Performance Enhancement of Wireless Sensor Networks Using a Multi-Channel Gaussian Mixture Model

Wireless Sensor Networks (WSNs) often suffer from performance degradation caused by high delay and data loss, especially in dense communication scenarios. To address these issues, this study proposes an enhanced routing protocol by integrating the Low Energy Adaptive Clustering Hierarchy (LEACH) with a Multi-Channel Gaussian Mixture Model (GMM). The LEACH protocol provides energy-efficient clustering, while GMM enables probabilistic multi-channel assignment to minimize interference and optimize packet transmission. This combined approach offers a more adaptive and data-driven mechanism for cluster formation and channel selection. Simulation results demonstrate notable improvements. The delay decreases from approximately 3.95×10⁻³ to 1.5×10⁻³ seconds, while packet loss is reduced from 0.55 to 0.40 before stabilizing at 0.56. Throughput performance also shows enhancement, rising from 125 kbps to 155 kbps after the proposed optimization. These outcomes indicate that the LEACH–GMM integration provides better communication reliability and efficiency compared to conventional LEACH, the findings highlight the potential of probabilistic multi-channel modeling for improving WSNs performance and offer a promising direction for intelligent and adaptive routing protocols in next-generation sensor networks.

Recommended citation: I. Ali, K. Adi, and R. Rizky, "Performance Enhancement of Wireless Sensor Networks Using a Multi-Channel Gaussian Mixture Model," in 2026 International Seminar on Intelligent Business and Edge-Computing Research (ISIBER), 2026, pp. 570–574. doi: 10.1109/ISIBER68248.2026.11469918.
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