Physical layer secret key generation schemes can provide lightweight encryption for resource constrained Inter?net of Things (IoT) devices. However, many IoT devices are stationary and the environment is usually static, which leads to ultra-low/zero secret key rates. To circumvent this problem, a novel secret key generation scheme based on Reconfigurable Intelligent Surface (RIS) is proposed for Multiple-Input Single?Output IoT communications. The RIS is introduced into the network, and the phase shifts at RIS are tuned randomly in each frame, which superimpose time-varying reflection channels on the orginal channels. And then the secret key capacity of all nodes within the cell coverage area can be remarkably improved. The closed-form expressions of secret key capacity taking into account eavesdropping are first derived for the case studies of independent and correlated channels. We can conclude that sharing the same phase shifts randomness will not bring key information leakage. Compared with three benchmark schemes, the proposed scheme can both maintain good performance whenever eavesdropping nodes exist. Further, the impact of parameters, such as the number of reflecting units, the transmit antennas and the Rician factor, on secret key performance are analyzed respectively, which can offer guidelines for system design. Finally, simulation results verify the theoretical analysis.
Physical layer secret key generation schemes can provide lightweight encryption for resource constrained Inter?net of Things (IoT) devices. However, many IoT devices are stationary and the environment is usually static, which leads to ultra-low/zero secret key rates. To circumvent this problem, a novel secret key generation scheme based on Reconfigurable Intelligent Surface (RIS) is proposed for Multiple-Input Single?Output IoT communications. The RIS is introduced into the network, and the phase shifts at RIS are tuned randomly in each frame, which superimpose time-varying reflection channels on the orginal channels. And then the secret key capacity of all nodes within the cell coverage area can be remarkably improved. The closed-form expressions of secret key capacity taking into account eavesdropping are first derived for the case studies of independent and correlated channels. We can conclude that sharing the same phase shifts randomness will not bring key information leakage. Compared with three benchmark schemes, the proposed scheme can both maintain good performance whenever eavesdropping nodes exist. Further, the impact of parameters, such as the number of reflecting units, the transmit antennas and the Rician factor, on secret key performance are analyzed respectively, which can offer guidelines for system design. Finally, simulation results verify the theoretical analysis.
Physical layer secret key generation schemes can provide lightweight encryption for resource-constrained Internet of Things (IoT) devices. However, many IoT devices are stationary and the environment is usually static, which leads to ultra-low/zero secret key rates. To circumvent this problem, a novel secret key generation scheme based on Reconfigurable Intelligent Surface (RIS) is proposed for Multiple-Input Single-Output IoT communications. The RIS is introduced into the network, and the phase shifts at RIS are tuned randomly in each frame, which makes time-varying reflection channels superimposed on the original channels. And then, the secret key capacity of all nodes within the cell coverage area can be remarkably improved. Compared with the existing methods, the scheme is loosely coupled with the existing communication system. The closed-form expressions of secret key capacity taking into account eavesdropping are first derived for the case studies of independent and correlated channels. We can conclude that sharing the same phase shifts randomness will not bring key information leakage. Compared with existing user-introduced randomness schemes, the proposed scheme can maintain good performance as the number of eavesdropping nodes increases. Further, the impact of parameters, such as the number of reflecting units, the transmit antennas, and the Rician factor, on secret key performance are analyzed respectively, which can offer guidelines for system design. Finally, simulation results verify the theoretical analysis.
Physical layer secret key generation schemes can provide lightweight encryption for resource-constrained Internet of Things (IoT) devices. However, many IoT devices are stationary and the environment is usually static, which leads to ultra-low/zero secret key rates. To circumvent this problem, a novel secret key generation scheme based on Reconfigurable Intelligent Surface (RIS) is proposed for Multiple-Input Single-Output IoT communications. The RIS is introduced into the network, and the phase shifts at RIS are tuned randomly in each frame, which makes time-varying reflection channels superimposed on the original channels. And then, the secret key capacity of all nodes within the cell coverage area can be remarkably improved. Compared with the existing methods, the scheme is loosely coupled with the existing communication system. The closed-form expressions of secret key capacity taking into account eavesdropping are first derived for the case studies of independent and correlated channels. We can conclude that sharing the same phase shifts randomness will not bring key information leakage. Compared with existing user-introduced randomness schemes, the proposed scheme can maintain good performance as the number of eavesdropping nodes increases. Further, the impact of parameters, such as the number of reflecting units, the transmit antennas, and the Rician factor, on secret key performance are analyzed respectively, which can offer guidelines for system design. Finally, simulation results verify the theoretical analysis.
