Hybrid Swarm-Autoencoder Model for Ene
Hybrid Swarm-Autoencoder Model for Ene

Summary/Abstract: Background: Environmental monitoring and data collection critically depend on wireless sensor networks (WSN). However, the limited battery life of these sensor nodes significantly affects the operational lifetime of the network. This often results in challenges related to energy efficiency and clustering tasks due to dynamic network topologies and limited resources. Objective: This paper aims to improve clustering efficiency and extend the network lifetime of WSN by proposing a hybrid model. Methods: The proposed framework was evaluated in terms of energy consumption, network lifetime, Packet Delivery Ratio (PDR), and clustering efficiency through extensive simulations. Modified Salp Swarm Algorithm (MSSA) -Deep Stacked Sparse Autoencoder (DSSA) -K-means is a promising solution for next-generation WSN in smart environments and industrial Internet of Things (IoT) applications because experimental results show that it achieves higher clustering accuracy, energy economy, and robustness against network failures than current methods. Results: Extensive simulations were conducted to evaluate the proposed framework based on energy consumption, network lifetime, PDR, and clustering efficiency. The experimental results demonstrate that the proposed MSSA-DSSA-K-means model achieves higher clustering accuracy, energy economy, and robustness against network failures compared to existing methods. Conclusion: The paper concludes that this hybrid model is a promising solution for next-generation WSN in smart environments and industrial IoT applications.

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