CN113347685A - Electric wireless sensor network route clustering method and device and electronic equipment - Google Patents

Electric wireless sensor network route clustering method and device and electronic equipment Download PDF

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CN113347685A
CN113347685A CN202110479034.3A CN202110479034A CN113347685A CN 113347685 A CN113347685 A CN 113347685A CN 202110479034 A CN202110479034 A CN 202110479034A CN 113347685 A CN113347685 A CN 113347685A
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particle
determining
clustering
fitness
factor
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白巍
陆阳
黄毕尧
张全明
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State Grid Corp of China SGCC
Global Energy Interconnection Research Institute
State Grid Sichuan Electric Power Co Ltd
Economic and Technological Research Institute of State Grid Sichuan Electric Power Co Ltd
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State Grid Corp of China SGCC
Global Energy Interconnection Research Institute
State Grid Sichuan Electric Power Co Ltd
Economic and Technological Research Institute of State Grid Sichuan Electric Power Co Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/24—Connectivity information management, e.g. connectivity discovery or connectivity update
    • H04W40/32—Connectivity information management, e.g. connectivity discovery or connectivity update for defining a routing cluster membership
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks

Abstract

The invention provides a method and a device for clustering routes of a power wireless sensor network and electronic equipment, wherein the method comprises the following steps: determining the current speed of the particles according to the last updated particle speed, the last updated particle position, the dynamically adjusted inertia weight and/or the dynamically adjusted factor, wherein each particle represents different clustering modes; determining the position of the particle after movement according to the current speed of the particle; determining each particle fitness of the particle population according to a predetermined fitness function, wherein the fitness function is determined according to the positions of the nodes; repeating the steps until the fitness of the particles reaches a preset condition; and when the fitness of the particles reaches a preset condition, determining the routing cluster according to the particles reaching the preset condition. By implementing the invention, the inertia weight and/or the dynamic adjustment factor are/is dynamically adjusted, so that the search range is dynamically limited when the particles are subjected to space search, the local optimization and the global optimization of the particles are more balanced, and the clustering result is more optimal.

Description

Electric wireless sensor network route clustering method and device and electronic equipment
Technical Field
The invention relates to the field of routing protocols in a wireless sensor network, in particular to a method and a device for clustering routes of a power wireless sensor network and electronic equipment.
Background
The internet technology is one of the important subjects of the current world development, and as the application of a Wireless Sensor Network (WSN) is gradually increased, the scale of the WSN is larger and larger, the overhead of an independent address in the IPv4 Network is larger and larger, a point-to-point communication mode cannot meet the data transmission requirement of the wireless sensor Network, and an all-IP communication mode combining the wireless sensor Network with the IPv6 is a main development direction in the future. In the smart grid, the wireless sensing equipment can sense the real-time running state of power transmission and distribution and various parameters of the power system, and the application of the wireless sensing network technology in the power system enables services such as power transmission and distribution to have lower cost and higher efficiency. When data transmission is performed between wireless sensing equipment and a base station, how to perform routing clustering to ensure the survival time of nodes is a hot research problem at present.
In the related art, a particle swarm optimization algorithm is adopted for local search and global search, but a weight parameter and a calculation factor in the particle swarm optimization algorithm are generally constant, so that the algorithm is biased to local search or global search, another search effect is weakened, and the performance of a final clustering result is poor.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method, an apparatus, and an electronic device for clustering a wireless power sensor network route, so as to solve the defect of poor performance of a clustering result in the prior art.
According to a first aspect, an embodiment of the present invention provides a method for clustering routes of a wireless power sensor network, including the following steps: determining the current speed of the particles according to the last updated particle speed, the last updated particle position, the dynamically adjusted inertia weight and/or the dynamically adjusted factor, wherein each particle represents different clustering modes; determining the position of the particle after movement according to the current speed of the particle; determining each particle fitness of the particle population according to a predetermined fitness function, wherein the fitness function is determined according to the particle position; repeating the step of determining the current speed of the particles according to the last updated particle speed, the last updated particle position, the dynamically adjusted inertial weight and/or the dynamically adjusted factor to the step of determining the fitness of each particle of the particle group according to the predetermined fitness function until the fitness of the particles reaches a preset condition; and when the fitness of the particles reaches a preset condition, determining the routing clustering according to the particles reaching the preset condition.
Optionally, the dynamically adjusted inertial weights and/or dynamically adjusted factors comprise: the inertial weights and/or factors are dynamically adjusted according to the trigonometric function.
