Age-of-sensing analysis and optimization method for wireless power supply sensor network

Abstract

Disclosed in the present invention are an age-of-sensing analysis and optimization method for a wireless power supply sensor network. The method comprises the following steps: S1, determining a network model; S2, determining a MAC layer model; S3, determining a channel model; S4, on the basis of a multi-segment formula, fitting a nonlinear energy-receiving model, and calculating the decoding success probability of a single sensor; S5, calculating the possibility of information being successfully received in a fusion center under a sensing information requirement threshold; S6, using a hypothesis testing theory to model a fusion-sensing reliability model, so as to obtain the probability of credible fusion; S7, by means of a joint sensing-transmission success possibility, acquiring a closed-form expression of age of sensing, and formulating an information timeliness optimization problem; and S8, respectively optimizing a sensor power supply duration, a fusion information demand threshold, and sensor deployment, so as to give a minimum age of sensing. By means of the present invention, age of sensing is optimized on the basis of the analysis thereof, thereby improving the timeliness of sensing information.

Description

Perception age analysis and optimization method for wireless powered sensor networks Technical Field

[0001] The present invention belongs to the technical field of wireless power transmission and information age, and specifically relates to a perception age analysis and optimization method for wireless power supply sensor networks. Background Art

[0002] As one of the most critical technologies for the future, the Internet of Everything (IoE) supports groundbreaking applications in agriculture, industry, transportation, and homes. Thousands of low-power sensing micro-devices are deployed on a large scale to support its wide range of functions. These sensing devices typically generate sensing information by monitoring physical processes or sensing the surrounding environment, which is then fed back to the access point (AP) for intelligent network management. Because a single sensor can only collect a portion of the required information, multiple sensors are deployed in a single network to expand sensing coverage. Transmitting sensing information from different sensors to a fusion center (FC) for adjudication gives rise to the concept of fused sensing in wireless sensor networks. For example, temperature and humidity sensors sense and perform wireless sensing information transmission (WSIT) to the FC.

[0003] Furthermore, real-time sensor information is required to reflect the true state of dynamic objects or environments. Because outdated sensor information is meaningless or even misleading, the Age of Information (AoI) has been proposed to measure the freshness of uploaded information. AoI is defined as the time elapsed between the current moment and the moment when newly uploaded information is generated. However, traditional AoI focuses only on a single information stream, making it ineffective for measuring the freshness of fused information in wireless sensor networks with multiple sensors. Therefore, a metric specifically addressing this issue is urgently needed.

[0004] Furthermore, due to the large number of devices present, energy shortages in the IoT have become a recognized problem. Sensor downtime can significantly reduce network availability and even lead to network paralysis. Consequently, long-distance wireless power delivery has attracted attention from both industry and academia.

[0005] Summary of the Invention

[0006] The purpose of the present invention is to overcome the shortcomings of the existing technology and provide a perception age analysis and optimization method for wireless power supply sensor networks, which uses classical hypothesis testing theory to model the reliability of fused information and construct a closed-form expression of perception age; optimizes the three parts of sensor power supply duration, fusion information demand threshold and sensor deployment, and optimizes them based on the analysis of perception age, thereby improving the timeliness of perception information.

[0007] The object of the present invention is achieved through the following technical solution: a method for analyzing and optimizing the perceived age of a wireless power supply sensor network, comprising the following steps:

[0008] S1. Determine the network model: The network includes a fusion center, multiple sensors, and a monitoring target. The fusion center transmits dedicated RF energy signals downlink to charge the sensors. It also receives and fuses the uplink information transmitted by the sensors, determining whether the fused information is reliable. Each sensor is equipped with two antennas: a rectenna for receiving energy and an information transmission antenna for transmitting the sensed wireless information. The sensor uses the rectenna to harvest the dedicated RF energy signals sent downlink from the fusion center. The sensor communicates with the fusion center via the information transmission antenna.

