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

Abstract

The invention discloses a wireless sensing encryption control method of a programmable metasurface and a wireless sensing system, and solves the problem that the complexity and the cost of the existing protection technology are too high. According to the invention, coding switching rate configuration approaching selected camouflage activity features is generated through a genetic optimization algorithm, then frequency deviation changing along with the switching rate is generated through reasonable switching of coding configuration of metasurface beam forming and coding configuration of metasurface null beam forming, and camouflage activity feature injection is realized at an unauthorized receiver. Therefore, real activity features leaked by the user are covered, and recognition result confusion of the wireless sensing system is completed.

Description

Technical Field

[0001] The present invention relates to the field of wireless sensing security, and in particular to a wireless sensing encryption control method of a programmable metasurface and a related wireless sensing system. Background Art

[0002] The widespread application of the Internet of Things (IoT) is driving the digital transformation of various industries, especially in the fields of smart home 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 the quality of life and safety. For example, smart home systems can automatically adjust indoor temperature, lighting, and monitor air quality, while smart medical devices can track patients' health data in real time, such as sleep monitoring and breathing detection. At the same time, with the popularization of technology, the demand for wireless sensors continues to increase, making them increasingly important in data collection, real-time feedback and early warning systems. However, how to ensure the security of data and prevent the leakage of sensitive information behind these conveniences remains an unresolved problem, especially in application scenarios involving personal privacy and life and health.

[0003] However, the openness of wireless media allows attackers to easily capture signals and obtain users' private information. This risk is particularly prominent in today's network environment. Studies have shown 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 may not only lead to the leakage of personal privacy, but also be exploited by others, causing major security risks. For example, if the monitoring equipment for the elderly is hacked, it may prevent the guardian from knowing their health status in time, and even affect the response speed of emergency rescue. In order to deal with these threats, it is particularly important to develop more secure communication protocols and encryption technologies. Many existing IoT devices lack sufficient security measures and are vulnerable to attacks. Therefore, the protection mechanism for wireless sensor networks needs to be strengthened to protect users' personal information and ensure the security and reliability of devices.

[0004] In wireless sensor networks that use wireless signals to sense human behavior, protection mechanisms can be further refined to address specific security challenges. They can be divided into the following three categories:

[0005] The first category: privacy-preserving algorithms. Using privacy-preserving techniques (such as differential privacy) to process user data can effectively prevent individual information leakage while still allowing the system to analyze behavioral patterns. However, the implementation is complex and may reduce the accuracy of data analysis.

[0006] The second category: device authentication and access control. Through a strong identity authentication mechanism, only authorized users and devices can access sensitive data, improving overall security. However, complex access control management may increase the difficulty of user operation and affect user experience.

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

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

[0009] To this end, the wireless sensing encryption control method of the programmable metasurface provided by the present invention comprises: according to the camouflage spectrum characteristic curve L act Obtaining a switching time sequence for encryption; then controlling the switching of the metasurface beamforming coding configuration and the metasurface zero-notch beamforming coding configuration according to the switching time sequence to achieve encryption of wireless sensor features;

[0010] The method for obtaining a camouflaged spectrum characteristic curve comprises: performing time-frequency conversion processing on a wireless sensor signal received by a receiver to obtain a corresponding spectrum characteristic, and extracting an outer contour curve of the spectrum characteristic as a camouflaged spectrum characteristic curve;

[0011] The switching time sequence acquisition method comprises: using a genetic algorithm to obtain an optimal switching time sequence between a metasurface beamforming coding configuration and a metasurface null-notch beamforming coding configuration, so as to obtain a switching time sequence corresponding to a spectrum characteristic curve close to a camouflaged spectrum characteristic curve; the method comprises:

[0012] Step 11, randomly generating multiple initial switching time sequences, each of which includes a number of switching times;

[0013] Step 12: Perform time-frequency conversion on each initial switching time series to obtain the spectrum characteristics corresponding to each initial switching time series, and extract the outer contour curve of each spectrum characteristic as the spectrum characteristic curve of the corresponding initial switching time series.

[0014] Step 13: Using a genetic algorithm to obtain the spectrum characteristic curves obtained in step 12 and the camouflage spectrum characteristic curve L act The difference or similarity between them is determined, and the initial switching time sequence corresponding to the spectrum characteristic curve with the smallest difference or the highest similarity is selected as the optimal switching time sequence.

[0015] An optional solution is that the difference calculation method is:

[0016]

[0017]

[0018] S n is the spectrum feature corresponding to any initial switching time series;

[0019] is the spectrum characteristic curve corresponding to any initial switching time series;

[0020] F s Any spectrum characteristic curve and camouflage spectrum characteristic curve L act The differences between

[0021] for With L act Pearson correlation coefficient of

[0022] for With L act The covariance of

[0023] and They are With L act The standard deviation of

[0024] An optional solution is that the wireless sensor signal is a human activity signal or an animal activity signal.

