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A learning automata-based algorithm for solving coverage problem in directional sensor networks

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Abstract

Wireless sensor networks have been used in a wide variety of applications. Recently, networks consisting of directional sensors have gained prominence. An important challenge facing directional sensor networks (DSNs) is maximizing the network lifetime while covering all the targets in an area. One effective method for saving the sensors’ energy and extending the network lifetime is to partition the DSN into several covers, each of which can cover all targets, and then to activate these covers successively. This paper first proposes a fully distributed algorithm based on irregular cellular learning automata to find a near-optimal solution for selecting each sensor’s appropriate working direction. Then, to find a near-optimal solution that can cover all targets with the minimum number of active sensors, a centralized approximation algorithm is proposed based on distributed learning automata. This algorithm takes advantage of learning automata (LA) to determine the sensors that must be activated at each stage. As the presented algorithm proceeds, the activation process is focused on the sensor nodes that constitute the cover set with the minimum number of active sensors. Through simulations, we indicate that the scheduling algorithm based on LA has better performance than the greedy algorithm-based scheme in terms of maximizing network lifetime.

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Authors and Affiliations

  1. Computer Department, Faculty of Computer Science and Information, Universiti Teknologi Malaysia, 81310, Johor Bahru, Malaysia

    Hosein Mohamadi & Abdul Samad Bin Haji Ismail

  2. Mathematics Department, Faculty of Science, Universiti Teknologi Malaysia, 81310, Johor Bahru, Malaysia

    Shaharuddin Salleh

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  1. Hosein Mohamadi
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  2. Abdul Samad Bin Haji Ismail
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  3. Shaharuddin Salleh
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Correspondence to Hosein Mohamadi.

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Mohamadi, H., Ismail, A.S.B.H. & Salleh, S. A learning automata-based algorithm for solving coverage problem in directional sensor networks. Computing 95, 1–24 (2013). https://doi.org/10.1007/s00607-012-0208-x

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  • DOI: https://doi.org/10.1007/s00607-012-0208-x

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