Optionally, the determining the current velocity of the particle according to the last updated velocity of the particle, the last updated position of the particle, and the dynamically adjusted inertial weight includes:
Figure BDA0003047724010000021
wherein v isxid(t) is the velocity component of the cluster head node in the clustering mode corresponding to the current particle on the x axis, vyid(t) is a cluster head section in a clustering mode corresponding to the current particleVelocity component of the point in the y-axis, vxid(t-1) velocity component of cluster head node in clustering mode corresponding to particle updated last time in x axis, vyid(t-1) is the velocity component of the cluster head node in the clustering mode corresponding to the particle updated last time on the y axis, omega is the inertia weight, and represents the influence rate of the velocity of the particle in the t-1 iteration on the t-th iteration,
Figure BDA0003047724010000022
Figure BDA0003047724010000023
a. b and m are any parameter; c. C1And c2Respectively representing a cognitive learning factor and a sociological factor, and respectively controlling the weight of the local optimal position and the global optimal position of the particle; p is a radical ofxid(t-1) x-axis component, p, of the locally optimal position of the cluster head node in the clustering mode corresponding to the particle updated last timeyid(t-1) is the y-axis component, p, of the locally optimal position of the cluster head node in the clustering mode corresponding to the particle updated last timexgd(t-1) x-axis component, p, of the global optimal position of the cluster head node in the clustering mode corresponding to the last updated particleygd(t-1) is the y-axis component of the global optimal position of the cluster head node in the clustering mode corresponding to the particle updated last time, r1、r2Is a random number between 0 and 1.
Optionally, the determining a current velocity of the particle according to the last updated velocity of the particle, the last updated position of the particle, and the dynamically adjusted inertial weight and/or the dynamically adjusted factor includes:
a cognitive learning factor and a sociological factor dynamically adjusted by the following formula:
c1＝c1s-(c1s-c1e)cos(ω)；
c2＝c2s-(c2s-c2e)cos(ω)；
wherein, c1sTo initiate cognitive learning factor size, c1eFor the final cognitive learning factor, ω is the inertia weight, c2sStarting the sociological factor size, c2eIs the size of the final sociological factor.
Optionally, the predetermined fitness function includes:
F1＝αf1+(1-α)f2；
wherein, F1For fitness, α is a weight coefficient, f1Is an energy factor, f2In order to be a position factor,
Figure BDA0003047724010000031
wherein N is the total number of surviving nodes in the electric wireless sensor network, K is the number of cluster head nodes, and Er CH(i) Representing cluster head node CH in the r-th iterationiResidual energy of, Er NCH(j) Representing non-cluster head node NCH in the r-th iterationjThe residual energy of (d);
Figure BDA0003047724010000032
wherein d (NCH)iBS) is the distance from the non-cluster head node to the base station, d (CH)jBS) is the distance from the cluster head node to the base station, d (NCH)i，CHj) The distance from the non-cluster head node to the cluster head node.
Optionally, before determining the current velocity of the particle according to the last updated velocity of the particle, the last updated position of the particle, and the dynamically adjusted inertial weight and/or the dynamically adjusted factor, the method includes: selecting a plurality of initial cluster heads according to sensor node data in the electric wireless sensor network to obtain a plurality of particles to form a particle group; selecting a plurality of initial cluster heads according to sensor node data in the electric wireless sensor network comprises the following steps: and screening the plurality of nodes according to a preset energy value, screening out the nodes with the energy value larger than the preset energy value, and selecting an initial cluster head from the nodes with the energy value larger than the preset energy value.
Optionally, determining the position of the particle after moving according to the current velocity of the particle includes: matching the moved positions of the nodes in the clustering mode corresponding to the particles with the currently alive nodes by the following formula:
Figure BDA0003047724010000041
wherein, CMnx、CMnySurviving nodes CM for a networknCoordinate of (2), Xxid、XyidIs the position coordinate of the current particle.
Optionally, the remaining energy of the node is determined by a LEACH energy consumption model.
According to a second aspect, an embodiment of the present invention provides a wireless power sensor network routing clustering device, including: the speed determining module is used for determining the current speed of the particles according to the last updated particle speed, the last updated particle position, the dynamically adjusted inertia weight and/or the dynamically adjusted factor, and each particle represents different clustering modes; the position determining module is used for determining the position of the particle after movement according to the current speed of the particle; a fitness determining module, configured to determine a fitness of each particle of the particle population according to a predetermined fitness function, where the fitness function is determined according to a particle position; a repeating module, configured to repeat the step of determining the current velocity of the particle according to the last updated particle velocity, the last updated particle position, and the dynamically adjusted inertial weight and/or the dynamically adjusted factor to the step of determining the fitness of each particle of the particle group according to the predetermined fitness function until the fitness of the particle reaches a preset condition; and the route clustering determining module is used for determining route clustering according to the particles meeting the preset condition when the fitness of the particles meets the preset condition.
