Compressive sensing (CS) encryption has attracted increasing attention as a lightweight encryption technique for the Internet of things (IoT) and wireless sensor networks (WSNs). Since devices and sensors may have limited hardware, it might be needed to consider quantization for CS encryption. In this paper, we consider a 1-bit CS encryption using artificial noise and a retransmission strategy with interleaving to mitigate degradation of detection performance at a legitimate receiver caused by the quantization errors. Through the analysis and simulation results, we demonstrate that the retransmission strategy can improve the detection performance at a legitimate receiver, while it slightly improves decision performance to estimate a measurement matrix at an eavesdropper. In addition, the proposed 1-bit CS encryption has a better performance than the existing CS encryption in terms of security, while mitigating the performance degradation caused by quantization errors of detection probability at a legitimate receiver.
Compressive sensing (CS) encryption has attracted increasing attention as a lightweight encryption technique for the Internet of things (IoT) and wireless sensor networks (WSNs). Since devices and sensors may have limited hardware, it might be needed to consider quantization for CS encryption. In this paper, we consider a 1-bit CS encryption using artificial noise and a retransmission strategy with interleaving to mitigate degradation of detection performance at a legitimate receiver caused by the quantization errors. Through the analysis and simulation results, we demonstrate that the retransmission strategy can improve the detection performance at a legitimate receiver, while it slightly improves decision performance to estimate a measurement matrix at an eavesdropper. In addition, the proposed 1-bit CS encryption has a better performance than the existing CS encryption in terms of security, while mitigating the performance degradation caused by quantization errors of detection probability at a legitimate receiver.
Compressive sensing (CS) has become a popular signal processing technique and has extensive applications in numerous fields such as wireless communications, image processing, magnetic resonance imaging, remote sensing imaging, and analogy to information conversion, since it can realize simultaneous sampling and compression. In the information security field, secure CS has received much attention due to the fact that CS can be regarded as a cryptosystem to attain simultaneous sampling, compression and encryption when maintaining the secret measurement matrix. Considering that there are increasing works focusing on secure wireless communications based on CS in recent years, we produce a detailed review for the state-of-the-art in this paper. To be specific, the survey proceeds with two phases. The first phase reviews the security aspects of CS according to different types of random measurement matrices such as Gaussian matrix, circulant matrix, and other special random matrices, which establishes theoretical foundations for applications in secure wireless communications. The second phase reviews the applications of secure CS depending on communication scenarios such as wireless wiretap channel, wireless sensor network, Internet of Things, crowd-sensing, smart grid, and wireless body area networks. Finally, some concluding remarks are given.
We consider sequential detection based on one-bit quantized data in the Internet of Things (IoT) with an eavesdropper. A lightweight physical-layer encryption algorithm, called stochastic encryption, is employed as a counter measure that flips the quantization bits at each IoT sensor according to certain probabilities, and the flipping probabilities are only known to the legitimate cloud (LC) but not the eavesdropping cloud (EC). Due to the optimality of the sequential probability ratio test (SPRT), the LC employs the SPRT for sequential detection whereas the EC employs a mismatched SPRT (MSPRT). We characterize the asymptotic performance of the MSPRT in terms of the expected sample size as a function of the vanishing error probabilities. We show that every symmetric stochastic encryption is ineffective in the sense that it leads to the same expected sample size at the LC and EC when the LC and EC have the same detection accuracy. Next, in the asymptotic regime of small detection error probabilities, we show that every stochastic encryption degrades the performance of the EC to a greater extent in the sense that the expected sample size required at the EC is no fewer than that is required at the LC. Moreover, the optimal stochastic encryption is derived in the sense of maximizing the difference between the expected sample sizes required at the EC and LC.
