CN112333810B - TMPA algorithm-based hierarchical wireless sensor network topology optimization method - Google Patents

TMPA algorithm-based hierarchical wireless sensor network topology optimization method Download PDF

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CN112333810B
CN112333810B CN202011221295.7A CN202011221295A CN112333810B CN 112333810 B CN112333810 B CN 112333810B CN 202011221295 A CN202011221295 A CN 202011221295A CN 112333810 B CN112333810 B CN 112333810B
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唐震洲
陈龙
蔡雪冰
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Wenzhou University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/02—Power saving arrangements
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/18—TPC being performed according to specific parameters
    • H04W52/24—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters
    • H04W52/242—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters taking into account path loss
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/30—TPC using constraints in the total amount of available transmission power
    • H04W52/36—TPC using constraints in the total amount of available transmission power with a discrete range or set of values, e.g. step size, ramping or offsets
    • H04W52/367—Power values between minimum and maximum limits, e.g. dynamic range
    • 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
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    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
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    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

The invention provides a TMPA algorithm-based hierarchical wireless sensor network topology optimization method, which comprises the steps of obtaining an arrangement range, and setting barriers and signal attenuation values; setting calculation variables and initializing parameters; calculating the lowest transmitting power and fitness of each sensing layer node under the condition that the connection rate is 1 in the k iteration; setting the population with the optimal fitness as a top predator, carrying out Tent chaotic disturbance strategy, and then comparing to obtain the population with the optimal fitness; copying top predators to construct a predator matrix; optimizing the node position of the convergence layer, comparing the fitness values by a Tent chaotic disturbance strategy again, and taking a group of solutions with optimal fitness; executing the FADS effect of the MPA algorithm until the iteration is finished; and outputting the position coordinates of the optimal convergence layer nodes and the transmitting power of all the sensing layer nodes. The invention provides the optimal position distribution of the nodes of the convergence layer and the minimum energy consumption of the transmitting power of all the nodes of the sensing layer, and realizes the purposes of saving energy, balancing energy consumption and prolonging the service life of a network.

Description

TMPA algorithm-based hierarchical wireless sensor network topology optimization method
Technical Field
The invention relates to the technical field of wireless local area networks, in particular to a TMPA algorithm-based hierarchical wireless sensor network topology optimization method.
Background
Wireless Sensors (WSNs) have gained academic and industrial attention due to their widespread use. The layered wireless sensor network has the advantages of expandability, efficient communication, fault tolerance and the like, so that the layered wireless sensor network is widely applied to a large-scale wireless sensor network. In a hierarchical structure, the whole network generally comprises a sensing layer and a convergence layer, wherein the convergence layer is mainly used for receiving related data acquired by a plurality of sensing layers, converging the data and communicating with other nodes of the convergence layer in a multi-hop manner. The sensing layer node is often an energy-limited system. Therefore, saving energy and extending its operating time as much as possible play a crucial role in the performance and lifetime of the entire network.
It has been shown that the main energy consumption of wireless sensor nodes is the transmission and reception of data. Therefore, when designing a layered wireless sensor network, the most important aspect to be considered is energy consumption and node scheduling, because it defines the life cycle of one wireless sensor node, and thus the life cycle of the whole wireless sensor network.
As shown in fig. 1, the layered wireless sensor network includes a sensing layer and a convergence layer; the Sensing layer nodes (SNs) are responsible for collecting information, and the Convergence layer nodes (CNs) are responsible for aggregating information collected by multiple Sensing layer nodes and communicating information between different Convergence layer nodes in a multi-hop manner. Wherein,
Figure BDA0002758355290000011
for a set of sense layer nodes, nsTo sense the number of layer nodes, SNi＝{xi,yi,ziThe position of the ith sensing layer node is used as the position of the ith sensing layer node;
Figure BDA0002758355290000012
for a set of sink nodes, ncFor the number of sink nodes, CNj＝{xj,yj,zjAnd is the position information of the jth aggregation layer node.
