Enhanced sensing age analysis optimization method for wireless power supply sensor network

Abstract

The invention discloses an enhanced perception age analysis optimization method for a wireless power supply sensor network, and relates to the field of time delay sensitive networks, comprising the following steps: S1, constructing a network model; s2, determining an MAC layer model; s3, determining a wireless channel model; s4, modeling an energy receiving process; s5, evaluating the information transmission capability; s6, modeling fusion reliability; s7, defining and updating a sensing age; s8, constructing an expected penalty function; s9, establishing a joint optimization problem, and solving to obtain a scheduling and distribution strategy with the minimum expected penalty; modeling credibility of fusion information by using a hypothesis testing theory in combination with an instantaneous channel state and a sensing information volume, and constructing a closed expression of a fusion success probability; defining an enhanced sensing age index, establishing a recursive evolution model of the enhanced sensing age index, and introducing a frame-level freshness penalty function; through joint optimization of a sensor scheduling matrix and a time slot allocation vector, precise modeling and minimization of the timeliness of sensing information in a dynamic wireless environment are realized.

Description

Technical Field

[0001] This invention relates to the field of time-delay-sensitive networks, and more particularly to an enhanced sensing age analysis optimization method for wirelessly powered sensor networks. Background Technology

[0002] With the rapid development of wireless communication and Internet of Things (IoT) technologies, ubiquitous sensing "Internet of Everything" systems are widely used in smart cities, industrial monitoring, and disaster response. These systems typically deploy a large number of low-power sensor nodes distributed across different geographical locations to collect and upload environmental sensing information in real time, forming a Wireless Sensor Network (WSN) system. To improve decision-making accuracy, sensing information from multiple sensors needs to be transmitted to a fusion center (FC) for fusion and decision-making, forming a "sensory fusion" mechanism. This process places higher demands on the freshness and timeliness of the fused information.

[0003] Traditional performance metrics such as latency and packet loss rate cannot accurately measure the freshness of information content itself. Therefore, Information Age (AoI) is introduced as a new indicator to measure the "timeliness" of information. However, AoI only focuses on the upload time of information from a single node and is not suitable for multi-sensor fusion scenarios. To address this, Perception Age (AoS) is proposed as a metric to measure the freshness of fused information. It is defined as the interval between the current moment and the earliest generation time of the perception information used in the most recent reliable fusion.

[0004] On the other hand, power supply sustainability remains a key bottleneck in the deployment of wireless sensor networks (WSNs). Wireless Powered Sensor Networks (WPSNs) decouple long-distance power supply and information sensing through downlink radio frequency power transmission and energy harvesting circuits, providing a new low-maintenance operating mode for wide-area distributed networks. However, existing technologies still suffer from problems such as inaccurate measurement of the freshness of sensed information, idealized fusion decision mechanisms, and a lack of time-varying adaptability in scheduling and allocation mechanisms. Summary of the Invention

[0005] The purpose of this invention is to design an enhanced sensing age analysis optimization method for wirelessly powered sensor networks in order to solve the above problems.

[0006] The present invention achieves the above objectives through the following technical solutions:

[0007] An enhanced sensing age analysis optimization method for wirelessly powered sensor networks includes:

[0008] S1. Network Model Construction: The network model includes a full-duplex fusion center (FC), M sensors distributed in different geographical locations, and the target to be monitored. The fusion center (FC) performs parallel operations of downlink radio frequency energy broadcasting and uplink sensing information reception in each frame period. The sensors store the harvested radio frequency energy through supercapacitors and upload the previously collected sensing information in the scheduling time slot. This process is called Wireless Sensing Information Transmission (WSIT). Each sensor is equipped with two antennas, one for receiving energy and the other for uploading information. After each frame period, the fusion center (FC) fuses the successfully decoded sensing information and judges the reliability of the fusion result based on the received content.

[0009] S2. Determine the MAC layer model; A complete frame structure is uniformly scheduled by the fusion center (FC), and includes the following stages in sequence;

[0010] S21, Control Phase: The fusion center FC operates for a fixed duration. Within the frame, channel estimation, frame synchronization, sensing information acquisition and resource allocation are completed, and the scheduling order and corresponding time slot of each sensor in the frame are determined.

