Performance Assessment of One-Bit Compressed Sensing Techniques Targeting IoT Applications: A Comparative Study

Abstract

One-bit Compressed Sensing (CS) is a novel approach in signal processing that focuses on the recovery of sparse signals from highly quantized measurements, specifically retaining only the sign information of the measurements. This extreme quantization reduces the data storage and transmission requirements, making it particularly suitable for resource constrained environments. Despite the loss of amplitude information, one-bit compressed sensing leverages advanced algorithms and theoretical frameworks to achieve accurate signal reconstruction. This technique has shown potential in various applications, including low-power sensor networks and medical imaging, where efficient data acquisition is crucial. This work provides a comparative study over various one-bit CS recovery techniques proposed in the literature with respect to measurement matrix structure, noise resilience, convexity, and side information requirement in terms of either noise or sparsity information. We conducted a comprehensive comparison of multiple algorithms on a certain setup, evaluating their performance in terms of Signal-to-Noise Ratio (SNR), Hamming Distance (HD), and Hamming Error (HE) to assess their effectiveness in signal recovery. We extend our validation process for the recovery techniques through an adaptive setup focused on image compression and reconstruction using one-bit compressed sensing, emphasizing their suitability for hardware implementation in resource-constrained IoT hardware applications. Results provide guidance over appropriate algorithm selection for different IoT use cases. Evaluation process is performed with respect to reconstruction accuracy, reconstruction latency, and storage resources. In noiseless setup, the one-bit LP algorithm supersedes in terms of reconstruction accuracy utilizing small storage resources with latency overhead, meanwhile the Epin algorithm represents a better option in terms of reconstruction latency sacrificing the storage utilization to reach the same accuracy. However, at high noise margin the Passive algorithm has the best reconstruction accuracy compared to other algorithms.