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

[0026] Alternatively, the metasurface null-notch beamforming coding configuration is obtained by the following method:

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

[0028]

[0029]

[0030]

[0031]

[0032]

[0033] In the above formula:

[0034] is the metasurface pattern gain function;

[0035] The pattern gain function when shaping the metasurface beam;

[0036] var is the variance function;

[0037] (θ l , ) is the direction of the constraint zero sink;

[0038] θ0 is the pitch angle of the programmable metasurface outgoing beam;

[0039] is the azimuth angle of the outgoing beam of the programmable metasurface;

[0040] BW1 is the beam width of the programmable metasurface outgoing beam in the pitch angle direction;

[0041] BW2 is the beam width of the programmable metasurface outgoing beam in the azimuth direction.

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

[0043] The present invention also provides a related encrypted wireless sensing system, which includes a transmitter, a receiver, a programmable metasurface and a controller. The controller uses the above method to perform encryption control on the programmable metasurface.

[0044] The present invention generates a coding switching rate configuration that approximates the selected camouflaged activity characteristics through a genetic optimization algorithm, and then generates a frequency offset that varies with the switching rate by reasonably switching the coding configuration of metasurface beamforming and the coding configuration of metasurface null-notch beamforming, thereby realizing the injection of camouflaged activity characteristics at unauthorized receivers or unauthorized users, thereby concealing the real activity characteristics leaked by the user and completing the confusion of the recognition results of the wireless sensor system. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 In the embodiment of the present invention, the comparison result is obtained by using the spectrum characteristics of the disguised activity signal generated by the present invention (B: disguised spectrum characteristics) and the spectrum characteristics of the encrypted wireless signal (A: real activity spectrum characteristics).

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

[0047] Figure 3 The figure is a comparison result of the unauthorized user identification success rate between the method using the present invention and the method not using the present invention.

[0048] Figure 4 The results are as follows: the impact of the present invention on the success rate of unauthorized user identification when using the present invention in different directions.

[0049] Figure 5 This is the result of the present invention's influence on the success rate of unauthorized user recognition in the gesture recognition task.

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

[0051] Unless otherwise specified, the scientific and technical terms and methods in this document are based on the understanding of ordinary technicians in the relevant fields or are implemented using relevant methods known to ordinary technicians in the relevant fields.

[0052] For a specific programmable metasurface, metasurface beamforming coding configuration and metasurface zero-notch beamforming coding configuration are well-known technologies. Those skilled in the art can obtain the metasurface beamforming coding configuration and metasurface zero-notch beamforming coding configuration of a 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 related methods disclosed in the prior art "Protego:Securing Wireless Communication viaProgrammable Metasurface". The metasurface zero-notch beamforming coding configuration can be obtained using a particle swarm optimization algorithm or an optimized particle swarm optimization algorithm activity. An optimized particle swarm optimization algorithm such as minimizing the set objective (1) under the conditions of satisfying objective functions (2)-(3) and constraints (4)-(5) to obtain the metasurface zero-notch beamforming coding configuration;

[0053]

[0054]

[0055]

[0056]

[0057]

[0058] In the solution of the present invention, the optimal switching time sequence is selected by calculating the difference or similarity between the spectrum characteristic curve and the camouflage spectrum characteristic curve. The calculation of the curve difference or similarity can select the existing related method or adopt the related method provided by the present invention.

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

[0060] Example:

[0061] The wireless sensing system of this embodiment includes a transmitter, a receiver and a programmable metasurface. Specifically, two IoT devices equipped with Intel 5300 network cards are used as the transmitter and receiver of the wireless sensing system respectively, and the distance between the two machines is 1.5m; the programmable metasurface is composed of M = 16 columns and N = 16 rows of reflective atoms, and specifically 2 phase shifters can provide 4 phase states (i.e., 0, π / 2, π and 3π / 2); the metasurface plate is divided into 4 areas, and for each area, two channels in the microcontroller are used to transmit 128-bit data streams to control 128 PIN diodes, and a DC regulator (MESTEK DP3005B) is used to apply a bias voltage to the metasurface, and a 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 the receiver in the system is 1.5 m, and other relevant position parameters are: the metasurface is placed 0.5 m behind the transmitter.

[0063] The camouflage spectrum feature of this embodiment is in the human activity signal (the spectrum feature of the signal is shown in FIG. Figure 1 B), the spectrum characteristics of the encrypted wireless characteristic signal are: Figure 1 As shown in A, the outer contour curves corresponding to the two signals are as follows Figure 2 As shown; the figure shows that the two have a very high similarity.

[0064] The metasurface beamforming coding configuration of this embodiment is obtained by using the related method disclosed in the prior art "Protego:Securing WirelessCommunication via Programmable Metasurface"; the metasurface zero-notch beamforming coding configuration can be obtained by using the above-mentioned optimized particle swarm optimization algorithm. The method of the present invention is used to encrypt and control the system signal transmission process. The switching time in step 11 of the scheme is greater than 0 and less than or equal to 20ms; in step 13, a genetic algorithm is used to obtain each spectrum characteristic curve and the camouflage spectrum characteristic curve L act The difference between them is selected, and the initial switching time sequence corresponding to the different spectrum characteristic curve is selected as the optimal switching time sequence, where the difference calculation method is:

[0065]

[0066]

[0067] S nis the spectrum feature corresponding to any initial switching time series;

[0068] is the spectrum characteristic curve corresponding to any initial switching time series;

[0069] F s Any spectrum characteristic curve and camouflage spectrum characteristic curve L act The differences between

[0070] for With L acc Pearson correlation coefficient of

[0071] for With L att The covariance of

[0072] and Respectively With L act The standard deviation of .