The Internet of Things (IoT) features pervasive sensing and control capabilities by deploying a massive machine-type communication (MTC) devices. However, low hardware complexity, low processing capacity and constrained energy of MTC devices raise lots of challenges to communication and security. Therefore, physical layer security (PLS) appears to be a promising method for securing wireless communication in which orthogonal frequency division multiplexing (OFDM) is widely used. However, most existing OFDM-based PLS schemes possess little dynamicity for their secret keys derived from pre-extracted information. The combination of OFDM and compressed sensing (CS) is a remarkable approach to address the weakness. So, with compressed sensing, we propose a physical layer security scheme for OFDM-based IoT systems, named PLSSCS. It can address the demerit of key extraction by channel measuring instead of pre-extracted information. To evaluate the performance of PLSSCS, the simulation is carried out in MATLAB, and PLSSCS provides better performance with dynamic property.
Devices in the Internet of Things (IoT) are usually limited in computing resources and energy capacity, which means that encryption schemes with higher complexity are not suitable for them to ensure secure communication. As a promising solution to this problem, physical layer key generation suggests that shared secret keys can be generated from noisy wireless channel measurements to enhance the security of wireless communications. In this article, we propose a key generation scheme with extremely low implementation complexity, which allows physical layer key generation to be implemented on IoT nodes. First, we preprocess the channel measurements with simple moving average filtering before quantization to improve channel reciprocity. Next, a bidirectional difference quantization scheme is proposed to realize reliable quantization of channel measurements, which is ingenious in that the quantization process does not depend on quantization thresholds, and thus, the mismatched key bits caused by measurements close to quantization thresholds can be effectively avoided. Then, we propose an improved Cascade protocol to achieve lightweight and efficient information reconciliation. The simulation results show that our scheme can well balance the reliability and efficiency of key generation, and has excellent performance in terms of implementation complexity and key randomness.
Secure information transmission over IoT-based wireless sensor networks is critically influenced by the reliability of electromagnetic wave propagation and the resilience of physical-layer communications against passive eavesdropping. This paper investigates cooperative secure transmission in multi-hop and multi-relay wireless sensor networks by proposing a unified framework for intelligent eavesdropping scenarios. To effectively mitigate passive interception threats, a lightweight information-theoretic secure communication scheme is developed by modeling data processing as a communication channel and introducing a bit-level probabilistic encryption mechanism based on random bit flipping, thereby minimizing the mutual information between confidential messages and intercepted signals. Furthermore, channel coding and bit interleaving are incorporated to make brute-force recovery by eavesdroppers computationally impractical, particularly in high-speed wireless transmission environments, without requiring prior knowledge of the legitimate user’s complete channel state information. An LLR-based fuse-then-decode decision rule is derived, and a low-complexity suboptimal fusion strategy based on the Min-Sum Algorithm is proposed to balance decoding efficiency and communication reliability. Simulation results verify the theoretical analysis and demonstrate that the proposed framework provides robust and efficient secure transmission performance for energy-constrained wireless sensor networks while offering valuable guidance for physical-layer security and electromagnetic-aware wireless communication systems.
The rapid development of Internet of Things (IoT) applications has led to the large-scale deployment of wireless sensor networks (WSNs) that incessantly generate and transmit sensitive data over wireless networks.Although the current mostly used symmetric encryption algorithm for securing such networks is the Advanced Encryption Standard (AES), the advent of quantum computing threatens the long-term security of AES, as well as the weaknesses inherent to the key generation, distribution, and lifecycle management.This research paper proposes a Quantum Enhanced AES framework for IoT based WSNs which integrates the use of Quantum Random Number Generator (QRNG) based high entropy key generation, key injection against side channel attacks and dynamic session-based rekeying.Compared with classical AES and two state-of-the-art security frameworks based on quantum computing, i.e., QKDassisted AES scheme and lightweight post-quantum security hybrid AES framework, the proposed scheme increases the entropy of the key by about 20% (from 0.82 to 0.99), and lowers the key exposure risk by more than 60% by using session-based rekeying.Simulation results indicate that the proposed method can achieve up to 8 to10% higher throughput and 22% lower end-to-end delay than post-quantum AES schemes, and also prolongs the network lifetime by almost 15% under realistic IoT-WSN workloads.The framework maintains and does not alter the AES standard compliance and does not make any changes to the AES core, thus it ensures practical applicability.These results show that the proposed approach offers a good quantum resilience with low performance and energy overhead, which is suitable for long-lived and security-critical IoT-WSN deployment.