CN117676785A - Power consumption optimization method and device in wireless sensor network and electronic equipment - Google Patents
Power consumption optimization method and device in wireless sensor network and electronic equipment Download PDFInfo
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Abstract
The application discloses a power consumption optimization method and device in a wireless sensor network and electronic equipment. Wherein the method comprises the following steps: acquiring an observed value of a data source acquired by a sensor, wherein the observed value comprises a first observed value and observed noise data of the first observed value; determining a transmission power allocated to the sensor according to a first variance in a first probability distribution to which the observed value obeys and an amplification factor corresponding to the sensor; determining the degree of difference between a data value generated by a data source and an estimated value of a signal corresponding to an observed value transmitted by a sensor and received by a sink node according to the observed value and the amplification factor; and determining the minimum value of an objective function under the condition that the difference degree meets the preset condition, wherein the objective function is used for representing the total power consumption of the sensor. The method and the device solve the technical problem that the service life of the whole sensing network is short due to the fact that the energy consumption of the wireless sensing network is large in the related technology.
Description
Technical Field
The present invention relates to the field of physical networks, and in particular, to a method and an apparatus for optimizing power consumption in a wireless sensor network, and an electronic device.
Background
With the continuous development of modern technology, a wireless sensor network is used as an important information collecting and transmitting tool and is widely applied to various fields such as environment monitoring, health monitoring and agriculture. The wireless sensor network is composed of a plurality of sensor nodes distributed at different places, and the nodes cooperate through wireless communication to transmit the observation data of the common source to a centralized central processing unit for source estimation and data processing. However, since sensor nodes are typically deployed in remote, dangerous, or difficult to maintain areas, the battery life of the sensor nodes becomes an important factor limiting the overall network life.
In the related art, the energy consumption in the wireless sensor network is larger, and a balance point cannot be found between the energy and the data transmission quality, so that the service life of the whole sensor network is shorter.
In view of the above problems, no effective solution has been proposed at present.
Disclosure of Invention
The embodiment of the application provides a power consumption optimization method, a power consumption optimization device and electronic equipment in a wireless sensor network, and aims to at least solve the technical problem that the service life of the whole sensor network is short due to the fact that energy consumption is large in the wireless sensor network in the related technology.
According to an aspect of the embodiments of the present application, there is provided a power consumption optimization method in a wireless sensor network, including: acquiring an observed value of a data source acquired by a sensor, wherein the observed value comprises a first observed value and observed noise data of the first observed value, and the first observed value is data acquired by the sensor in a data value generated by the data source; determining the transmitting power allocated to the sensor according to a first variance in a first probability distribution obeyed by the observed value and an amplification factor corresponding to the sensor, wherein the amplification factor is used for amplifying signals acquired by the sensor; determining the degree of difference between a data value generated by a data source and an estimated value of a signal corresponding to an observed value transmitted by a sensor and received by a sink node according to the observed value and the amplification factor; and determining the minimum value of an objective function under the condition that the difference degree meets the preset condition, wherein the objective function is used for representing the total power consumption of the sensor.
Optionally, acquiring observations of a data source acquired by a sensor includes: determining a standard deviation corresponding to the first observation value according to a second variance in a second probability distribution obeyed by the first observation value; determining a first observation value according to a standard deviation corresponding to the first observation value, an attenuation relation between the data source and the first observation value, a first variable and a data value generated by the data source, wherein the first variable is used for representing loss between the data source and the sensor; and determining an observed value according to the first observed value and the observed noise data.
Optionally, the attenuation relationship between the data source and the first observation is determined by: determining a distance between the data source and the sensor, and determining a first parameter and a second parameter, wherein the first parameter is used for controlling the attenuation rate of the attenuation relation, and the second parameter is used for controlling the smoothness of the attenuation relation; and determining an attenuation relation between the data source and the first observed value according to the distance, the first parameter and the second parameter.
Optionally, the estimated value of the signal corresponding to the observed value transmitted by the sensor and received by the sink node is determined by: determining additional noise corresponding to the receiving observation value of the sink node; and determining an estimated value according to the additional noise, the channel gain, the amplification factor and the observed value, wherein the channel gain is the channel gain between the sensor and the sink node.
Optionally, determining a degree of difference between a data value generated by the data source and an estimated value of a signal corresponding to an observed value of a sensor transmission received by the sink node includes: determining a first covariance matrix corresponding to a data value and a first observed value generated by a data source; determining a second covariance matrix corresponding to the first observed value and the observed noise data; determining an additional matrix corresponding to the additional noise according to a third variance and channel gain in a third probability distribution obeyed by the additional noise; and determining the degree of difference according to the first covariance matrix, the second covariance matrix, the additional matrix and the amplification factor matrix, wherein the amplification factor matrix is determined by the amplification factors corresponding to each sensor.
