Wireless sensing encryption control method for programmable metasurface and wireless sensing system

Abstract

This invention discloses a wireless sensing encryption control method and wireless sensing system using programmable metasurfaces, addressing the problems of excessive complexity and cost in existing protection technologies. The invention generates a coding switching rate configuration that approximates selected spoofing activity features using a genetic optimization algorithm. Then, by appropriately switching the coding configurations of metasurface beamforming and metasurface null beamforming, a frequency offset that varies with the switching rate is generated. This allows for the injection of spoofing activity features at unlicensed receivers, thereby masking the user's leaked true activity features and obfuscating the identification results of the wireless sensing system.

Description

Technical Field

[0001] This invention relates to the field of wireless sensing security, and in particular to a wireless sensing encryption control method and related wireless sensing system based on a programmable metasurface. Background Technology

[0002] The widespread adoption of the Internet of Things (IoT) is driving digital transformation across industries, particularly in smart homes and smart healthcare. By integrating various sensors and devices, users can monitor their home environment, health status, and other life indicators in real time, thereby improving their quality of life and safety. For example, smart home systems can automatically adjust indoor temperature and lighting, and monitor air quality, while smart medical devices can track patients' health data in real time, such as sleep monitoring and respiratory detection. Meanwhile, with the proliferation of technology, the demand for wireless sensors is constantly increasing, making them increasingly important in data collection, real-time feedback, and early warning systems. However, ensuring data security and preventing the leakage of sensitive information remains an unresolved issue, especially in applications involving personal privacy and health.

[0003] However, the openness of wireless media allows attackers to easily capture signals and obtain users' private information, a risk particularly prominent in today's network environment. Research shows that, through appropriate technical means, malicious users can monitor the daily activities of family members and even obtain physiological data such as heart rate, body temperature, and activity patterns. This can not only lead to the leakage of personal privacy but also be exploited by others, causing significant security risks. For example, if monitoring devices for the elderly are hacked, caregivers may be unable to obtain timely information about their health status, and it may even affect the speed of emergency response. To address these threats, developing more secure communication protocols and encryption technologies is crucial. Many existing IoT devices lack sufficient security measures and are vulnerable to attack. Therefore, protection mechanisms for wireless sensor networks need to be strengthened to protect users' personal information and ensure the security and reliability of devices.

[0004] In wireless sensor networks that utilize wireless signals for human behavior sensing, protection mechanisms can be further refined to address specific security challenges. These can be categorized into the following three types:

[0005] Category 1: Privacy-preserving algorithms. These algorithms employ privacy-preserving technologies (such as differential privacy) to process user data, effectively preventing the leakage of individual information while still allowing the system to analyze behavioral patterns. However, they are complex to implement and may reduce the accuracy of data analysis.

[0006] The second category: Device authentication and access control. Strong authentication mechanisms ensure that only authorized users and devices can access sensitive data, improving overall security. However, complex access control management may increase the difficulty of user operations and negatively impact user experience.

[0007] The third category: Real-time monitoring and response. Building a real-time monitoring system can detect abnormal activity in a timely manner, respond quickly to potential threats, and enhance security protection. However, it requires continuous resource investment and technical support, resulting in high maintenance costs. Summary of the Invention

[0008] In view of the defects or deficiencies of the prior art, the present invention provides a wireless sensing encryption control method for programmable metasurfaces.

[0009] Therefore, the wireless sensing encryption control method for programmable metasurfaces provided by this invention includes: based on the camouflage spectral characteristic curve L act Obtain the switching time series for encryption; then control the switching between the metasurface beamforming coding configuration and the metasurface null beamforming coding configuration according to the switching time series to achieve encryption of wireless sensing features;

[0010] The method for obtaining the spoofed spectrum feature curve includes: performing time-frequency conversion processing on the wireless sensing signal received by the receiver to obtain the corresponding spectrum feature, and extracting the outer contour curve of the spectrum feature as the spoofed spectrum feature curve.

[0011] The switching time series acquisition method includes: using a genetic algorithm to obtain the optimal switching time series between the metasurface beamforming coding configuration and the metasurface null beamforming coding configuration, so as to obtain the switching time series corresponding to the spectral characteristic curve that is close to the camouflage spectral characteristic curve; the method includes:

[0012] Step 11: Randomly generate multiple initial switching time series, each initial switching time series containing several switching times;

[0013] Step 12: Perform time-frequency conversion on each initial handover time series to obtain the spectral features corresponding to each initial handover time series, and extract the outer contour curve of each spectral feature as the spectral feature curve of the corresponding initial handover time series.

