Energy-efficiency and reliability are vital metrics of the robustness of Wireless Sensor Networks (WSNs). Various data reduction techniques are used to improve them, among them compressive sensing (CS) is a data reduction technique used to recover extensive data from fewer samples in case of sparse representation of sensor-readings. Unfortunately, energy-efficiency and accuracy are contradictory metrics, as increased accuracy requires a large number of measurements, and data transmissions. Therefore, in this paper, a CS-based algorithm is proposed for efficient data transfer through WSNs, which uses multiple objective genetic algorithms (MOGA) to optimize the number of measurements, transmission range, and the sensing matrix. The algorithm aims at striking the right balance between energy-efficiency and accuracy. It constructs a path in a multi hop manner based on the optimized values. Numerical simulations and experiments show that Pareto-front, which is the output of MOGA, helps the user to select the right combination of the number of measurements and the transmission range fitting the application at hand, and to strike a good balance between energy efficiency and accuracy. The results also demonstrate the existence of measurement matrices which lower mutual coherency improve the accuracy of CS.

中文翻译：

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基于压缩感知和多目标遗传算法的无线传感器网络多跳路由算法

能源效率和可靠性是无线传感器网络（WSN）健壮性的重要指标。各种数据缩减技术用于改进它们，其中压缩感测（CS）是一种数据缩减技术，用于在传感器读数稀疏表示的情况下从较少的样本中恢复大量数据。不幸的是，能量效率和准确性是相互矛盾的指标，因为提高准确性需要大量的测量和数据传输。因此，本文提出了一种基于CS的WSN高效数据传输算法，该算法使用多目标遗传算法（MOGA）来优化测量次数，传输范围和传感矩阵。该算法旨在在能效和准确性之间取得适当的平衡。它基于优化值以多跳方式构造路径。数值模拟和实验表明，MOGA的输出是Pareto-front，它可以帮助用户选择合适的测量数量和适合当前应用的传输范围的组合，并在能效和能耗之间取得良好的平衡。准确性。结果还证明了存在测量矩阵，该矩阵降低了彼此的相干性，从而提高了CS的准确性。