However, the existing layered wireless sensor network has disadvantages and shortcomings, mainly because the used models are all uniform models and each node has the same coverage radius, the deployment in the non-uniform environment has deviation, and the wireless signal not only has path loss in the propagation process, but also suffers loss when penetrating through an obstacle.
Therefore, a topology optimization method for a layered wireless sensor network is needed, which can provide optimal distribution of the positions of the nodes in the convergence layer and minimum energy consumption of the transmission power of all the nodes in the sensing layer on the premise of ensuring the coverage, thereby achieving the purposes of saving energy, balancing energy consumption and prolonging the service life of the network.
Disclosure of Invention
The technical problem to be solved by the embodiments of the present invention is to provide a TMPA algorithm-based topology optimization method for a layered wireless sensor network, which can provide optimal distribution of sink layer node positions and minimum energy consumption of transmission power of all sensing layer nodes on the premise of ensuring a coverage, thereby achieving the purpose of energy saving, energy consumption balancing and network life prolonging.
In order to solve the above technical problem, an embodiment of the present invention provides a TMPA algorithm-based hierarchical wireless sensor network topology optimization method, including the following steps:
step S1, acquiring an arrangement area of the wireless sensor network, and setting obstacles and attenuation values thereof in the arrangement area; the sensing layer nodes and the convergence layer nodes are randomly distributed in the arrangement area;
step S2, setting the position coordinates (x, y, z) of the nodes of the convergence layer and the transmitting power p of the nodes of the perception layer as calculation variables, and setting algorithm related parameters; the step of setting the algorithm-related parameters is specifically to define the position coordinate (x, y, z) of the aggregation layer node as one group in the group, wherein x, y, z respectively correspond to one individual in the group, the number of the groups is n, and the maximum iteration number of optimization of the algorithm is Mmax(ii) a Definition of jth prey population as { Xj k，Yj k，Zj k}，
Figure BDA0002758355290000021
Coordinate vectors of the nodes of the convergence layer are obtained;
step S3, initializing a prey matrix, which is calculated by the following formula:
Figure BDA0002758355290000031
wherein L isx，Ly，LzThe length, width and height of the arrangement area are respectively;
step S4, in the k iteration, according to the positions of the current sensing layer node and the aggregation layer node, calculating the lowest transmitting power P of each sensing layer node under the condition that the connection rate is 1 through a formula (2)kAnd make an order
Figure BDA0002758355290000032
Is PkAverage value of all elements of the vector, and randomly generating a sum PkVectors of the same dimension
Figure BDA0002758355290000033
And further the amount of orientation
Figure BDA0002758355290000034
The larger data in the position corresponding to the vector P constitutes the vector PminSubstituting into the fitness function formula (3) to calculate:
Figure BDA0002758355290000038
Figure BDA0002758355290000035
wherein p isi,kRepresents the transmit power of the i-th sensing layer node at the k-th iteration, and pi,k＝β+αo,i＝1,2,K,ns；
Wherein eta is a penalty factor which does not satisfy the constraint condition and does not satisfy the constraint condition
Figure BDA0002758355290000036
In any of the three constraints, the penalty factor η is a sufficiently large positive integer, and conversely is 0; constraint C1The current sensing layer node and the convergence layer node are constrained to realize full connection; c2The emission power of all sensing layer nodes is restricted to reach energy balance; c3The transmission power range of all sensing layer nodes is restricted;
wherein p isiThe transmission power of the ith sensing layer node is obtained;
wherein L isΩIs the connection rate of the network, and LΩWhen the sensing layer is 1, the full connection is realized;
Figure BDA0002758355290000037
Figure BDA0002758355290000041
the distance from the ith sensing layer node to the jth aggregation layer node is calculated; gamma is a path loss exponent, representing the rate of increase of path loss with distance; dαIs a reference distance; alpha is a reference distance dαA lower received power; beta is aoA signal attenuation value caused for an obstacle;