[0009] S2. Determine the MAC layer model: A complete frame structure includes the following four stages:

[0010] S21, Energy transfer phase: All sensors will harvest energy in this phase, and the duration of this phase is recorded as T wpt , contains K time slots;

[0011] S22, perception phase: All sensors perceive the target and collect perception information in this phase. This phase lasts for a very short time, so the analysis of its duration is ignored;

[0012] S23, wireless perception information transfer phase: In this phase, the sensors will upload perception information in sequence. The duration of this phase is recorded as T wsit , containing M time slots;

[0013] S24, information fusion stage: the fusion center integrates all successfully received information and makes decisions;

[0014] S3. Determine the channel model: The wireless channel between the fusion center and the sensor experiences uncorrelated block Rayleigh fading; assume that the wireless channel remains flat within a single transmission frame and varies in different frames; from the fusion center to the mth sensor S m The normalized multipath fading coefficient of the downlink channel is expressed as h wpt,m The normalized multipath fading coefficient of the uplink channel from the mth sensor to the fusion center is represented by h wsit,m Indicates that |h wpt,m | 2 and h wsit,m | 2 Both obey the exponential distribution with parameter λ. The path loss coefficients for downlink and uplink are expressed as Ω wpt,m and Ω wsit,m ;

[0015] S4. Fitting a nonlinear energy receiving model according to a multi-segment formula to calculate the decoding success probability of a single sensor;

[0016] S5. Calculate the probability of successful information reception in the fusion center under the threshold of perception information requirement: Let the threshold of fusion information requirement be M th , the probability of successful reception of fusion information is expressed as π m The probability of successfully decoding the signal for the fusion center;

[0017] S6. By using hypothesis testing theory to model the fusion perception reliability model, the probability of fusion credibility Pr is obtained. IF ;

[0018] S7. By combining the perception-transmission success probability, we can obtain a closed-form expression of the perceived age and establish an information timeliness optimization problem. The quantitative perceived age is defined as the difference between the current moment and the moment when the latest reliable fusion information was generated. The formula for perceived age is:

[0019] Where T0 represents the time slot length, Pr SC is the joint perception-transmission success probability, Pr SC =Pr WSIT Pr IF ;

[0020] The information timeliness optimization problem is modeled as

[0021] Step S8: Optimize the sensor power supply time, fusion information demand threshold and sensor deployment respectively to give the minimum perception age; solve the three variables in an iterative way: for variables M and K, use the integer optimization method based on Fibonacci idea to obtain the optimal solution; for the distance d between the sensor and the fusion center, t , a one-dimensional search algorithm is used to solve it; finally, the current variable is optimized while the other two variables are fixed each time, and the final optimized solution and perceived age are obtained by iterating repeatedly until convergence.

[0022] The specific method of step S4 is: the mth sensor S m The received downlink signal power is modeled as Among them, P FC is the downlink signal power sent by the fusion center; the energy receiving curve of each sensor is a nonlinear saturation curve. Use a piecewise linear function to fit this nonlinear saturation curve to obtain the sensor S m The harvested power is expressed as:

[0023] Where i = 1, 2, ..., I, I is the number of matching segments in the growth interval of the energy harvesting curve; P1 = P th , P I+1 =P st ;P th Indicates the minimum signal power that can activate the energy receiving circuit, P st Indicates the signal power when the energy receiving circuit reaches saturation, P sat That is, the output power when the energy receiving circuit is saturated; η i and μ i are the slope and intercept of the fitting curve;

[0024] Sensor S m The signal-to-noise ratio of the uploaded signal is Among them, P wsit,m It is sensor S m The transmission power, is the Gaussian white noise variance;

[0025] Assume S m Each time you upload information, the energy storage is cleared, so there is P wsit,m =KP dc,m ; When the signal-to-noise ratio is greater than the given threshold γ th The uplink signal is considered to be decoded successfully, so the probability of successful decoding is expressed as Pr{γ wsit,m ≥γ th} means that the calculation is:

[0026] λ is h wpt,m | 2 and h wsit,m | 2 The parameters of the exponential distribution followed.