Optionally, the speed determination module includes: and the parameter adjusting module is used for dynamically adjusting the inertia weight and/or the factor according to the trigonometric function.
According to a third aspect, an embodiment of the present invention provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the steps of the power wireless sensor network clustering method according to the first aspect or any of the embodiments of the first aspect when executing the program.
According to a fourth aspect, an embodiment of the present invention provides a storage medium, on which computer instructions are stored, and the instructions, when executed by a processor, implement the steps of the power wireless sensor network routing clustering method according to the first aspect or any one of the embodiments of the first aspect.
The technical scheme of the invention has the following advantages:
compared with the scheme that the inertia weight and the factor are generally used as constants, the method/device for clustering the routes of the power wireless sensor network dynamically limits the search range when the particles perform space search by dynamically adjusting the inertia weight and/or the dynamic adjustment factor, so that the local optimization and the global optimization of the particles are effectively balanced, the problem that the other optimization effect is weakened due to the fact that the local optimization is biased or the global optimization is biased is avoided, and the clustering result is better.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of a specific example of a routing clustering method for a wireless power sensor network according to an embodiment of the present invention;
fig. 2 is a schematic block diagram of a specific example of a routing clustering device of a wireless power sensor network according to an embodiment of the present invention;
fig. 3 is a schematic block diagram of a specific example of an electronic device in the embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; the two elements may be directly connected or indirectly connected through an intermediate medium, or may be communicated with each other inside the two elements, or may be wirelessly connected or wired connected. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
In addition, the technical features involved in the different embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
In the clustering method for the wireless power sensor network provided by the present embodiment, considering that the calculation of the particle swarm algorithm requires more calculation resources and the calculation resources of the sensor nodes are difficult to support, each sensor node can transmit its own position and energy to the base station, and the base station performs the calculation, as shown in fig. 1, the method includes the following steps:
s101, determining the current speed of the particles according to the last updated particle speed, the last updated particle position, the dynamically adjusted inertia weight and/or the dynamically adjusted factor, wherein each particle represents different clustering modes.
Illustratively, the factors include cognitive learning factors and sociological factors. For a plurality of sensor nodes in the same electric power wireless sensor network, the clustering mode is various, and each particle represents one clustering mode, so that a plurality of particles can be formed for one electric power wireless sensor network to form a particle swarm. And selecting the optimal particles from the particle swarm through particle swarm optimization, and taking the clustering mode represented by the optimal particles as a clustering result.
The particle speed comprises the speeds of all nodes in a clustering mode corresponding to the particles; the particle positions comprise positions of all nodes in a clustering mode corresponding to the particles; the current speed of the particle comprises the speeds of all nodes in the clustering mode corresponding to the particle at the current moment.
According to the particle velocity, the particle position, the inertial weight, and the factor of the last update, the current velocity of the particle may be determined by:
Figure BDA0003047724010000071
wherein v isxid(t) is the velocity component of the cluster head node in the clustering mode corresponding to the current particle on the x axis, vyid(t) is the velocity component of the cluster head node in the clustering mode corresponding to the current particle on the y axis, vxid(t-1) velocity component of cluster head node in clustering mode corresponding to particle updated last time in x axis, vyid(t-1) is the velocity component of the cluster head node in the clustering mode corresponding to the particle updated last time on the y axis, omega is the inertia weight, and represents the influence rate of the velocity of the particle in the t-1 iteration on the t-th iteration,
Figure BDA0003047724010000072
Figure BDA0003047724010000073
a. b and m are any parameter; c. C1And c2Respectively representing a cognitive learning factor and a sociological factor, and respectively controlling the weight of the local optimal position and the global optimal position of the particle; p is a radical ofxid(t-1) x-axis component, p, of the locally optimal position of the cluster head node in the clustering mode corresponding to the particle updated last timeyid(t-1) is the y-axis component, p, of the locally optimal position of the cluster head node in the clustering mode corresponding to the particle updated last timexgd(t-1) x-axis component, p, of the global optimal position of the cluster head node in the clustering mode corresponding to the last updated particleygd(t-1) is the y-axis component of the global optimal position of the cluster head node in the clustering mode corresponding to the particle updated last time, r1、r2Is a random number between 0 and 1, and increases the randomness of the search.
General inertia weight omega and cognitive learning factor c1And social department factor c2Is constant, but the final result is biased to be locally optimal or biased to be globally optimal, so that the other optimization effect is weakened, and therefore, the inertia weight ω and the cognitive learning factor c in the embodiment1And social department factor c2A trigonometric function can be introduced, and the oscillation characteristic of the trigonometric function is utilized to enable the particles to have an oscillatory limited search range during optimization, so that the local optimization and the global optimization of the particles are effectively balanced.
