Semitensor Product Compressive Sensing for Big Data Transmission in Wireless Sensor Networks

Peng, Haipeng; Tian, Ye; Kurths, Jürgen

doi:10.1155/2017/8158465

Cited by 8 publications

(6 citation statements)

References 27 publications

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“…Currently, the NPCR and UACI [51] are two quantitative indicators that can effectively appraise the capability of encryption scheme to withstand the differential attacks. Their mathematical definitions are displayed in equation (23), where the matrices As pointed out in [15], the closer the NPCR and UACI are to 99.6094% and 33.4635%, respectively, for a cryptosystem, the stronger it is in withstanding the differential attacks. In this experiment, the asymmetric embedding phase is first removed, and seven plain images with different scales are adopted to analyze the performance of the proposed privacy-preserving scheme against the differential analysis.…”

Section: Differential Attack Analysismentioning

confidence: 99%

“…Actually, it has been certified that the measurement matrix as the secret key can provide sufficient computational security to withstand some security attack models, such as brute-force attack and ciphertext only attack [20]. Therefore, various compressive sensing models including parallel CS [21,22], semi-tensor product CS [23], Kronecker CS [24], etc were applied to image encryption. For example, the 2D compressive sensing combined with fractional-order Chen hyperchaotic system was adopted by Yang et al [25] to encrypt private images.…”

Section: Introductionmentioning

confidence: 99%

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Privacy data protection scheme using memristive hyperchaos and multi-scale block compressive sensing

et al. 2023

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Recently, most meaningful image encryption schemes incorporated with various compressive sensing models have been developed to achieve dual protection of private image data and appearance. However, there exist performance constraints in these schemes in terms of anti-chosen-plaintext attack capability and key management. Aiming at the above issues, a new visually secure image encryption scheme is proposed using multi-scale block compressive sensing (MSB-CS) model and asymmetric integer wavelet transform (IWT) embedding. In this scheme, a memristor model with locally active tristable is first introduced into the oscillator to construct a new 5-D memristive hyperchaotic system to generate cipher flows. Then, the non-linear MSB-CS model is designed to compress sparsely-represented plaintext coefficients. After a series of encryption operations, secret image without semantic features is asymmetrically embedded into the same-scale non-secret-involved carrier image. Additionally, both communicating parties, Alice and Bob, acquire shared secret key through the key sharing protocol based on matrix factorization problem. Finally, simulation experiments and comprehensive analysis indicate that the 5-D memristive system has complicated hyperchaotic behaviours. In the meantime, the designed encryption scheme possesses better the anti-differential attack capability and reconstruction performance than other recently proposed schemes.

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Section: Differential Attack Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Privacy data protection scheme using memristive hyperchaos and multi-scale block compressive sensing

et al. 2023

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“…However, the traditional data compression sensing technology has strict restrictions on the dimension of signal matrices, which is not conducive to dealing with ubiquitous heterogeneous medical signals. Peng et al [36] proposed an STPCS algorithm to save computing resources, and applied it to the communication of wireless sensor networks. Ping et al [23] leveraged STPCS to reduce computational and spatial resources when designing a visually secure image encryption scheme.…”

Section: Related Workmentioning

confidence: 99%

Two-Tier Cooperation Based High-Reliable and Lightweight Forwarding Strategy in Heterogeneous WBAN

Xiao

Yuan

2023

Sustainability

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Due to the limited and difficult access to sensor energy, energy conservation has always been an important issue in wireless body area network (WBAN). How to make full use of the limited energy of heterogeneous sensors in WBAN to achieve lightweight and high-reliable data transmission has also become key to the sustainable development of telemedicine services. This paper proposes a two-tier cooperation based high-reliable and lightweight forwarding (TTCF) mechanism via minimizing the amount of transmitted data and optimizing forwarding performance, so as to improve the efficiency and reliability of WBAN and reduce system energy consumption. In TTCF, an adaptive semi-tensor product compressed sensing evolution (STPCSE) model is first constructed to minimize the amount of data to be transmitted and extend the lifetime of sensors. Then, the important factors closely related to the energy consumption of human body sensors, including sampling frequency, residual energy and their importance in the network, are analyzed and redefined, and a high-reliable and lightweight forwarding model based on a multi-factor dynamic fusion is built. Finally, the performance and energy-saving effect of TTCF in a dynamic WBAN environment are compared and analyzed. Simulation results show that the system with our TTCF always performs the best in terms of data reconstruct accuracy, cumulative delivery rata, energy consumption and throughput. For example, its cumulative delivery rate is about 12% and 20.8% higher than that of UC-MPRP and CRPBA, and its residual energy and throughput are 1.22 times and 1.41 times, 1.35 times and 1.6 times of the latter two, respectively.

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“…Column number in matrix A must be equal to row number in matrix x. The theory of semitensor product (STP) breaks through this limitation, able to perform matrix multiplication when two matrices do not meet the dimension-matching condition [31].…”

Section: Semitensor-product Compressed Sensingmentioning

confidence: 99%

“…An application of STP-CS was presented to reduce calculation energy consumption, and it was applied to the communication of wireless sensor networks (WSNs) [31]. In terms of recovery quality, STP-CS is almost equal to conventional CS and CCS.…”

Section: Semitensor-product Compressed Sensingmentioning

confidence: 99%

Overview of Compressed Sensing: Sensing Model, Reconstruction Algorithm, and Its Applications

Liu

Peng

et al. 2020

Applied Sciences

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With the development of intelligent networks such as the Internet of Things, network scales are becoming increasingly larger, and network environments increasingly complex, which brings a great challenge to network communication. The issues of energy-saving, transmission efficiency, and security were gradually highlighted. Compressed sensing (CS) helps to simultaneously solve those three problems in the communication of intelligent networks. In CS, fewer samples are required to reconstruct sparse or compressible signals, which breaks the restrict condition of a traditional Nyquist–Shannon sampling theorem. Here, we give an overview of recent CS studies, along the issues of sensing models, reconstruction algorithms, and their applications. First, we introduce several common sensing methods for CS, like sparse dictionary sensing, block-compressed sensing, and chaotic compressed sensing. We also present several state-of-the-art reconstruction algorithms of CS, including the convex optimization, greedy, and Bayesian algorithms. Lastly, we offer recommendation for broad CS applications, such as data compression, image processing, cryptography, and the reconstruction of complex networks. We discuss works related to CS technology and some CS essentials.

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Semitensor Product Compressive Sensing for Big Data Transmission in Wireless Sensor Networks

Cited by 8 publications

References 27 publications

Privacy data protection scheme using memristive hyperchaos and multi-scale block compressive sensing

Privacy data protection scheme using memristive hyperchaos and multi-scale block compressive sensing

Two-Tier Cooperation Based High-Reliable and Lightweight Forwarding Strategy in Heterogeneous WBAN

Overview of Compressed Sensing: Sensing Model, Reconstruction Algorithm, and Its Applications

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