wherein σpIs a standard deviation of transmission power of the node of the sensing layer, and
Figure BDA0002758355290000042
Figure BDA0002758355290000043
epsilon is a given standard deviation threshold;
step S5, according to the value of the fitness function, the population with the optimal fitness is set as the top predator TtopAnd using formula (4) to pair top predators TtopPerforming Tent chaos one-dimensional disturbance strategy, and replacing top-level predators T with individuals subjected to Tent chaos sequence disturbancetopAfter the data of the corresponding position in the database are obtained, the fitness of the individual before and after replacement is respectively calculated, and a group of a fitness optimal solution is further selected;
XI i,j＝Xmin j+Rt·(Xmax j-Xmin j) (4)；
wherein XI i,jRandomly acquiring one-dimensional data from the top predators, and performing one-dimensional chaotic traversal on the acquired position to obtain a final solution; xmin jAnd Xmax jRespectively a lower bound and an upper bound of a jth dimension solution space; rtIs the t-th element in Tent sequence, an
Figure BDA0002758355290000044
Num represents the total number of elements in the Tent sequence; mod1 represents the value after the divisor decimal point is taken;
step S6, top predator TtopCopying n times to construct a predator matrix;
s7, optimizing the position of the node of the convergence layer by using a preset MPA algorithm;
step S8, solving the group with the optimal fitness again according to the optimized position of the convergence layer node and the position of the corresponding sensing layer node, and setting the group as a top-level predator TtopAnd using formula (4) to pair top predators TtopCarrying out Tent chaos one-dimensional disturbance strategy, and replacing top predator T with individuals subjected to Tent chaos sequence disturbancetopAfter the data of the corresponding position in the database are obtained, the fitness of the individual before and after replacement is respectively calculated, and a group of a fitness optimal solution is further selected;
step S9, executing FADs effect of MPA algorithm, and k is k + 1; if k is less than or equal to MmaxThen return to step S4; otherwise, the iteration is ended;
and S10, outputting the optimal position coordinates of the nodes of the convergence layer and the transmitting power of all the nodes of the sensing layer.
The embodiment of the invention has the following beneficial effects:
the invention introduces Tent chaotic sequences into the MPA algorithm, effectively improves the local search capability of the optimal solution position of the MPA algorithm by utilizing the characteristics of the chaotic sequences, ensures the connection rate to be 100 percent, and ensures that the sum of the transmission power of all the sensing layer nodes is minimum and the standard deviation of the transmission power among all the sensing nodes is as small as possible by jointly optimizing the deployment positions of all the convergent layer nodes and the transmission power of all the sensing layer nodes, thereby achieving the purposes of saving energy, balancing energy consumption and prolonging the service life of a network.
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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 introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is within the scope of the present invention for those skilled in the art to obtain other drawings based on the drawings without inventive exercise.
FIG. 1 is a topology diagram of a layered wireless sensor network in the prior art;
fig. 2 is a flowchart of a topology optimization method for a hierarchical wireless sensor network based on a TMPA algorithm according to an embodiment of the present invention;
fig. 3 is a comparison graph of convergence curves of a TMPA algorithm and a traditional MPA algorithm in the TMPA algorithm-based hierarchical wireless sensor network topology optimization method provided in the embodiment of the present invention;
fig. 4 is a network data connection diagram optimized by using a TMPA algorithm in the TMPA algorithm-based hierarchical wireless sensor network topology optimization method provided in the embodiment of the present invention;
fig. 5 is a comparison diagram of a hierarchical wireless sensor network deployed by using a TMPA algorithm and a hierarchical wireless sensor network deployed by using a uniform algorithm in the TMPA algorithm-based hierarchical wireless sensor network topology optimization method provided in the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings.
