In the sixth-generation (6G) communications, how to deploy and manage massively connected Internet-of-Things (IoT) nodes will be one of the key technical challenges, because 6G is expected to provide 10 times higher connection, compared to 5G. At the same time, due to the sharp growth in connected devices and newly adopted technologies, learning-based attacks and big data breaches are expected to occur more frequently. With the advances of quantum computing in the future, conventional cryptography-based security protocols may be obsolete in the future wireless networks, which makes physical layer security (PLS) an attractive alternative or complement for secure communications. In this context, cooperative beamforming (CB)-based PLS schemes are known to be effective solutions to guarantee high secrecy rate with IoT devices, which have limited power and hardware complexity. However, the existing CB-based PLS algorithms suffer from extremely low secrecy rate, in case that eavesdroppers are close to the intended receiver. To overcome such critical issue, in this paper, we propose a CB-based PLS with artificial noise (AN) injection, which can be realized in a fully distributed manner to minimize the overhead in the IoT networks with a large number of devices. We analyze the array factor using the virtual antenna array (VAA) created by the proposed PLS algorithm. Then, the secrecy rate is derived in a closed-form expression, which can be used to optimize the performance for given system parameters in both the absence and presence of channel state information (CSI) error. The proposed scheme provides considerably higher secrecy rate compared to the conventional CB-based PLS schemes, when an eavesdropper exists near to the intended receiver. Furthermore, through simulation and numerical results, we show that the secrecy rate of the proposed scheme can be maximized by adjusting the ratio between the data beamformer and AN injection beamformer components. As a result, the proposed method shows a performance improvement of up to two times compared to the conventional CB-based PLS schemes, in terms of the secrecy rate. Such performance gain increases as the angular location of Eve becomes closer to that of Bob, which corresponds to the most vulnerable situation of the conventional CB-based PLS algorithms.
In the sixth-generation (6G) communications, how to deploy and manage massively connected Internet-of-Things (IoT) nodes will be one of the key technical challenges, because 6G is expected to provide 10 times higher connection, compared to 5G. At the same time, due to the sharp growth in connected devices and newly adopted technologies, learning-based attacks and big data breaches are expected to occur more frequently. With the advances of quantum computing in the future, conventional cryptography-based security protocols may be obsolete in the future wireless networks, which makes physical layer security (PLS) an attractive alternative or complement for secure communications. In this context, cooperative beamforming (CB)-based PLS schemes are known to be effective solutions to guarantee high secrecy rate with IoT devices, which have limited power and hardware complexity. However, the existing CB-based PLS algorithms suffer from extremely low secrecy rate, in case that eavesdroppers are close to the intended receiver. To overcome such critical issue, in this paper, we propose a CB-based PLS with artificial noise (AN) injection, which can be realized in a fully distributed manner to minimize the overhead in the IoT networks with a large number of devices. We analyze the array factor using the virtual antenna array (VAA) created by the proposed PLS algorithm. Then, the secrecy rate is derived in a closed-form expression, which can be used to optimize the performance for given system parameters in both the absence and presence of channel state information (CSI) error. The proposed scheme provides considerably higher secrecy rate compared to the conventional CB-based PLS schemes, when an eavesdropper exists near to the intended receiver. Furthermore, through simulation and numerical results, we show that the secrecy rate of the proposed scheme can be maximized by adjusting the ratio between the data beamformer and AN injection beamformer components. As a result, the proposed method shows a performance improvement of up to two times compared to the conventional CB-based PLS schemes, in terms of the secrecy rate. Such performance gain increases as the angular location of Eve becomes closer to that of Bob, which corresponds to the most vulnerable situation of the conventional CB-based PLS algorithms.
With the rapid proliferation of the Internet of Things (IoT), small devices in various applications generate, process, and exchange security and safety-critical data as well as privacy-sensitive information. However, due to limited hardware capabilities of such small devices, the IoT networks are inherently vulnerable to security attacks. In this context, the analog cooperative beamforming (ACB)-based physical layer security (PLS) technique can be a suitable solution to guarantee security in the IoT networks by creating a virtual antenna array with multiple IoT devices. Nevertheless, such techniques suffer from poor secrecy performance, when an eavesdropper is located within the main beam toward the legitimate receiver. Motivated by this limitation, in this article, we propose a fully distributed null-steering beamformer, which is suitable for IoT networks following decentralized architecture. Through analysis and simulation, we show that the proposed PLS algorithm outperforms the existing ACB-based PLS scheme. Furthermore, we show that there exists an optimal degree of nulling that maximizes the secrecy performance, which can be estimated based on the analytical results.
With the rapid increase in connectivity and complexity of 6G-enabled Internet-of-Things (IoT) networks, security has become a critical concern due to the integration of various devices. Analog cooperative beamforming (ACB)-based physical layer security (PLS) algorithms have been investigated to effectively cope with such a security issue in massive IoT networks with limited hardware capabilities. However, in the existing work on ACB-based PLS, IoT nodes are typically assumed to follow Gaussian or uniform distributions, which limits the applicability in general IoT networks with arbitrary node distributions. For this reason, we propose a novel approach to analyze the secrecy rate of the ACB-based PLS scheme with arbitrarily distributed nodes using the non-parametric kernel method, where a kernel function is introduced to better estimate the actual node distribution from survey data.