Optionally, the objective function is determined by: determining a penalty term of the objective function, wherein the penalty term is defined by l corresponding to the basic circuit power consumption and the transmitting power of the sensor 0 Determining a norm item; and determining an objective function according to the transmitting power and the penalty term.
Optionally, the total power consumption includes the sensor's transmit power and the base circuit power consumption.
According to another aspect of the embodiments of the present application, there is also provided a power consumption optimization apparatus in a wireless sensor network, including: the acquisition module is used for acquiring the observed value of the data source acquired by the sensor, wherein the observed value comprises a first observed value and observed noise data of the first observed value, and the first observed value is data acquired by the sensor in the data value generated by the data source; a first determining module, configured to determine a transmitting power allocated to the sensor according to a first variance in a first probability distribution obeyed by the observed value and an amplification factor corresponding to the sensor, where the amplification factor is used to amplify a signal acquired by the sensor; the second determining module is used for determining the degree of difference between the data value generated by the data source and the estimated value of the signal corresponding to the observed value transmitted by the sensor and received by the sink node according to the observed value and the amplification factor; and the third determining module is used for determining the minimum value of an objective function under the condition that the difference degree meets the preset condition, wherein the objective function is used for representing the total power consumption of the sensor.
According to still another aspect of the embodiments of the present application, there is also provided an electronic device, including: a memory for storing program instructions; a processor coupled to the memory for executing program instructions that perform the following functions: acquiring an observed value of a data source acquired by a sensor, wherein the observed value comprises a first observed value and observed noise data of the first observed value, and the first observed value is data acquired by the sensor in a data value generated by the data source; determining the transmitting power allocated to the sensor according to a first variance in a first probability distribution obeyed by the observed value and an amplification factor corresponding to the sensor, wherein the amplification factor is used for amplifying signals acquired by the sensor; determining the degree of difference between a data value generated by a data source and an estimated value of a signal corresponding to an observed value transmitted by a sensor and received by a sink node according to the observed value and the amplification factor; and determining the minimum value of an objective function under the condition that the difference degree meets the preset condition, wherein the objective function is used for representing the total power consumption of the sensor.
According to still another aspect of the embodiments of the present application, there is further provided a nonvolatile storage medium, where the nonvolatile storage medium includes a stored computer program, and a device where the nonvolatile storage medium is located executes the power consumption optimization method in the wireless sensor network by running the computer program.
In the embodiment of the application, the data acquired by the sensor in the data value generated by the data source is acquired through acquiring the observed value of the data source acquired by the sensor, wherein the observed value comprises a first observed value and observed noise data of the first observed value; determining the transmitting power allocated to the sensor according to a first variance in a first probability distribution obeyed by the observed value and an amplification factor corresponding to the sensor, wherein the amplification factor is used for amplifying signals acquired by the sensor; determining the degree of difference between a data value generated by a data source and an estimated value of a signal corresponding to an observed value transmitted by a sensor and received by a sink node according to the observed value and the amplification factor; the method and the device achieve the aim of determining the minimum value of the objective function under the condition that the difference degree meets the preset condition, thereby realizing the technical effect of prolonging the service life of the sensing network, and further solving the technical problem that the service life of the whole sensing network is short due to larger energy consumption of the related technology in the wireless sensing network.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation to the application. In the drawings:
Fig. 1 is a hardware block diagram of a computer terminal for implementing a power consumption optimization method in a wireless sensor network according to an embodiment of the present application;
FIG. 2 is a flow chart of a method of power consumption optimization in a wireless sensor network according to an embodiment of the present application;
fig. 3a is a data transmission system model diagram according to an embodiment of the present application;
FIG. 3b is a flow chart of data transmission in a sensor network according to an embodiment of the present application;
fig. 4 is a block diagram of a power consumption optimizing apparatus in a wireless sensor network according to an embodiment of the present application.
Detailed Description
In order to make the present application solution better understood by those skilled in the art, the following description will be made in detail and with reference to the accompanying drawings in the embodiments of the present application, it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, shall fall within the scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that embodiments of the present application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In wireless sensor networks, energy consumption is a critical issue due to the small size, low power consumption characteristics of the sensor nodes, and battery-powered limitations. In order to ensure long-term stable operation of the sensor network, a way to maximize energy utilization must be sought to extend the life of the entire network.
Aiming at the energy consumption problem in the wireless sensor network, the power consumption optimization method in the wireless sensor network is provided, and the total power consumption of the sensor nodes is minimized by considering the radiation power and the circuit power consumption of the sensor nodes at the same time. The radiation power refers to the power consumed by the sensor node during data transmission, and the circuit power consumption is the fixed power required for activating the sensor node. The application aims to optimize the energy use of the sensor nodes, thereby prolonging the service life of the whole network to the greatest extent. The power consumption optimization method in the wireless sensor network provided by the embodiment of the application can be operated in the computer terminal shown in fig. 1, and the computer terminal is explained below.