[0027] The specific method of step S6 is: using Neyman-Pearson in hypothesis testing theory to model the reliability of fusion information; specifically, assuming that the fusion perception information z obeys two Gaussian distributions with different parameters under the unreliable hypothesis H0 and the reliable hypothesis H1, respectively, and their mean and variance are u0, and u1, Divide z into two non-overlapping regions D0 and D1 in its domain. When z is in region D0, i.e. z∈D0, the fusion center makes a decision Indicates that the fusion information z is unreliable; when z is in region D1, i.e. z∈D1, the fusion center makes a decision Indicates that the fused information z is credible; therefore, the probability of fusion credibility is expressed as:

[0028] Pr(H0) and Pr(H1) represent the probability of the occurrence of hypothesis H0 and H1 respectively. Indicates making a decision under the hypothesis H0 The probability of Indicates making a decision under the assumption H1 The probability of f and Pr d They are the abbreviations of two conditional probabilities; Pr(H1)=exp(-κd s ), Pr(H0)=1-exp(-κd s ), κ represents the sensor perception distortion coefficient;

[0029] Finally, through the Neyman-Pearson criterion, Pr f At the acceptance threshold Pr' f Under the condition of d Maximize; get the following formula Where v represents the value of Pr' f A threshold map of The relationship between the two is as follows:

[0030] in,

[0031] Q(·) represents the Q function; the formula is monotonic, so v is obtained by bisection; finally, we get Pr d It is in Pr f =Pr' f So we finally get Pr IF =Pr' f Pr(H0)+Pr d Pr(H1).

[0032] The beneficial effects of the present invention are as follows: the present invention uses the classical hypothesis testing theory to model the reliability of fusion information and construct a closed-form expression of perception age; it optimizes the three parts of sensor power supply duration, fusion information demand threshold and sensor deployment, and optimizes them based on the analysis of perception age, thereby improving the timeliness of perception information. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] FIG1 is a flow chart of a method for analyzing and optimizing the perceived age of a wireless power supply sensor network according to the present invention;

[0034] FIG2 is a schematic diagram of a network model of the present invention;

[0035] FIG3 shows the perceived age trend of the present invention. DETAILED DESCRIPTION

[0036] To overcome the shortcomings of information age metrics for single data streams, a perception age is proposed for multi-stream information fusion networks. This is defined as the difference between the generation time of fresh, reliable fusion information and the current moment. By considering a fusion center, multiple sensor nodes, and a monitoring target, the reliability of fusion information is modeled using classical hypothesis testing theory to quantify the perception age of the network, construct a closed-form expression, and analyze it. Further optimization is performed on sensor power supply duration, fusion information demand threshold, and sensor deployment to significantly improve network information freshness. The technical solution of the present invention is further illustrated below with reference to the accompanying drawings.

[0037] As shown in FIG1 , a method for analyzing and optimizing the perceived age of a wireless power supply sensor network according to the present invention includes the following steps:

[0038] S1. Determine the network model: The network includes a fusion center (FC), multiple sensors (Sensor1 to SensorM), and a monitoring target, as shown in Figure 2. The fusion center transmits RF energy signals downlink to charge the sensors. It also receives and fuses the uplink information transmitted by the sensors to determine whether the fused information is reliable. Each sensor is equipped with two antennas: a rectenna dedicated to receiving energy and an information transmission antenna for transmitting the sensed wireless information. The sensor uses the rectenna to harvest the dedicated RF energy signals sent downlink from the fusion center, stores and consumes them to charge the sensor, which is the RF Chain-Energy Harvester branch in Figure 2. The sensor communicates with the fusion center via the information transmission antenna to exchange information, which is the RF Chain-Info.Encoder branch in the figure. After charging, the sensor immediately senses the monitoring target and sends the sensed signal to the fusion center via the information transmission antenna.

