Backscatter communication (BackCom) is widely adopted in Internet of Things (IoT) thanks to its characteristics of low power consumption and low cost. However, it is vulnerable to jamming due to double channel fading of backscattering signal, and the lack of encryption capability in tags increases the risk of eavesdropping. In this paper, we propose a monostatic BackCom system with a multi-antenna reader under hybrid attack, which leverages artificial noise (AN) to degrade eavesdropper’s channel capacity. We also design the transmit beamforming and the combiner to enhance the secrecy rate of the system. Specifically, we formulate a secrecy rate maximization problem with transmit power constraint, which is difficult to solve due to its non-convexity and coupled variables. Hence, we divide it into two subproblems through decoupling the variables and solve them alternately to obtain an overall suboptimal solution. Finally, our numerical results validate our optimization scheme by comparing it with selected benchmarks and showing the fast convergence speed of the proposed algorithm.
Backscatter communication (BackCom) is widely adopted in Internet of Things (IoT) thanks to its characteristics of low power consumption and low cost. However, it is vulnerable to jamming due to double channel fading of backscattering signal, and the lack of encryption capability in tags increases the risk of eavesdropping. In this paper, we propose a monostatic BackCom system with a multi-antenna reader under hybrid attack, which leverages artificial noise (AN) to degrade eavesdropper’s channel capacity. We also design the transmit beamforming and the combiner to enhance the secrecy rate of the system. Specifically, we formulate a secrecy rate maximization problem with transmit power constraint, which is difficult to solve due to its non-convexity and coupled variables. Hence, we divide it into two subproblems through decoupling the variables and solve them alternately to obtain an overall suboptimal solution. Finally, our numerical results validate our optimization scheme by comparing it with selected benchmarks and showing the fast convergence speed of the proposed algorithm.
Backscatter communication (BackCom) has emerged as a sustainable and green paradigm for large-scale Internet of Things deployments, enabling energy-efficient and low-cost connectivity. However, its open nature makes it highly vulnerable to hybrid attacks combining jamming and eavesdropping. This paper investigates a novel physical-layer security framework for secure data collection in multi-tag monostatic BackCom systems under hybrid attacks. We formulate a joint optimization problem of the transmit precoder, artificial noise, and receive combiner to maximize the sum-secrecy spectral efficiency. To address this, semidefinite relaxation and mixed max-min fractional programming are employed to obtain the optimal precoder and artificial noise, while a closed-form solution is derived for the receive beamformer. By integrating these into a block coordinate descent framework, we develop an iterative algorithm with guaranteed convergence to a near-optimal solution. Simulation results validate the robustness of the proposed design and show substantial performance enhancement, achieving up to 26.45dB gain over conventional benchmarks and 0.47dB over the existing scheme.
In this paper, we investigate the physical layer secrecy (PLS) performance of a bi-static backscatter communication (BackCom) system, where a tag reliably communicates with a reader node by reflecting the radio frequency (RF) signal emitted by a source, while a malicious eavesdropper (Eve) attempts to intercept the transmission. To enhance the system's security, we propose an artificial noise (AN) injection scheme, in which the RF source transmits AN to confuse Eve while concurrently serving a legitimate communication user (CU). The objective is to maximize the tag's secrecy spectral efficiency (SSE) by jointly optimizing power allocation and beamforming for both the information signal and AN, subject to practical constraints including a minimum spectral efficiency (SE) requirement for the CU and a total power budget at the RF source. To tackle the resulting non-convex optimization problem, we develop an efficient algorithm based on alternating optimization (AO) to obtain a high-quality suboptimal solution. Simulation results confirm that the proposed design significantly improves secrecy performance compared to conventional schemes based on the null-space projection (NSP) technique.