The power consumption optimization method in the wireless sensor network provided by the embodiment of the application can be executed in a mobile terminal, a computer terminal or a similar computing device. Fig. 1 shows a hardware block diagram of a computer terminal for implementing a power consumption optimization method in a wireless sensor network. As shown in fig. 1, the computer terminal 10 may include one or more processors (shown as 102a, 102b, … …,102n in the figures) which may include, but are not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA, a memory 104 for storing data, and a transmission module 106 for communication functions. In addition, the method may further include: a display, a keyboard, a cursor control device, an input/output interface (I/O interface), a Universal Serial BUS (USB) port (which may be included as one of the ports of the I/O interface), a network interface, a BUS. It will be appreciated by those of ordinary skill in the art that the configuration shown in fig. 1 is merely illustrative and is not intended to limit the configuration of the electronic device described above. For example, the computer terminal 10 may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1.
It should be noted that the one or more processors and/or other data processing circuits described above may be referred to herein generally as "data processing circuits. The data processing circuit may be embodied in whole or in part in software, hardware, firmware, or any other combination. Furthermore, the data processing circuitry may be a single stand-alone processing module or incorporated, in whole or in part, into any of the other elements in the computer terminal 10. As referred to in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of the path of the variable resistor termination to interface).
The memory 104 may be used to store software programs and modules of application software, such as program instructions/data storage devices corresponding to the power consumption optimization method in the wireless sensor network in the embodiment of the present application, and the processor executes the software programs and modules stored in the memory 104, thereby executing various functional applications and data processing, that is, implementing the power consumption optimization method in the wireless sensor network. Memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory located remotely from the processor, which may be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission module 106 is used to receive or transmit data via a network. The specific examples of the network described above may include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission module 106 includes a network adapter (Network Interface Controller, NIC) that can connect to other network devices through a base station to communicate with the internet. In one example, the transmission module 106 may be a Radio Frequency (RF) module for communicating with the internet wirelessly.
The display may be, for example, a touch screen type Liquid Crystal Display (LCD) that may enable a user to interact with a user interface of the computer terminal 10.
It should be noted here that, in some alternative embodiments, the computer terminal shown in fig. 1 may include hardware elements (including circuits), software elements (including computer code stored on a computer readable medium), or a combination of both hardware and software elements. It should be noted that fig. 1 is only one example of a specific example, and is intended to illustrate the types of components that may be present in the computer terminals described above.
In the above-described operating environment, the embodiments of the present application provide an embodiment of a method for optimizing power consumption in a wireless sensor network, it should be noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system such as a set of computer-executable instructions, and that although a logical order is illustrated in the flowchart, in some cases, the steps illustrated or described may be performed in an order other than that illustrated herein.
Fig. 2 is a flowchart of a power consumption optimization method in a wireless sensor network according to an embodiment of the present application, as shown in fig. 2, the method includes the following steps:
step S202, obtaining an observed value of a data source acquired by a sensor, wherein the observed value comprises a first observed value and observed noise data of the first observed value, and the first observed value is data acquired by the sensor in a data value generated by the data source.
In the step S202, the sensor may be, for example, a wireless sensor, and the sensor monitors the data source and sends the observed value observed or collected by the sensor to the sink node. Since the sensor may be affected by the surrounding environment when observing the data source, each first observation corresponds to observation noise data, and these first observations and observation noise data together form an observation. In addition, the data generated by the data source cannot be all collected by the sensor, so that only the data collected by the sensor can be used as the first observation value, and the data which is not collected by the sensor in the data generated by the data source cannot be used as the first observation value.
Step S204, determining the transmitting power allocated to the sensor according to the first variance in the first probability distribution obeyed by the observed value and the amplification factor corresponding to the sensor, wherein the amplification factor is used for amplifying the signal acquired by the sensor.
In the above step S204, the observed value x i Obeying a first probability distribution having a mean of 0 and a variance of(i.e., the first variance) the data collected by the sensors is transmitted to the sink node by the amplification forwarding protocol, each sensor using an amplification factor alpha i The transmit power P allocated to the sensor can thus be determined by the following formula i ：
Wherein the maximum transmittable power of each sensor is P max The corresponding maximum amplification factor is alpha max I.e.
Step S206, determining the difference degree between the data value generated by the data source and the estimated value of the signal corresponding to the observed value transmitted by the sensor and received by the sink node according to the observed value and the amplification factor.
In the above step S206, after the sink node receives the data sent by the sensor, the data is estimated by using a Linear Minimum Mean Square Error (LMMSE) estimation, so as to obtain an estimated value, so as to predict the error (i.e. the degree of difference) between the estimated value and the data generated by the data source.