Specifically, taking a cosine function as an example, the inertia weight ω can be determined by:
Figure BDA0003047724010000081
wherein a, b and m are any parameter formula, a can be 0.1, and b can be 0.2.
For the cognitive learning factor c1 and the societies factor c2, the calculation method is as follows:
c1＝c1s-(c1s-c1e)cos(ω)；
c2＝c2s-(c2s-c2e)cos(ω)；
wherein, c1sTo initiate cognitive learning factor size, c1eFor the final cognitive learning factor, ω is the inertia weight, c2sStarting the sociological factor size, c2eIs the size of the final sociological factor. Through the formula, the self-cognition and the social cognition of the particles change along with time, the self-cognition ability of the particles in the early stage is enhanced integrally, and the social cognition ability in the later stage is stronger, so that the search range can be expanded, and a better convergence effect can be obtained. According to the embodiment, dynamic parameters are adopted, so that the convergence rate of the particle swarm optimization algorithm is more balanced, premature trapping in local optimization is avoided to a certain extent, and the clustering requirement of the power sensing system is better met.
And S102, determining the position of the particle after movement according to the current speed of the particle.
For example, according to the current velocity of the particle, the manner of determining the position of the particle after movement may be obtained according to a kinematic relation of velocity and displacement. Specifically, the current positions of all nodes in the clustering mode corresponding to the particle may be added to the current velocity to obtain the positions of all nodes in the clustering mode corresponding to the particle.
S103, determining each particle fitness of the particle population according to a predetermined fitness function, wherein the fitness function is determined according to the positions of the particles.
For example, the predetermined fitness function may be determined only according to the positions of the particles (positions of all nodes in the clustering mode corresponding to the particles), or may be determined according to the positions and the remaining energy together. And then updating the global optimal position and the local optimal position of the particle according to the fitness.
The way in which the fitness function is determined only from the particle positions may be:
Figure BDA0003047724010000091
wherein, F1To be adaptive, d (NCH)iBS) is a non-clusterhead node toDistance of base station, d (CH)jBS) is the distance from the cluster head node to the base station, d (NCH)i，CHj) The distance from a non-cluster head node to a cluster head node is obtained, N is the total number of surviving nodes in the electric wireless sensor network, and K is the number of the cluster head nodes.
The way that the fitness function is determined jointly according to the particle position and the residual energy (the residual energy of all nodes in the clustering way corresponding to the particle) may be:
F1＝αf1+(1-α)f2；
wherein, F1For fitness, alpha is a weight coefficient, and alpha belongs to (0, 1)]The requirements of the degree of energy and position equalisation can be adjusted, f1Is an energy factor, f2In order to be a position factor,
Figure BDA0003047724010000092
wherein N is the total number of surviving nodes in the electric wireless sensor network, K is the number of cluster head nodes, and Er CH(i) Representing cluster head node CH in the r-th iterationiResidual energy of, Er NCH(j) Representing non-cluster head node NCH in the r-th iterationjThe residual energy of (d);
Figure BDA0003047724010000101
wherein d (NCH)iBS) is the distance from the non-cluster head node to the base station, d (CH)jBS) is the distance from the cluster head node to the base station, d (NCH)i，CHj) The distance from the non-cluster head node to the cluster head node.
The residual energy of the nodes is determined through an LEACH energy consumption model, and the model is mainly used for providing a calculation formula for the energy consumption of the nodes in the calculation process. Meanwhile, other WSN energy consumption models can be used for simulation calculation, which is not limited in this embodiment and can be determined by those skilled in the art as needed. The specific calculation method of the LEACH energy consumption model is as follows:
ERX(m,d)＝mEelec；
Figure BDA0003047724010000102
EDA(m,d)＝mEDA；
wherein d represents the distance between nodes; d0Representing the threshold distance, the threshold distance is usually set by itself, and is usually about 80 in a 100m × 100m range simulation. EfsAnd EmpRespectively representing power amplification factors of a free space model and a multipath attenuation model; m represents a bit size of each data group; eelecRepresenting the energy consumption for transmitting data per bit, EDAWhether it represents power consumption for fusing 1bit data, ETX(m, d) and ERX(m, d) and EDAAnd (m and d) are respectively energy consumption for transmitting mbit data, energy consumption for receiving mbit data and energy consumption for fusing mbit data by the nodes with the distance d. The amount of data and parameters transferred are determined by the specific device energy consumption data during the simulation.
