Wireless sensor network energy optimization method and device, and nonvolatile storage medium

Abstract

The invention discloses a wireless sensor network energy optimization method, a wireless sensor network energy optimization device and a nonvolatile storage medium. The method comprises the following steps: constructing a wireless sensor network model; optimizing the respective initial phases and initial powers of the plurality of antennas through an energy beam forming technology to obtain a first optimization result of the plurality of antennas; on the basis of the first optimization result of the plurality of antennas, optimizing the respective initial transmitting power and initial time slot of the plurality of sensor nodes to obtain a first optimization result of the plurality of sensor nodes; and performing alternative iterative optimization on the first optimization results of the plurality of antennas and the first optimization results of the plurality of sensor nodes until a predetermined condition is reached, and obtaining an energy optimization result of the wireless sensor network model, thereby solving the technical problem that the energy consumption of the wireless sensor network cannot be effectively optimized in the prior art.

Description

Technical Field

[0001] The present invention relates to the field of wireless sensor network energy consumption optimization, and in particular to a wireless sensor network energy optimization method, device, and non-volatile storage medium. Background Art

[0002] In order to effectively meet the growing power load, beautify the urban landscape, and deal with the damage to the urban power system caused by natural disasters, cables are widely deployed in underground pipe corridors to ensure stable power transmission. At the same time, wireless sensor networks achieve the coverage of monitoring sensors by deploying low-cost wireless sensor network systems to meet the IoT monitoring needs such as water level, gas, temperature and humidity, and partial discharge in the pipe corridor. However, the performance of wireless sensor networks is severely restricted by the limited energy storage of sensor nodes, and the space limitation of cable channels makes it difficult to frequently replace the battery of sensors. On the one hand, the existing technology has developed energy management solutions for wireless sensor networks, aiming to make full use of the energy resources of the network. However, this solution cannot solve the fundamental problem of limited sensor energy and it is difficult to ensure the long-term stable operation of wireless sensor networks. On the other hand, some studies introduce wireless energy transmission into wireless sensor networks and use radio frequency signals to remotely charge sensors. Although this solution can effectively replenish sensor energy, most existing studies focus on using omnidirectional antennas for wireless energy transmission, which will cause serious energy loss in narrow cable channel environments.

[0003] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0004] The embodiments of the present invention provide a method, device, and non-volatile storage medium for optimizing energy in a wireless sensor network, so as to at least solve the technical problem that the prior art cannot effectively optimize the energy consumption of a wireless sensor network.

[0005] According to one aspect of an embodiment of the present invention, a method for energy optimization of a wireless sensor network is provided, comprising: constructing a wireless sensor network model, wherein the wireless sensor network model comprises a plurality of sensor nodes and a collection node, and a plurality of antennas are configured at the collection node; optimizing the initial phase and initial power of each of the plurality of antennas by energy beamforming technology to obtain a first optimization result of the plurality of antennas, wherein the energy beamforming technology concentrates the energy sent to the plurality of sensor nodes in a specific direction by adjusting the phase and power of the plurality of antennas; optimizing the initial transmission power and initial time slot of each of the plurality of sensor nodes on the basis of the first optimization result of the plurality of antennas to obtain a first optimization result of the plurality of sensor nodes, wherein the initial time slot of each of the plurality of sensor nodes comprises an initial time slot for energy signal transmission and an initial time slot for data transmission; performing alternate iterative optimization on the first optimization results of the plurality of antennas and the first optimization results of the plurality of sensor nodes until a predetermined condition is met to obtain an energy optimization result of the wireless sensor network model.

[0006] Optionally, the predetermined condition is that a first difference between target optimization results of multiple antennas and last optimization results, and a second difference between target optimization results of multiple sensor nodes and last optimization results are both smaller than a predetermined threshold.

[0007] Optionally, the initial phase and initial power of each of the multiple antennas are optimized through energy beamforming technology to obtain a first optimization result for the multiple antennas, including: determining a covariance matrix corresponding to the energy signals transmitted by the multiple antennas based on the initial phases and initial powers of the multiple antennas, wherein the covariance matrix is ​​used to describe the directional characteristics and power characteristics of the energy signals transmitted by the multiple antennas; constructing a first objective function based on the covariance matrix with the goal of maximizing the energy signal transmission efficiency; using a predetermined optimization solver, solving the first objective function while satisfying a first constraint condition to obtain a first optimization result for the multiple antennas, wherein the first constraint condition includes that the direction of the energy signals sent by the multiple antennas points to the corresponding sensor node, and the transmission powers of the multiple antennas are all within their respective power limit ranges.

[0008] Optionally, a predetermined solver is used to solve the first objective function to obtain a first optimization result for multiple antennas, including: obtaining a Taylor expansion of the first objective function, wherein the first objective function is a non-convex function, wherein the Taylor expansion is a local approximate representation of the first objective function; constructing a lower bound function corresponding to the Taylor expansion, wherein the lower bound function is used to convert the non-convex function into a convex function; using a predetermined optimization solver, the lower bound function is solved to obtain a first optimization result for multiple antennas.