Step S208, determining the minimum value of an objective function under the condition that the difference degree meets the preset condition, wherein the objective function is used for representing the total power consumption of the sensor, and the total power consumption comprises the transmitting power of the sensor and the power consumption of a basic circuit.
In the above step S208, when the degree of difference satisfies the preset condition, the minimum value of the objective function is calculated, and since the objective function represents the total power consumption of the sensor, the minimum value of the objective function represents the minimum total power consumption of the sensor.
Through the steps S202 to S208, the purpose of determining the minimum value of the objective function under the condition that the difference degree meets the preset condition is achieved, so that the technical effect of prolonging the service life of the sensing network is achieved, and the technical problem that the service life of the whole sensing network is short due to the fact that the energy consumption of the wireless sensing network is large in the related technology is solved. The following is a detailed description.
As shown in fig. 3a, in a monitoring area, one Sink node (Sink) and N wireless sensor nodes are deployed, and these sensors are used to observe data sources. The observation data (or referred to as observation value) with noise acquired by each sensor is transmitted to a sink node through an amplification forwarding protocol, and a central processing unit of the sink node performs estimation processing on the data.
FIG. 3b is a flow chart of data transmission in a sensor network according to an embodiment of the present application, wherein a first observed value s is obtained after a data value θ generated by a data Source is observed by a sensor i First observed value and observed noise data n i Together form an observed value x i Through the amplification factor alpha i After processing, the observed value is sent to Sink node Sink, and in the transmission process, the needed data comprises channel gain g i Additive noise v i The signal received by the sink node is denoted as y i 。
In step S202 in the power consumption optimization method in the wireless sensor network, an observed value of a data source acquired by a sensor is acquired, and specifically includes the following steps: determining a standard deviation corresponding to the first observation value according to a second variance in a second probability distribution obeyed by the first observation value; determining a first observation value according to a standard deviation corresponding to the first observation value, an attenuation relation between the data source and the first observation value, a first variable and a data value generated by the data source, wherein the first variable is used for representing loss between the data source and the sensor; and determining an observed value according to the first observed value and the observed noise data.
In the embodiment of the application, the first observed value may be s i Representation s i Obeying the mean value was 0, and the variance wasA Gaussian distribution (i.e., the second probability distribution) of (i.e., the second variance) according to the variance ∈>It is possible to obtain a first observation value corresponding to a standard deviation of +.>The first observation may be determined by the following formula:
wherein θ represents data generated by a data source, p i Representing the attenuation relationship between the data source and the first observation, η i Representing the first variable, which follows an N (0, 1) distribution, is a random variable independent of the data source for capturing losses from the data source to the sensors, N representing the number of sensors.
Observations x obtained by the ith sensor i Can be determined by the following formula:
x i ＝s i +n i
wherein, the data value theta generated by the data source obeys Gaussian distribution, the mean value is 0, the variance is the standard variance, namely theta-N (0, 1), N i Represents observed noise data, n i Mean value of 0, variance ofI.e. < ->Due to the similarity of the environments->Has a correlation with each other. Wherein (1)>
In the power consumption optimization method in the wireless sensor network, the attenuation relation between the data source and the first observed value is determined by the following method: determining a distance between the data source and the sensor, and determining a first parameter and a second parameter, wherein the first parameter is used for controlling the attenuation rate of the attenuation relation, and the second parameter is used for controlling the smoothness of the attenuation relation; and determining an attenuation relation between the data source and the first observed value according to the distance, the first parameter and the second parameter.
In an embodiment of the present application, the attenuation relationship p between the data source and the first observation i Can be determined by the following formula:
wherein d i Representing the distance between the data source and the ith sensor, a first parameter θ 1 Is an attenuation rate for controlling the attenuation of the attenuation relation with the increase of the distance, and the second parameter theta 2 Is a parameter for controlling the smoothness in the decay relation.
In the power consumption optimization method in the wireless sensor network, the estimated value of the signal corresponding to the observed value transmitted by the sensor and received by the sink node is determined by the following method: determining additional noise corresponding to the receiving observation value of the sink node; and determining an estimated value according to the additional noise, the channel gain, the amplification factor and the observed value, wherein the channel gain is the channel gain between the sensor and the sink node.
In the embodiment of the application, the signal y received by the sink node i Can be determined by the following formula:
y i ＝g i α i x i +v i
wherein g i Is the channel gain between the sensor and the sink node,(i.e., the third probability distribution described below) is the additive noise corresponding to the observed value received by the sink node, +.>Independent of each other. By->Receiving signal corresponding to observation value representing sensor transmission received by combined sink node >The obtained estimated value.