[0039] S2. Determine the MAC layer model: Considering the time division multiple access method, sensors are connected to the network one by one. The network completes four operation phases in a single transmission frame. As shown in Figure 2, a complete frame (A Single Transmission Frame) structure includes the following four phases:

[0040] S21, Energy Transfer Phase (WPT): All sensors will harvest energy in this phase, and the duration of this phase is recorded as T wpt , contains K time slots;

[0041] S22, perception phase: All sensors perceive the target and collect perception information in this phase. This phase lasts for a very short time, so the analysis of its duration is ignored;

[0042] S23, Wireless Sensing Information Transfer (WSIT): In this stage, the sensors will upload sensing information in sequence. The duration of this stage is recorded as T wsit , containing M time slots;

[0043] S24, information fusion stage: The fusion center integrates the information just successfully received and makes a decision;

[0044] S3. Determine the channel model: The wireless channel between the fusion center and the sensor experiences uncorrelated block Rayleigh fading; assume that the wireless channel remains flat within a single transmission frame and varies in different frames; from the fusion center to the mth sensor S m The normalized multipath fading coefficient of the downlink channel is expressed as h wpt,m The normalized multipath fading coefficient of the uplink channel from the mth sensor to the fusion center is represented by h wsit,m Indicates that |h wpt,m | 2 and h wsit,m | 2 Both obey the exponential distribution with parameter λ. The path loss coefficients for downlink and uplink are expressed as Ω wpt,m and Ω wsit,m ; Since the distance between the sensor's transmitting and receiving antennas is close, when d t,m When ≥d0, the path loss coefficient is modeled as Ω wpt,m =Ω wsit,m =Ω0(d t,m / d0) α ; When d t,m When <d0, the path loss coefficient is modeled as Ω wpt,m =Ω wsit,m =Ω0. Where d t,m Represents the distance between the sensor m and FC, and Ω0 represents the path loss at the reference distance d0. Since d0 is very small in practice and the path loss of the sensor at any position within the distance FCd0 is the same, only d is usually considered in the research process. t,m ≥d0, so as to study the impact of the distance between the sensor and FC on the system performance. In addition, for the convenience of modeling, we consider the case where the distance between sensors is relatively close, so the distance between all sensors and FC can be approximately expressed as d t , the distance from the sensor to the monitoring target is expressed as d s .

[0045] S4. Fit the nonlinear energy receiving model according to the multi-segment linear formula to calculate the decoding success probability of a single sensor. The specific method is as follows:

[0046] The mth sensor S mThe received downlink signal power is modeled as Among them, P FC is the downlink signal power sent by the fusion center. The energy receiving curve of each sensor is a nonlinear saturation curve. It is very difficult to analyze the network performance based on this curve theory. Therefore, the present invention uses a piecewise linear function to fit this nonlinear saturation curve to obtain the sensor S m The harvested power is expressed as:

[0047] Where i = 1, 2, ..., I, I is the number of matching segments in the growth interval of the energy harvesting curve; P1 = P th , P I+1 =P st ; The parameter size in the formula should be determined by the actual energy harvester circuit. Specifically, we use the above multi-segment linear formula to fit the input-output test curve of the actual energy harvester circuit, where the input is the received downlink RF energy signal power and the output is the power harvested by the circuit. Considering that all sensors use the same energy harvesting circuit, Indicates that the circuit receives a signal with power P wpt Based on this, we use P th Indicates the minimum signal power that can activate the energy receiving circuit, P st Indicates the signal power when the energy receiving circuit reaches saturation, P sat That is, the output power when the energy receiving circuit is saturated. In addition, in order to determine the slope η i and intercept μ i , perform the following operations: divide the growth interval of the test curve of the circuit into I segments evenly, and the received signal power range in the i-th segment is [P i ,P i+1 ), so there is Because we consider replacing the original circuit curve in each section with a straight line,

[0048] Sensor S m The signal-to-noise ratio of the uploaded signal is Among them, P wsit,m It is sensor S m The transmission power, is the Gaussian white noise variance;

[0049] Assume S m Each time you upload information, the energy storage is cleared, so there is P wsit,m =KP dc,m ; When the signal-to-noise ratio is greater than the given threshold γ th It is considered that the uplink signal (WSIT signal) is decoded successfully, so the probability of successful decoding can be expressed as Pr{γwsit,m ≥γ th} indicates that the probability of the fusion center successfully decoding the signal is calculated as:

[0050] λ is h wpt,m | 2 and h wsit,m | 2 The parameters of the exponential distribution obeyed. Use f m,i (x) indicates that since the integral is not elementary, the integral is approximated by Gaussian method. Approximately, where x max and x min Represents π m The upper and lower limits of the integral, n', ω j 、y j The Gaussian approximation method is used to obtain the corresponding ω for different n'. j and y j Value table, just directly correspond to the value.