[0009] Optionally, based on the first optimization results of the multiple antennas, the initial transmission power and the initial time slot of each of the multiple sensor nodes are optimized to obtain the first optimization results of the multiple sensor nodes, including: based on the first optimization results of the multiple antennas, according to the initial transmission power and the initial time slot of each of the multiple sensor nodes, a second objective function is constructed with the goal of minimizing the energy consumption of transmitting data by the multiple sensor nodes; using a predetermined optimization solver, while satisfying a second constraint condition, the second objective function is solved to obtain the first optimization results of the multiple sensor nodes, wherein the second constraint condition includes that the transmission power of the multiple sensor nodes is within their respective power ranges, and time slot allocation ensures that the multiple sensor nodes can transmit data at a predetermined minimum data transmission rate.

[0010] Optionally, alternating iterative optimization means optimizing multiple sensor nodes based on the first optimization results of the multiple antennas, optimizing the first optimization results of the multiple antennas for a second time based on the first optimization results of the multiple sensor nodes, and continuously optimizing the parameters of the multiple sensor nodes and the parameters of the multiple antennas alternately until predetermined conditions are met.

[0011] According to another aspect of an embodiment of the present invention, a wireless sensor network energy optimization device is provided, comprising: a construction module, used to construct a wireless sensor network model, wherein the wireless sensor network model includes multiple sensor nodes and a collection node, and multiple antennas are configured at the collection node; a first optimization module, used to optimize the initial phase and initial power of each of the multiple antennas through energy beamforming technology to obtain a first optimization result of the multiple antennas, wherein the energy beamforming technology concentrates the energy sent to the multiple sensor nodes in a specific direction by adjusting the phase and power of the multiple antennas; a second optimization module, used to optimize the initial transmission power and initial time slot of each of the multiple sensor nodes on the basis of the first optimization result of the multiple antennas to obtain the first optimization result of the multiple sensor nodes, wherein the initial time slot of each of the multiple sensor nodes includes an energy signal transmission initial time slot and a data transmission initial time slot; a third optimization module, used to alternately iteratively optimize the first optimization results of the multiple antennas and the first optimization results of the multiple sensor nodes until a predetermined condition is met to obtain the energy optimization result of the wireless sensor network model.

[0012] According to another aspect of an embodiment of the present invention, a non-volatile storage medium is provided. The non-volatile storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing any one of the wireless sensor network energy optimization methods.

[0013] According to another aspect of an embodiment of the present invention, an electronic device is provided, comprising: one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement any one of the wireless sensor network energy optimization methods.

[0014] According to yet another aspect of an embodiment of the present invention, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, any one of the above-mentioned wireless sensor network energy optimization methods is implemented.

[0015] In an embodiment of the present invention, a wireless sensor network model is constructed, wherein the wireless sensor network model includes multiple sensor nodes and a collection node, and multiple antennas are configured at the collection node; the initial phase and initial power of each of the multiple antennas are optimized by energy beamforming technology to obtain the first optimization results of the multiple antennas, wherein the energy beamforming technology concentrates the energy sent to the multiple sensor nodes in a specific direction by adjusting the phase and power of the multiple antennas; on the basis of the first optimization results of the multiple antennas, the initial transmission power and initial time slot of each of the multiple sensor nodes are optimized to obtain the first optimization results of the multiple sensor nodes, wherein the initial time slot of each of the multiple sensor nodes includes an initial time slot for energy signal transmission and an initial time slot for data transmission; the first optimization results of the multiple antennas and the first optimization results of the multiple sensor nodes are alternately iteratively optimized until a predetermined condition is met, and the energy optimization result of the wireless sensor network model is obtained, thereby achieving the purpose of optimizing the energy consumption of the wireless sensor network from two perspectives of reducing the energy consumption of the sensor node sending data to the collection node and improving the efficiency of the collection node transmitting energy to the sensor node, solving the technical problem that the prior art cannot effectively optimize the energy consumption of the wireless sensor network, thereby achieving the technical effect of improving the degree of optimization of the energy consumption of the wireless sensor network. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0017] Figure 1 is a flow chart of a wireless sensor network energy optimization method provided by an embodiment of the present invention;

[0018] Figure 2 A schematic diagram of an optional wireless sensor network model provided according to an embodiment of the present invention;

[0019] Figure 3 is a schematic diagram of an optional curve of energy consumption of a line sensor network changing with the length of a pipe gallery provided according to an embodiment of the present invention;

[0020] Figure 4 is a schematic diagram of a wireless sensor network energy optimization device provided according to an embodiment of the present invention;

[0021] Figure 5 is a schematic diagram of an electronic device provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. 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 creative work should fall within the scope of protection of the present invention.

