Context-Aware Lossless and Lossy Compression of Radio Frequency Signals

Martí, Aniol; Portell, J.; Riba, Jordi-Roger; Mas, Orestes

doi:10.3390/s23073552

Cited by 2 publications

(1 citation statement)

References 26 publications

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“…Gilles et al [13] used a compressed sensing approach based on a sub-Nyquist scheme, known as a modulated wideband converter, to solve wideband spectrum sensing. Aniol et al [14] proposed an algorithm based on linear prediction that can perform both the lossless and near-lossless compression of RF signals.…”

Section: Data Compressionmentioning

confidence: 99%

Autonomous Internet of Things (IoT) Data Reduction Based on Adaptive Threshold

Zhang,

Na,

Zhang

2023

Sensors

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With the development of intelligent IoT applications, vast amounts of data are generated by various volume sensors. These sensor data need to be reduced at the sensor and then reconstructed later to save bandwidth and energy. As the reduced data increase, the reconstructed data become less accurate. Usually, the trade-off between reduction rate and reconstruction accuracy is controlled by the reduction threshold, which is calculated by experiments based on historical data. Considering the dynamic nature of IoT, a fixed threshold cannot balance the reduction rate with the reconstruction accuracy adaptively. Aiming to dynamically balance the reduction rate with the reconstruction accuracy, an autonomous IoT data reduction method based on an adaptive threshold is proposed. During data reduction, concept drift detection is performed to capture IoT dynamic changes and trigger threshold adjustment. During data reconstruction, a data trend is added to improve reconstruction accuracy. The effectiveness of the proposed method is demonstrated by comparing the proposed method with the basic Kalman filtering algorithm, LMS algorithm, and PIP algorithm on stationary and nonstationary datasets. Compared with not applying the adaptive threshold, on average, there is an 11.7% improvement in accuracy for the same reduction rate or a 17.3% improvement in reduction rate for the same accuracy.

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Section: Data Compressionmentioning

confidence: 99%

Autonomous Internet of Things (IoT) Data Reduction Based on Adaptive Threshold

Zhang,

Na,

Zhang

2023

Sensors

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Context-Aware Lossless and Lossy Compression of Radio Frequency Signals

Martí

Portell

Riba

et al. 2023

Sensors

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We propose an algorithm based on linear prediction that can perform both the lossless and near-lossless compression of RF signals. The proposed algorithm is coupled with two signal detection methods to determine the presence of relevant signals and apply varying levels of loss as needed. The first method uses spectrum sensing techniques, while the second one takes advantage of the error computed in each iteration of the Levinson–Durbin algorithm. These algorithms have been integrated as a new pre-processing stage into FAPEC, a data compressor first designed for space missions. We test the lossless algorithm using two different datasets. The first one was obtained from OPS-SAT, an ESA CubeSat, while the second one was obtained using a SDRplay RSPdx in Barcelona, Spain. The results show that our approach achieves compression ratios that are 23% better than gzip (on average) and very similar to those of FLAC, but at higher speeds. We also assess the performance of our signal detectors using the second dataset. We show that high ratios can be achieved thanks to the lossy compression of the segments without any relevant signal.

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Context-Aware Lossless and Lossy Compression of Radio Frequency Signals

Cited by 2 publications

References 26 publications

Autonomous Internet of Things (IoT) Data Reduction Based on Adaptive Threshold

Autonomous Internet of Things (IoT) Data Reduction Based on Adaptive Threshold

Context-Aware Lossless and Lossy Compression of Radio Frequency Signals

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