The goal of this paper is to address the physical layer (PHY) security problem for multi-user multi-input single-output (MU-MISO) Internet of Things (IoT) systems in the presence of passive eavesdroppers (Eves). To this end, we propose an artificial noise (AN)-aided rate-splitting (RS)-based secure beamforming scheme. Our design considers the dual use of common messages and places the research emphasis on hiding the private messages for secure communication. In particular, leveraging AN-aided RS-based beamforming, we aim to maximize the focused secrecy sum-rate (F-SSR) by jointly designing transmit information and AN beamforming while satisfying the desired received constraints for the private messages at IoT devices (IoDs), and per-antenna transmit power constraint at base station. Then, we proposed a two-stage algorithm to iteratively find the optimal solution. By transforming non-convex terms into linear terms, we first reformulate the original problem as a convex program. Next, we recast the optimization problem to an unconstrained problem to obtain the global optimal solutions. Utilizing the duality framework, we further develop an efficient algorithm based on a barrier interior point method to solve the reformulated problem. Simulation results validate the superior performance of our proposed schemes.
In this work, we exploit a multi-antenna cooperative jammer to enable secure short packet communications in Internet of Things (IoT) networks. Specifically, we propose three jamming schemes to combat eavesdropping, i.e., the zero forcing beamforming (ZFB) scheme, null-space artificial noise (NAN) scheme, and transmit antenna selection (TAS) scheme. Assuming Rayleigh fading, we derive new closed-form approximations for the secrecy throughput with finite blocklength coding. To gain further insights, we also analyze the asymptotic performance of the secrecy throughput in the case of infinite blocklength. Furthermore, we investigate the optimization problem in terms of maximizing the secrecy throughput with the latency and reliability constraints to determine the optimal blocklength. Simulation results validate the accuracy of the approximations and evaluate the impact of key parameters such as the jamming power and the number of antennas at the jammer on the secrecy throughput.
Physical layer security is a promising way to secure the wireless communications in the Internet of Things (IoT). Motivated by the fact that the limited feedback resources in the IoT network would degrade the secrecy advantage of the multiple-antenna technique, we attempt to investigate the problem of how many transmit antennas should be utilized to perform secure communications. In particular, we consider the heterogeneous IoT downlink network and design a multiuser secure transmission scheme. In this scheme, the zero-forcing beamforming technique is adopted to serve the IoT legitimate users, and the remaining spatial freedoms are utilized to send artificial noise (AN) for confusing the passive eavesdroppers. Given the secrecy outage constraints, we derive the closed-form expression for the network secrecy throughput and formulate a non-convex optimization problem with multiple parameters, e.g., the number of transmit antennas, the wiretap codes, the feedback bits allocation strategy, and the power allocation ratio between the information bearing signal and the AN. To effectively tackle this problem, we develop an optimization framework involving the block coordinate descent algorithm and the 1-D search method. Simulation results validate the effectiveness of our proposed optimization framework and show that the optimal number of transmit antennas increases as the secrecy outage constraints become stricter, or the feedback resources become scarcer.
Backscatter wireless communication lies at the heart of many practical low-cost, low-power, distributed passive sensing systems. The inherent cost restrictions coupled with the modest computational and storage capabilities of passive sensors, such as RFID tags, render the adoption of classical security techniques challenging; which motivates the introduction of physical layer security approaches. Despite their promising potential, little has been done to study the prospective benefits of such physical layer techniques in backscatter systems. In this paper, the physical layer security of wireless backscatter systems is studied and analyzed. First, the secrecy rate of a basic single-reader, single-tag model is studied. Then, the unique features of the backscatter channel are exploited to maximize this secrecy rate. In particular, the proposed approach allows a backscatter system's reader to inject a noise-like signal, added to the conventional continuous wave signal, in order to interfere with an eavesdropper's reception of the tag's information signal. The benefits of this approach are studied for a variety of scenarios while assessing the impact of key factors, such as antenna gains and location of the eavesdropper, on the overall secrecy of the backscatter transmission. Numerical results corroborate our analytical insights and show that, if properly deployed, the injection of artificial noise yields significant performance gains in terms of improving the secrecy of backscatter wireless transmission.