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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. In the second part of the paper, based on the proposed model a new clustering algorithm for sensor networks is designed. The proposed clustering algorithm is fully distributed and the nodes in the network don't need to be fully synchronized with each other. The proposed clustering algorithm consists of two phases; initial clustering and reclustering. Unlike existing methods in which the reclustering phase is performed periodically on the entire network, reclustering phase in the proposed method is performed locally whenever it is needed. This results in a reduction in the consumed energy for reclustering phase and also allows reclustering phase to be performed as the network operates. The proposed clustering method in comparison to existing methods produces a clustering in which each cluster has higher number of nodes and higher residual energy for the cluster head. Local reclustering, higher residual energy in cluster heads and higher number of nodes in each cluster results in a network with longer lifetime. To evaluate the performance of the proposed algorithm several experiments have been conducted. The results of experiments have shown that the proposed clustering algorithm outperforms existing clustering methods in terms of quality of clustering measured by the total number of clusters, the number of sparse clusters and the remaining energy level of the cluster heads. Experiments have also shown that the proposed clustering algorithm in comparison to other existing methods prolongs the network lifetime.

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 automata-based 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 GPS-based 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.

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Deployment of a wireless sensor network is a challenging problem, especially when the environment of the network does not allow either of the random deployment or the exact placement of sensor nodes. If sensor nodes are mobile, then one approach to overcome this problem is to first deploy sensor nodes randomly in some initial region within the area of the network, and then let the sensor nodes to move around and cooperatively and gradually increase the covered section of the area. Recently, a cellular learning automata-based deployment strategy, called CLA-DS, is introduced in literature which follows this approach and is robust against inaccuracies which may occur in the measurements of sensor positions or in the movements of sensor nodes. Despite its advantages, this deployment strategy covers every point within the area of the network with only one sensor node, which is not enough for applications with k-coverage requirement. In this paper, we extend CLA-DS so that it can address the k-coverage requirement. This extension, referred to as CLA-EDS, is also able to address k-coverage requirement with different values of k in different regions of the network area. Experimental results have shown that the proposed deployment strategy, in addition to the advantages it inherits from CLA-DS, outperforms existing algorithms such as DSSA, IDCA, and DSLE in covering the network area, especially when required degree of coverage differs in different regions of the network.

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One of the main challenges in wireless sensor networks is the energy constraints of sensor nodes which must be considered precisely when designing algorithms for such networks. Clustering is known as one of the approaches which can be used for addressing this challenge. In this paper, an efficient method for clustering wireless sensor networks by means of cellular learning automata has been presented (LaClustering). Proposed method selects cluster head (CHs) through several stages; each considers one parameter affecting the overall performance of the clustering. Parameters considered in different stages of the proposed algorithm are energy levels of the sensor nodes, number of neighbors of each node, network connectivity, and formation of balanced clusters. To evaluate the performance of the proposed method, several experiments have been conducted using the J-sim simulator and the proposed method has been compared with some of the best clustering algorithms reported in literature. The simulation results have shown that the proposed algorithm can provide clustering infrastructure with higher overall quality than the existing algorithms, especially in balancing the number of sensor nodes in different clusters and selecting CHs with higher energy levels.