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Efficient Optimization Algorithms for Minimizing Delay and Packet Loss in Doppler and Geometric Spreading Environment in Underwater Sensor Networks

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Abstract

This paper proposes a Cooperative Ray Optimization Algorithm (CoROA) algorithm that helps minimizing the delay and packet loss arising as result of Doppler and the geometric spreading environment in underwater acoustic networks. The existing algorithms perform routing and energy management for an underwater network in the temperature and salinity environment. The proposed CoROA algorithm is known for the efficient performance in different environments such as spatial and temporal variation for improving the battery life, network lifetime and throughput. The CoROA algorithm has more than one path through relay node to reach the destination node and improves the packet delivery, throughput and minimizes the delay and packet drop. The CoROA algorithm compares well with the existing algorithms that include AODV, Lion Optimized Cognitive Acoustic Network and Cat Optimization Algorithm in cognitive networks and shows better performance in terms of efficiency.

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Availability of data and material

The data used to support the findings of this study are available from the corresponding author upon request.

Code availability

NS2 code in aquasim available from corresponding author upon request.

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Authors and Affiliations

  1. Velammal Engineering College, Chennai, TN, India

    A. Rajeswari & N. Duraipandian

  2. Department of Computer Science and Engineering, Aalim Muhammed Salegh College of Engineering, Chennai, TN, India

    N. R. Shanker

  3. Jazan University, Jazan, Kingdom of Saudi Arabia

    Betty Elezebeth Samuel

Authors
  1. A. Rajeswari
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  2. N. Duraipandian
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  3. N. R. Shanker
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  4. Betty Elezebeth Samuel
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Correspondence to A. Rajeswari.

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Rajeswari, A., Duraipandian, N., Shanker, N.R. et al. Efficient Optimization Algorithms for Minimizing Delay and Packet Loss in Doppler and Geometric Spreading Environment in Underwater Sensor Networks. Wireless Pers Commun 121, 49–67 (2021). https://doi.org/10.1007/s11277-021-08623-9

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  • DOI: https://doi.org/10.1007/s11277-021-08623-9

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