Resource allocation method for wireless power sensor networks based on information freshness

Abstract

The application belongs to the field of wireless energy supply sensor network communication, and particularly relates to a wireless energy supply sensor network resource allocation method based on information freshness; the method comprises the following steps: introducing an intelligent reflecting surface IRS into a wireless energy supply sensor network communication system, and constructing an IRS-assisted communication wireless energy supply sensor network; based on the uncertain channel state from an energy station to a wireless sensor and the uncertain state of the wireless sensor from the IRS to a base station, a channel uncertainty model is constructed; an information age robust optimization model of the IRS-assisted communication wireless energy supply sensor is established; based on a conditional value at risk (CVaR) method and an alternating iteration algorithm, the information age robust optimization problem is converted into two sub-problems for solving, and an information age optimization scheme is obtained; the application improves the information timeliness and robustness of the IRS-assisted communication wireless energy supply sensor network system, and has high practicability.

Description

Technical Field

[0001] The present invention belongs to the field of wireless power supply sensor network communication, and in particular relates to a wireless power supply sensor network resource allocation method based on information freshness. Background Art

[0002] With the continuous development of Internet of Things (IoT) technology, more and more IoT applications require wireless sensors to be deployed in communication networks to perceive the status of the surrounding environment in real time. However, in practical applications, wireless sensors cannot send status updates in a timely manner due to the lack of continuous energy supply, which affects the freshness of the information. Wireless power supply communication networks are considered to be one of the effective solutions to the problem of limited device energy. At the same time, obstacles can easily block the distance between wireless sensors and base stations, affecting the timeliness of information delivery. Intelligent Reflecting Surfaces (IRS) intelligently reconfigure the wireless transmission environment by integrating a large number of low-cost passive reflective elements on a plane. This can effectively solve the problem of communication channel obstacles and improve the performance of wireless communication networks.

[0003] Currently, in IRS-assisted wireless energy communication networks, resource allocation can be optimized by increasing network speed, throughput, and energy efficiency. However, these approaches fail to consider information timeliness and cannot guarantee the reliability and accuracy of real-time allocation decisions. Furthermore, due to the complexity of actual communication scenarios, obtaining perfect channel state information is difficult. Imperfect channel state information affects the probability of interruptions in wireless sensor energy collection and information transmission, severely impacting the timeliness of system information. Summary of the Invention

[0004] In view of the shortcomings of the existing technology and to facilitate practical engineering applications, the present invention proposes a wireless power supply sensor network resource allocation method based on information freshness, which includes:

[0005] According to the wireless energy supply sensor network architecture with intelligent reflective surface assisted communication, the energy collection process information of the wireless sensor to collect energy from the energy station and the information transmission process information of using the collected energy to transmit information to the base station via the intelligent reflective surface are determined;

[0006] According to the interruption probability of wireless sensors in the energy collection process and the interruption probability of wireless sensors in the information transmission process, the objective function of minimizing the average information age is constructed;

[0007] According to the uncertain channel state from the energy station to the wireless sensor and the uncertain channel state from the wireless sensor to the base station through the smart reflector, the uncertain channel state constraint of the distribution of the channel state information error under the fuzzy set is constructed;

[0008] According to the energy collected by wireless sensors during energy collection, the interruption probability and the energy consumed during information transmission, the worst-case collection interruption constraint is constructed under the distribution of channel state information error under fuzzy sets.

[0009] According to the information transmission rate, interruption probability and minimum information transmission probability of wireless sensors in the information transmission process, the transmission interruption probability constraint is constructed under the distribution of channel state information error under fuzzy sets.

[0010] According to the interruption probability of wireless sensors in the energy collection process and the information transmission process, the collection interruption probability constraint and the transmission interruption probability constraint are constructed respectively.

[0011] Construct the transmission power constraint based on the covariance matrix of the energy station during the energy collection process and the maximum transmission power;

[0012] According to the reflection angle of the smart reflective surface, the reflection phase shift constraint of the smart reflective surface is constructed;

[0013] By solving the robust optimization model of information age of wireless sensors consisting of minimizing the average information age objective function and various constraints, the covariance matrix of the energy station, the reflection phase shift of the smart reflector, the interruption probability of the energy collection process and the interruption probability of the information transmission process are obtained.