[0014] Step 13: Use a genetic algorithm to obtain the spectral characteristic curves and the masquerading spectral characteristic curve L obtained in Step 12. act Based on the differences or similarities between them, the initial switching time series corresponding to the spectral feature curve with the smallest difference or the highest similarity is selected as the optimal switching time series.

[0015] An optional approach is that the difference calculation method is as follows:

[0016]

[0017]

[0018] S n The spectral characteristics corresponding to any initial switching time series;

[0019] The spectral characteristic curve corresponding to any initial switching time series;

[0020] F s For any spectral characteristic curve and the masquerading spectral characteristic curve L act The differences between them;

[0021] for With L act The Pearson correlation coefficient;

[0022] for With L act covariance;

[0023] and They are respectively With L act Standard deviation;

[0024] Alternatively, the wireless sensing signal may be a human activity signal or an animal activity signal.

[0025] An alternative is that the programmable metasurface is a 2-phase shifter metasurface.

[0026] An alternative approach is to obtain the metasurface zero-dimple beamforming coding configuration using the following method:

[0027] The particle swarm optimization algorithm is used to minimize the ensemble objective (1) under the conditions of satisfying the objective functions (2)-(3) and the constraints (4)-(5), and the metasurface zero-dimple beamforming coding configuration is obtained.

[0028]

[0029]

[0030]

[0031]

[0032]

[0033] In the above formula:

[0034] The metasurface pattern gain function;

[0035] Pattern gain function for metasurface beamforming;

[0036] var is the variance function;

[0037] (θ l , ) represents the direction of the constraint null trap;

[0038] θ0 is the elevation angle of the beam emitted from the programmable metasurface;

[0039] The azimuth angle of the beam emitted from the programmable metasurface;

[0040] BW1 is the beamwidth in the elevation direction of the emitted beam from the programmable metasurface;

[0041] BW2 is the beamwidth in the azimuth direction of the emitted beam from the programmable metasurface.

[0042] An alternative is that the switching time in step 11 is greater than 0 and less than or equal to 20ms.

[0043] The present invention also provides a related encrypted wireless sensing system, the system comprising a transmitter, a receiver, a programmable metasurface, and a controller, wherein the controller uses the above-described method to perform encrypted control of the programmable metasurface.

[0044] This invention generates a coding switching rate configuration that approximates the selected camouflage activity features through a genetic optimization algorithm. Then, by reasonably switching the coding configuration of metasurface beamforming and the coding configuration of metasurface null beamforming, a frequency offset that varies with the switching rate is generated. Camouflage activity features are injected at unauthorized receivers or unauthorized users, thereby masking the real activity features leaked by the user and completing the confusion of the identification results of the wireless sensing system. Attached Figure Description

[0045] Figure 1 In this embodiment of the invention, the comparison results are obtained by using the spectral characteristics (B: spoofed spectral characteristics) of the spoofed activity signal generated by the present invention and the spectral characteristics (A: real activity spectral characteristics) of the encrypted wireless signal.

[0046] Figure 2 for Figure 1 The outer contour curves of the two sample signals.

[0047] Figure 3 This is a comparison of the success rate of identifying unauthorized users using this invention versus those not using this method.

[0048] Figure 4 The results show the impact of using this invention on the success rate of unauthorized user identification in different orientations.

[0049] Figure 5 This is the result of the impact of the present invention on the success rate of unauthorized user recognition in gesture recognition tasks.

[0050] Figure 6 The results show the impact of different user scenarios on the success rate of unauthorized user identification. Detailed Implementation

[0051] Unless otherwise specified, the scientific and technical terms and methods used in this document are based on the understanding of a person skilled in the art or are implemented using relevant methods known to a person skilled in the art.

[0052] For a specific programmable metasurface, the metasurface beamforming coding configuration and the metasurface null beamforming coding configuration are known technologies. Those skilled in the art can obtain the metasurface beamforming coding configuration and the metasurface null beamforming coding configuration of the specific programmable metasurface using existing methods or methods optimized based on existing methods. For example, the metasurface beamforming coding configuration can be obtained using the relevant methods disclosed in the existing technology "Protego: Securing Wireless Communication via Programmable Metasurface". The metasurface null beamforming coding configuration can be optimized using a particle swarm optimization algorithm or an optimized particle swarm optimization algorithm. An optimized particle swarm optimization algorithm can minimize the set objective (1) under the conditions of satisfying the objective function (2)-(3) and the constraint conditions (4)-(5) to obtain the metasurface null beamforming coding configuration.

