<p>Owing to limited storage and battery power, wireless sensor nodes often face challenges in maintaining long-term energy sustainability. To address this, only a subset of sensors remains active to monitor different sensor parameters while others get predicted to minimize sensor node energy consumption. In prediction, not all active parameters are equally important, as low-correlated parameters increase computational complexity and decrease accuracy. Researchers use highly correlated active parameters, though existing solutions often use polynomial time and don’t ensure optimal parameter set. This paper proposes a cross-correlation-based parameter selection <InlineEquation ID="IEq3"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41598_2025_21171_Article_IEq3.gif" Format="GIF" Height="19" Rendition="HTML" Resolution="72" Type="Linedraw" Width="70" /> </InlineMediaObject> <EquationSource Format="TEX">\((C_cBPS)\)</EquationSource> </InlineEquation> approach, ensuring the selected parameter set is stable and Pareto-optimal. Simulations are performed on nine publicly available datasets of environmental data collected from different places and at different sampling intervals to validate the effectiveness of the <InlineEquation ID="IEq4"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41598_2025_21171_Article_IEq1.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="60" /> </InlineMediaObject> <EquationSource Format="TEX">\(C_cBPS\)</EquationSource> </InlineEquation> approach. It has been observed that <InlineEquation ID="IEq5"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41598_2025_21171_Article_IEq1.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="60" /> </InlineMediaObject> <EquationSource Format="TEX">\(C_cBPS\)</EquationSource> </InlineEquation> approach selects a subset of active parameters faster than existing approaches and reduces energy consumption at the edge node ranges from <InlineEquation ID="IEq6"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41598_2025_21171_Article_IEq6.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="35" /> </InlineMediaObject> <EquationSource Format="TEX">\(6.5\%\)</EquationSource> </InlineEquation> - <InlineEquation ID="IEq7"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41598_2025_21171_Article_IEq7.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="43" /> </InlineMediaObject> <EquationSource Format="TEX">\(34.2\%\)</EquationSource> </InlineEquation> in the prediction of sleep sensor parameters on various datasets.</p>

错误:搜索内容不能为空,请输入英文关键词
错误:关键词超出字数限制,请精简
高级检索

Energy consumption minimisation at edge node using \(C_cBPS\) approach in predicting sensor parameters in WSNs

  • Vipin Maurya,
  • Sumit Kumar,
  • Sonali Raj,
  • Ruchir Gupta

摘要

Owing to limited storage and battery power, wireless sensor nodes often face challenges in maintaining long-term energy sustainability. To address this, only a subset of sensors remains active to monitor different sensor parameters while others get predicted to minimize sensor node energy consumption. In prediction, not all active parameters are equally important, as low-correlated parameters increase computational complexity and decrease accuracy. Researchers use highly correlated active parameters, though existing solutions often use polynomial time and don’t ensure optimal parameter set. This paper proposes a cross-correlation-based parameter selection \((C_cBPS)\) approach, ensuring the selected parameter set is stable and Pareto-optimal. Simulations are performed on nine publicly available datasets of environmental data collected from different places and at different sampling intervals to validate the effectiveness of the \(C_cBPS\) approach. It has been observed that \(C_cBPS\) approach selects a subset of active parameters faster than existing approaches and reduces energy consumption at the edge node ranges from \(6.5\%\) - \(34.2\%\) in the prediction of sleep sensor parameters on various datasets.