CS-LeCT: Chained Secure and Low-Energy Consumption Data Transmission Based on Compressive Sensing

【Author】 Zhang, Jun; Zhou, Jiaxin; Gu, Zhenghui; Zhang, Zhi; Wang, Luhua; Yu, Zhu Liang; Li, Yuanqing

【Source】IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT

【影响因子】5.332

【Abstract】Energy consumption and security are two major challenges faced by telemedicine systems. Due to the simplicity and the randomness in information acquisition process, compressive sensing (CS) has a vast prospect in dealing with these two problems. Recently, a chained CS (CCS) scheme has been proposed, which can implement a secure and low-energy consumption data transmission. However, due to its chain computation and the lossy compression nature of CS, there exists an accumulative error in the existing CCS scheme. Moreover, block-based CS decoding also leads to significant performance deterioration. In this article, a new chained secure and low-energy consumption data transmission (CS-LeCT) scheme has been proposed, whose reconstruction performance is far superior to the CCS. First, to overcome the accumulative error of the CCS scheme, square matrices instead of underdetermined matrices were used to encode the signal so that in the proposed CS-LeCT, the decoding process can be carried out by adopting a lossless inverse operation. Second, inspired by the structurally random matrix (SRM), an active packet drop strategy was designed to achieve low-energy consumption data transmission, whose decoding can be implemented by means of the SRM-based CS reconstruction. The performance gain of the CS-LeCT has not only been validated through simulation experiments but has also been analyzed theoretically. Safety analysis also shows that the proposed CS-LeCT can resist several potential attacks, such as ciphertext-only attacks (COAs), known-plaintext attacks (KPAs), and man-in-the-middle attacks (MiTMs).

【Keywords】Blockchain; compressive sensing (CS); data security; structurally random matrix (SRM); telemedicine

【发表时间】2023

【收录时间】2023-08-06

【文献类型】实验仿真

【主题类别】

区块链应用-实体经济-数据管理