[0053]

[0054]

[0055]

[0056]

[0057]

[0058] In this invention, the optimal switching time series is selected by calculating the difference or similarity between the spectral characteristic curve and the masquerading spectral characteristic curve. The calculation of the curve difference or similarity can be performed using existing related methods or the related methods provided by this invention.

[0059] The following are specific examples provided by the inventor to further explain the present invention.

[0060] Example:

[0061] The wireless sensing system in this embodiment includes a transmitter, a receiver, and a programmable metasurface. Specifically, two IoT devices equipped with Intel 5300 network cards serve as the transmitter and receiver of the wireless sensing system, respectively, with a distance of 1.5m between the two devices. The programmable metasurface consists of M=16 columns and N=16 rows of reflective atoms, specifically two phase shifters that can provide four phase states (i.e., 0, π / 2, π, and 3π / 2). The metasurface is divided into four regions. For each region, two channels in the microcontroller transmit a 128-bit data stream to control 128 PIN diodes. A DC regulator (MESTEK DP3005B) is used to apply a bias voltage to the metasurface. Discrete 2-bit phase shift is achieved by changing the diode state in each unit. The system's microcontroller uses an STM32 controller and 64 SN74LV595 shift registers.

[0062] The distance between the transmitter and receiver in the system is 1.5m, and other relevant positional parameters are as follows: the metasurface is placed 0.5m behind the transmitter.

[0063] The spoofed spectral characteristics of this embodiment are in human activity signals (see the spectral characteristics of this signal). Figure 1 Based on (as shown in B), the spectral characteristics of the encrypted wireless signal are obtained as follows: Figure 1 As shown in Figure A, the outer contour curves corresponding to the two signals are as follows: Figure 2 As shown in the figure, the two have a very high degree of similarity.

[0064] In this embodiment, the metasurface beamforming coding configuration is obtained using the relevant methods disclosed in the existing technology "Protego: Securing Wireless Communication via Programmable Metasurface"; the metasurface null-depression beamforming coding configuration can be obtained using the optimized particle swarm optimization algorithm described above. The method of this invention is used to encrypt and control the system signal transmission process. In step 11, the switching time is greater than 0 and less than or equal to 20ms; in step 13, a genetic algorithm is used to obtain each spectral characteristic curve and the camouflaged spectral characteristic curve L. act The differences between them are considered, and the initial switching time series corresponding to the spectral characteristic curves of the differences are selected as the optimal switching time series. The method for calculating the differences is as follows:

[0065]

[0066]

[0067] S nThe spectral characteristics corresponding to any initial switching time series;

[0068] The spectral characteristic curve corresponding to any initial switching time series;

[0069] F s For any spectral characteristic curve and the masquerading spectral characteristic curve L act The differences between them;

[0070] for With L acc The Pearson correlation coefficient;

[0071] for With L att covariance;

[0072] and They are respectively for With L act The standard deviation.

[0073] Furthermore, the inventors evaluated the effects of the present invention from the following aspects:

[0074] Comparison of unauthorized user identification accuracy under different protection schemes:

[0075] Figure 3 The comparison results of the accuracy of unauthorized users' perception of leaked wireless signals under different protection schemes specifically include: (1) no metasurface protection (i.e., no metasurface in the system); (2) random switching between two configurations, metasurface beamforming coding configuration and metasurface null beamforming coding configuration, based on the above embodiments (i.e., only the metasurface coding configuration is applicable); (3) the above embodiments (i.e., the method of the present invention is fully applicable). It can be seen that the accuracy of eavesdropper identification is significantly reduced when using the scheme of the present invention, and the method can obviously achieve chaotic camouflage of the eavesdropping channel.

[0076] The impact of metasurface orientation on the success rate of unauthorized user identification: Figure 4 To measure the recognition success rate of different recognition methods (CNN classifier, SVM classifier, KNN classifier, Bayes classifier, and decision tree classifier) ​​when the metasurface is deployed in different directions of the transmitter, the results show that the metasurface exhibits robustness when deployed in different directions; the horizontal axis in the figure represents the angle of the metasurface deployment in different orientations; the protection success rate is calculated or obtained as follows: the protection success rate is calculated as: number of recognition failures / total number of recognition attempts.

