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.
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.
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.
Parameter estimation in wireless sensor networks (WSN) using encrypted non-binary quantized data is studied. In a WSN, sensors transmit their observations to a fusion center through a wireless medium where the observations are susceptible to unauthorized eavesdropping. Encryption approaches for WSNs with fixed threshold binary quantization were previously explored. However, fixed threshold binary quantization limits parameter estimation to scalar parameters. In this paper, we propose a stochastic encryption approach for WSNs that can operate on non-binary quantized observations and has the capability for vector parameter estimation. We extend a binary stochastic encryption approach proposed previously, to a non-binary generalized case. Sensor outputs are quantized using a quantizer with R + 1 levels, where R ϵ {1, 2, 3,...}, encrypted by flipping them with certain flipping probabilities, and then transmitted. Optimal estimators using maximum-likelihood estimation are derived for both a legitimate fusion center (LFC) and a third party fusion center (TPFC) perspectives. We assume the TPFC is unaware of the encryption. Asymptotic analysis of the estimators is performed by deriving the Cramer-Rao lower bound for LFC estimation, and the asymptotic bias and variance for TPFC estimation. Numerical results validating the asymptotic analysis are presented.
In Internet of Things (IoT) applications requiring parameter estimation, sensors often transmit quantized observations to a fusion center through a wireless medium where the observations are susceptible to unauthorized eavesdropping. The fusion center uses the received data to estimate desired parameters. To provide security to such networks, some low complexity encryption approaches have been proposed. In this paper, we generalize those approaches and present an analysis of their estimation and secrecy capabilities. We show that the dimension of the unknown parameter that can be efficiently estimated using an unbiased estimator when using these approaches, is upper bounded. Assuming that an unauthorized eavesdropper is aware of the low complexity encryption process but is unaware of the encryption key, we show successful eavesdropping, even with a large number of observations, is impossible with unbiased estimators and independent observations for these approaches. Numerical results validating our analysis are presented.
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.
In this article, we consider physical-layer security (PLS) of the Internet-of-Things (IoT) communications based on hybrid automatic repeat request (HARQ) protocols in multiple-input single-output single-eavesdropper (MISOSE) channels. Owing to the reciprocity of wireless channels, a negative acknowledge frame can be used to obtain the channel state information (CSI) from a source with multiple antennas to an IoT destination device, and this CSI of a legitimate link is used for beamforming for the next transmission round (TR). Therefore, a higher secrecy rate can be expected from retransmissions with beamforming based on the CSI as compared to the first TR without CSI. To evaluate the secrecy throughput under time-invariant and time-varying channels, connection and secrecy outage probabilities (OPs) are derived. By exploiting the characteristics of the OPs, we suggest an approximated secrecy throughput. We then consider the optimization of the approximated secrecy throughput considering the rate parameters for legitimate and eavesdropping links. With an alternating optimization approach, an algorithm to find convergent system parameters, i.e., the rates for legitimate and eavesdropping links, is proposed. Moreover, an asymptotic analysis with the large number of transmit antennas indicates an interesting behavior of the secrecy rate with regard to the number of TRs. The analytical results are verified by rigorous numerical simulations and they can serve as a useful guideline to those involved in the design of secure IoT communication systems with HARQ in MISOSE channels.
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.