2009
"... A solution to channel assignment problem in cellular networks is selforganizing channel assignment algorithm with distributed control. In this paper, we propose three cellular learning automata based dynamic channel assignment algorithms. In the first two algorithms, no information about the status ..."
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Cited by 5 (5 self)
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A solution to channel assignment problem in cellular networks is selforganizing channel assignment algorithm with distributed control. In this paper, we propose three cellular learning automata based dynamic channel assignment algorithms. In the first two algorithms, no information about the status of channels in the whole network will be used by cells for channel assignment whereas in the third algorithm, the additional information regarding status of channels may be gathered and then used by cells in order to allocate channels. The simulation results show that by using the proposed channel assignment algorithms the microcellular network can selforganize itself. The simulation results also show that the additional information used by the third algorithm help the cellular learning automata to find an assignment which results in lower blocking probability of calls for the network.
A Cellular Learning Automatabased Deployment Strategy for Mobile Wireless Sensor Networks
"... Abstract: One important problem which may arise in designing a deployment strategy for a wireless sensor network is how to deploy a specific number of sensor nodes throughout an unknown network area so that the covered section of the area is maximized. In a mobile sensor network, this problem can be ..."
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Cited by 5 (3 self)
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Abstract: One important problem which may arise in designing a deployment strategy for a wireless sensor network is how to deploy a specific number of sensor nodes throughout an unknown network area so that the covered section of the area is maximized. In a mobile sensor network, this problem can be addressed by first deploying sensor nodes randomly in some initial positions within the area of the network, and then letting sensor nodes to move around and find their best positions according to the positions of their neighboring nodes. The problem becomes more complicated if sensor nodes have no information about their positions or even their relative distances to each other. In this paper, we propose a cellular learning automatabased deployment strategy which guides the movements of sensor nodes within the area of the network without any sensor to know its position or its relative distance to other sensors. In the proposed algorithm, the learning automaton in each node in cooperation with the learning automata in the neighboring nodes controls the movements of the node in order to attain high coverage. Experimental results have shown that in noise free environments, the proposed algorithm can compete with the existing algorithms such as PF, DSSA, IDCA, and VEC in terms of network coverage. It has also been shown that in noisy environments, where utilized location estimation techniques such as GPSbased devices and localization algorithms experience inaccuracies in their measurements, or the movements of sensor nodes are not perfect and follow a probabilistic motion model, the proposed algorithm outperforms the existing algorithms in terms of network coverage.
Irregular Cellular Learning Automata and Its Application to Clustering in Sensor Networks
"... In the first part of this paper, we propose a generalization of cellular learning automata (CLA) called irregular cellular learning automata (ICLA) which removes the restriction of rectangular grid structure in traditional CLA. This generalization is expected because there are a number of applicatio ..."
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Cited by 2 (1 self)
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In the first part of this paper, we propose a generalization of cellular learning automata (CLA) called irregular cellular learning automata (ICLA) which removes the restriction of rectangular grid structure in traditional CLA. This generalization is expected because there are a number of applications which cannot be adequately modeled with rectangular grids. One category of such applications is in the area of wireless sensor networks. In these networks, nodes are usually scattered randomly throughout the environment, so no regular structure can be assumed for modeling their behavior. In the second part of the paper, based on the proposed model we design a clustering algorithm for sensor networks. Simulation results show that the proposed clustering algorithm is very efficient and outperforms similar existing methods.
Cellular Learning Automata based Scheduling Method for Wireless Sensor Networks
"... In wireless sensor network often microbattery with very limited power provides the energy of sensor nodes. Since sensors are usually utilized in remote or hostile environments, recharging or replacing the battery of the sensors is something quite undesirable or even impossible. Thus long system lif ..."