[0011] S22, Energy Charging Phase: Lasting for a duration of... During the energy pre-charging interval, all sensors simultaneously receive the radio frequency energy transmitted by the FC downlink from the fusion center and store it in their supercapacitors, providing energy for subsequent uploading of sensing information;

[0012] S23, Sensing Information Upload Phase: A total of L upload sub-slots Time Division Multiple Access (TDMA) is allocated to each sensor, and each sensor... Only in the time slots it is allocated Upload information within the app, and only use it in [the app / system]. to The accumulated energy satisfies the causal requirements of energy;

[0013] S24. Information Fusion Stage: After all sensors have uploaded their sensing information, the Fusion Center (FC) fuses the successfully received sensing information and evaluates whether the fusion result meets the reliability requirements.

[0014] S3. Determine the wireless channel model: The channel between the fusion center (FC) and each sensor is an independent Rician fading channel, with channel gain... It includes both line-of-sight (LOS) and non-line-of-sight (NLOS) components; path loss coefficient. Distance from sensor to fusion center FC Related data is collected and modeled based on the channel reference loss model; a residual self-interference channel is constructed to address the self-interference problem caused by the full-duplex operation mode of the fusion center FC. Introducing the inhibition coefficient Depicting the overall effect of analog and digital interference cancellation;

[0015] S4. Modeling the energy receiving process: Use a multi-segment linear model to fit the energy rectification characteristics and calculate the received power of each sensor;

[0016] S5. Evaluate information transmission capability: Combine energy causality and instantaneous channel state information to determine whether the information threshold required for fusion is met;

[0017] S6. Modeling the reliability of fusion: Introducing hypothesis testing theory to estimate the reliability probability of the fusion result based on the information volume;

[0018] S7. Defining and Updating Perceived Age: Constructing an enhanced perceived age index and recursive expression, quantifying the freshness loss function, and enhancing perceived age. Defined as the difference between the current time and the time when the latest reliable fused information was generated, and enhanced perceived age. The update at the beginning and end of each frame specifically involves: introducing a fusion success indicator variable. If fusion is successful within frame x, then Furthermore, enhanced perception decreases age. Otherwise, add one frame length. The update rule is expressed as: ;

[0019] in, This represents the enhanced perceived age value in the first time slot of frame x. This represents the enhanced perceived age value in the Lth time slot of the xth frame. This represents the enhanced perceived age value in the first time slot of the (x+1)th frame.

[0020] S8. Construct the expected penalty function based on fusion reliability;

[0021] S9. Establish a joint optimization problem with the expected penalty function as the objective, and use the Hungarian algorithm and recursive algorithm to solve it to obtain the sensor scheduling and time slot allocation strategy with the minimum expected penalty.

[0022] An enhanced sensing age analysis optimization device for wirelessly powered sensor networks includes:

[0023] Storage; storage is used to store computer programs;

[0024] An actuator; the actuator is used to execute a computer program in a storage device, which, when executing the computer program, implements the enhanced sensing age analysis optimization method for wirelessly powered sensor networks as described above.

[0025] A computer-readable storage medium storing a computer program that is executed by a processor to implement the enhanced sensing age analysis optimization method for wirelessly powered sensor networks as described above.

[0026] The beneficial effects of this invention are as follows: by combining instantaneous channel state and the volume of sensed information, the reliability of fused information is modeled using hypothesis testing theory, and a closed expression of the probability of successful fusion is constructed; on this basis, an enhanced sensed age index is defined and its recursive evolution model is established, and a frame-level freshness penalty function is introduced; by jointly optimizing the sensor scheduling matrix and the time slot allocation vector, accurate modeling and minimization of the timeliness of sensed information in a dynamic wireless environment are achieved. Attached Figure Description

[0027] Figure 1 This is a flowchart of the enhanced sensing age analysis optimization method for wirelessly powered sensor networks according to the present invention.