As shown in fig. 1, in an embodiment of the present invention, a method for optimizing a topology of a layered wireless sensor network based on a TMPA algorithm is provided, where the method includes the following steps:
step S1, acquiring an arrangement area of the wireless sensor network, and setting obstacles and attenuation values thereof in the arrangement area; the sensing layer nodes and the convergence layer nodes are randomly distributed in the arrangement area;
step S2, setting the position coordinates (x, y, z) of the nodes of the convergence layer and the transmitting power p of the nodes of the perception layer as calculation variables, and setting algorithm related parameters; the step of setting the algorithm-related parameters is specifically to define the position coordinate (x, y, z) of the aggregation layer node as one group in the group, wherein x, y, z respectively correspond to one individual in the group, the number of the groups is n, and the maximum iteration number of optimization of the algorithm is Mmax(ii) a Definition of jth prey population as { Xj k，Yj k，Zj k}，
Figure BDA0002758355290000061
Coordinate vectors of the nodes of the convergence layer are obtained;
step S3, initializing a prey matrix, which is calculated by the following formula:
Figure BDA0002758355290000062
wherein L isx，Ly，LzThe length, width and height of the arrangement area are respectively;
step S4, in the k iteration, according to the positions of the current sensing layer node and the aggregation layer node, calculating the lowest transmitting power P of each sensing layer node under the condition that the connection rate is 1 through a formula (2)kAnd make an order
Figure BDA0002758355290000063
Is PkAverage value of all elements of the vector, and randomly generating a sum PkVectors of the same dimension
Figure BDA0002758355290000064
And further the amount of orientation
Figure BDA0002758355290000066
The larger data in the position corresponding to the vector P constitutes the vector PminSubstituting into the fitness function formula (3) to calculate:
Figure BDA0002758355290000065
Figure BDA0002758355290000071
wherein p isi,kRepresents the transmit power of the i-th sensing layer node at the k-th iteration, and pi,k＝β+αo,i＝1,2,K,ns；
Wherein eta is a penalty factor which does not satisfy the constraint condition and does not satisfy the constraint condition
Figure BDA0002758355290000072
In any of the three constraints, the penalty factor η is a sufficiently large positive integer, and conversely is 0; constraint C1The current sensing layer node and the convergence layer node are constrained to realize full connection; c2The emission power of all sensing layer nodes is restricted to reach energy balance; c3The transmission power range of all sensing layer nodes is restricted;
wherein p isiThe transmission power of the ith sensing layer node is obtained;
wherein L isΩIs the connection rate of the network, and LΩWhen the sensing layer is 1, the full connection is realized;
Figure BDA0002758355290000073
Figure BDA0002758355290000074
the distance from the ith sensing layer node to the jth aggregation layer node is calculated; gamma is a path loss exponent, representing the rate of increase of path loss with distance; dαIs a reference distance; alpha is a reference distance dαA lower received power; beta is aoA signal attenuation value caused for an obstacle;
wherein σpIs a standard deviation of transmission power of the node of the sensing layer, and
Figure BDA0002758355290000075
Figure BDA0002758355290000076
epsilon is a given standard deviation threshold;
step S5, according to the value of the fitness function, the population with the optimal fitness is set as the top predator TtopAnd using formula (4) to pair top predators TtopPerforming Tent chaos one-dimensional disturbance strategy, and treating the individuals after the Tent chaos sequence is disturbedReplacement top predator TtopAfter the data of the corresponding position in the database are obtained, the fitness of the individual before and after replacement is respectively calculated, and a group of a fitness optimal solution is further selected;
XI i,j＝Xmin j+Rt·(Xmax j-Xmin j) (4)；
wherein, XI i,jRandomly acquiring one-dimensional data from the top predators, and performing one-dimensional chaotic traversal on the acquired position to obtain a final solution; xmin jAnd Xmax jRespectively a lower bound and an upper bound of a jth dimension solution space; rtIs the t-th element in Tent sequence, an
Figure BDA0002758355290000081
Num represents the total number of elements in the Tent sequence; mod1 represents the value after the divisor decimal point is taken;
step S6, top predator TtopCopying n times to construct a predator matrix;
s7, optimizing the position of the node of the convergence layer by using a preset MPA algorithm;
step S8, solving the group with the optimal fitness again according to the optimized position of the convergence layer node and the position of the corresponding sensing layer node, and setting the group as a top-level predator TtopAnd using formula (4) to pair top predators TtopCarrying out Tent chaos one-dimensional disturbance strategy, and replacing top predator T with individuals subjected to Tent chaos sequence disturbancetopAfter the data of the corresponding position in the database are obtained, the fitness of the individual before and after replacement is respectively calculated, and a group of a fitness optimal solution is further selected;
step S9, executing FADs effect of MPA algorithm, and k is k + 1; if k is less than or equal to MmaxThen return to step S4; otherwise, the iteration is ended;
and S10, outputting the optimal position coordinates of the nodes of the convergence layer and the transmitting power of all the nodes of the sensing layer.