[0014] Beneficial effects of the present invention:

[0015] This paper addresses the issues of obstacles between wireless sensors and base stations, which can affect the timeliness of information delivery, and insufficient sensor energy supply, which can also cause information aging performance degradation. By constructing a wireless powered sensor network system model with IRS-assisted communication, this paper also considers the impact of imperfect channel state information on the probability of energy collection and information transmission interruptions. Furthermore, during the solution process, the paper proposes a new improved algorithm that utilizes methods such as CVaR and semi-definite relaxation to ensure the timeliness of sensor information under limited energy station transmit power and worst-case communication conditions, employing an alternating optimization algorithm. Furthermore, compared with existing solutions, this paper improves the information freshness and robustness of wireless powered sensor networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is an overall flow chart of the method of the present invention;

[0017] Figure 2 This is a model diagram of the wireless energy supply sensor network system with IRS-assisted communication in the present invention;

[0018] Figure 3 This is the AoI evolution diagram of sensor k of the present invention.

[0019] Figure 4 This is the convergence diagram of the average information age in the system of the present invention.

[0020] Figure 5 Graph showing the relationship between the average information age and channel error for the present invention and the comparative method. DETAILED DESCRIPTION

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0022] The present invention proposes a wireless energy sensor network resource allocation method based on information freshness, such as Figure 1 As shown, the method includes the following contents:

[0023] 101. According to the wireless energy supply sensor network architecture with intelligent reflective surface assisted communication, determine the energy collection process information of the wireless sensor collecting energy from the energy station, and the information transmission process information of using the collected energy to transmit information to the base station via the intelligent reflective surface;

[0024] In the embodiment of the present invention, it is necessary to build a wireless power supply sensor network architecture with IRS auxiliary communication, such as Figure 2 As shown, the embodiment of the present invention considers a wireless energy supply sensor network with IRS assisted communication. The network scenario consists of energy stations, wireless sensors, IRS and base stations. Specifically, there is an N T An energy station with 1 antenna, K single-antenna sensors, a single-antenna base station and N S IRS of reflection units, where N T ≥2, N S ≥2. Energy-constrained wireless sensors collect energy through energy stations and then send the collected status update information to the base station. Due to the obstruction of obstacles between the sensor and the base station, the IRS is used to reflect the signal to the base station. Define the wireless sensor set K≥2.

[0025] In this embodiment of the present invention, energy-constrained wireless sensors collect energy through energy stations and then use the collected energy to transmit collected status updates to a base station. Because obstacles between the sensor and the base station obstruct the signal, an IRS is used to reflect the signal back to the base station. This process information may include channel information, transmission information, and reception information.

[0026] 102. Based on the interruption probability of wireless sensors in the energy collection process and the interruption probability of wireless sensors in the information transmission process, construct an objective function to minimize the average information age;

[0027] This embodiment establishes a robust optimization model for the information age of wireless power supply sensors with IRS-assisted communication. In order to characterize the freshness of transmitted information, the information age is used as a performance indicator. Figure 3 The AoI evolution process of sensor k in the system is described. In the system time, it is assumed that sensor k needs to successfully send M data packets, so there are M update cycles. The total AoI in each update cycle can be Q m Indicates that m∈{1,...,M}. In the mth update cycle, the arrival time of the data packet is recorded as S m , the update time is recorded as D m . Use W m X represents the waiting time from the base station successfully receiving the m-1th data packet to the sensor generating the next data packet, m It can be seen that the actual transmission time of the mth state update information is W. m =S m -D m-1 and X m =D m -S m So the time required for an update cycle is I m =W m +X m .

[0028] In system time N, M data packets are successfully sent, and the average AoI is expressed as:

[0029]

[0030] Known When n→∞, the average AoI of sensor k is expressed as:

[0031]

[0032] Among them, Q m The area is represented as I m The width is 1 and the length is d(1+X m-1 ≤d≤I m +X m-1 ), that is:

[0033]

[0034] The average AoI of sensor k is further expressed as:

[0035]

[0036] In the system model, the waiting time W m It is related to the energy harvesting probability constraint, and its compliance probability is ω k =1-γ k At the same time, the interruption probability in each update cycle is considered to be the same. Then W m The probability mass function is Pr{W m =t}=γ k [m] t-1 (1-γ k [m]). From this we can get:

[0037]

[0038]

[0039] Similarly, assuming that the transmission of a data packet can be completed within one time slot, the transmission time X m It is related to the probability constraint of information transmission interruption, and the probability of compliance is β k =1-τ k The geometric distribution of X m The probability mass function is Pr{X m =t}=τ k [m] t-1 (1-τ k [m]). From this we can get:

[0040]

[0041]

[0042] according to We can get:

[0043]

[0044] according to and The average information age of the kth sensor in the system time is:

[0045]

[0046] Among them, γ k [m] represents the interruption probability of the energy harvesting process of the kth sensor in the mth update cycle, τ k [m] represents the interruption probability of the kth sensor information transmission in the mth update cycle.