[0051] S5. Calculate the probability of successful information reception in the fusion center under the threshold of the perception information requirement: First, calculate the probability of successful transmission of the fusion information. Let the threshold of the fusion information requirement be M th , when we regard the information uploaded by a single sensor as a single amount of information, the threshold can be regarded as the number of sensors that successfully upload information required by the fusion center to perform the fusion operation. Therefore, when the distance between sensors is negligible, the probability of successful reception of fusion information is expressed as π m is the probability that the fusion center successfully decodes the signal.

[0052] S6. By using hypothesis testing theory to model the fusion perception reliability model, the probability of fusion credibility Pr is obtained. IF The specific method is to use Neyman-Pearson in hypothesis testing theory to model the reliability of fusion information. Specifically, it is assumed that the fusion perception information z obeys two Gaussian distributions with different parameters under two different hypotheses (unreliable hypothesis H0 and reliable hypothesis H1), and their mean and variance are u0, and u1, Divide z into two non-overlapping regions D0 and D1 in its domain. When z is in region D0, i.e. z∈D0, FC makes a decision It means that the fusion information z is unreliable; accordingly, when z is in the region D1, that is, z∈D1, the decision is made Indicates that the fused information z is credible; Therefore, the probability of fusion credibility is expressed as:

[0053] Pr(H0) and Pr(H1) represent the probability of the occurrence of hypothesis H0 and H1 respectively. Indicates making a decision under the hypothesis H0 The probability of Indicates making a decision under the assumption H1 The probability of f and Pr d They are the abbreviations of two conditional probabilities; Pr(H1)=exp(-κd s ), Pr(H0)=1-exp(-κd s ), κ represents the sensor perception distortion coefficient;

[0054] Finally, through the Neyman-Pearson criterion, Pr f At the acceptance threshold Pr' f Under the condition of d Maximize; get the following formula Where v represents the value of Pr' f A threshold map of The relationship between the two is as follows:

[0055] in, Q(·) represents the Q function; the formula is monotonic, so v is obtained by bisection; finally, we get Note that Pr d It is in Pr f =Pr' f Therefore, we finally get Pr IF =Pr' f Pr(H0)+Pr d Pr(H1).

[0056] S7. By combining the perception-transmission success probability, we can obtain a closed-form expression of the perceived age and establish an information timeliness optimization problem. The quantitative perceived age is defined as the difference between the current moment and the moment when the latest reliable fusion information was generated. Figure 3 shows a schematic diagram of the perceived age trend under this definition. i represents the moment when the sensor generates the perception information i, t' i Indicates the moment when the fusion center decides that it is reliable information. In addition, X i Indicates the time between two consecutive reliable fusion information, Y i Is the number of frames experienced in this short time. By calculating X iThe perceived age area corresponding to the period, the formula for obtaining the perceived age is:

[0057] Where T0 represents the time slot length, Pr SC is the joint perception-transmission success probability, Pr SC =Pr WSIT Pr IF ;

[0058] The information timeliness optimization problem is modeled as

[0059] Step S8: Optimize the sensor power supply duration, fusion information demand threshold, and sensor deployment respectively to determine the minimum perception age. Use an iterative approach to solve the three variables: For variables M and K, use an integer optimization method based on Fibonacci theory to obtain the optimal solution. The specific algorithm is implemented as follows:

[0060] Input: M, K, d t , P FC wait;

[0061] Create: a Fibonacci sequence F = [1, 1, 2, 3, 5, ...] containing N elements; let δ1 and δ2 represent the search values ​​of the variables to be optimized;

