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<p>In wireless sensor networks (WSNs), sensor nodes often collect highly sparse data. To achieve energy-efficient data gathering while maintaining data quality, compressive sensing (CS) has been widely adopted, allowing for accurate signal recovery at low sampling rates. Existing approaches primarily focus on either “sparsely” selecting sensor nodes for data delivery, or selecting cluster heads with special transmission capabilities to perform CS before sending data to the sink. However, only a few works have explored in-network CS in WSNs where sensor nodes have homogeneous transmission abilities, and existing approaches still have room for improvement in data transmission efficiency. To address this challenge, we propose an efficient in-network CS algorithm, adaptive dominant set-minimum spanning tree (ADS-MST), which leverages adaptive dominating node ability to optimize data processing and routing. ADS-MST builds on the Sota dominant set-minimum spanning tree (DS-MST) algorithm and K-hop dominant set-minimum spanning tree (KDS-MST) algorithm, enabling the selection of suitable nodes for in-network processing and identifying efficient transmission routes for both processed and unprocessed data. Extensive simulations were conducted by MATLAB, and the results demonstrate that ADS-MST significantly reduces energy consumption compared to DS-MST and KDS-MST, making it a promising approach for energy-efficient data gathering in WSNs.</p>

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An Adaptive In-network Compressive Sensing Routing Scheme in Wireless Sensor Networks

  • 期刊论文

  • 发表日期：

    2025年2月1日

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  • Yiyi Zhang,
  • Peng Guo,
  • Renjie Guo,
  • Chi Zhang,
  • Zhe Tian,
  • Jiang Liu

摘要

In wireless sensor networks (WSNs), sensor nodes often collect highly sparse data. To achieve energy-efficient data gathering while maintaining data quality, compressive sensing (CS) has been widely adopted, allowing for accurate signal recovery at low sampling rates. Existing approaches primarily focus on either “sparsely” selecting sensor nodes for data delivery, or selecting cluster heads with special transmission capabilities to perform CS before sending data to the sink. However, only a few works have explored in-network CS in WSNs where sensor nodes have homogeneous transmission abilities, and existing approaches still have room for improvement in data transmission efficiency. To address this challenge, we propose an efficient in-network CS algorithm, adaptive dominant set-minimum spanning tree (ADS-MST), which leverages adaptive dominating node ability to optimize data processing and routing. ADS-MST builds on the Sota dominant set-minimum spanning tree (DS-MST) algorithm and K-hop dominant set-minimum spanning tree (KDS-MST) algorithm, enabling the selection of suitable nodes for in-network processing and identifying efficient transmission routes for both processed and unprocessed data. Extensive simulations were conducted by MATLAB, and the results demonstrate that ADS-MST significantly reduces energy consumption compared to DS-MST and KDS-MST, making it a promising approach for energy-efficient data gathering in WSNs.