[0023] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0024] According to an embodiment of the present invention, a method embodiment of energy optimization of a wireless sensor network is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0025] Figure 1 is a flow chart of a wireless sensor network energy method according to an embodiment of the present invention. Figure 1 As shown, the method comprises the following steps:

[0026] Step S102, constructing a wireless sensor network model, wherein the wireless sensor network model includes a plurality of sensor nodes and a sink node, and a plurality of antennas are configured at the sink node;

[0027] In this step, a cable channel wireless sensor network model with energy beamforming is constructed. The model includes N sensor nodes, a sink node S and an access point. Specifically, the sensor is responsible for sensing a variety of environmental parameters, such as water level, gas, temperature, humidity, and partial discharge. In addition, the sensing data of each sensor is wirelessly transmitted in a multi-hop manner, and then the data is collected at the sink node. At the same time, the sink node uses energy beamforming to transmit energy to the sensor, and further uploads the collected data to the access point through the optical fiber link. Figure 2 As shown in the figure, it is a wireless sensor network model diagram.

[0028] Step S104, optimizing the initial phase and initial power of each of the multiple antennas by energy beamforming technology to obtain a first optimization result of the multiple antennas, wherein the energy beamforming technology concentrates the energy sent to the multiple sensor nodes in a specific direction by adjusting the phase and power of the multiple antennas;

[0029] In this step, before starting the first optimization, each antenna has an initial phase and power. If multiple sensor nodes send energy signals according to the initial phase and power of the antenna, energy will be wasted due to inaccurate selection of the direction or path of energy transmission, such as Figure 3 As shown in the figure, according to the curve of the change of energy consumption of the wireless sensor network with the length of the corridor, it is shown that the energy consumption increases with the increase of the path length. Therefore, it is necessary to optimize the phase and power of multiple antennas through energy beamforming technology to form a directional energy beam and efficiently transmit energy to specific sensor nodes.

[0030] In an optional embodiment, the initial phase and initial power of each of the multiple antennas are optimized through energy beamforming technology to obtain a first optimization result of the multiple antennas, including: determining a covariance matrix corresponding to the energy signals transmitted by the multiple antennas according to the initial phases and initial powers of the multiple antennas, wherein the covariance matrix is ​​used to describe the directional characteristics and power characteristics of the energy signals transmitted by the multiple antennas; constructing a first objective function based on the covariance matrix with the goal of maximizing the energy signal transmission efficiency; using a predetermined optimization solver, solving the first objective function while satisfying a first constraint condition to obtain a first optimization result of the multiple antennas, wherein the first constraint condition includes that the direction of the energy signals sent by the multiple antennas points to the corresponding sensor node, and the transmission powers of the multiple antennas are within their respective power limit ranges.

[0031] Optionally, based on the initial phase and initial power settings of each antenna, a covariance matrix describing the direction and power characteristics of the energy signals emitted by multiple antennas is constructed. The covariance matrix reflects the statistical characteristics of the signal transmitted by the antenna array. The elements in the matrix can be used to understand the intensity distribution of the signal in different directions and the relationship between the signals between the antennas. Then, using the constructed covariance matrix, we define a first objective function with the goal of maximizing the energy signal transmission efficiency. The energy transmission efficiency reflects the effective transmission degree of the energy signal from the antenna to the sensor node. The objective function comprehensively considers factors such as the loss of the signal transmission path and the receiving efficiency of the sensor, aiming to find the antenna phase and power configuration that can maximize the energy transmission to each sensor. Subsequently, by using a predetermined optimization solver, the first objective function is solved under the premise of satisfying the following first constraints: first, the direction of the energy beam must be accurately pointed to the sensor node to ensure effective absorption of energy; second, the transmission power of each antenna cannot exceed its power limit range, because too high power will cause signal interference or equipment damage, while too low power cannot guarantee effective energy transmission. The optimization solver gradually approaches the optimal solution by iteratively adjusting the phase and power of the antenna until a set of antenna configurations that can simultaneously meet the above constraints and maximize energy transmission efficiency is found, thereby obtaining the first optimization results of multiple antennas.

[0032] In an optional embodiment, a predetermined solver is used to solve the first objective function to obtain a first optimization result for multiple antennas, including: obtaining a Taylor expansion of the first objective function, wherein the first objective function is a non-convex function, wherein the Taylor expansion is a local approximate representation of the first objective function; constructing a lower bound function corresponding to the Taylor expansion, wherein the lower bound function is used to convert the non-convex function into a convex function; using a predetermined optimization solver, the lower bound function is solved to obtain a first optimization result for multiple antennas.

[0033] Optionally, the first objective function describes the energy signal transmission efficiency of the energy beamforming technology in the wireless sensor network. Due to the non-convexity of the first objective function, directly solving it will obtain a local optimal solution instead of a global optimal solution, resulting in inaccurate optimization results. In order to solve this problem, a mathematical method-Taylor expansion is used to locally approximate the first objective function. A lower bound function is constructed using the Taylor expansion, and the original non-convex optimization problem is converted into a convex optimization problem through the lower bound function, and a predetermined optimization solver is used to solve this lower bound function to obtain a first optimized structure of multiple antennas.