[0062] Initialization parameters: n←N, which is the number of elements in the search interval, and n is the mapping of the number of elements remaining in the search interval, used to identify how many elements are left; the lower limit of the search interval δ min ←1, the upper limit of the search interval δ max ←F(n)+δ min , variable search value δ1←F(n-2)+δ min ,δ2←F(n-1)+δ min , where F(n) represents the nth element in the Fibonacci sequence; obtained by the perceived age formula in step S7 and

[0063] (1) If Update n←n-1,δ max ←δ2, δ2←δ1, δ1←F(n-2)+δ min , and obtain the corresponding and

[0064] If δ1=δ2: end the loop;

[0065] (2) If Update n←n-1,δ min ←δ1, δ1←δ2, δ2←F(n-1)+δmin , and obtain the corresponding and

[0066] If δ1=δ2: end the loop;

[0067] (3) If Update n←n-3,δ min ←δ1,δ max ←δ2;

[0068] If |δ1-δ2|>1: update δ1←F(n-2)+δ min ,δ2←F(n-1)+δ min , and obtain the corresponding and Otherwise: end the loop;

[0069] Repeat the cycle;

[0070] Get δ'←[δ min ,δ1,δ2,δ max ]and in Represents a vector The jth element in ;

[0071] Output: K * or in is the j*th element in the vector δ'.

[0072] For the distance d between the sensor and the fusion center t , a one-dimensional search algorithm is used to solve it; finally, the current variable is optimized while the other two variables are fixed each time, and the final optimized solution and perceived age are obtained by iterating repeatedly until convergence.

[0073] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and it should be understood that the scope of protection of the present invention is not limited to such specific descriptions and embodiments. Those skilled in the art can make various other specific variations and combinations based on the technical teachings disclosed in the present invention without departing from the essence of the present invention, and such variations and combinations are still within the scope of protection of the present invention.

Claims

1. Perception age analysis and optimization methods for wireless powered sensor networks, It is characterized in that The following steps are involved: S1. Determine the network model: The network includes a fusion center, multiple sensors and a monitoring target. The fusion center transmits a dedicated RF energy signal downlink to charge the sensor. It is also responsible for receiving the information transmitted uplink by the sensor and fusing the received uplink information to determine whether the fused information is reliable. Each sensor is equipped with two antennas: a rectifying antenna dedicated to receiving energy and an information transmission antenna for transmitting the perceived wireless information. The sensor harvests the dedicated RF energy signal sent downlink from the fusion center through the rectifying antenna. The sensor communicates with the fusion center through the information transmission antenna. S2. Determine the MAC layer model: A complete frame structure includes the following four stages: S21, Energy transfer phase: All sensors will harvest energy in this phase, and the duration of this phase is recorded as T wpt , contains K time slots; S22, perception phase: All sensors perceive the target and collect perception information in this phase. This phase lasts for a very short time, so the analysis of its duration is ignored; S23, wireless sensing information transfer phase: In this phase, the sensors will upload sensing information in sequence. The duration of this phase is recorded as T wsit , including M time slots; S24, information fusion stage: the fusion center integrates all successfully received information and makes decisions; S3. Determine the channel model: The wireless channel between the fusion center and the sensor undergoes uncorrelated block Rayleigh fading; assume that the wireless channel remains flat within a single transmission frame and varies in different frames; from the fusion center to the mth sensor S m The normalized multipath fading coefficient of the downlink channel is denoted by h wpt,m The normalized multipath fading coefficient of the uplink channel from the mth sensor to the fusion center is represented by h wsit,m Indicates that |h wpt,m | 2 and |h wsit,m | 2 All obey the exponential distribution with parameter λ. The path loss coefficients for downlink and uplink are expressed as Ω wpt,m and Ω wsit,m ; S4, fitting a nonlinear energy receiving model according to a multi-segment formula to calculate the decoding success probability of a single sensor; S5. Calculate the possibility of successful information reception in the fusion center under the threshold of perception information requirement: Assume that the threshold of fusion information requirement is M th , the probability of successful reception of fusion information is expressed as π m The probability of successfully decoding the signal for the fusion center; S6. By using hypothesis testing theory to model the fusion perception reliability model, the probability of fusion credibility Pr is obtained. IF ; S7. By combining the perception-transmission success probability, a closed-form expression of the perceived age is obtained, and an information timeliness optimization problem is established; the quantitative perceived age is defined as the difference between the current moment and the moment when the latest reliable fusion information is generated; the formula for perceived age is: Where T 0 Indicates the time slot length, Pr SC is the joint perception-transmission success probability, Pr SC =Pr WSIT Pr IF ; The information timeliness optimization problem is modeled as Step S8, respectively optimize the sensor power supply duration, fusion information demand threshold and sensor deployment to give the minimum perception age; solve the three variables in an iterative manner: for variables M and K, use the integer optimization method based on Fibonacci idea to obtain the optimal solution; for the distance d between the sensor and the fusion center t , using a one-dimensional search algorithm to solve; finally, by fixing the other two variables each time while optimizing the current variable, and iterating until convergence to obtain the final optimized solution and perceived age.