A conventional secret key generation scheme achieves a low secret key rate in static environments. To solve this problem, the reconfigurable intelligent surface (RIS) antenna is merged with the device at the base station and is firstly proposed for secret key generation. By tuning the phase shifts at the RIS, a random varying beam can be generated and combined with the original wireless channel to form a new equivalent channel so that the key capacity is not subject to the time variance of the wireless channel. Then the closed-form solution is derived. Theoretical analysis and simulation show that the performance can be increased to that of a fast varying channel with limited radiating units. Moreover, the proposed scheme is superior to the existing schemes, in which the RIS is used as a reflective surface, over performance gain and preventing information disclosure. To further develop the gain of RIS, a joint design scheme is proposed, where some units are used for key generation, and some units are used for beamforming. Finally, the joint's closed-form solution scheme is derived. Finally, on the basis of satisfying the key capacity and communication performance, an optimal resource allocation strategy is proposed. Simulation results illustrate that the key capacity and communication performance can be improved simultaneously.
Physical layer secret key generation (SKG) technology can provide lightweight encryption for resource-constrained Internet of Things (IoT) devices. However, many IoT devices are stationary, and the environment is usually static, which leads to ultralow/zero secret key rates. To circumvent this problem, a novel SKG scheme based on reconfigurable intelligent surface (RIS) is proposed for multiple-input single-output (MISO) communications. First, the phase shifts at the RIS are tuned randomly in each frame, which makes time-varying reflection channels superimposed on the original channels. Correspondingly, the key capacity of all nodes within the cell coverage area can be remarkably improved. Compared with the existing methods, the scheme is loosely coupled with the original system. Besides, the closed-form expressions of key capacity taking into account eavesdropping are derived for the case studies of independent and correlated channels. Based on these derivations, we can conclude that sharing the same phase shifts randomness will not lead to key information leakage. More importantly, compared with user-introduced randomness schemes, the proposed scheme can maintain good performance even the number of eavesdropping nodes increases. Furthermore, the impacts of critical parameters on key performance are analyzed, respectively, which offers guidelines for system design. Finally, simulation results verify the theoretical analysis.
Physical-layer key generation (PLKG) can significantly enhance the security of classic encryption schemes by efficiently providing secret keys in resource-limited network like the Internet of Things (IoT). However, reaching a high key generation rate (KGR) is challenging in applications like smart home or remote area sensing with quasi-static channels. Recently, exploiting reconfigurable intelligent surface (RIS) to induce randomness in quasi-static wireless channels has received significant research interest. However, the inherent spatial correlation among the RIS elements is rarely studied, which can alter the optimum physical-layer key generation (PLKG) approach in terms of KGR and randomness in the key sequence. Specifically, for the first time, in this contribution, we take into account a spatially correlated RIS, which intends to enhance the KGR in a quasi-static medium. Novel closed-form analytical expressions for KGR are derived for the two cases of random phase shift (RPS) and our proposed equal phase shift (EPS) in the RIS elements. We also analyze the correlation between the channel samples to ensure the randomness of the generated secret key sequence. It is shown that the EPS scheme can effectively exploit the inherent spatial correlation between the RIS elements and it leads to a higher KGR compared to the widely used RPS strategy. We further formulate an optimization problem in which we determine the optimal portion of time dedicated to direct and indirect channel estimation, which has never been addressed in previous studies. We show the accuracy and the fast convergence of our sequential convex programming (SCP)-based algorithm and discuss the various parameters affecting spatially correlated RIS-assisted PLKG.
Physical layer key generation (PLKG) is a promising method to generate secret keys by harnessing the randomness of wireless channels. In this letter, a reconfigurable intelligent surface (RIS)-assisted multi-carrier PLKG framework is proposed to overcome the lack of channel randomness in static environments. Specifically, the secret key generation rate (SKGR) is analyzed based on the inter-frequency correlation among the channel coefficients across subcarriers. Building upon this analysis, a novel scheme for configuring RIS elements is proposed to enhance the SKGR by introducing correlations into the random phase shifts of RIS elements. Simulation results are presented to validate the effectiveness of the proposed scheme.