[0034] Step S106, based on the first optimization results of the multiple antennas, optimizing the initial transmission power and the initial time slot of each of the multiple sensor nodes to obtain the first optimization results of the multiple sensor nodes, wherein the initial time slots of each of the multiple sensor nodes include an energy signal transmission initial time slot and a data transmission initial time slot;

[0035] In this step, each sensor node has an initial transmit power setting and an initial time slot allocation scheme. The time slot allocation determines when the sensor sends an energy signal (for receiving energy beams) and when it sends a data signal (for uploading monitoring data). The initial transmit power and initial time slot can achieve the purpose of sending data to the aggregation node, but it will lead to energy waste. Therefore, it is necessary to optimize the initial transmit power and initial time slot allocation scheme of multiple sensor nodes from the perspective of data transmission based on the first optimization of multiple antennas, so as to reduce the energy consumption of multiple sensor nodes during data transmission as much as possible and extend the operation time of the network.

[0036] In an optional embodiment, based on the first optimization results of the multiple antennas, the initial transmission power and the initial time slot of each of the multiple sensor nodes are optimized to obtain the first optimization results of the multiple sensor nodes, including: based on the first optimization results of the multiple antennas, according to the initial transmission power and the initial time slot of each of the multiple sensor nodes, a second objective function is constructed with the goal of minimizing the energy consumption of transmitting data by the multiple sensor nodes; using a predetermined optimization solver, while satisfying a second constraint condition, the second objective function is solved to obtain the first optimization results of the multiple sensor nodes, wherein the second constraint condition includes that the transmission power of the multiple sensor nodes is within their respective power ranges, and the time slot allocation ensures that the multiple sensor nodes can transmit data at a predetermined minimum data transmission rate.

[0037] Optionally, based on the first optimization results of multiple antennas, a second objective function is constructed with the goal of minimizing the energy consumption of data transmission by multiple sensor nodes. This objective function takes the data transmission energy consumption of all sensors in the network as the optimization target, aiming to find a set of power and time slot configurations so that the energy consumption of the entire network is minimized while ensuring the quality of data transmission and stable network operation. Using a predetermined optimization solver, we solve the second objective function while satisfying the second constraint. The second constraint includes two aspects: first, the transmission power of each sensor node must be within its power range to avoid exceeding the power limit and causing equipment damage or energy waste; second, the time slot allocation must ensure that all sensor nodes can transmit data at a predetermined minimum data transmission rate, which is to ensure the communication quality of the network. Even in the case of energy consumption optimization, the data transmission cannot be lower than a certain speed to maintain the normal operation of the network. Through the calculation of the optimization solver, we can find the optimal transmission power and time slot allocation scheme of the sensor nodes that meets the above constraints. This optimization result, that is, the first optimization result of multiple sensor nodes, will significantly improve the data transmission efficiency of the wireless sensor network, while reducing the overall energy consumption, thereby achieving longer operation.

[0038] Step S108, performing alternate iterative optimization on the first optimization results of the multiple antennas and the first optimization results of the multiple sensor nodes until a predetermined condition is met, thereby obtaining an energy optimization result of the wireless sensor network model.

[0039] In this step, alternating iterative optimization means that based on the first optimization results of multiple sensor nodes, the first optimization results of multiple antennas are optimized for the second time to obtain the second optimization results of the antennas; then, based on the second optimization results of multiple antennas, the first optimization results of multiple sensor nodes are optimized to obtain the second optimization results of the sensor nodes. This process will continue, and each iteration will be based on the results of the previous optimization. Until a balance point is found, that is, an optimal state is reached between the energy transmission of multiple antennas and the data transmission of multiple sensors, so that the energy consumption of the overall network is minimized, while ensuring the efficiency and reliability of data transmission.

[0040] The final optimization result includes the final phase and power configuration of the antenna, as well as the final transmission power and time slot allocation scheme of the sensor node. This optimization result ensures efficient energy management and data transmission of the wireless sensor network in the cable channel environment, greatly improves the energy utilization efficiency and operation stability of the network, and also solves the core problem of limited energy of sensor nodes.

[0041] In an optional embodiment, the predetermined condition is that a first difference between target optimization results of multiple antennas and previous optimization results, and a second difference between target optimization results of multiple sensor nodes and previous optimization results are both less than a predetermined threshold.

[0042] Optionally, the termination condition of the iterative optimization is to reach a predetermined convergence standard, that is, in two consecutive iterations, the changes in the phase and power optimization results of the antenna and the transmission power and time slot allocation optimization results of the sensor node are less than a preset threshold.

[0043] In an optional embodiment, alternating iterative optimization refers to optimizing multiple sensor nodes based on the first optimization results of the multiple antennas, optimizing the first optimization results of the multiple antennas for a second time based on the first optimization results of the multiple sensor nodes, and continuously optimizing the parameters of the multiple sensor nodes and the parameters of the multiple antennas alternately until predetermined conditions are met.

[0044] Optionally, based on the first optimization results of the multiple antennas, the multiple sensor nodes in the wireless sensor network are optimized for the first time. After the transmission power and time slot allocation of the multiple sensor nodes are optimized, the first optimization results of the multiple sensor nodes are obtained. The first results of the multiple sensor nodes are used to guide the next step of optimization, that is, the second optimization of the phase and power of the multiple antennas. The purpose of the secondary optimization is to further adjust the direction and intensity of the energy beam based on the optimized state of the multiple sensor nodes to more accurately meet the energy requirements and data transmission requirements of the sensor nodes. According to the first and second optimization processes, the parameters of the multiple sensor nodes and the multiple antennas are continuously and alternately optimized until the predetermined conditions are met.