Specifically, the inventor finds that the wireless signal not only has path loss but also penetrates through an obstacle in the process of propagatingSuffers from loss of time, the propagation of the radio signal is formulated
Figure BDA0002758355290000082
To indicate. Wherein,
Figure BDA0002758355290000083
the distance from the ith sensing layer node to the jth aggregation layer node is calculated; gamma is a path loss exponent, representing the rate of increase of path loss with distance; dαIs a reference distance; alpha is a reference distance dαA lower received power; beta is aoThe attenuation values of the signals caused by the obstacles are different.
At this time, the inventors introduced the Boolean expression l (SN) on the one handi,CNj)，i∈[1,ns]，j∈[1,nc]To judge the sensing layer node SNiWhether to contact the node CN of the convergence layerjThere is an active link between them, i.e. a sensing layer node SNiWhether to effectively cover the node CN of the convergence layerj。
Meanwhile, in order to collect data completely, it is necessary to ensure that an effective link exists between any sensing node and an aggregation node. For this purpose, the connection rate L of the entire network is definedΩ. Obviously, when all the sensing layer nodes can have effective links with the aggregation layer node, i.e. LΩThe sensing layer implements full connectivity as 1.
On the other hand, the standard deviation sigma of the transmission power of the node of the sensing layer is introducedpTo measure the energy consumption balance among the nodes of the sensing layer.
Finally, the inventor introduces an MPA algorithm to optimize the hierarchical wireless sensor network, and improves the MPA algorithm based on the above-introduced related contents. Wherein, each predator group corresponds to a convergence layer node CN position deployment mode.
The above optimization problem of the layered wireless sensor network can be summarized as follows: on the premise of ensuring that the connection rate is 100%, deployment positions of all the aggregation layer nodes and transmission power of all the sensing layer nodes are jointly optimized, so that the network node is enabled to be capable of transmitting the data at the same timeThe sum of the transmitting power of the nodes with the sensing layer is minimum, and the standard deviation of the transmitting power among all the sensing nodes is made to be as small as possible, so that the purposes of saving energy, balancing energy consumption and prolonging the service life of the network are achieved. Wherein the optimization problem can be defined as
Figure BDA0002758355290000091
The method can restrict the full connection condition of the current sensing layer node and the aggregation layer node, restrict the transmitting power of all the sensing layer nodes to reach energy balance, and restrict the transmitting power range of all the sensing layer nodes. Wherein p isiThe transmission power of the ith sensing layer node is obtained;
Figure BDA0002758355290000092
ε is the given standard deviation threshold.
Meanwhile, in order to increase the searching capability of the MPA algorithm, the Tent chaotic sequence is introduced into the MPA algorithm, and the local searching capability of the optimal solution position of the MPA algorithm is effectively improved by utilizing the characteristics of the chaotic sequence. The chaotic motion is non-repetitive, so that the chaotic motion has higher search speed than random search, and the Tent chaotic motion has the characteristics of pseudo-randomness, ergodicity and sensitivity to initial values, and strict mathematical reasoning proves that the ergodic uniformity and the convergence speed of Tent mapping are superior to Logistic mapping, so that the Tent chaotic mapping is selected.
