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<p>The technique of clustering and using cluster heads that are connected to a sink node has been a very effective approach to reducing energy consumption and increasing the service life of sensor networks. In the present study, a density-based clustering technique is used, and then, three parameters, including residual energy, link quality, and data delivery rate, are considered as three evidence sources to determine the scores of sensor nodes and cluster heads. These three parameters are used for data fusion at signal and decision levels based on the Dempster-Shafer evidence theory so that the scores of the sensor nodes are determined. After fusion, cluster heads are selected. Moreover, appropriate sensor nodes are chosen from the neighbors for intra-cluster routing based on their scores. As an advantage of the method proposed in this study, the number of clusters is not selected by an external component. Moreover, evidence-based data fusion allows aggregating data from different heterogeneous sources. A comparison of the proposed scheme with the Fuzzy logic-based unequal clustering (FBUC), energy-aware unequal clustering algorithm (EAUCF), and low energy adaptive clustering hierarchy (LEACH) methods shows, respectively, 0.04, 0.14, and 0.18 joules improvement in residual energy, and 1.9%, 7.2%, and 10.6% improvement in network lifetime.</p>

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Improving routing and energy consumption in wireless sensor networks by data fusion-based clustering

  • 期刊论文

  • 发表日期：

    2025年7月7日

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  • Mojdeh. Mahdavi,
  • Rezvan. Khandani

摘要

The technique of clustering and using cluster heads that are connected to a sink node has been a very effective approach to reducing energy consumption and increasing the service life of sensor networks. In the present study, a density-based clustering technique is used, and then, three parameters, including residual energy, link quality, and data delivery rate, are considered as three evidence sources to determine the scores of sensor nodes and cluster heads. These three parameters are used for data fusion at signal and decision levels based on the Dempster-Shafer evidence theory so that the scores of the sensor nodes are determined. After fusion, cluster heads are selected. Moreover, appropriate sensor nodes are chosen from the neighbors for intra-cluster routing based on their scores. As an advantage of the method proposed in this study, the number of clusters is not selected by an external component. Moreover, evidence-based data fusion allows aggregating data from different heterogeneous sources. A comparison of the proposed scheme with the Fuzzy logic-based unequal clustering (FBUC), energy-aware unequal clustering algorithm (EAUCF), and low energy adaptive clustering hierarchy (LEACH) methods shows, respectively, 0.04, 0.14, and 0.18 joules improvement in residual energy, and 1.9%, 7.2%, and 10.6% improvement in network lifetime.