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首页> 外文期刊>Journal of network and computer applications >Novel Light Weight Compressed Data Aggregation using sparse measurements for IoT networks
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Novel Light Weight Compressed Data Aggregation using sparse measurements for IoT networks

机译：针对物联网的稀疏测量的新型轻量级压缩数据聚合

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Optimal data aggregation aimed at maximizing loT network lifetime by minimizing constrained on-board resource utilization continues to be a challenging task. The existing data aggregation methods have proven that compressed sensing is promising for data aggregation. However, they compromise either on energy efficiency or recovery fidelity and require complex on-node computations. In this paper, we propose a novel Light Weight Compressed Data Aggregation (LWCDA) algorithm that randomly divides the entire network into non overlapping clusters for data aggregation. The random non-overlapping clustering offers two important advantages: 1) energy efficiency, as each node has to send its measurement only to its cluster head, 2) highly sparse measurement matrix, which leads to a practically implementable framework with low complexity. We analyze the properties of our measurement matrix using restricted isometry property, the associated coherence and phase transition. Through extensive simulations on practical data, we show that the measurement matrix can reconstruct data with high fidelity. Further, we demonstrate that the LWCDA algorithm reduces transmission cost significantly against baseline approaches, implying thereby the enhancement of the network lifetime.
机译：旨在通过最小化受限的板载资源利用率来最大化loT网络寿命的最佳数据聚合仍然是一项艰巨的任务。现有的数据聚合方法已证明压缩感知技术有望用于数据聚合。但是，它们在能量效率或恢复保真度上都有折衷，并且需要复杂的节点计算。在本文中，我们提出了一种新颖的轻量级压缩数据聚合（LWCDA）算法，该算法将整个网络随机划分为非重叠簇以进行数据聚合。随机不重叠的群集具有两个重要优点：1）能源效率高，因为每个节点仅将其测量结果发送到其群集头，2）高度稀疏的测量矩阵，这导致了可实际实现的，具有低复杂度的框架。我们使用受限的等轴测特性，相关的相干性和相变来分析测量矩阵的特性。通过对实际数据的大量仿真，我们表明测量矩阵可以高保真地重建数据。此外，我们证明了LWCDA算法相对于基线方法可显着降低传输成本，从而延长了网络寿命。

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