In step S206 in the power consumption optimization method in the wireless sensor network, determining a degree of difference between a data value generated by a data source and an estimated value of a signal corresponding to an observed value of sensor transmission received by a sink node, specifically includes the following steps: determining a first covariance matrix corresponding to a data value and a first observed value generated by a data source; determining a second covariance matrix corresponding to the first observed value and the observed noise data; determining an additional matrix corresponding to the additional noise according to a third variance and channel gain in a third probability distribution obeyed by the additional noise; and determining the degree of difference according to the first covariance matrix, the second covariance matrix, the additional matrix and the amplification factor matrix, wherein the amplification factor matrix is determined by the amplification factors corresponding to each sensor.
In this embodiment of the present application, the degree of difference between the data value generated by the data source and the estimated value of the signal corresponding to the observed value of the sensor transmission received by the sink node may be represented by using a Mean Square Error (MSE), specifically, the formula corresponding to the degree of difference is as follows:
wherein x= [ x ] 1 ,.x. N .] T .，s＝[s 1 ,....,s N ] T ，P＝[P 1 ,....,P N ] T ，n＝[n 1 ,....,n N ] T ，Wherein R is θx ＝E[θx]＝E[θs]Representing the first covariance matrix D p ＝diag(α 1 ,...,α N ) Representing the amplification factor matrix, R x ＝E[xx T ]＝E[ss T ]+E[nn T ]Representing the second covariance matrix,/>Representing the additional matrix->Representing the third variance.
In the power consumption optimization method in the wireless sensor network, the objective function is determined by: determining a penalty term of the objective function, wherein the penalty term is defined by l corresponding to the basic circuit power consumption and the transmitting power of the sensor 0 Determining a norm item; and determining an objective function according to the transmitting power and the penalty term.
In the embodiment of the application, on the premise of meeting the distortion (i.e. the above-mentioned difference degree) and the sensor power constraint, the power consumption of the whole node is minimized so as to jointly determine a group of optimal active sensors and the corresponding transmission power thereof, and the optimal active sensors and the transmission power thereof are converted into an optimization problem, and the expression of the objective function is as follows:
0≤P i ≤P max
wherein D is T Representing target distortion, the target function simulates the total power consumption in the wireless sensor network, including the actual transmission power P i And basic circuit power consumption P active In a wireless sensor network, even if the sensor does not transmit data, some power is required to maintain its normal operation, such as maintaining the state of an electronic component, handling some basic communication tasks, etc. This basic circuit power consumption P active Is fixed regardless of whether the sensor is actually transmitting data. Therefore, for a sensor transmitting data, its total power consumption is P total ＝P active +P i However, for a sensor that does not send data, it is not necessary to continue to consume basic circuit power consumption to stay on-line. Thus, when a sensor is not transmitting data, it can be placed in an inactive state, thereby saving P active This part of the power consumption.
The second term in the above objective function, P active ||P|| 0 As a sparse induction penalty term, the number of transmission nodes is affected, where P 0 Indicating the corresponding l of the above-mentioned transmitting power 0 And a norm term. Specifically, P active Is a sparse induction parameter, larger P active Will make the solution more sparse, when P active When large, each transmitted sensor increases the total power consumption significantly, where it is important to limit the number of active sensors. In the other extreme case, when P active When=0, the optimization problem becomes:
at this point, the distortion constraint is a convex function of P. Since the optimization problem is limited by linear and convex constraints and the objective function minimizes a linear objective function, the optimization problem becomes a convex optimization problem and can be solved in a global scope by using techniques such as interior point method. However, reducing the number of active sensors means a reduction in the received data at the sink node, which in turn may result in the distortion condition in the constraint not being met.
Analyzing the optimization problem described above, the objective function is of a combinatorial nature in nature. In the problem of combinatorial nature, the decision space is typically composed of various possible combinations, and the choice of each combination may lead to different results or costs. In view of the current problem, the optimization problem requires determining the optimal allocation of active and inactive sensors to achieve the best balance of power consumption and distortion. Of course, search 2 can be performed by exhaustion N The possible active/inactive combinations are found but when the value of N is large, the computational cost of an exhaustive search will be very large, even for a medium-scale network, not viable. Moreover, when the optimization problem relates toIn the case of the norm term, the problem solving becomes more difficult, because +.>The norm represents the number of non-zero elements whose non-convex nature makes the problem less effective to solve. In comprehensive consideration, the embodiment of the application adopts a reconstruction strategy by introducing the weighting +.>Norm minimization algorithm to deal with +.>And the norm term, thereby optimizing the sensor selection and the power distribution. The core idea is to add +.>The norm term is approximately +.>Norms terms, then adjusting ++by introducing appropriate weights >And optimizing the norm. />The norm is the sum of absolute values, and +.>The norm is similar, but it is convex and therefore easier to optimise. In each iteration, weight +.>The norm minimization algorithm weights ++by introducing a set of weights in the optimization problem>The norm term is optimized along with other constraints. These weights are adjusted in each iteration to gradually approximate the optimal solution of the problem. In this way, the original problem can be approximated in a continuously optimizable manner without having to directly handle the difficult-to-solve +.>Norms. Specifically, in each iteration, weight +.>The norm minimization algorithm solves the following optimization problem:
wherein w=diag (W), w= [ W ] 1 ,...,w N ] T Is a weight vector weighting that adjusts in each iteration of the algorithm, algorithm 1 details the weight adjustment during the iteration, where l represents the iterationCount, l max Representing the maximum iteration number, epsilon is introduced to maintain the numerical stability, and the situation that the denominator is 0 is avoided.
