Toward Efficient Compressed-Sensing-Based RFID Identification: A Sparsity-Controlled Approach

Abstract

Radio-frequency identification (RFID) has pervasive applications in building ultralow-power ubiquitous networks, where backscatter communication during tag identification is neither reliable nor efficient. Inspired by the sparsity that only a few RFID tags communicate with the reader simultaneously, many compressed sensing (CS)-based schemes have been proposed to exploit the colliding tag responses to facilitate efficient tag identification. However, most of them suffer from huge ID search space and signature collision during the CS recovery process. To address these issues, we propose SCRIC, a novel sparsity-controlled RFID identification scheme using a random signature assignment, which achieves a faster and more robust identification performance. Specifically, to tackle identification failure caused by severe signature collision, we assign each active tag an access probability to control the sparsity and reduce signature collision. Theoretical analysis is given to prove that the signature collision probability of the proposed scheme is reduced compared with the existing random signature scheme and an optimal access probability is derived. Moreover, considering that conventional CS recovery algorithm relies heavily on the unpractical assumption that the active tag number is known precisely in advance, we integrate the cross validation (CV) into CS recovery algorithms and propose a greedy algorithm called the CV-based orthogonal matching pursuit (OMP-CV), which can reduce the tag identification false alarm rate without any prior knowledge. Extensive experimental results show that the proposed mechanism significantly outperforms the existing CS-based tag identification methods in terms of identification speed and robustness to noise.