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<p>The proposed study introduces a novel and hybrid wireless sensor network packet transmission framework (H-WSN-PTF) that synergistically integrates two bio-inspired optimization paradigms namely Namib Beetle Optimization (NBO) and Jellyfish Optimization Algorithm (JFOA) for achieving energy-efficient and robust cluster head (CH) selection. The NBO, inspired by the fog-basking and adaptive moisture-harvesting behavior of desert beetles, is used to evaluate initial CH candidates based on local environmental sensing and energy retention potential. The JFOA, which models the passive–active oscillatory movements of jellyfish within ocean currents, refines the cluster configuration through global optimization of communication load, inter-cluster distance, and packet delivery reliability. This hybridization ensures multi-layer adaptability in which the lower layer manages sensor density and energy heterogeneity, the middle layer handles CH optimization through NBO–JFOA co-evolution, and the upper layer manages data aggregation and packet routing stability. Simulation experiments conducted over NS-3 and MATLAB show 2.4 times improvement in energy efficiency, 1.8 times reduction in packet loss, and 2.1 times enhancement in network lifetime compared to LEACH (Low-Energy Adaptive Clustering Hierarchy), PSO-CHS (Particle Swarm Optimization-based Cluster Head Selection), and FCM (Fuzzy c-means) methods.</p>

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A novel and hybrid wireless sensor network packet transmission framework based on optimal cluster head selection using NBO and JFOA

  • 期刊论文

  • 发表日期：

    2025年12月18日

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  • Jyothi R,
  • S. Rashmi,
  • Mohana Kumar S,
  • Nagamani H. Shahapure,
  • R. B. Madhumala,
  • S. N. Saranya

摘要

The proposed study introduces a novel and hybrid wireless sensor network packet transmission framework (H-WSN-PTF) that synergistically integrates two bio-inspired optimization paradigms namely Namib Beetle Optimization (NBO) and Jellyfish Optimization Algorithm (JFOA) for achieving energy-efficient and robust cluster head (CH) selection. The NBO, inspired by the fog-basking and adaptive moisture-harvesting behavior of desert beetles, is used to evaluate initial CH candidates based on local environmental sensing and energy retention potential. The JFOA, which models the passive–active oscillatory movements of jellyfish within ocean currents, refines the cluster configuration through global optimization of communication load, inter-cluster distance, and packet delivery reliability. This hybridization ensures multi-layer adaptability in which the lower layer manages sensor density and energy heterogeneity, the middle layer handles CH optimization through NBO–JFOA co-evolution, and the upper layer manages data aggregation and packet routing stability. Simulation experiments conducted over NS-3 and MATLAB show 2.4 times improvement in energy efficiency, 1.8 times reduction in packet loss, and 2.1 times enhancement in network lifetime compared to LEACH (Low-Energy Adaptive Clustering Hierarchy), PSO-CHS (Particle Swarm Optimization-based Cluster Head Selection), and FCM (Fuzzy c-means) methods.