When the fitness function is determined by the particle position and the residual energy together, the energy consumption requirement and the communication efficiency requirement can be met, the residual energy and the position are used as the basis of the fitness function, the more uniform the energy use of the nodes is, the shorter the distance between the cluster head and the non-cluster head node and the distance between the base stations are, the larger the fitness value is, and the better the algorithm result is.
S104, repeating the steps of determining the current speed of the particles according to the last updated particle speed, the last updated particle position, the dynamically adjusted inertia weight and/or the dynamically adjusted factor, and determining the fitness of each particle of the particle group according to a predetermined fitness function until the fitness of the particles reaches a preset condition, and executing S105 when the fitness of the particles reaches the preset condition;
and S105, determining routing clustering according to the particles reaching the preset condition.
Illustratively, the present embodiment may take a globally optimal solution as a final clustering result. The preset condition may be that the fitness value reaches a self-defined expected value, for example, 1, and the representation mode that the fitness of the particle reaches the preset condition may be that the fitness is higher than the preset fitness, or may be that a fixed optimization frequency is set according to a relationship between the fitness and the optimization frequency, and the optimization frequency reaches a target frequency. The method for determining routing clustering according to the particles meeting the preset condition may be that when the fitness of the particles is higher than the preset fitness, the particles are indicated to achieve local optimum and global optimum, and then according to the distance between the non-cluster-head nodes and the cluster-head nodes in the clustering mode represented by the particles, all the non-cluster-head nodes are added to the nearest cluster-head node to complete clustering.
Compared with a scheme that inertia weight and factors are generally used as constants, the method for clustering the routes of the power wireless sensor network determines the speed of the particles by dynamically adjusting the inertia weight and/or the factors, so that the search range is dynamically limited when the particles perform space search, local optimization and global optimization of the particles are effectively balanced, the problem that another optimization effect is weakened due to the fact that the local optimization or the global optimization is biased is avoided, and a clustering result is optimized.
As an optional implementation manner of this embodiment, determining the current velocity of the particle according to the last updated particle velocity, the last updated particle position, and the dynamically adjusted inertial weight and/or the dynamically adjusted factor includes:
selecting a plurality of initial cluster heads according to sensor node data in the electric wireless sensor network to obtain a plurality of particles to form a particle group;
selecting a plurality of initial cluster heads according to sensor node data in the electric wireless sensor network comprises the following steps:
and screening the plurality of nodes according to a preset energy value, screening out the nodes with the energy value larger than the preset energy value, and selecting an initial cluster head from the nodes with the energy value larger than the preset energy value.
For example, according to the data of the sensor nodes in the wireless power sensor network, the manner of selecting the plurality of initial cluster heads may be to receive energy values returned by the sensor nodes in the wireless power sensor network, and determine the initial cluster heads according to the energy of each sensor node. The specific mode can be that K nodes are randomly selected from all nodes with energy larger than a preset energy value to serve as a cluster head node set, and then non-cluster head nodes are added into the nearest cluster head node to form a particle. Repeating the above steps for M times to obtain M initial clusters, i.e. M initial particles, wherein the preset energy value is an average value of energy of surviving nodes in the network:
Figure BDA0003047724010000121
wherein E isavgFor a preset energy value, N is the total number of surviving nodes in the electric wireless sensor network, and e (i) is the remaining energy of the ith node.
According to the routing clustering method for the electric wireless sensor network, when the initial cluster head is screened, the initial cluster head node is selected from the nodes with the energy exceeding the preset energy value through energy limitation, so that the problem that some low-energy nodes are selected as the cluster head due to the fact that the cluster head nodes are selected in a random mode in a traditional routing clustering protocol, and therefore premature death is caused is solved, and the survival time of the nodes is prolonged.
As an optional implementation manner of this embodiment, determining the position of the particle after the movement according to the current velocity of the particle includes: after the location is updated, there may be no surviving node in the current location in the network, so the updated location needs to be matched with the current surviving node, and the moved location of the node in the clustering mode corresponding to the particle is matched with the current surviving node by the following formula:
Figure BDA0003047724010000122
wherein, CMnx、CMnySurviving nodes CM for a networknCoordinate of (2), Xxid、XyidIs the position coordinate of the current particle.
An embodiment of the present invention provides a clustering device for a wireless power sensor network, as shown in fig. 2, including:
a velocity determining module 201, configured to determine a current velocity of a particle according to a velocity of the particle updated last time, a position of the particle updated last time, and a dynamically adjusted inertial weight and/or a dynamically adjusted factor, where each of the particles represents a different clustering manner; for details, reference is made to the corresponding parts of the above method embodiments, which are not described herein again.