[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 include: (1) no metasurface protection (i.e., no metasurface in the system); (2) random switching of metasurface beamforming coding configuration and metasurface zero-notch beamforming coding configuration based on the above embodiment (i.e., only the metasurface coding configuration is applicable); (3) the above embodiment (i.e., the method of the present invention is fully applicable). It can be seen that the recognition accuracy of the eavesdropper is significantly reduced when the scheme of the present invention is used, and the method can obviously achieve chaotic camouflage of the eavesdropping channel.

[0076] The results of the impact of the metasurface in different directions on the success rate of unauthorized user identification: Figure 4 To calculate 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 can be robust when deployed in different directions; the abscissa in the figure is: where the abscissa is the angle of the metasurface deployed in different orientations; the protection success rate calculation or acquisition method is: the protection success rate calculation method is: number of recognition failures / total number of recognitions.

[0077] The impact of the present invention on the success rate of unauthorized user recognition in gesture recognition tasks: Figure 5 In order to use the present invention to protect users against unauthorized eavesdroppers in gesture recognition tasks, the evaluation results show that the recognition success rate of passive eavesdropping is significantly reduced after using the present invention compared to when the present invention is not used (i.e., the two configurations of metasurface beamforming coding configuration and metasurface zero-notch beamforming coding configuration are randomly switched based on the above-mentioned 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 on the identification of unauthorized eavesdroppers in different user scenarios show that the present invention is insensitive to user changes and has robust generalization.

Claims

1. A wireless sensing encryption control method for a programmable metasurface, characterized in that: The method comprises: according to the camouflage spectrum characteristic curve L act Obtaining a switching time sequence for encryption; then controlling the switching of the metasurface beamforming coding configuration and the metasurface zero-notch beamforming coding configuration according to the switching time sequence to achieve encryption of wireless sensor features; The method for obtaining a camouflaged spectrum characteristic curve comprises: performing time-frequency conversion processing on a wireless sensor signal received by a receiver to obtain a corresponding spectrum characteristic, and extracting an outer contour curve of the spectrum characteristic as a camouflaged spectrum characteristic curve; The switching time sequence acquisition method comprises: using a genetic algorithm to obtain an optimal switching time sequence between a metasurface beamforming coding configuration and a metasurface null-notch beamforming coding configuration, so as to obtain a switching time sequence corresponding to a spectrum characteristic curve close to a camouflaged spectrum characteristic curve; the method comprises: Step 11, randomly generating multiple initial switching time sequences, each of which includes a number of switching times; Step 12: Perform time-frequency conversion on each initial switching time series to obtain the spectrum characteristics corresponding to each initial switching time series, and extract the outer contour curve of each spectrum characteristic as the spectrum characteristic curve of the corresponding initial switching time series. Step 13: Using a genetic algorithm to obtain the spectrum characteristic curves obtained in step 12 and the camouflage spectrum characteristic curve L act The difference or similarity between them is used to select the initial switching time sequence corresponding to the spectrum characteristic curve with the smallest difference or the highest similarity as the optimal switching time sequence.

2. The wireless sensing encryption control method of programmable metasurface according to claim 1 is characterized in that: The difference calculation method is: S n is the spectrum feature corresponding to any initial switching time series; is the spectrum characteristic curve corresponding to any initial switching time series; F s Any spectrum characteristic curve and camouflage spectrum characteristic curve L act The differences between for With L acc Pearson correlation coefficient of for With L act The covariance of and They are With L act The standard deviation of .

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

4. The wireless sensing encryption control method of a programmable metasurface according to claim 1 is characterized in that: The programmable metasurface is a 2D phase shifter metasurface.

5. The wireless sensing encryption control method of programmable metasurface according to claim 1 is characterized in that: The metasurface null-notched beamforming coding configuration is obtained using the following method: The particle swarm optimization algorithm is used to minimize the set objective (1) under the conditions of satisfying the objective functions (2)-(3) and the constraints (4)-(5), and the metasurface null-notch beamforming coding configuration is obtained; In the above formula: is the metasurface pattern gain function; The pattern gain function when shaping the metasurface beam; var is the variance function; is the direction of the constraint zero sink; θ0 is the pitch angle of the programmable metasurface outgoing beam; is the azimuth angle of the outgoing beam of the programmable metasurface; BW1 is the beam width of the programmable metasurface outgoing beam in the pitch angle direction; BW2 is the beam width of the programmable metasurface outgoing beam in the azimuth direction.

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

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

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