[0045] By constructing a wireless sensor network model as described above, the wireless sensor network model includes multiple sensor nodes and a collection node, and multiple antennas are configured at the collection node; by using energy beamforming technology, the initial phase and initial power of each of the multiple antennas are optimized to obtain the first optimization results of the multiple antennas, wherein the energy beamforming technology concentrates the energy sent to the multiple sensor nodes in a specific direction by adjusting the phase and power of the multiple antennas; on the basis of the first optimization results of the multiple antennas, the initial transmission power and initial time slot of each of the multiple sensor nodes are optimized to obtain the first optimization results of the multiple sensor nodes, wherein the initial time slot of each of the multiple sensor nodes includes the initial time slot for energy signal transmission and the initial time slot for data transmission; the first optimization results of the multiple antennas and the first optimization results of the multiple sensor nodes are alternately iteratively optimized until the predetermined conditions are met, and the energy optimization result of the wireless sensor network model is obtained, thereby achieving the purpose of optimizing the energy consumption of the wireless sensor network from two perspectives of reducing the energy consumption of the sensor node sending data to the collection node and improving the efficiency of the collection node transmitting energy to the sensor node, solving the technical problem that the existing technology cannot effectively optimize the energy consumption of the wireless sensor network, thereby achieving the technical effect of improving the degree of optimization of the energy consumption of the wireless sensor network.

[0046] Based on the above embodiments and optional embodiments, the present invention proposes a specific optional implementation manner.

[0047] Step S1, constructing a cable channel wireless sensor network model, which includes N sensors, a sink node S and an access point. Specifically, the sensor is responsible for sensing a variety of environmental parameters, such as water level, gas, temperature, humidity, and partial discharge. In addition, the sensing data of each sensor is wirelessly transmitted in a multi-hop manner, and then the data is collected at the sink node. At the same time, the sink node uses energy beamforming to transmit energy to the sensor, and further uploads the collected data to the access point through the optical fiber link;

[0048] Step S11, in order to realize low power operation of wireless sensor network, the whole data collection period T is divided into an energy beamforming time slot and multiple data transmission time slots. In the energy beamforming time slot, the aggregation node transmits a signal carrying energy, and the sensor collects the signal to support data perception and communication. In the xth data transmission time slot, sensor y merges the data from other sensors with the data collected by sensor y, and then transmits it to the next node. Let δ and τ represent the length of energy beamforming time slot and a data transmission time slot respectively, then δ+Nτ≤T.

[0049] Step S12: The sink node is equipped with M antennas to achieve energy beamforming, and the transmitted signal is written as: v∈C M×1 , the covariance is: V = E{vv HThe Rice channel model is used to model the channel h between the sink node and any sensor n. S,n , where the sight distance part is: where θ n is the azimuth angle, and the non-line-of-sight part obeys Rayleigh fading. Therefore, the received energy of sensor n is: In particular, considering the actual nonlinear energy harvesting model, the harvested energy of sensor n is calculated as:

[0050] in, Q sat ,Ω,c 1 , c 2 is a constant.

[0051] Step S13: For each sensor n, its energy consumption includes data sensing, receiving and transmitting. Definition d n The amount of sensing data is determined by the monitoring information of the pipeline corridor that the sensor needs to collect. sen and e rec Represent the sensing and receiving energy consumption per bit of data respectively. In addition, p n represents the transmission power of sensor n. Therefore, the total energy consumption of sensor n is calculated as:

[0052]

[0053] Where I(n) represents the set of sensors with sensor n as the relay, so n needs to receive all the data from these nodes. To ensure the energy self-sustainability of the wireless sensor network, the energy consumption of each sensor should not exceed its collection capacity, that is,

[0054] In step S14, the aggregation node needs to consume energy for wireless energy transmission and collect the sensing data of all sensors, so its energy consumption is calculated as:

[0055]

[0056] Considering the data transmission of any hop between sensor nodes, the channel gain from sensor n to j is The achievable transmission rate is calculated as

[0057]

[0058] Where B is the bandwidth, σ 2 represents the noise power. In addition, the total amount of data that sensor n needs to transmit is Therefore, the amount of data transmitted is constrained

[0059] Step S15, describing the process of minimizing the energy consumption of the sink node to ES The variables to be designed include the energy beamforming time slot length δ, the covariance matrix V, the amount of sensing data of each sensor, and the transmission power The specific expression is:

[0060] P1:

[0061] C 1 :δ+Nτ≤T,

[0062] C 2 :V is a semi-positive definite matrix,

[0063] C 3 :

[0064] C 4 :

[0065] C 5 :

[0066] C 6 :

[0067] Among them, C 3 The transmit power of the representative sink node should not exceed the power budget C 4 The sensor transmission power does not exceed the maximum power

[0068] Step S2, using alternating optimization techniques to decouple the original problem P1 into two sub-problems, namely, the problem of multiple sensor data transmission to optimize V and the problem of multiple antenna energy transmission to optimize

[0069] Step S21, for the problem of multiple sensor data transmission, given Reframe the problem as

[0070] SP1:

[0071] sC 2 ,C 3 ,

[0072]

[0073] Due to the nonlinear n , this subproblem is still non-convex. Given that Ψ n is for If it is a convex function, its lower bound can be approximately expressed by Taylor expansion as:

[0074]

[0075] in, Obtained from the previous iteration of the continuous convex approximation, Will n Replace with C 6 ′ is a convex constraint. Therefore, the transformed subproblem (denoted as SP1′) is a standard positive definite programming problem, which can be solved by convex optimization tools.