A position determining module 202, configured to determine a position of the particle after movement according to the current velocity of the particle; for details, reference is made to the corresponding parts of the above method embodiments, which are not described herein again.
A fitness determining module 203, configured to determine a fitness of each particle of the particle group according to a predetermined fitness function, where the fitness function is determined according to a particle position; for details, reference is made to the corresponding parts of the above method embodiments, which are not described herein again.
A repeating module 204, configured to repeat the step of determining the current velocity of the particle according to the last updated particle velocity, the last updated particle position, and the dynamically adjusted inertia weight and/or the dynamically adjusted factor to the step of determining the fitness of each particle of the particle group according to the predetermined fitness function until the fitness of the particle reaches a preset condition; for details, reference is made to the corresponding parts of the above method embodiments, which are not described herein again.
A routing cluster determining module 205, configured to determine, when the fitness of the particle reaches a preset condition, a routing cluster according to the particle that reaches the preset condition. For details, reference is made to the corresponding parts of the above method embodiments, which are not described herein again.
As an optional implementation manner of this embodiment, the speed determining module 201 includes: and the parameter adjusting module is used for dynamically adjusting the inertia weight and/or the factor according to the trigonometric function. For details, reference is made to the corresponding parts of the above method embodiments, which are not described herein again.
As an optional implementation manner of this embodiment, the speed determining module 201 includes: a speed calculation module for calculating a speed according to the following formula:
Figure BDA0003047724010000141
wherein v isxid(t) is the velocity component of the cluster head node in the clustering mode corresponding to the current particle on the x axis, vyid(t) is the velocity component of the cluster head node in the clustering mode corresponding to the current particle on the y axis, vxid(t-1) velocity component of cluster head node in clustering mode corresponding to particle updated last time in x axis, vyid(t-1) is the velocity component of the cluster head node in the clustering mode corresponding to the particle updated last time on the y axis, omega is the inertia weight, and represents the influence rate of the velocity of the particle in the t-1 iteration on the t-th iteration,
Figure BDA0003047724010000142
Figure BDA0003047724010000143
a. b and m are any parameter; c. C1And c2Respectively representing a cognitive learning factor and a sociological factor, and respectively controlling the weight of the local optimal position and the global optimal position of the particle; p is a radical ofxid(t-1) x-axis component, p, of the locally optimal position of the cluster head node in the clustering mode corresponding to the particle updated last timeyid(t-1) is the y-axis component, p, of the locally optimal position of the cluster head node in the clustering mode corresponding to the particle updated last timexgd(t-1) x-axis component, p, of the global optimal position of the cluster head node in the clustering mode corresponding to the last updated particleygd(t-1) is the y-axis component of the global optimal position of the cluster head node in the clustering mode corresponding to the particle updated last time, r1、r2Is a random number between 0 and 1. For details, reference is made to the corresponding parts of the above method embodiments, which are not described herein again.
As an optional implementation manner of this embodiment, the speed determining module 201 includes:
the dynamic factor determining module is used for dynamically adjusting the cognitive learning factor and the sociological factor through the following formulas:
c1＝c1s-(c1s-c1e)cos(ω)；
c2＝c2s-(c2s-c2e)cos(ω)；
wherein, c1sTo initiate cognitive learning factor size, c1eFor the final cognitive learning factor, ω is the inertia weight, c2sStarting the sociological factor size, c2eIs the size of the final sociological factor. For details, reference is made to the corresponding parts of the above method embodiments, which are not described herein again.
As an optional implementation manner of this embodiment, the fitness determining module 203 includes:
a fitness calculating module for calculating the fitness according to the following formula:
F1＝αf1+(1-α)f2；
wherein, F1For fitness, α is a weight coefficient, f1Is an energy factor, f2In order to be a position factor,
Figure BDA0003047724010000151
wherein N is the total number of surviving nodes in the electric wireless sensor network, K is the number of cluster head nodes, and Er CH(i) Representing cluster head node CH in the r-th iterationiResidual energy of, Er NCH(j) Representing non-cluster head node NCH in the r-th iterationjThe residual energy of (d);
Figure BDA0003047724010000152
wherein d (NCH)iBS) is the distance from the non-cluster head node to the base station, d (CH)jBS) is the distance from the cluster head node to the base station, d (NCH)i，CHj) The distance from the non-cluster head node to the cluster head node. For details, reference is made to the corresponding parts of the above method embodiments, which are not described herein again.
As an optional implementation manner of this embodiment, the method further includes:
the particle swarm initialization module is used for selecting a plurality of initial cluster heads according to sensor node data in the electric wireless sensor network to obtain a plurality of particles and form a particle swarm; for details, reference is made to the corresponding parts of the above method embodiments, which are not described herein again.
