Dynamic Point Coverage in Wireless Sensor Networks: A Learning Automata Approach

Esnaashari, Mehdi; Meybodi, Mohammad Reza

doi:10.1007/978-3-540-89985-3_97

Cited by 16 publications

(15 citation statements)

References 15 publications

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“…The first publication that introduced LA to the science community was a survey conducted by Narendra in 1970s [9]. Since the first introduction of LA, there have been many applications of this learning method such as traffic congestion [11], channel assignment [12] in cellular mobile networks and dynamic point coverage in wireless sensor networks [13]. More recently, LA has been successfully applied to the context of PSO for adaptive parameter selection [14].…”

Section: *Manuscriptmentioning

confidence: 99%

Adaptive cooperative particle swarm optimizer

2013

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Section: *Manuscriptmentioning

confidence: 99%

Adaptive cooperative particle swarm optimizer

2013

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“…The coverage calculation here is independent of paths traveled by the target, which is different from [15]. Esnaashari and Meybodi in [11] proposed a learning automata-based scheduling solution to the dynamic point coverage problem. In the proposed scheduling algorithm, the learning automaton of each node learns the sleep duration of that node based on the movement pattern of a single moving target point in the network.…”

Section: Related Workmentioning

confidence: 99%

Maximizing Lifetime of Target Coverage in Wireless Sensor Networks Using Learning Automata

Mostafaei

Meybodi

2012

Wireless Pers Commun

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In wireless sensor networks, when each target is covered by multiple sensors, we can schedule sensor nodes to monitor deployed targets in order to improve lifetime of network. In this paper, we propose an efficient scheduling method based on learning automata, in which each node is equipped with a learning automaton, which helps the node to select its proper state (active or sleep), at any given time. To study the performance of the proposed method, computer simulations are conducted. Results of these simulations show that the proposed scheduling method can better prolong the lifetime of the network in comparison to similar existing methods.

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“…Despite its irregular structure, ICLA operation is equivalent to that of CLA. ICLA has found a number of applications in wireless ad hoc and sensor networks [89][90][91].…”

Section: Irregular Clamentioning

confidence: 99%

Deployment of a mobile wireless sensor network with k-coverage constraint: a cellular learning automata approach

Esnaashari

Meybodi

2012

Wireless Netw

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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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Dynamic Point Coverage in Wireless Sensor Networks: A Learning Automata Approach

Cited by 16 publications

References 15 publications

Adaptive cooperative particle swarm optimizer

Adaptive cooperative particle swarm optimizer

Maximizing Lifetime of Target Coverage in Wireless Sensor Networks Using Learning Automata

Deployment of a mobile wireless sensor network with k-coverage constraint: a cellular learning automata approach

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