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<p>The Internet of Things (IoT) consists of a vast network of interconnected devices generating large volumes of data, requiring efficient and secure data transmission. Clustering methods in the IoT can reduce data volume while maintaining security. This paper proposes a novel trust-based clustering approach for IoT. Network nodes are evaluated based on parameters like energy, centrality, trust, and neighbor count. Trust calculations consider direct, indirect, and historical factors. The method selects the most reliable node as the cluster head and also chooses a backup head using coefficients optimized by a genetic algorithm. During the stable phase, data is exclusively accepted from trusted nodes, ensuring a robust and secure data collection process. The results of comparing the proposed method with previous methods demonstrate its superior performance in terms of average energy consumption, delay, communication overhead, and throughput. Specifically, the proposed method reduces energy consumption by approximately 18%, decreases end-to-end delay by 22%, lowers communication overhead by 16%, and improves throughput by 25% compared to existing approaches such as eeTMFO/GA (Sharma in Telecommun Syst 74: 253–268, 2020) and the Qureshi model (Qureshi in Wirel Pers Commun, 128: 67–88, 2023). These improvements confirm the effectiveness of our method in enhancing security, efficiency, and reliability in IoT clustering.</p>

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A dynamic trust based clustering method for secure data gathering in Internet of Things

  • 期刊论文

  • 发表日期：

    2025年3月21日

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  • Maryam Naghibi,
  • Hamid Barati,
  • Ali Barati

摘要

The Internet of Things (IoT) consists of a vast network of interconnected devices generating large volumes of data, requiring efficient and secure data transmission. Clustering methods in the IoT can reduce data volume while maintaining security. This paper proposes a novel trust-based clustering approach for IoT. Network nodes are evaluated based on parameters like energy, centrality, trust, and neighbor count. Trust calculations consider direct, indirect, and historical factors. The method selects the most reliable node as the cluster head and also chooses a backup head using coefficients optimized by a genetic algorithm. During the stable phase, data is exclusively accepted from trusted nodes, ensuring a robust and secure data collection process. The results of comparing the proposed method with previous methods demonstrate its superior performance in terms of average energy consumption, delay, communication overhead, and throughput. Specifically, the proposed method reduces energy consumption by approximately 18%, decreases end-to-end delay by 22%, lowers communication overhead by 16%, and improves throughput by 25% compared to existing approaches such as eeTMFO/GA (Sharma in Telecommun Syst 74: 253–268, 2020) and the Qureshi model (Qureshi in Wirel Pers Commun, 128: 67–88, 2023). These improvements confirm the effectiveness of our method in enhancing security, efficiency, and reliability in IoT clustering.