2. The method for analyzing and optimizing the perceived age of wireless power supply sensor networks according to claim 1, It is characterized in that The specific method of step S4 is: m The received downlink signal power is modeled as Among them, P FC is the downlink signal power sent by the fusion center; the energy receiving curve of each sensor is a nonlinear saturation curve. The piecewise linear function is used to fit this nonlinear saturation curve to obtain the sensor S m The harvested power is expressed as: Where i = 1, 2, ..., I, I is the number of matching segments in the growth interval of the energy harvesting curve; P 1 =P th , P I+1 =P st ;P th Indicates the minimum signal power that can activate the energy receiving circuit, P st Indicates the signal power when the energy receiving circuit reaches saturation, P sat That is, the output power when the energy receiving circuit is saturated; η i and μ i are the slope and intercept of the fitting curve; Sensor S m The signal-to-noise ratio of the upload signal is Among them, P wsit,m It is sensor S m The transmission power, is the Gaussian white noise variance; Assume S m Each time you upload information, the energy storage is cleared, so P wsit,m =KP dc,m ; When the signal-to-noise ratio is greater than the given threshold γ th The uplink signal is considered to be decoded successfully, so the probability of successful decoding is expressed as Pr{γ wsit,m ≥γ th } means that the calculation is: λ is |h wpt,m | 2 and |h wsit,m | 2 The parameters of the exponential distribution that follows.

3. The method for analyzing and optimizing the perceived age of wireless power supply sensor networks according to claim 1, It is characterized in that The specific method of step S6 is: using Neyman-Pearson in hypothesis testing theory to model the reliability of fusion information; specifically, assuming that the fusion perception information z is under the unreliable hypothesis H 0 and reliable hypothesis H 1 They are respectively subject to two Gaussian distributions with different parameters, and their means and variances are u 0 , and u 1 , Divide z into two non-intersecting regions D in its domain 0 and D 1 , when z is in region D 0 In the above equation, z∈D 0 , the fusion center makes decisions Indicates that the fusion information z is unreliable; when z is in region D 1 In the above equation, z∈D 1 , the fusion center makes decisions It means that the fusion information z is credible; therefore, the probability of fusion credibility is expressed as: Pr(H 0 ) and Pr(H 1 ) represent the hypothesis H 0 and H 1 The probability of occurrence, In the assumption that H 0 Make a decision The probability of In the assumption that H 1 Make a decision The probability, Pr f and Pr d They are the abbreviations of two conditional probabilities; Pr(H 1 )=exp(-κd s ), Pr(H 0 )=1-exp(-κd s ), κ represents the sensor perception distortion coefficient; Finally, through the Neyman-Pearson criterion, Pr f At the acceptable threshold Pr' f Under the condition of d Maximize; get the following formula Where v represents the value of Pr' f A threshold map of The relationship between the two is as follows: in, Q(·) represents the Q function; the formula is monotonic, so v is obtained by bisection; finally, we get Pr d It is in Pr f =Pr' f So we finally get Pr IF =Pr' f Pr(H 0 )+Pr d Pr(H 1 ).

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