[0047] Therefore, based on the above interruption probability, this embodiment constructs an objective function for minimizing the average information age, which can be expressed as:

[0048]

[0049] By minimizing the average information age, the covariance matrix of the energy station, the reflection phase shift of the smart reflector, the interruption probability of the energy collection process and the interruption probability of the information transmission process are obtained.

[0050] 103. Based on the uncertain channel state from the energy station to the wireless sensor and the uncertain channel state from the wireless sensor to the base station through the smart reflective surface, an uncertain channel state constraint for the distribution of channel state information error under a fuzzy set is constructed;

[0051] In order to characterize the uncertain channel state from the energy station to the wireless sensor and the uncertain channel state from the wireless sensor to the base station through the smart reflective surface, this embodiment can construct a channel uncertainty model.

[0052] In practical applications, there are errors in the channel state information. The actual channel state h k and H k Expressed as:

[0053]

[0054]

[0055] in, and Respectively expressed as h k and H k The statistical channel gain, and Indicates h k and H k The uncertainty channel state information estimation error. Compared with obtaining the exact distribution from the channel information state estimation, it is obviously easier to directly obtain the mean and variance of the channel state information error. Therefore, the definition of the fuzzy set This set contains all probability distributions with the same mean and variance, specifically expressed as

[0056]

[0057] in, Represents a fuzzy set Any distribution with mean and variance, and Respectively and The mean of and express and The variance of

[0058] Based on this, the uncertain channel state constraint of the distribution of the channel state information error under the fuzzy set of this embodiment can be expressed as:

[0059]

[0060] in, Represents a fuzzy set Any distribution with mean and variance, represents the probability distribution, and They represent the estimation errors of the uncertain channel state information from the energy station to the kth sensor and from the kth sensor to the base station through the smart reflector surface, respectively.

[0061] 104. Based on the energy collected by the wireless sensor during the energy collection process, the interruption probability, and the energy consumed during the information transmission process, the worst-case collection interruption constraint under the distribution of channel state information error under fuzzy sets is constructed;

[0062] In this embodiment, in the first phase, i.e., the wireless energy collection phase, the wireless sensor collects energy from the energy station. The energy collected by the kth sensor in each time slot of the mth update cycle is:

[0063]

[0064] in, represents the channel vector from the energy station to the kth sensor, represents the energy covariance matrix, is the radio frequency energy signal transmitted by the energy station, and χ represents the energy conversion efficiency.

[0065] Based on the above analysis, the worst energy harvesting interruption constraint under the distribution of channel state information error under fuzzy sets can be obtained, which is expressed as:

[0066]

[0067] in, Indicates the probability distribution The probability that A>B is true is, represents the probability function of the distribution of channel state information error under the fuzzy set, Indicates the minimum energy consumed to transmit information.

[0068] 105. According to the information transmission rate, interruption probability and minimum information transmission probability of wireless sensors in the information transmission process, the transmission interruption probability constraint under the distribution of channel state information error under fuzzy sets is constructed;

[0069] In this embodiment, in the second phase, i.e., the wireless information transmission phase, the wireless sensor uses the energy collected in the first phase to transmit information. The information transmission rate of the kth sensor in the update period m is expressed as:

[0070]

[0071] Where B represents the channel bandwidth, represents the transmission power of the kth sensor, represents the reflection phase shift vector of IRS, represents the cascade channel from the kth sensor to the base station through the IRS, σ 2 Represents the noise power. and denote the channel vectors from the kth sensor to the IRS and from the IRS to the base station, respectively.

[0072] Based on the above analysis, we can derive the transmission interruption probability constraint under the distribution of channel state information error under the fuzzy set, which can be expressed as:

[0073]

[0074] in, represents the information transmission rate of the kth sensor, Indicates the minimum information transmission rate.