Physical layer security (PLS) has grown in popularity as it guarantees secure and reliable communication between wireless devices without restricting the ability of data analysts to eavesdrop. PLS benefits from the randomization property of the wireless channel, which improves immunity and protects against many dangers of information breaches from eavesdroppers. MIMO and Massive MIMO, which use numerous antennas at the transmitter and receiver ends, are fundamental technologies for improving efficiency in 5G and future network systems. The PLS still requires a significant amount of work before it can be employed in practical systems. The interception of eavesdroppers can be made difficult by adding artificial noise. This paper focuses on the physical layer security considering maximal ratio combining, and artificial noise with beamforming techniques to further enhance the secrecy rate. The proposed work demonstrates that the use of transmitter beamforming with artificial noise can significantly improve the degree of confidentiality of information being transmitted between a transmitter and an intended receiver while making it more difficult for eavesdroppers to intercept. The simulation results in terms of bit error rate show that the proposed method has a significant impact on the error rate performance of the eavesdropper.
Physical-layer security (PLS) is a promising alternative or complement to cryptography-based solutions, because security key management and distribution required in such schemes are challenging in future wireless technologies with a distributed nature (e.g., Internet-of-Things (IoT) and massive machine-type communication). Motivated by this fact, in this paper, we investigate a PLS algorithm using linear virtual antenna array (VAA) constructed by multiple single-antenna nodes in mobile ad hoc networks such as unmanned aerial vehicles (UAVs) and mobile robots. Digital cooperative beamforming (CB) has been extensively explored such as cooperative null-steering or jamming. However, in spite of their effectiveness, the digital CB-based schemes incur prohibitive overhead and complexity, which creates difficulty in networks with limited hardware (e.g., IoT). To overcome this issue, we consider an analog CB-based PLS with linearly distributed VAA, which can be implemented in a fully distributed manner. In the proposed scheme, the received signal at the intended receiver is co-phased, while other receivers capture noise-like signals through randomized radiation patterns using the linear VAA. Assuming Rician channels, we derive secrecy rate of the proposed algorithm in a closed-from expression for an arbitrary K-factor, which is not available in previous work on the analog CB-based PLS. Furthermore, we show that the random location perturbations of the VAA elements following Gaussian distributions can enhance the secrecy performance by resolving the angle-of-departure ambiguity issue in linear arrays.
As the importance to guarantee confidentiality against security attacks by eavesdroppers has attracted significant attention, there has been much effort to enhance the security performance through cooperative beamforming (CB) in the context of physical layer security (PLS). In particular, analog CB (ACB), which requires vastly lower overhead compared to digital CB, can provide significantly higher secrecy rate compared to the PLS methods with co-located array. In this letter, we focus on ACB with open-loop architecture that requires low complexity, and consider phase offsets across multiple virtual antenna array (VAA) elements for ACB, which are caused by position estimation error of VAA. We investigate the impact of the phase offsets in ACB in terms of the secrecy throughput loss.
An increasing number of devices exchanges data via the wireless medium into large scale, decentralized, and heterogeneous networks, leading to the concept of Internet of Things (IoT). This PhD work proposes a new practical scheme to perform physical layer security (PLS) in this IoT context.Wireless communications in IoT networks are prone to passive eavesdropping. Therefore, this work introduces a scheme that guarantees the reliability of a communication from a transmitter (Alice) towards a legitimate user (Bob), while preventing multiple passive eavesdroppers (Eve) to correctly decode the data in a worst-cases scenario, thereby considering i) Eve to be noiseless, ii) Eve equipped with an arbitrarily large number of antennas, iii) Alice imperfectly estimating Bob's channel state information (CSI). To do so, a frequency domain time-reversal (TR) precoder is proposed using orthogonal frequency-division multiplexing (OFDM) and artificial noise (AN) injection. The scheme exploits the frequency diversity selective behaviour in multipath channels, and can be incorporated into existing standards, such as in LTE or 5G networks. Different configurations are investigated, namely, single-input single-output (SISO), multiple-input single-output (MISO), and single-input multiple-output (SIMO) systems.The handshake procedures between Alice and Bob are described, both in time-division duplexing (TDD) and frequency-division duplexing (FDD) systems, highlighting the amount of CSI acquisition at Eve, which influences the secrecy performances. Depending on the handshake procedures, different decoding structures are investigated at Eve for each system configuration, considering a block-fading environment. Closed-form approximations of the signal-to-noise ratio required at Bob and the maximal CSI error that can be made at Alice, in order to guarantee a communication ergodic secrecy rate (ESR), are derived. Furthermore, the optimal amount of AN energy to inject, as well as the maximal number of eavesdropper's antennas allowed, considering imperfect CSI, are also given as closed-form expressions. A trade-off on the choice of the spreading factor of the TR precoder is established between maximizing the ESR and decreasing the percentage of outage, leading to information leaked to Eve. Finally, thanks to these results, Alice can be a-priori aware of the ESR over which she can establish a secure communication, assuming worst-case assumptions regarding the eavesdroppers. The PLS scheme presented in this PhD work therefore emerges as a promising solution to secure wireless communications in IoT networks.