Algorithm 1 adjusts the power allocation of the sensors step by step through a series of iterations such that one portion of the sensors are assigned a lower power value and another portion of the sensors are assigned a higher power value. This approach can in fact be seen as a kind of "polarization" of the power distribution, i.e. a significant differentiation in the power values, so that a part of the sensors can transmit signals more efficiently, while another part of the sensors is more advantageous in terms of saving power consumption. To further optimize the transmission power, embodiments of the present application introduce a threshold that eliminates those sensors that have a relatively low allocated power. Then, for the selected sensors, an additional power distribution step is performed to determine the transmission power P of each selected sensor optimal . As shown in algorithm 2:
in summary, algorithm 1 selects the most informative part from the sensors to be active, avoiding unnecessary power consumption. Algorithm 2 then optimizes the power allocation based on the selected sensor to further reduce overall power consumption. The comprehensive strategy ensures accurate estimation of the target while prolonging the service life of the sensor network.
It should be noted that, the power consumption optimization method in the wireless sensor network provided by the embodiment of the application can be suitable for application scenarios of various wireless sensor networks, especially under the condition of higher requirements on energy efficiency and service life of the sensor network. The following are examples of some usage scenarios:
1. environmental monitoring: in the field of environmental monitoring, sensor networks generally need to operate for a long period of time to monitor parameters such as temperature, humidity, air quality, etc. in real time. By optimized sensor selection and power distribution, the life of the sensor network can be prolonged, thereby better serving environmental monitoring needs.
2. Agricultural field: in the agricultural field, the wireless sensor network can be used for monitoring soil humidity, crop growth conditions and the like. By selecting proper sensors and optimizing power distribution, energy consumption can be effectively reduced, and reliability and persistence of the sensor network are improved.
Specifically, in an agricultural monitoring project for a farm field, a sensor network is used to monitor soil humidity and weather information. By applying the algorithm provided by the embodiment of the application, a group of optimal sensor nodes are selected, and appropriate transmission power is distributed for each node so as to meet the requirements of soil humidity and meteorological data, so that the energy consumption can be reduced, and meanwhile, the accuracy and the instantaneity of the data are ensured.
3. Industrial automation: in industrial automation, wireless sensor networks may be used to monitor information such as device status, energy consumption, etc. The optimized sensor selection and power distribution can reduce maintenance cost and prolong the service life of the sensor network.
Specifically, in the equipment monitoring project of one factory, a wireless sensor network is deployed for monitoring the state and the operation condition of equipment. By applying the technology in the embodiment of the application, the sensor nodes of the key equipment are selected, and proper power is distributed to each node so as to ensure that equipment abnormality is timely monitored and reported. Thus, unnecessary energy consumption can be reduced, and normal operation of the industrial automation system is ensured.
4. Application of the Internet of things: in the internet of things, a large number of sensor nodes need to work cooperatively, but energy limitation is a common problem. By the technology, the energy consumption of the sensor node can be better managed, and longer-time operation and more efficient data transmission are realized.
5. Health monitoring: in the medical health field, wireless sensor networks may be used to monitor physiological parameters of patients. The optimized energy distribution strategy can prolong the service life of the sensor and ensure long-time monitoring and data collection.
The power consumption optimization method in the wireless sensor network provided by the embodiment of the application has the following beneficial effects in different fields: 1) Energy efficiency: the method provided by the embodiment of the application can intelligently allocate the transmission power of the sensor node according to the actual demand so as to achieve the optimal energy utilization effect. The sensor nodes are dynamically adjusted according to the position, the monitoring task and the environmental characteristics, so that the running time of the whole sensor network is prolonged to the greatest extent. 2) Accuracy of data: by optimizing power distribution and selecting sensor nodes, the system can ensure that data of a critical area is timely and accurately transmitted to a central control center, for example, a decision maker can be helped to know farmland conditions more accurately, and appropriate measures are taken to optimize irrigation strategies. 3) Flexibility and adaptability: the method provided by the embodiment of the application can adapt to different environmental requirements, and the system can adjust the state and power distribution of the sensor nodes according to actual conditions, so that an optimal monitoring and control scheme is provided under different situations. 4) The cost is reduced: by accurate power distribution and sensor node selection, the system can reduce unnecessary energy consumption, thereby reducing energy costs. In addition, the system can only activate necessary sensor nodes according to actual demands, so that the maintenance and management cost of equipment is reduced.