[0028] Figure 2 This is a schematic diagram of the network model of the present invention;

[0029] Figure 3 This is a frame structure diagram of the present invention;

[0030] Figure 4 This invention relates to the enhanced perceived age trend. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0032] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0033] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0034] In the description of this invention, it should be understood that the terms "upper," "lower," "inner," "outer," "left," "right," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product of this invention is in use, or the orientation or positional relationship commonly understood by those skilled in the art. They are only used to facilitate the description of this invention and to simplify the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0035] Furthermore, the terms "first," "second," etc., are used only to distinguish descriptions and should not be interpreted as indicating or implying relative importance.

[0036] In the description of this invention, it should also be noted that, unless otherwise explicitly specified and limited, terms such as "set" and "connection" should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0037] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0038] An enhanced sensing age analysis optimization method for wirelessly powered sensor networks includes:

[0039] S1. Network Model Construction: The network model includes a full-duplex fusion center (FC), M sensors distributed in different geographical locations, and the target to be monitored. The fusion center (FC) performs parallel operations of downlink radio frequency energy broadcasting and uplink sensing information reception in each frame period. The sensors store the harvested radio frequency energy through supercapacitors and upload the previously collected sensing information in the scheduling time slot. This process is called Wireless Sensing Information Transmission (WSIT). Each sensor is equipped with two antennas, one for receiving energy and the other for uploading information. After each frame period, the fusion center (FC) fuses the successfully decoded sensing information and judges the reliability of the fusion result based on the received content.

[0040] S2. Determine the MAC layer model; A complete frame structure is uniformly scheduled by the fusion center (FC), and includes the following stages in sequence;

[0041] S21, Control Phase: The fusion center FC operates for a fixed duration. Within the frame, channel estimation, frame synchronization, sensing information acquisition and resource allocation are completed, and the scheduling order and corresponding time slot of each sensor in the frame are determined.

[0042] S22, Energy Charging Phase: Lasting for a duration of... During the energy pre-charging interval, all sensors simultaneously receive the radio frequency energy transmitted by the FC downlink from the fusion center and store it in their supercapacitors, providing energy for subsequent uploading of sensing information;

[0043] S23, Sensing Information Upload Phase: A total of L upload sub-slots Time Division Multiple Access (TDMA) is allocated to each sensor, and each sensor... Only in the time slots it is allocated Upload information within the app, and only use it in [the app / system]. to The accumulated energy satisfies the causal requirements of energy;

[0044] S24. Information Fusion Stage: After all sensors have uploaded their sensing information, the Fusion Center (FC) fuses the successfully received sensing information and evaluates whether the fusion result meets the reliability requirements.

[0045] S3. Determine the wireless channel model: The wireless link between the fusion center (FC) and each sensor is modeled as an uncorrelated Rician fading channel, with composite channel gain. It includes both line-of-sight (LOS) and non-line-of-sight (NLOS) components, and the Rician factor. The path loss coefficient measures the degree of LOS dominance. Channel gain remains stable within each frame but varies between frames, reflecting the actual slow-varying channel characteristics. Distance from sensor to fusion center FC Relatedly, if the sensor is at a distance from the fusion center FC Less than Then the path loss is constant. Otherwise ,in, The path loss exponent is used, and modeling is performed based on the channel reference loss model. The fusion center (FC) operates in full-duplex mode, transmitting downlink energy signals while receiving uplink sensing information, resulting in some residual self-interference. Its channel model follows a complex Gaussian distribution, and a residual self-interference channel is constructed. Introducing the inhibition coefficient Depicting the overall effect of analog and digital interference cancellation, Indicates complete offsetting. This indicates no inhibition.

[0046] S4. Modeling the energy receiving process: A multi-segment linear model is used to fit the energy rectification characteristics, and the received power of each sensor is calculated; the fusion center FC is used with a fixed power. Continuous transmission of radio frequency signals If the power supply is sufficient, then the effective power received by the m-th sensor is... , is represented as: Because the energy rectifier circuit has an activation threshold in the low-power region and exhibits saturation characteristics in the high-power region, the traditional continuous model is difficult to use directly for optimization design. Therefore, a nonlinear energy receiving model is fitted with a multi-segment linear function to obtain the DC output power. , is represented as: Each interval used for fitting is Only one segment is activated to ensure model uniqueness, and each sensor accumulates data. to Accumulated energy And upload the information within the allocated time slot; The cumulative energy received before each upload time slot is: Subsequently, it is used for uplink transmission of sensing information, among which and These represent the circuit activation and saturation thresholds, respectively. , Let be the slope and intercept of the k-th segment, respectively. For channel gain, This represents the large-scale path loss factor. This piecewise linear rectification modeling method can accurately fit the nonlinear behavior of the energy harvesting circuit while also possessing high modeling simplicity, making it suitable for subsequent optimization analysis.