[0075] 106. According to the interruption probability of the wireless sensor in the energy collection process and the interruption probability in the information transmission process, the collection interruption probability constraint and the transmission interruption probability constraint are respectively constructed;

[0076] In this embodiment, the probability intervals of the collection interruption probability and the transmission interruption probability need to be limited to between 0 and 1, so the following constraints are constructed:

[0077] C4:0<γ k [m]<1

[0078] C5:0<τ k [m]<1

[0079] 107. Construct a transmit power constraint based on the covariance matrix of the energy station during the energy collection process and the maximum transmit power;

[0080] In this embodiment, the transmit power constraint can be expressed as:

[0081]

[0082] Where Tr(·) represents the trace of the matrix, W represents the energy covariance matrix of the energy station, Indicates the maximum transmission power of the energy station.

[0083] 108. Construct a reflection phase shift constraint of the smart reflective surface based on the reflection angle of the smart reflective surface;

[0084] In this embodiment, the reflection phase shift constraint is expressed as:

[0085] C6:|v n |=1

[0086] Where ν represents the reflection phase shift vector of IRS, v n Indicates the phase shift angle of the nth reflection unit.

[0087] 109. By solving the robust optimization model of the information age of wireless sensors composed of the objective function of minimizing the average information age and various constraints, the covariance matrix of the energy station, the reflection phase shift of the intelligent reflector, the interruption probability of the energy collection process and the interruption probability of the information transmission process are obtained.

[0088] Combining the sensor energy constraint, information transmission rate constraint, IRS reflection phase shift constraint and channel conditions, the robust optimization problem of the system average information age is established, which can be expressed as:

[0089]

[0090]

[0091]

[0092]

[0093] C4:0<γ k [m]<1

[0094] C5:0<τ k [m]<1

[0095] C6:|v n |=1

[0096]

[0097] In this embodiment, the information age robust optimization problem is transformed into two sub-problems to be solved, and an information age optimization solution is obtained.

[0098] The information age optimization solution process includes:

[0099] The CVaR method is used to transform the outage probability constraint into a deterministic constraint, and the rewritten information age robust optimization problem is obtained.

[0100] Considering the uncertainty of channel information in the worst case, the constraint is always established, and the upper bound of the information age of the objective function under the distributed robust optimization opportunity constraint is solved. According to the above fuzzy set The distribution of channel state information error in the worst case is expressed as:

[0101]

[0102]

[0103] To transform the probabilistic constraints into deterministic constraints, a method based on CVaR is introduced, which is a convex approximation of the worst-case chance constraints. and Can be rewritten as:

[0104]

[0105]

[0106] Among them, β1, M1 and β2, M2 are auxiliary variables of energy harvesting opportunity constraint and information transmission opportunity constraint respectively.

[0107] Based on the CVaR method, P1 is rewritten as the following optimization problem:

[0108]

[0109] stC3-C7

[0110]

[0111]

[0112] An alternating iterative algorithm is used to decompose the robust optimization of rewritten information age into two sub-problems.

[0113] Fix v and τ in P2 k , P2 can be transformed into an information age optimization model with the optimization variables being the energy covariance matrix and the energy collection interruption probability, that is, it can be transformed into optimizing W and γ k The sub-problem is expressed as:

[0114]

[0115]

[0116]

[0117]

[0118] C4:0<γ k [m]<1

[0119] Fix W and γ in P2 k , P2 can be transformed into an information age optimization model with the optimization variables being the IRS reflection phase shift and the probability of information transmission interruption, that is, it can be transformed into optimizing v and τ k The sub-problem is expressed as:

[0120]

[0121]

[0122]

[0123] C6:|v n |=1

[0124] The convex optimization tool is used to solve the two sub-problems, and the energy covariance matrix of the energy station, the IRS reflection phase shift, the energy collection interruption probability and the information transmission interruption probability, that is, the information age minimization transmission scheme, are obtained.

[0125] Let V = νν H , where Rank(V)=1 and V≥0. Using the semi-positive definite relaxation method, the information transmission subproblem P4 is transformed into:

[0126]

[0127]

[0128]

[0129] Subproblems P3 and P5 are both standard semidefinite convex optimization problems that can be solved directly using the CVX toolbox. Since P5 relaxes the rank constraint of Rank(V)=1, a Gaussian random method is used to construct a unique solution for V. The eigenvalue decomposition of V, that is, V=UΣU H , U and Σ are unitary and diagonal matrices respectively. The suboptimal solution is ν=UΣ 1 / 2 r, r are circularly symmetric Gaussian random variables with r~CN(0;I). Therefore, the optimal solution to problem P5 is to use The biggest one.

