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Cat Swarm Optimization based autonomous recovery from network partitioning in heterogeneous underwater wireless sensor network

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Abstract

Underwater wireless sensor network is characterized with dynamic network topology owing to node mobility. Frequent changes in position of nodes due to water current cause network partitioning. This often results in frequent network failures and causes void spaces. Frequent network failures lead to unreliable data transmissions where nodes injudiciously drain their power resource. Such a network needs to adapt its network routing in order to diminish the challenges caused by node mobility. A dynamic approach addressing the issues of node mobility will also enhance the network lifetime. Node mobility creates articulation points (AP) in the network topology. AP is similar to bridges in a graph. AP leads to partitioning of the network that only yields failed and unreliable transmission. Cat Swarm Optimization is explored here for detecting a possible partition in the network prior to its occurrence in the seeking mode of the algorithm. In the tracking mode of the algorithm the cat with the closest distance to the predicted AP is selected to move towards the predicted AP in order to avoid the network from partitioning. The proposed approach enhances the network lifetime as it avoids disconnections and failed transmissions. It would conserve energy consumption of nodes by reducing the possibilities of failed and retried transmissions.

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Authors and Affiliations

  1. Department of Computer Science, Siksha ‘O’ Anusandhan University, Bhubaneswar, Odisha, India

    Madhuri Rao

  2. Biju Patnaik University of Technology, Rourkela, Odisha, India

    Madhuri Rao & Narendra Kumar Kamila

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  1. Madhuri Rao
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  2. Narendra Kumar Kamila
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Correspondence to Madhuri Rao.

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Rao, M., Kamila, N.K. Cat Swarm Optimization based autonomous recovery from network partitioning in heterogeneous underwater wireless sensor network. Int J Syst Assur Eng Manag 12, 480–494 (2021). https://doi.org/10.1007/s13198-021-01095-x

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  • DOI: https://doi.org/10.1007/s13198-021-01095-x

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