[0076] Step S22, for the problem of multiple antenna energy transmission, given V, δ, The joint optimization subproblem is described as

[0077] SP2:

[0078] sC 1 :δ≤T-Nτ,

[0079] C 4 :

[0080] C 5 :

[0081] C 6 :

[0082] By checking the convexity of the objective function and constraints, it can be seen that SP2 is a strictly convex optimization problem and can be directly solved by the solver.

[0083] Step S3, construct an initial solution for time slot allocation and sensor power As the start of alternating optimization; according to step 21 and step 22, the energy beamforming subproblem and the resource allocation subproblem are iteratively solved, and finally converge to the approximate optimal solution of the energy consumption optimization problem.

[0084] Based on the above embodiments and optional embodiments, the present invention proposes another optional implementation. That is, multiple sensor data transmission problems are optimized to optimize V and to optimize

[0085] Step A1: The specific implementation scenario of the present invention includes N = 20 sensors, data collection period T = 30s, each transmission time slot τ = 1s, the number of aggregation node antennas M = 25, and the aggregation node power limit Amount of perception data n =100, sensor power upper limit The wireless channel is modeled by a Rice channel with a reference gain of g 0 =10 -3, path loss factor α = 3, Rice factor κ = 3.16, unit perceived energy consumption e sen =200nJ, unit receiving energy consumption e rec =50nJ, the nonlinear energy harvester parameter is Q sat =10.73mW,Ω=0.2247,c 1 =0.2308,c 2 =5.365, bandwidth is B = 1KHz, noise power is σ 2 =5×10 -10 W.

[0086] Step A11: Construct a cable channel wireless sensor network model with energy beamforming, and use the Rice channel model to model the channel h between the sink node and any sensor n S,n , where the sight distance part is where θ n is the direction angle, and the non-line-of-sight part obeys Rayleigh fading;

[0087] Step A12: Construct an initial solution for time slot allocation and sensor power As the starting point of alternating optimization, where δ (0) =20s, Given δ, Solve the sub-problem of the multiple sensor data transmission problem: Define the M×M dimensional complex matrix variable V in MATLAB's CVX solver to represent the covariance matrix of the beamforming transmission signal, and define in Initialize to the identity matrix;

[0088] Step A13: Enter the objective function in the CVX solver Constrain V to be a semi-positive definite matrix, in Call the solver to solve and obtain the optimization result of V, and update in

[0089] Step A14: Repeat steps A2 and A3 until V converges, that is, the change in the optimization results of V between two consecutive times is less than a threshold value of 0.01.

[0090] Step A2: Given V, solve the problem of energy transfer among multiple antennas.

[0091] Step A21: Define the one-dimensional optimization variable δ and the N-dimensional optimization variable in the CVX solver of MATLAB

[0092] Step A22: Enter the objective function in the CVX solver Constraint δ≤T-Nτ,

[0093] Call the solver to solve directly and get Optimization results.

[0094] Step A3: Repeat steps 1 and 2, calculating the function each time. Until the difference between two consecutive objective function values ​​is less than the threshold of 0.01, record the final optimization result

[0095] The above optional implementation achieves at least the following effects: by constructing a wireless sensor network model, wherein the wireless sensor network model includes multiple sensor nodes and a collection node, and multiple antennas are configured at the collection node; by using energy beamforming technology, the initial phase and initial power of each of the multiple antennas are optimized to obtain a first optimization result of the multiple antennas, wherein the energy beamforming technology concentrates the energy sent to the multiple sensor nodes in a specific direction by adjusting the phase and power of the multiple antennas; based on the first optimization result of the multiple antennas, the initial transmission power and initial time slot of each of the multiple sensor nodes are optimized to obtain a first optimization result of the multiple sensor nodes. As a result, the initial time slots of multiple sensor nodes respectively include the initial time slots for energy signal transmission and the initial time slots for data transmission; the first optimization results of multiple antennas and the first optimization results of multiple sensor nodes are alternately iterated and optimized until the predetermined conditions are met, and the energy optimization results of the wireless sensor network model are obtained, thereby achieving the purpose of optimizing the energy consumption of the wireless sensor network from two perspectives: reducing the energy consumption of sending data from sensor nodes to aggregation nodes and improving the efficiency of energy transmission from aggregation nodes to sensor nodes, solving the technical problem that the existing technology cannot effectively optimize the energy consumption of wireless sensor networks, and thus achieving the technical effect of improving the degree of optimization of energy consumption of wireless sensor networks.

