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This research explores strategies to enhance communication efficiency in swarm intelligence (SI) for network optimization. SI, inspired by social insects’ collective behaviour, offers decentralized problem-solving, making it ideal for dynamic and evolving network conditions. Key strategies include topology optimization, dynamic neighbourhood structures, message compression, asynchronous communication, decentralized control, data fusion, adaptive communication protocols, and energy-efficient communication. These methods improve network routing, quality of service, congestion management, and security. By incorporating SI into IEEE 802.11, IEEE 802.16, and IEEE 802.20 networks, this study highlights SI's transformative potential. Practical insights are provided for researchers and practitioners, fostering new avenues for future research.

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Enhancing Communication Efficiency in Swarm Intelligence for Network Optimization

  • 会议论文

  • 发表日期：

    2025年

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  • R. Usha,
  • G. Lakshmi Praveena,
  • Pokuri Deepika,
  • T. Jyotsna,
  • Ganapavarapu Surekha,
  • T. Pratyusha

摘要

This research explores strategies to enhance communication efficiency in swarm intelligence (SI) for network optimization. SI, inspired by social insects’ collective behaviour, offers decentralized problem-solving, making it ideal for dynamic and evolving network conditions. Key strategies include topology optimization, dynamic neighbourhood structures, message compression, asynchronous communication, decentralized control, data fusion, adaptive communication protocols, and energy-efficient communication. These methods improve network routing, quality of service, congestion management, and security. By incorporating SI into IEEE 802.11, IEEE 802.16, and IEEE 802.20 networks, this study highlights SI's transformative potential. Practical insights are provided for researchers and practitioners, fostering new avenues for future research.