Fig. 4 is a block diagram of a power consumption optimizing apparatus in a wireless sensor network according to an embodiment of the present application, as shown in fig. 4, the apparatus includes:
an acquisition module 42, configured to acquire an observed value of a data source acquired by the sensor, where the observed value includes a first observed value and observed noise data of the first observed value, and the first observed value is data acquired by the sensor in a data value generated by the data source;
a first determining module 44, configured to determine a transmit power allocated to the sensor according to a first variance in a first probability distribution to which the observed value obeys and an amplification factor corresponding to the sensor, where the amplification factor is used to amplify a signal acquired by the sensor;
a second determining module 46, configured to determine, according to the observed value and the amplification factor, a degree of difference between a data value generated by the data source and an estimated value of a signal corresponding to the observed value transmitted by the sensor and received by the sink node;
a third determining module 48, configured to determine a minimum value of an objective function, where the objective function is used to represent the total power consumption of the sensor, in a case where the degree of difference satisfies a preset condition.
Through the acquisition module 42, the first determination module 44, the second determination module 46 and the third determination module 48 in the power consumption optimization device in the wireless sensor network, the purpose of determining the minimum value of the objective function under the condition that the difference degree meets the preset condition is achieved, so that the technical effect of prolonging the service life of the sensor network is achieved, and the technical problem that the service life of the whole sensor network is short due to the fact that the energy consumption of the wireless sensor network is large in the related art is solved.
It should be noted that, the power consumption optimizing device in the wireless sensor network shown in fig. 4 is configured to execute the power consumption optimizing method in the wireless sensor network shown in fig. 2, so the explanation related to the power consumption optimizing method in the wireless sensor network is also applicable to the power consumption optimizing device in the wireless sensor network, which is not described herein again.
The embodiment of the application also provides electronic equipment, which comprises: a memory for storing program instructions; a processor coupled to the memory for executing program instructions that perform the following functions: acquiring an observed value of a data source acquired by a sensor, wherein the observed value comprises a first observed value and observed noise data of the first observed value, and the first observed value is data acquired by the sensor in a data value generated by the data source; determining the transmitting power allocated to the sensor according to a first variance in a first probability distribution obeyed by the observed value and an amplification factor corresponding to the sensor, wherein the amplification factor is used for amplifying signals acquired by the sensor; determining the degree of difference between a data value generated by a data source and an estimated value of a signal corresponding to an observed value transmitted by a sensor and received by a sink node according to the observed value and the amplification factor; and determining the minimum value of an objective function under the condition that the difference degree meets the preset condition, wherein the objective function is used for representing the total power consumption of the sensor.
It should be noted that, the above electronic device is configured to execute the power consumption optimization method in the wireless sensor network shown in fig. 2, so the explanation related to the power consumption optimization method in the wireless sensor network is also applicable to the electronic device, and will not be repeated herein.
The embodiment of the application also provides a nonvolatile storage medium, which comprises a stored computer program, wherein the equipment where the nonvolatile storage medium is located executes the power consumption optimization method in the following wireless sensor network by running the computer program: acquiring an observed value of a data source acquired by a sensor, wherein the observed value comprises a first observed value and observed noise data of the first observed value, and the first observed value is data acquired by the sensor in a data value generated by the data source; determining the transmitting power allocated to the sensor according to a first variance in a first probability distribution obeyed by the observed value and an amplification factor corresponding to the sensor, wherein the amplification factor is used for amplifying signals acquired by the sensor; determining the degree of difference between a data value generated by a data source and an estimated value of a signal corresponding to an observed value transmitted by a sensor and received by a sink node according to the observed value and the amplification factor; and determining the minimum value of an objective function under the condition that the difference degree meets the preset condition, wherein the objective function is used for representing the total power consumption of the sensor.
It should be noted that, the above-mentioned nonvolatile storage medium is used to execute the power consumption optimization method in the wireless sensor network shown in fig. 2, so the explanation related to the power consumption optimization method in the wireless sensor network is also applicable to the nonvolatile storage medium, and will not be repeated here.
The foregoing embodiment numbers of the present application are merely for describing, and do not represent advantages or disadvantages of the embodiments.
In the foregoing embodiments of the present application, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed technology content may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of the units, for example, may be a logic function division, and may be implemented in another manner, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including several instructions to cause a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely a preferred embodiment of the present application and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present application and are intended to be comprehended within the scope of the present application.