[0047] S5. Evaluate information transmission capability: Combine energy causality and instantaneous channel state information to determine whether the required information threshold for fusion is met; Sensor N m In its allocated upload slot Internal power Transmission information, power Satisfying the energy causality constraint, power Represented as: The FC received signal at the fusion center contains the desired signal, residual self-interference, and additive white Gaussian noise. Based on the Shannon capacity formula, the effective information content of each sensor is calculated. And sum them up to get the total amount of information. ,like Exceeding the information threshold If the frame is successfully transmitted, the transmission is considered successful, using the indicator. Indicates the transmission status.

[0048] S6. Modeling the reliability of fusion: Introducing hypothesis testing theory to estimate the reliability probability of the fusion result based on the information volume; specifically: introducing a discriminant variable Y to characterize the credibility of the fused information, and respectively under reliable assumptions. With unreliable assumptions The model is based on two means. Differences, Variance With information volume The relevant complex Gaussian distribution, This indicates the sensitivity of information volume to reliability assessment, i.e. ,in , It is a scaling factor related to variance. Based on the Neyman-Pearson decision criterion, the fusion confidence probability is derived. Represented as: Its form is two Gaussian tail functions. The weighted combination of the differences varies with the sensor's sensing distance. and perceived information content Joint change, where 'a' is the perceptual factor. As the lower limit for decision-making, Upper limit for decision-making; fusion of credible probabilities With transmission success indicators Combining factors increases the probability of successful integration. .

[0049] S7. Defining and Updating Perceived Age: Constructing an enhanced perceived age index and recursive expression, quantifying the freshness loss function, and enhancing perceived age. Defined as the difference between the current time and the time when the latest reliable fused information was generated, and enhanced perceived age. The update at the beginning and end of each frame specifically involves: introducing a fusion success indicator variable. If fusion is successful within frame x, then Furthermore, enhanced perception decreases age. Otherwise, add one frame length. The update rule is expressed as: ;

[0050] in, This represents the enhanced perceived age value in the first time slot of frame x. This represents the enhanced perceived age value in the Lth time slot of the xth frame. This represents the enhanced perceived age value in the first time slot of frame x+1.

[0051] S8. Constructing the expected penalty function based on fusion reliability , is represented as: The expected penalty function quantifies the information aging caused by fusion failure and the freshness gain brought by successful fusion, thereby constructing a system based on... The objective function is to minimize the timeliness of fused information in a joint scheduling and time slot allocation optimization problem, where... This represents the probability of success for the x-th transmitted frame. express The ideal information freshness gain that a transmission frame can obtain when the value is 1;

[0052] S9. Establish a joint optimization problem with the expected penalty function as the objective, and use the Hungarian algorithm and recursive algorithm to solve it to obtain the sensor scheduling and time slot allocation strategy with the minimum expected penalty.

[0053] The scheduling and allocation strategy that uses the Hungarian algorithm and a recursive algorithm to obtain the minimum expected penalty is as follows:

[0054] 1) Inputs: Total number of sensors M, total number of upload sub-slots L, frame length Fixed power of fusion center FC The initial time allocation vector is uniformly divided into time slots. Scheduling matrix Set the matrix to all zeros, the iteration step n=1, and the maximum number of iterations. ;

[0055] 2) Scheduling optimization: Given Below, based on the perceptual information matrix Construct the benefit matrix, which is expressed as: ,in The optimal scheduling matrix is ​​obtained by applying the Hungarian algorithm. ;

[0056] 3) Time allocation optimization: In a fixed time allocation... Next, construct the Lagrange function, expressed as: ,in, Represents the Lagrange multipliers. The channel state is represented as: , The signal-to-interference-plus-noise ratio (SIR / NNR) is expressed by the equation condition. Solve the optimal solution The optimal time vector is calculated using the recursive relation and is expressed as: ;in ;

[0057] 4) Update ;

[0058] 5) Calculate the frame-level freshness penalty. ;

[0059] 6) Let n = n + 1, and determine if n is greater than 1. If yes, proceed to step 7); otherwise, return to step 2).