[0130] Under the influence of imperfect channel information, the resource allocation method of this invention aims to improve the information timeliness of the entire system. It considers the energy collection and information transmission probability constraints under the worst-case communication conditions, ensuring the information freshness of wireless sensor communications. The optimal robust optimization scheme for wireless sensor information age is obtained.

[0131] Verification and evaluation of the distribution effect of the present invention:

[0132] The application effect of the present invention is described in detail with reference to simulation:

[0133] 1) Simulation conditions

[0134] Assume that this system contains one energy station, one intelligent reflector, two wireless sensors, and one base station. Assume that the energy station is located at (-4, 0), the two wireless sensors are randomly distributed within a circle centered at (1, 0) with a radius of 2 meters, the IRS is located at (2, 2), and the base station is located at (30, 0). Other simulation parameter settings are given in Table 1:

[0135] Table 1 Simulation parameters

[0136]

[0137] 2) Simulation results

[0138] Figure 4 The convergence of the present invention under different conditions of the number of energy station antennas and the number of reflective units is shown. Figure 4 It can be seen that the average AoI of the wireless sensor in the present invention can converge to a stable point after several iterations. T and the number of reflection units N S As the value of increases, the average information age of the system decreases. Figure 5 Shows the channel state information error and Impact on Average AoI. As channel state information error increases and channel conditions worsen, the average AoI of the system increases using our method, while the average information age remains unchanged in the non-robust application model. Furthermore, our method outperforms the non-robust method, demonstrating that it is sensitive to variations in channel estimation error and can maintain information freshness even under relatively poor channel conditions, further demonstrating its robustness and practicality.

[0139] The above embodiments further illustrate the purpose, technical solutions and advantages of the present invention in detail. It should be understood that the above embodiments are only preferred implementation plans of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made to the present invention within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

[0140] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the relevant hardware through a program, and the program can be stored in a computer-readable storage medium, which may include: ROM, RAM, disk or CD, etc.

[0141] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A wireless energy sensor network resource allocation method based on information freshness, characterized in that: include: According to the wireless energy supply sensor network architecture with intelligent reflective surface assisted communication, the energy collection process information of the wireless sensor to collect energy from the energy station and the information transmission process information of using the collected energy to transmit information to the base station via the intelligent reflective surface are determined; According to the interruption probability of wireless sensors in the energy collection process and the interruption probability of wireless sensors in the information transmission process, the objective function of minimizing the average information age is constructed; According to the uncertain channel state from the energy station to the wireless sensor and the uncertain channel state from the wireless sensor to the base station through the smart reflector, the uncertain channel state constraint of the distribution of the channel state information error under the fuzzy set is constructed; According to the energy collected by wireless sensors during energy collection, the interruption probability and the energy consumed during information transmission, the worst-case collection interruption constraint is constructed under the distribution of channel state information error under fuzzy sets. According to the information transmission rate, interruption probability and minimum information transmission probability of wireless sensors in the information transmission process, the transmission interruption probability constraint is constructed under the distribution of channel state information error under fuzzy sets. According to the interruption probability of wireless sensors in the energy collection process and the information transmission process, the collection interruption probability constraint and the transmission interruption probability constraint are constructed respectively. Construct the transmission power constraint based on the covariance matrix of the energy station during the energy collection process and the maximum transmission power; According to the reflection angle of the smart reflective surface, the reflection phase shift constraint of the smart reflective surface is constructed; By solving the robust optimization model of wireless sensor information age, which consists of minimizing the average information age objective function and various constraints, the covariance matrix of the energy station, the reflection phase shift of the smart reflector, the interruption probability of the energy collection process, and the interruption probability of the information transmission process are obtained. The calculation process of the average information age includes determining the waiting time and information transmission time of each update cycle based on the arrival time of the data packet and the update time; and calculating the time required for an update cycle based on the waiting time and information transmission time of each update cycle; The average information age is calculated based on the expected time required for an update cycle and the expected information transmission time; Formula for calculating average information age include: in, represents the average information age of the kth sensor in the system time, γ k [m] represents the interruption probability of the kth sensor in the energy harvesting process during the mth update cycle, τ k [m] represents the interruption probability of the kth sensor in the information transmission process during the mth update cycle; The calculation formula of fuzzy set is expressed as: in, represents a fuzzy set, Express expectations, h k represents the channel state from the energy station to the kth wireless sensor, H k represents the channel status from the kth wireless sensor to the base station through the smart reflective surface, and Respectively expressed as h k and H k The statistical channel gain, and Δ Hk Indicates h k and H k The uncertainty channel state information estimation error, Represents a fuzzy set Any distribution with mean and variance, and Respectively and The mean vector of and express and The variance matrix of The information age robust optimization model is expressed as: C4:0<γ k [m]<1 C5:0<τ k [m]<1 C6:|v n |=1 in, represents the average information age of the kth sensor, ν represents the reflection phase shift vector of the intelligent reflector IRS, W represents the energy covariance matrix of the energy station, γ k represents the interruption probability of the energy harvesting process of the kth sensor, τ k represents the interruption probability of the k-th sensor information transmission process, C1 represents the worst energy collection interruption constraint under the distribution of channel state information error under the fuzzy set, C2 represents the transmission interruption probability constraint under the distribution of channel state information error under the fuzzy set, C3 represents the transmission power constraint, C4 represents the collection interruption probability constraint, C5 represents the transmission interruption probability constraint, C6 represents the reflection phase shift constraint, and C7 represents the uncertain channel state constraint under the distribution of channel state information error under the fuzzy set. represents the probability function of the distribution of channel state information error under the fuzzy set, represents the energy collected by the kth sensor in each time slot of the mth update cycle, Indicates the minimum energy consumed to transmit information, represents the information transmission rate of the kth sensor, Indicates the minimum information transmission rate, Indicates the maximum transmission power of the energy station, and They represent the uncertainty channel state information estimation errors from the energy station to the kth sensor and from the kth sensor to the base station through the smart reflector, Represents a fuzzy set Any distribution of mean and variance in , Tr(·) represents the trace of the matrix, Indicates the probability distribution The probability that A>B is true.

