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Neural networks for MANET AODV: an optimization approach

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Abstract

To find a route with good stability and less cost is a hot issue because of MANET’s mobility. AODV is one of the most widely used routing protocols in MANET because of its wide application, good performance and expansion. However, AODV is only an optional route instead of an optimized one. In this paper, continuous Hopfield Neural Networks is used to optimize the route to seek an optimal or nearly-optimal route, which can improve the usability and survivability of MANET. The simulation results show that CHNN-AODV is more effective and advantageous than AODV in the measurement of packet receiving rate, end-to-end average delay and routing recovery frequency.

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Acknowledgements

Supported by the Opening Project of Guangxi Colleges and Universities Key Laboratory of robot & welding. The project of Guangxi education Department (KY2016YB531, 2017KY0868).

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Authors and Affiliations

  1. Guangxi Colleges and Universities Key Laboratory of Robot & Welding, Guilin University of Aerospace Technology, Guilin, Guangxi, 541004, China

    Hua Yang, Zhimei Li & Zhiyong Liu

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  1. Hua Yang
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  2. Zhimei Li
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  3. Zhiyong Liu
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Correspondence to Hua Yang.

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Yang, H., Li, Z. & Liu, Z. Neural networks for MANET AODV: an optimization approach. Cluster Comput 20, 3369–3377 (2017). https://doi.org/10.1007/s10586-017-1086-y

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  • DOI: https://doi.org/10.1007/s10586-017-1086-y

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