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Cited by 2 (2 self)
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In wireless sensor network often microbattery with very limited power provides the energy of sensor nodes. Since sensors are usually utilized in remote or hostile environments, recharging or replacing the battery of the sensors is something quite undesirable or even impossible. Thus long system lifetime is a must. Sleep scheduling is a mechanism in wireless sensor network to save energy. In this paper, we propose an energyefficient distributed scheduling method considering mobile target tracking also called dynamic target coverage. The algorithm is based on cellular learning automata. In this algorithm, each node is equipped with a learning automaton which will learn (schedule) the proper on and off times of that node based on the movement nature of a single moving target. To evaluate the proposed method it is tested under straight with constant velocity movement model of target. The results of experimentations have shown that the proposed scheduling algorithm outperforms two existing dynamic target coverage scheduling methods. 1.
Cellular Learning Automatabased Channel Assignment Algorithms in Mobile Ad Hoc Network
"... Abstract. The wireless mobile ad hoc network (MANET) architecture has received a lot of attention recently. This paper considers the access of multiple channels in a MANET with multihop communication behavior. We point out several interesting issues when using multiple channels. Our proposed algori ..."
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Cited by 1 (1 self)
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Abstract. The wireless mobile ad hoc network (MANET) architecture has received a lot of attention recently. This paper considers the access of multiple channels in a MANET with multihop communication behavior. We point out several interesting issues when using multiple channels. Our proposed algorithms enables hosts to utilize multiple channels by switching channels dynamically, thus increasing network throughput and decrease packet delay. In this paper, we first introduce the model of cellular learning automata in which learning automata are used to adjust the state transition probabilities of cellular automata. Then a cellular learning automata based channel assignment algorithm is proposed.
Distributed Learning AutomataBased Clustering Algorithm in Wireless Ad Hoc Networks
"... In ad hoc networks, the performance is significantly degraded as the size of the network grows. The network clustering is a method by which the nodes are hierarchically organized on the basis of the proximity and thus the scalability problem is alleviated. Finding the weakly connected dominating set ..."
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Cited by 1 (0 self)
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In ad hoc networks, the performance is significantly degraded as the size of the network grows. The network clustering is a method by which the nodes are hierarchically organized on the basis of the proximity and thus the scalability problem is alleviated. Finding the weakly connected dominating set (WCDS) is a wellknown approach, proposed for clustering the wireless ad hoc networks. Finding the minimum WCDS in the unit disk graph is an NPHard problem, and a host of approximation algorithms have been proposed. In this paper, an approximation algorithm based on distributed learning automata is first proposed for finding a near optimal solution to the minimum WCDS problem in a unit disk graph. Then, a distributed learning automatabased algorithm is proposed for clustering the wireless ad hoc networks. This clustering method is a generalization of the algorithm proposed for solving the WCDS problem, in which the dominator nodes and their closed neighbors assume the role of the clusterheads and cluster members, respectively. The proposed clustering algorithm, in an iterative process tries to find a policy that determines a clusterhead set with the minimum cardinality for the network. For both algorithms, the simulation results show that they outperform the best existing algorithms in terms of the number of hosts (nodes) in the clusterhead set (dominating set).
Fast Rule Identification and Neighbourhood Selection for Cellular Automata
"... Abstract—Cellular automata (CA) with given evolution rules have been widely investigated, but the inverse problem of extracting CA rules from observed data is less studied. Current CA rule extraction approaches are both timeconsuming, and inefficient when selecting neighbourhoods. We give a novel a ..."
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Abstract—Cellular automata (CA) with given evolution rules have been widely investigated, but the inverse problem of extracting CA rules from observed data is less studied. Current CA rule extraction approaches are both timeconsuming, and inefficient when selecting neighbourhoods. We give a novel approach to identifying CA rules from observed data, and selecting CA neighbourhoods based on the identified CA model. Our identification algorithm uses a model linear in its parameters, and gives a unified framework for representing the identification problem for both deterministic and probabilistic cellular automata. Parameters are estimated based on a minimumvariance criterion. An incremental procedure is applied during CA identification to select an initial coarse neighbourhood. Redundant cells in the neighbourhood are then removed based on parameter estimates, and the neighbourhood size is determined using a Bayesian information criterion. Experimental results show the effectiveness of our algorithm, and that it outperforms other leading CA identification algorithms. Index Terms—Cellular automata, rule identification, neighbourhood selection. I.