2. The method for allocating wireless energy sensor network resources based on information freshness according to claim 1, characterized in that: The wireless energy supply sensor network architecture of the intelligent reflective surface assisted communication includes an N T Energy station with one antenna, K wireless sensors with one antenna, a base station with one antenna, N S Intelligent reflective surface with 1 reflective unit; In the energy harvesting phase, wireless sensors collect energy from the energy station; In the information transmission stage, the wireless sensor uses the collected energy to transmit information to the base station through intelligent reflection, where N T ≥2, K≥2, N S ≥2.

3. The method for allocating wireless energy sensor network resources based on information freshness according to claim 1, characterized in that: The energy collected by the kth sensor in each time slot of the mth update cycle is: Among them, h k represents the channel vector from the energy station to the kth sensor, W represents the energy covariance matrix of the energy station, χ represents the energy conversion efficiency, Tr(·) represents the trace of the matrix, and the superscript H represents the conjugate matrix.

4. The method for allocating wireless energy sensor network resources based on information freshness according to claim 1, characterized in that: The information transmission rate of the kth sensor in the mth update cycle is: Where B represents the channel bandwidth, represents the transmission power of the kth sensor, represents the reflection phase shift vector of IRS, the superscript H represents the conjugate matrix, Indicates the Nth S The reflection angle of each reflection unit, N S Indicates the number of reflective units in the smart reflective surface, H k represents the channel state from the kth wireless sensor to the base station through the smart reflective surface, σ 2 Represents the noise power.

5. The wireless energy sensor network resource allocation method based on information freshness according to claim 1 is characterized in that: The process of solving the information age robust optimization model includes: The conditional value at risk (CVaR) method is used to transform the interruption probability constraint into a deterministic constraint, and a rewritten information age robust optimization model is obtained. An alternating iterative algorithm is used to decompose the rewritten information age robust optimization model into two sub-models, including an energy collection optimization sub-model whose optimization variables are the energy covariance matrix and the collection interruption probability, and an information transmission optimization sub-model whose optimization variables are the reflection phase shift of the smart reflector and the transmission interruption probability. The convex optimization tool is used to solve the two sub-models to obtain the energy covariance matrix of the energy station, the reflection phase shift of the smart reflector, the energy collection interruption probability and the information transmission interruption probability, namely the information age robust optimization scheme.

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