The particle swarm initialization module comprises a cluster head determining module and is used for selecting a plurality of initial cluster heads according to sensor node data in the electric wireless sensor network, and the initial cluster heads comprise: and screening the plurality of nodes according to a preset energy value, screening out the nodes with the energy value larger than the preset energy value, and selecting an initial cluster head from the nodes with the energy value larger than the preset energy value. For details, reference is made to the corresponding parts of the above method embodiments, which are not described herein again.
As an optional implementation manner of this embodiment, the position determining module 202 includes:
a position matching module, configured to match the moved position of the node in the clustering mode corresponding to the particle with a currently alive node according to the following formula:
Figure BDA0003047724010000161
wherein, CMnx、CMnySurviving nodes CM for a networknCoordinate of (2), Xxid、XyidIs the position coordinate of the current particle. For details, reference is made to the corresponding parts of the above method embodiments, which are not described herein again.
As an optional implementation manner of this embodiment, the fitness calculating module includes: and the energy consumption calculation module is used for determining the residual energy of the nodes through an LEACH energy consumption model. For details, reference is made to the corresponding parts of the above method embodiments, which are not described herein again.
The embodiment of the present application also provides an electronic device, as shown in fig. 3, including a processor 310 and a memory 320, where the processor 310 and the memory 320 may be connected by a bus or in other manners.
Processor 310 may be a Central Processing Unit (CPU). The Processor 310 may also be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, or any combination thereof.
The memory 320 is a non-transitory computer readable storage medium, and can be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the power wireless sensor network routing clustering method in the embodiment of the present invention. The processor executes various functional applications and data processing of the processor by executing non-transitory software programs, instructions, and modules stored in the memory.
The memory 320 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created by the processor, and the like. Further, the memory may include high speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, memory 320 may optionally include memory located remotely from the processor, which may be connected to the processor 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 one or more modules are stored in the memory 320 and, when executed by the processor 310, perform a power wireless sensor network routing clustering method as in the embodiment shown in fig. 1.
The details of the electronic device may be understood with reference to the corresponding related description and effects in the embodiment shown in fig. 1, and are not described herein again.
The present embodiment also provides a computer storage medium, where the computer storage medium stores computer-executable instructions, where the computer-executable instructions can execute any method described above in embodiment 1 of the clustering method for wireless power sensor network routing. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD), a Solid State Drive (SSD), or the like; the storage medium may also comprise a combination of memories of the kind described above.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications therefrom are within the scope of the invention.

Claims (12)

1. A clustering method for a power wireless sensor network is characterized by comprising the following steps:
determining the current speed of the particles according to the last updated particle speed, the last updated particle position, the dynamically adjusted inertia weight and/or the dynamically adjusted factor, wherein each particle represents different clustering modes;
determining the position of the particle after movement according to the current speed of the particle;
determining each particle fitness of the particle population according to a predetermined fitness function, wherein the fitness function is determined according to the particle position;
repeating the step of determining the current speed of the particles according to the last updated particle speed, the last updated particle position, the dynamically adjusted inertial weight and/or the dynamically adjusted factor to the step of determining the fitness of each particle of the particle group according to the predetermined fitness function until the fitness of the particles reaches a preset condition;
and when the fitness of the particles reaches a preset condition, determining the routing clustering according to the particles reaching the preset condition.
2. The method of claim 1, wherein the dynamically adjusted inertial weights and/or dynamically adjusted factors comprise: the inertial weights and/or factors are dynamically adjusted according to the trigonometric function.
3. The method of claim 1, wherein determining the current velocity of the particle based on the last updated velocity of the particle, the last updated position of the particle, and the dynamically adjusted inertial weight comprises:
Figure FDA0003047723000000011
wherein v isxid(t) is the velocity component of the cluster head node in the clustering mode corresponding to the current particle on the x axis, vyid(t) is the velocity component of the cluster head node in the clustering mode corresponding to the current particle on the y axis, vxid(t-1) velocity component of cluster head node in clustering mode corresponding to particle updated last time in x axis, vyid(t-1) is the velocity component of the cluster head node in the clustering mode corresponding to the particle updated last time on the y axis, omega is the inertia weight, and represents the influence rate of the velocity of the particle in the t-1 iteration on the t-th iteration,
Figure FDA0003047723000000021
Figure FDA0003047723000000022
a. b and m are any parameter; c. C1And c2Respectively representing a cognitive learning factor and a sociological factor, and respectively controlling the weight of the local optimal position and the global optimal position of the particle; p is a radical ofxid(t-1) x-axis component, p, of the locally optimal position of the cluster head node in the clustering mode corresponding to the particle updated last timeyid(t-1) is the y-axis component, p, of the locally optimal position of the cluster head node in the clustering mode corresponding to the particle updated last timexgd(t-1) x-axis component, p, of the global optimal position of the cluster head node in the clustering mode corresponding to the last updated particleygd(t-1) is the score corresponding to the particle updated last timeY-axis component, r, of global optimum position of cluster head node in cluster mode1、r2Is a random number between 0 and 1.
