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Learning Automata-Based Algorithms for Solving the Target Coverage Problem in Directional Sensor Networks

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Abstract

Recently, directional sensor networks have received a great deal of attention due to their wide range of applications in different fields. A unique characteristic of directional sensors is their limitation in both sensing angle and battery power, which highlights the significance of covering all the targets and, at the same time, extending the network lifetime. It is known as the target coverage problem that has been proved as an NP-complete problem. In this paper, we propose four learning automata-based algorithms to solve this problem. Additionally, several pruning rules are designed to improve the performance of these algorithms. To evaluate the performance of the proposed algorithms, several experiments were carried out. The theoretical maximum was used as a baseline to which the results of all the proposed algorithms are compared. The obtained results showed that the proposed algorithms could solve efficiently the target coverage problem.

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Authors and Affiliations

  1. Computer Department, Faculty of Computing, Universiti Teknologi Malaysia, 81310 , Johor Bahru, Malaysia

    Hosein Mohamadi, Abdul Samad Ismail & Ali Nodehi

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

    Shaharuddin Salleh

Authors
  1. Hosein Mohamadi
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  2. Abdul Samad Ismail
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  3. Shaharuddin Salleh
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  4. Ali Nodehi
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Correspondence to Hosein Mohamadi.

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Mohamadi, H., Ismail, A.S., Salleh, S. et al. Learning Automata-Based Algorithms for Solving the Target Coverage Problem in Directional Sensor Networks. Wireless Pers Commun 73, 1309–1330 (2013). https://doi.org/10.1007/s11277-013-1279-5

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