Claims (10)
1. A method for optimizing power consumption in a wireless sensor network, comprising:
acquiring an observed value of a data source acquired by a sensor, wherein the observed value comprises a first observed value and observed noise data of the first observed value, and the first observed value is data acquired by the sensor in a data value generated by the data source;
determining the transmitting power allocated to the sensor according to a first variance in a first probability distribution obeyed by the observed value and an amplification factor corresponding to the sensor, wherein the amplification factor is used for amplifying signals acquired by the sensor;
determining the degree of difference between a data value generated by the data source and an estimated value of a signal corresponding to the observed value transmitted by the sensor and received by the sink node according to the observed value and the amplification factor;
and determining a minimum value of an objective function under the condition that the difference degree meets a preset condition, wherein the objective function is used for representing the total power consumption of the sensor.
2. The method of claim 1, wherein acquiring observations of a data source acquired by a sensor comprises:
determining a standard deviation corresponding to the first observed value according to a second variance in a second probability distribution obeyed by the first observed value;
determining the first observed value according to a standard deviation corresponding to the first observed value, an attenuation relation between the data source and the first observed value, a first variable and a data value generated by the data source, wherein the first variable is used for representing loss between the data source and the sensor;
and determining the observed value according to the first observed value and the observed noise data.
3. The method of claim 2, wherein the attenuation relationship between the data source and the first observation is determined by:
determining a distance between the data source and the sensor, and determining a first parameter for controlling an attenuation rate of the attenuation relationship and a second parameter for controlling smoothness of the attenuation relationship;
and determining an attenuation relation between the data source and the first observed value according to the distance, the first parameter and the second parameter.
4. The method according to claim 1, wherein the estimated value of the signal corresponding to the observed value transmitted by the sensor received by the sink node is determined by:
determining that the sink node receives additional noise corresponding to the observed value;
and determining the estimated value according to the additional noise, the channel gain, the amplification factor and the observed value, wherein the channel gain is the channel gain between the sensor and the sink node.
5. The method of claim 4, wherein determining a degree of difference between a data value generated by the data source and an estimated value of a signal received by the sink node corresponding to the observed value transmitted by the sensor comprises:
determining a first covariance matrix corresponding to the data value generated by the data source and the first observed value;
determining a second covariance matrix corresponding to the first observed value and the observed noise data;
determining an additional matrix corresponding to the additional noise according to a third variance in a third probability distribution obeyed by the additional noise and the channel gain;
and determining the degree of difference according to the first covariance matrix, the second covariance matrix, the additional matrix and an amplification factor matrix, wherein the amplification factor matrix is determined by the amplification factors corresponding to each sensor.
6. The method of claim 1, wherein the objective function is determined by:
determining a penalty term of the objective function, wherein the penalty term is defined by the basic circuit power consumption of the sensor and the l corresponding to the transmission power 0 Determining a norm item;
and determining the objective function according to the transmitting power and the penalty term.
7. The method of claim 1, wherein the total power consumption comprises a transmit power of the sensor and a base circuit power consumption.
8. A power consumption optimizing apparatus in a wireless sensor network, comprising:
the acquisition module is used for acquiring an observed value of a data source acquired by a sensor, wherein the observed value comprises a first observed value and observed noise data of the first observed value, and the first observed value is data acquired by the sensor in a data value generated by the data source;
a first determining module, configured to determine a transmitting power allocated to the sensor according to a first variance in a first probability distribution obeyed by the observed value and an amplification factor corresponding to the sensor, where the amplification factor is used to amplify a signal acquired by the sensor;
The second determining module is used for determining the degree of difference between the data value generated by the data source and the estimated value of the signal corresponding to the observed value transmitted by the sensor and received by the sink node according to the observed value and the amplification factor;
and the third determining module is used for determining the minimum value of an objective function under the condition that the difference degree meets a preset condition, wherein the objective function is used for representing the total power consumption of the sensor.
9. An electronic device, comprising:
a memory for storing program instructions;
a processor, coupled to the memory, for executing program instructions that perform the following functions: acquiring an observed value of a data source acquired by a sensor, wherein the observed value comprises a first observed value and observed noise data of the first observed value, and the first observed value is data acquired by the sensor in a data value generated by the data source; determining the transmitting power allocated to the sensor according to a first variance in a first probability distribution obeyed by the observed value and an amplification factor corresponding to the sensor, wherein the amplification factor is used for amplifying signals acquired by the sensor; determining the degree of difference between a data value generated by the data source and an estimated value of a signal corresponding to the observed value transmitted by the sensor and received by the sink node according to the observed value and the amplification factor; and determining a minimum value of an objective function under the condition that the difference degree meets a preset condition, wherein the objective function is used for representing the total power consumption of the sensor.
10. A non-volatile storage medium, characterized in that the non-volatile storage medium comprises a stored computer program, wherein the device in which the non-volatile storage medium is located performs the power consumption optimization method in the wireless sensor network according to any one of claims 1 to 7 by running the computer program.
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