[0060] 7) Output the optimal scheduling strategy Optimal time vector and minimum penalty index .

[0061] An enhanced sensing age analysis optimization device for wirelessly powered sensor networks includes:

[0062] Storage; storage is used to store computer programs;

[0063] An actuator; the actuator is used to execute a computer program in a storage device, which, when executing the computer program, implements the enhanced sensing age analysis optimization method for wirelessly powered sensor networks as described above.

[0064] A computer-readable storage medium storing a computer program that is executed by a processor to implement the enhanced sensing age analysis optimization method for wirelessly powered sensor networks as described above.

[0065] The technical solutions of the present invention are not limited to the specific embodiments described above. Any technical modifications made in accordance with the technical solutions of the present invention fall within the protection scope of the present invention.

Claims

1. An enhanced sensing age analysis optimization method for wirelessly powered sensor networks, characterized in that, include: S1. Constructing the network model: The network model includes a full-duplex fusion center (FC), M sensors distributed in different geographical locations, and the target to be monitored; The Fusion Center (FC) performs parallel operations of downlink radio frequency energy broadcasting and uplink sensing information reception within each frame period; the sensors store the harvested radio frequency energy through supercapacitors and upload the previously collected sensing information within the scheduling time slots; each sensor is equipped with two antennas, used for receiving energy and uploading information respectively; after each frame period, the FC fuses the successfully decoded sensing information and judges the reliability of the fusion result based on the received content. S2. Determine the MAC layer model; A complete frame structure is uniformly scheduled by the fusion center (FC), and includes the following stages in sequence; S21, Control Phase: The fusion center FC operates for a fixed duration. Within the frame, channel estimation, frame synchronization, sensing information acquisition and resource allocation are completed, and the scheduling order and corresponding time slot of each sensor in the frame are determined. S22, Energy Charging Phase: Lasting for a duration of... During the energy pre-charging interval, all sensors simultaneously receive the radio frequency energy transmitted downlink from the FC fusion center and store it in their supercapacitors, providing energy for subsequent uploading of sensing information; S23, Sensing Information Upload Phase: A total of L upload sub-slots Time Division Multiple Access (TDMA) is allocated to each sensor, and each sensor... Only in the time slots it is allocated Upload information within the app, and only use it in [the app / system]. to The accumulated energy satisfies the causal requirements of energy; S24. Information Fusion Stage: After all sensors have uploaded their sensing information, the Fusion Center (FC) fuses the successfully received sensing information and evaluates whether the fusion result meets the reliability requirements. S3. Determine the wireless channel model: The channel between the fusion center (FC) and each sensor is an independent Rician fading channel, with channel gain... It includes both line-of-sight (LOS) and non-line-of-sight (NLOS) components; Path loss coefficient Distance from sensor to fusion center FC Related data is collected and modeled based on the channel reference loss model; a residual self-interference channel is constructed to address the self-interference problem caused by the full-duplex operation mode of the fusion center FC. Introducing the inhibition coefficient Depicting the overall effect of analog and digital interference cancellation; S4. Modeling the energy receiving process: Use a multi-segment linear model to fit the energy rectification characteristics and calculate the received power of each sensor; S5. Evaluate information transmission capability: Combine energy causality and instantaneous channel state information to determine whether the information threshold required for fusion is met; S6. Modeling the reliability of fusion: Introducing hypothesis testing theory to estimate the reliability probability of the fusion result based on the information volume; S7. Defining and Updating Perceived Age: Constructing an enhanced perceived age index and recursive expression, quantifying the freshness loss function, and enhancing perceived age. Defined as the difference between the current time and the time when the latest reliable fused information was generated, and enhanced perceived age. Update at the beginning and end of each frame; specifically: introduce a fusion success indicator variable. If fusion is successful within frame x, then Furthermore, enhanced perception decreases age. Otherwise, add one frame length. The update rule is expressed as: ; in, This represents the enhanced perceived age value in the first time slot of frame x. This represents the enhanced perceived age value in the Lth time slot of the xth frame. This represents the enhanced perceived age value in the first time slot of the (x+1)th frame. S8. Construct the expected penalty function based on fusion reliability; S9. Establish a joint optimization problem with the expected penalty function as the objective, and use the Hungarian algorithm and recursive algorithm to solve it to obtain the sensor scheduling and time slot allocation strategy with the minimum expected penalty.

