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Ocean Buoy Communication Node Selection Strategy with Intelligent Ant Behavior

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Advances in Swarm Intelligence (ICSI 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7332))

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Abstract

In this paper, we propose a novel ant system algorithm for balancing node energy distribution with maximum the number of complete data transmission in ocean buoy communication sensor network. In our algorithm, a complete transmission process is regarded as an ant tour, and each ant stochastically select corresponding node based on such information as energy function, heuristic function, and pheromone amount. An appropriate objective function is carefully designed with the expectation of maximizing the number of complete transmission and uniform minimum energy distribution. Simulation results are presented to support obtained favorable performance of our algorithm.

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Author information

Authors and Affiliations

  1. School of Electrical & Automatic Engineering, Changshu Institute of Technology, 215500, Changshu, China

    Benlian Xu, Wan Shi & Xiaoying Wang

  2. School of Mechanical Engineering, Changshu Institute of Technology, 215500, Changshu, China

    Qinglan Chen

Authors
  1. Benlian Xu
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  2. Qinglan Chen
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  3. Wan Shi
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  4. Xiaoying Wang
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Editor information

Editors and Affiliations

  1. Key Laboratory of Machine Perception (MOE), Peking University, Department of Machine Intelligence, School of Electronics Engineering and Computer Science, Peking University, 100871, Beijing, China

    Ying Tan

  2. Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

  3. Shenzhen City Key Laboratory of Embedded System Design, College of Computer Science and Software Engineering, Shenzhen University, 518060, Shenzhen, China

    Zhen Ji

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© 2012 Springer-Verlag Berlin Heidelberg

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Xu, B., Chen, Q., Shi, W., Wang, X. (2012). Ocean Buoy Communication Node Selection Strategy with Intelligent Ant Behavior. In: Tan, Y., Shi, Y., Ji, Z. (eds) Advances in Swarm Intelligence. ICSI 2012. Lecture Notes in Computer Science, vol 7332. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31020-1_71

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  • DOI: https://doi.org/10.1007/978-3-642-31020-1_71

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31019-5

  • Online ISBN: 978-3-642-31020-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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