[0096] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0097] In this embodiment, a wireless sensor network energy optimization device is also provided, which is used to implement the above embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the terms "module" and "device" can implement a combination of software and / or hardware for a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0098] According to an embodiment of the present invention, there is also provided an apparatus embodiment for implementing a wireless sensor network energy optimization method. Figure 4 is a schematic diagram of a wireless sensor network energy optimization device according to an embodiment of the present invention. Figure 4 As shown, the above-mentioned wireless sensor network energy optimization device includes a construction module 41, a first optimization module 42, a second optimization module 43, and a third optimization module 44. The device is described below.

[0099] A construction module 41 is used to construct a wireless sensor network model, wherein the wireless sensor network model includes a plurality of sensor nodes and a collection node, and a plurality of antennas are configured at the collection node;

[0100] A first optimization module 42 is connected to the construction module 41 and is used to optimize the initial phase and initial power of each of the multiple antennas through an energy beamforming technology to obtain a first optimization result of the multiple antennas, wherein the energy beamforming technology concentrates the energy sent to the multiple sensor nodes in a specific direction by adjusting the phase and power of the multiple antennas;

[0101] The second optimization module 43 is connected to the first optimization module 42, and is used to optimize the initial transmission power and the initial time slot of each of the multiple sensor nodes based on the first optimization results of the multiple antennas to obtain the first optimization results of the multiple sensor nodes, wherein the initial time slots of the multiple sensor nodes include the energy signal transmission initial time slot and the data transmission initial time slot;

[0102] The third optimization module 44 is connected to the second optimization module 43, and is used to perform alternate iterative optimization on the first optimization results of multiple antennas and the first optimization results of multiple sensor nodes until a predetermined condition is met to obtain an energy optimization result of the wireless sensor network model.

[0103] In a wireless sensor network energy optimization device provided by an embodiment of the present invention, a construction module is set to construct a wireless sensor network model, wherein the wireless sensor network model includes multiple sensor nodes and a collection node, and multiple antennas are configured at the collection node; a first optimization module is used to optimize the initial phase and initial power of each of the multiple antennas through energy beamforming technology to obtain a first optimization result of the multiple antennas, wherein the energy beamforming technology concentrates the energy sent to the multiple sensor nodes in a specific direction by adjusting the phase and power of the multiple antennas; a second optimization module is used to optimize the initial transmission power and initial time slot of each of the multiple sensor nodes based on the first optimization result of the multiple antennas to obtain The first optimization results of multiple sensor nodes, wherein the initial time slots of the multiple sensor nodes respectively include the energy signal transmission initial time slot and the data transmission initial time slot; the third optimization module is used to alternately iteratively optimize the first optimization results of the multiple antennas and the first optimization results of the multiple sensor nodes until the predetermined conditions are met, and the energy optimization result of the wireless sensor network model is obtained, so as to achieve the purpose of optimizing the energy consumption of the wireless sensor network from two perspectives of reducing the energy consumption of the sensor nodes sending data to the aggregation nodes and improving the efficiency of the energy transmission from the aggregation nodes to the sensor nodes, solve the technical problem that the existing technology cannot effectively optimize the energy consumption of the wireless sensor network, and thus achieve the technical effect of improving the degree of optimization of the energy consumption of the wireless sensor network.

[0104] It should be noted that the above modules can be implemented by software or hardware. For example, for the latter, it can be implemented in the following ways: the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0105] It should be noted that the above-mentioned construction module 41, the first optimization module 42, the second optimization module 43, and the third optimization module 44 correspond to steps S102 to S108 in the embodiment, and the examples and application scenarios implemented by the above-mentioned modules and the corresponding steps are the same, but are not limited to the contents disclosed in the above-mentioned embodiment. It should be noted that the above-mentioned modules can be run in a computer terminal as part of the device.

[0106] It should be noted that the optional or preferred implementation of this embodiment can refer to the relevant description in the embodiment, which will not be repeated here.

[0107] The above-mentioned wireless sensor network energy optimization device may also include a processor and a memory. The construction module 41, the first optimization module 42, the second optimization module 43, the third optimization module 44, etc. are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to realize the corresponding functions.

[0108] The processor includes a kernel, which retrieves the corresponding program unit from the memory. There can be one or more kernels. The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one storage chip.

[0109] An embodiment of the present invention provides a non-volatile storage medium on which a program is stored. When the program is executed by a processor, a method for optimizing energy in a wireless sensor network is implemented.

[0110] like Figure 5 It is shown that an embodiment of the present invention provides an electronic device, the electronic device 10 includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, the following steps are implemented: the memory is used to store a computer program, wherein, when the computer program is executed by the processor, the processor implements the above-mentioned wireless sensor network energy optimization method. The device in this article may be a server, a PC, etc.

[0111] The present invention also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program that initializes the following method steps: computer instructions are executed by a processor to implement the above-mentioned wireless sensor network energy optimization method.