2. The enhanced sensing age analysis optimization method for wirelessly powered sensor networks according to claim 1, characterized in that, In S4, the fusion center FC is powered by continuously transmitting radio frequency signals, so the effective power received by a single sensor is... , is represented as: The DC output power is obtained by fitting a nonlinear energy receiving model with a multi-segment linear function. , is represented as: Each sensor accumulates to Accumulated energy And upload information and accumulate energy within the allocated time slots. express: ,in and These represent the circuit activation and saturation thresholds, respectively. , and represent the slope and intercept of the k-th segment, respectively.

3. The enhanced sensing age analysis optimization method for wirelessly powered sensor networks according to claim 1, characterized in that, In S5, sensor N m In its allocated upload slot Internal power Transmission information, power Satisfy energy causality constraints; calculate the effective information content of each sensor. And sum them up to get the total amount of information. ,like Exceeding the information threshold If the frame is successfully transmitted, the transmission is considered successful, using the indicator. Indicates the transmission status.

4. The enhanced sensing age analysis optimization method for wirelessly powered sensor networks according to claim 3, characterized in that, In S6, a discriminant variable Y is introduced to characterize the credibility of the fused information, under the assumption of reliability. With unreliable assumptions The model is based on two means. Differences, Variance With information volume The relevant complex Gaussian distribution, This indicates the sensitivity of information volume to reliability assessment, i.e. ,in , It is a scaling factor related to variance. Based on the Neyman-Pearson decision criterion, the fusion confidence probability is derived. Represented as: Fusion of credible probabilities With transmission success indicators Combining factors increases the probability of successful integration. Where a is the perception factor. As the lower limit for decision-making, This is the upper limit for decision-making.

5. The enhanced sensing age analysis optimization method for wirelessly powered sensor networks according to claim 4, characterized in that, In S8, the expected penalty function Represented as: ,in, This represents the probability of success for the x-th transmitted frame. express The ideal information freshness gain that a transmission frame can obtain when the value is equal to 1.

6. The enhanced sensing age analysis optimization method for wirelessly powered sensor networks according to claim 1, characterized in that, The scheduling and allocation strategy that uses the Hungarian algorithm and a recursive algorithm to obtain the minimum expected penalty is as follows: 1) Inputs: Total number of sensors M, total number of upload sub-slots L, frame length Fixed power of fusion center FC The initial time allocation vector is uniformly divided into time slots. Scheduling matrix Set the matrix to all zeros, the iteration step n=1, and the maximum number of iterations. ; 2) Scheduling optimization: Given Below, based on the perceptual information matrix Construct the benefit matrix, which is expressed as: ,in The optimal scheduling matrix is ​​obtained by applying the Hungarian algorithm. ; 3) Time allocation optimization: In a fixed time allocation... Next, construct the Lagrange function, expressed as: ,in, Represents the Lagrange multipliers. The channel state is represented as: , The signal-to-interference-plus-noise ratio (SIR / NNR) is expressed by the equation condition. Solve the optimal solution The optimal time vector is calculated using the recursive relation and is expressed as: ;in ; 4) Update ; 5) Calculate the frame-level freshness penalty. ; 6) Let n+1=n, and determine whether n is greater than n. If yes, proceed to step 7); otherwise, return to step 2). 7) Output the optimal scheduling strategy Optimal time vector and minimum penalty index .

7. An enhanced sensing age analysis and optimization device for wirelessly powered sensor networks, characterized in that, include: Storage; Storage is used to store computer programs; Actuator; The actuator is used to execute a computer program in the storage, and when executing the computer program, implements the enhanced sensing age analysis optimization method for wirelessly powered sensor networks as described in any one of claims 1-6.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which is executed by a processor to implement the enhanced sensing age analysis optimization method for wirelessly powered sensor networks as described in any one of claims 1-6.

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