4. The method of claim 3, wherein determining the current velocity of the particle based on the last updated velocity of the particle, the last updated position of the particle, and the dynamically adjusted inertial weight and/or the dynamically adjusted factor comprises:
a cognitive learning factor and a sociological factor dynamically adjusted by the following formula:
c1＝c1s-(c1s-c1e)cos(ω)；
c2＝c2s-(c2s-c2e)cos(ω)；
wherein, c1sTo initiate cognitive learning factor size, c1eFor the final cognitive learning factor, ω is the inertia weight, c2sStarting the sociological factor size, c2eIs the size of the final sociological factor.
5. The method of claim 1, wherein the predetermined fitness function comprises:
F1＝αf1+(1-α)f2；
wherein, F1For fitness, α is a weight coefficient, f1Is an energy factor, f2In order to be a position factor,
Figure FDA0003047723000000023
wherein N is the total number of surviving nodes in the electric wireless sensor network, K is the number of cluster head nodes, and Er CH(i) Representing cluster head node CH in the r-th iterationiResidual energy of, Er NCH(j) Representing non-cluster head node NCH in the r-th iterationjThe residual energy of (d);
Figure FDA0003047723000000024
wherein d (NCH)iBS) is the distance from the non-cluster head node to the base station, d (CH)jBS) is the distance from the cluster head node to the base station, d (NCH)i，CNj) The distance from the non-cluster head node to the cluster head node.
6. The method of claim 1, wherein determining the current velocity of the particle based on the last updated velocity of the particle, the last updated position of the particle, and the dynamically adjusted inertial weight and/or the dynamically adjusted factor comprises:
selecting a plurality of initial cluster heads according to sensor node data in the electric wireless sensor network to obtain a plurality of particles to form a particle group;
selecting a plurality of initial cluster heads according to sensor node data in the electric wireless sensor network comprises the following steps:
and screening the plurality of nodes according to a preset energy value, screening out the nodes with the energy value larger than the preset energy value, and selecting an initial cluster head from the nodes with the energy value larger than the preset energy value.
7. The method of claim 1, wherein determining the position of the particle after the movement based on the current velocity of the particle comprises:
matching the moved positions of the nodes in the clustering mode corresponding to the particles with the currently alive nodes by the following formula:
Figure FDA0003047723000000031
wherein, CMnx、CMnySurviving nodes CM for a networknCoordinate of (2), Xxid、XyidIs the position coordinate of the current particle.
8. The method of claim 5, wherein the residual energy of the nodes is determined by a LEACH energy consumption model.
9. The utility model provides a wireless sensor network route clustering device of electric power which characterized in that includes:
the speed determining module is used for determining the current speed of the particles according to the last updated particle speed, the last updated particle position, the dynamically adjusted inertia weight and/or the dynamically adjusted factor, and each particle represents different clustering modes;
the position determining module is used for determining the position of the particle after movement according to the current speed of the particle;
a fitness determining module, configured to determine a fitness of each particle of the particle population according to a predetermined fitness function, where the fitness function is determined according to a particle position;
a repeating module, configured to repeat the step of determining the current velocity of the particle according to the last updated particle velocity, the last updated particle position, and the dynamically adjusted inertial weight and/or the dynamically adjusted factor to the step of determining the fitness of each particle of the particle group according to the predetermined fitness function until the fitness of the particle reaches a preset condition;
and the route clustering determining module is used for determining route clustering according to the particles meeting the preset condition when the fitness of the particles meets the preset condition.
10. The apparatus of claim 9, wherein the speed determination module comprises: and the parameter adjusting module is used for dynamically adjusting the inertia weight and/or the factor according to the trigonometric function.
11. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the power wireless sensor network routing clustering method according to any one of claims 1 to 8 when executing the program.
12. A storage medium having stored thereon computer instructions, which when executed by a processor, perform the steps of the power wireless sensor network routing clustering method of any one of claims 1 to 8.
CN202110479034.3A 2021-04-29 2021-04-29 Electric wireless sensor network route clustering method and device and electronic equipment Pending CN113347685A (en)

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