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"A Target Coverage Scheduling Scheme Based On Genetic Algorithms In Directional Sensor Networks." and the language of the book is English.


“A Target Coverage Scheduling Scheme Based On Genetic Algorithms In Directional Sensor Networks.” Metadata:

  • Title: ➤  A Target Coverage Scheduling Scheme Based On Genetic Algorithms In Directional Sensor Networks.
  • Authors:
  • Language: English

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  • Internet Archive ID: pubmed-PMC3274050

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"A Target Coverage Scheduling Scheme Based On Genetic Algorithms In Directional Sensor Networks." Description:

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This article is from <a href="//archive.org/search.php?query=journaltitle%3A%28Sensors%20(Basel,%20Switzerland)%29" rel="nofollow">Sensors (Basel, Switzerland)</a>, <a href="//archive.org/search.php?query=journaltitle%3A%28Sensors%20(Basel,%20Switzerland)%29%20AND%20volume%3A%2811%29" rel="nofollow">volume 11</a>.<h2>Abstract</h2>As a promising tool for monitoring the physical world, directional sensor networks (DSNs) consisting of a large number of directional sensors are attracting increasing attention. As directional sensors in DSNs have limited battery power and restricted angles of sensing range, maximizing the network lifetime while monitoring all the targets in a given area remains a challenge. A major technique to conserve the energy of directional sensors is to use a node wake-up scheduling protocol by which some sensors remain active to provide sensing services, while the others are inactive to conserve their energy. In this paper, we first address a Maximum Set Covers for DSNs (MSCD) problem, which is known to be NP-complete, and present a greedy algorithm-based target coverage scheduling scheme that can solve this problem by heuristics. This scheme is used as a baseline for comparison. We then propose a target coverage scheduling scheme based on a genetic algorithm that can find the optimal cover sets to extend the network lifetime while monitoring all targets by the evolutionary global search technique. To verify and evaluate these schemes, we conducted simulations and showed that the schemes can contribute to extending the network lifetime. Simulation results indicated that the genetic algorithm-based scheduling scheme had better performance than the greedy algorithm-based scheme in terms of maximizing network lifetime.

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