[0077] The impact of this invention on the success rate of unauthorized user recognition in gesture recognition tasks: Figure 5 To protect the evaluation results of users against unauthorized eavesdroppers in gesture recognition tasks using this invention, the success rate of passive eavesdropping is significantly reduced when using this invention compared to when it is not used (i.e., randomly switching between two configurations: metasurface beamforming coding configuration and metasurface null beamforming coding configuration based on the above embodiment scheme).

[0078] The impact of different user scenarios on the success rate of unauthorized user identification: Figure 6 The evaluation results of the present invention for identifying unauthorized eavesdroppers in different user scenarios show that the present invention is insensitive to changes in users and has robust generalization.

Claims

1. A wireless sensing encryption control method for a programmable metasurface, characterized in that, The method includes: based on the camouflage spectral characteristic curve L act Obtain the switching time series for encryption; then control the switching between the metasurface beamforming coding configuration and the metasurface null beamforming coding configuration according to the switching time series to achieve encryption of wireless sensing features; The method for obtaining the spoofed spectrum feature curve includes: performing time-frequency conversion processing on the wireless sensing signal received by the receiver to obtain the corresponding spectrum feature, and extracting the outer contour curve of the spectrum feature as the spoofed spectrum feature curve. The switching time series acquisition method includes: using a genetic algorithm to obtain the optimal switching time series between the metasurface beamforming coding configuration and the metasurface null beamforming coding configuration, so as to obtain the switching time series corresponding to the spectral characteristic curve that is close to the camouflage spectral characteristic curve; the method includes: Step 11: Randomly generate multiple initial switching time series, each initial switching time series containing several switching times; Step 12: Perform time-frequency conversion on each initial handover time series to obtain the spectral features corresponding to each initial handover time series, and extract the outer contour curve of each spectral feature as the spectral feature curve of the corresponding initial handover time series. Step 13: Use a genetic algorithm to obtain the spectral characteristic curves and the masquerading spectral characteristic curve L obtained in Step 12. act Based on the differences or similarities between them, the initial switching time series corresponding to the spectral feature curve with the smallest difference or the highest similarity is selected as the optimal switching time series.

2. The wireless sensing encryption control method for programmable metasurfaces according to claim 1, characterized in that, The method for calculating the difference is as follows: S n The spectral characteristics corresponding to any initial switching time series; The spectral characteristic curve corresponding to any initial switching time series; F s For any spectral characteristic curve and the masquerading spectral characteristic curve L act The differences between them; for With L acc The Pearson correlation coefficient; for With L act covariance; and They are respectively With L act The standard deviation.

3. The wireless sensing encryption control method for programmable metasurfaces according to claim 1, characterized in that, The wireless sensing signal is a human activity signal or an animal activity signal.

4. The wireless sensing encryption control method for programmable metasurfaces according to claim 1, characterized in that, The programmable metasurface is a 2-phase shifter metasurface.

5. The wireless sensing encryption control method for programmable metasurfaces according to claim 1, characterized in that, The metasurface zero-dimple beamforming encoding configuration is obtained using the following method: The particle swarm optimization algorithm is used to minimize the ensemble objective (1) under the conditions of satisfying the objective functions (2)-(3) and the constraints (4)-(5), and the metasurface zero-dimple beamforming coding configuration is obtained. In the above formula: The metasurface pattern gain function; Pattern gain function for metasurface beamforming; var is the variance function; To constrain the direction of the null trap; θ0 is the elevation angle of the beam emitted from the programmable metasurface; The azimuth angle of the beam emitted from the programmable metasurface; BW1 is the beamwidth in the elevation direction of the emitted beam from the programmable metasurface; BW2 is the beamwidth in the azimuth direction of the emitted beam from the programmable metasurface.

6. The wireless sensing encryption control method for programmable metasurfaces according to claim 1, characterized in that, In step 11, the switching time value is greater than 0 and less than or equal to 20ms.

7. An encrypted wireless sensing system, comprising a transmitter, a receiver, a programmable metasurface, and a controller, characterized in that, The controller employs the method described in any one of claims 1-6 to perform encrypted control of the programmable metasurface.

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