[0112] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0113] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0114] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0115] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0116] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0117] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0118] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0119] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0120] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0121] The above are only embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A wireless sensor network energy optimization method, characterized in that: include: Constructing a wireless sensor network model, wherein the wireless sensor network model includes a plurality of sensor nodes and a collection node, and the collection node is configured with a plurality of antennas; By using an energy beamforming technology, an initial phase and an initial power of each of the multiple antennas are optimized to obtain a first optimization result of the multiple antennas, wherein the energy beamforming technology focuses the energy sent to the multiple sensor nodes in a specific direction by adjusting the phase and power of the multiple antennas; Based on the first optimization results of the multiple antennas, the initial transmission power and the initial time slot of each of the multiple sensor nodes are optimized to obtain the first optimization results of the multiple sensor nodes, wherein the initial time slots of each of the multiple sensor nodes include an energy signal transmission initial time slot and a data transmission initial time slot; The first optimization results of the multiple antennas and the first optimization results of the multiple sensor nodes are alternately iterated and optimized until a predetermined condition is met, thereby obtaining an energy optimization result of the wireless sensor network model.

2. The method according to claim 1, characterized in that The predetermined condition is that a first difference between the target optimization results of the multiple antennas and the last optimization results, and a second difference between the target optimization results of the multiple sensor nodes and the last optimization results are both smaller than a predetermined threshold.

3. The method according to claim 1, characterized in that The step of optimizing the initial phase and initial power of each of the multiple antennas by using the energy beamforming technology to obtain a first optimization result of the multiple antennas includes: Determine, according to the initial phases and initial powers of the multiple antennas, a covariance matrix corresponding to the energy signals transmitted by the multiple antennas, wherein the covariance matrix is ​​used to describe the directional characteristics and power characteristics of the energy signals transmitted by the multiple antennas; According to the covariance matrix, constructing a first objective function with the goal of maximizing energy signal transmission efficiency; Using a predetermined optimization solver, the first objective function is solved while satisfying a first constraint condition to obtain a first optimization result for the multiple antennas, wherein the first constraint condition includes that the direction of the energy signals sent by the multiple antennas points to the corresponding sensor nodes, and the transmission powers of the multiple antennas are all within their respective power limit ranges.

4. The method according to claim 3, characterized in that The using a predetermined solver to solve the first objective function to obtain a first optimization result of the plurality of antennas includes: Obtaining a Taylor expansion of the first objective function, wherein the first objective function is a non-convex function, and wherein the Taylor expansion is a local approximate representation of the first objective function; Constructing a corresponding lower bound function of the Taylor expansion, wherein the lower bound function is used to convert the non-convex function into a convex function; The lower bound function is solved using the predetermined optimization solver to obtain a first optimization result of the multiple antennas.

5. The method according to claim 1, characterized in that: The step of optimizing the initial transmission power and the initial time slot of each of the plurality of sensor nodes based on the first optimization results of the plurality of antennas to obtain the first optimization results of the plurality of sensor nodes includes: On the basis of the first optimization results of the plurality of antennas, according to the initial transmission power and initial time slot of each of the plurality of sensor nodes, a second objective function is constructed with the goal of minimizing the energy consumption of data transmission by the plurality of sensor nodes; Using a predetermined optimization solver, the second objective function is solved while satisfying a second constraint condition to obtain a first optimization result for the multiple sensor nodes, wherein the second constraint condition includes that the transmission powers of the multiple sensor nodes are all within their respective power ranges, and time slot allocation ensures that the multiple sensor nodes can transmit data at a predetermined minimum data transmission rate.

6. The method according to claim 1, characterized in that The alternating iterative optimization refers to optimizing the multiple sensor nodes based on the first optimization results of the multiple antennas, optimizing the first optimization results of the multiple antennas for a second time based on the first optimization results of the multiple sensor nodes, and continuously optimizing the parameters of the multiple sensor nodes and the parameters of the multiple antennas alternately until the predetermined conditions are met.

7. A wireless sensor network energy optimization device, characterized in that: include: A construction module, used to construct a wireless sensor network model, wherein the wireless sensor network model includes a plurality of sensor nodes and a collection node, and the collection node is configured with a plurality of antennas; A first optimization module is used to optimize the initial phase and initial power of each of the multiple antennas by using an energy beamforming technology to obtain a first optimization result of the multiple antennas, wherein the energy beamforming technology concentrates the energy sent to the multiple sensor nodes in a specific direction by adjusting the phase and power of the multiple antennas; A second optimization module is used to optimize the initial transmission power and initial time slot of each of the multiple sensor nodes based on the first optimization results of the multiple antennas to obtain the first optimization results of the multiple sensor nodes, wherein the initial time slots of each of the multiple sensor nodes include an energy signal transmission initial time slot and a data transmission initial time slot; The third optimization module is used to perform alternate iterative optimization on the first optimization results of the multiple antennas and the first optimization results of the multiple sensor nodes until a predetermined condition is met, thereby obtaining an energy optimization result of the wireless sensor network model.

8. A non-volatile storage medium, characterized in that: The non-volatile storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the wireless sensor network energy optimization method according to any one of claims 1 to 6.

9. An electronic device, characterized in that: include: One or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the wireless sensor network energy optimization method described in any one of claims 1 to 6.

10. A computer program product comprising computer instructions, characterized in that: The computer instructions are executed by the processor to implement the wireless sensor network energy optimization method described in any one of claims 1 to 6.

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