In order to avoid the phenomenon that the occurrence of small period and unstable period points in Tent chaotic sequence affects the performance of the algorithm, an improved expression is adopted
Figure BDA0002758355290000101
And is expressed as after being transformed by the Bernoulli shift
Figure BDA0002758355290000102
Where Num represents the total number of elements in the Tent sequence and mod1 represents the value obtained by dividing the decimal point.
One-dimensional data is randomly selected from top predators, and one-dimensional chaotic traversal search is carried out on the selected position. Better solutions can be found by one-dimensional chaotic ergodic search while preservingAnd (4) optimal dimension information of the optimal solution is maintained. Therefore, the process is called Tent chaos one-dimensional perturbation strategy, and the expression is XI i,j＝Xmin j+Rt·(Xmax j-Xmin j)；XI i,jRandomly acquiring one-dimensional data from the top predators, and performing one-dimensional chaotic traversal on the acquired position to obtain a final solution; xmin jAnd Xmax jRespectively, the lower bound and the upper bound of the jth dimension solution space. Therefore, top predator T can be replaced by individuals perturbed by Tent chaotic sequencestopRespectively calculating the fitness of the individuals before and after replacement, and reserving the population with the optimal fitness.
The embodiment of the invention provides a TMPA algorithm-based hierarchical wireless sensor network topology optimization method, which is specifically realized as follows:
in step S1, the wireless sensor arrangement range (e.g., a cube of 100m × 100m × 10 m) is first subjected to network discretization (e.g., a cube domain is discretized into 100 grids, and the grid center is regarded as a coverage target point), where the sensing layer nodes and the convergence layer nodes are randomly distributed in the arrangement area.
Three different types of obstacles (e.g., load bearing walls, brick walls, and metal doors) were then introduced to the target area, with different obstacles corresponding to different values of signal attenuation.
In step S2, first, the position coordinates (x, y, z) of the sink layer node and the transmission power p of the sensing layer node are set as calculation variables.
Then, setting algorithm related parameters, specifically defining the position coordinate (x, y, z) of the node of the convergence layer as a group in the group, wherein x, y and z respectively correspond to an individual in the group, the number of the groups is n, and the maximum iteration number of optimization of the algorithm is Mmax；
The jth prey population was also defined as { Xj k，Yj k，Zj k}，
Figure BDA0002758355290000111
Is a coordinate vector of the sink node.
In step S3, a prey matrix is initialized.
In step S4, first, at the kth iteration, the lowest transmission power P of each sensing layer node with the connection rate of 1 is calculated by formula (2) according to the positions of the current sensing layer node and the aggregation layer nodek。
Then, let
Figure BDA0002758355290000112
Is PkAverage value of all elements of the vector, and randomly generating a sum PkVectors of the same dimension
Figure BDA0002758355290000113
And further the amount of orientation
Figure BDA0002758355290000114
The larger data in the position corresponding to the vector P constitutes the vector PminAnd substituting the fitness function into the fitness function formula (3) for calculation.
In step S5, first, a population with the optimal fitness is determined as the top predator T based on the fitness function value determined in step S4topAnd performing Tent chaos one-dimensional disturbance strategy. Then, the fitness of the population before and after disturbance is compared, and the population with low fitness is selected.
In step S6, the top predator is replicated n times to build a predator matrix.
In step S7, optimization of the position of the aggregation layer node CN is achieved using three phases of the MPA algorithm.
In step S8, first, in view of the optimization of the position of the convergence layer node CN and the position of the corresponding sensing layer node SN in step S7, the group with the optimal fitness is found again as the top predator T in step S4topAnd performing Tent chaos one-dimensional disturbance strategy. Then, the fitness of the population before and after disturbance is compared, and the population with low fitness is selected.
In step S9, the FADs effect of the MPA algorithm is performed, k ═ k + 1. If k is less than or equal to MmaxThen, the process returns to step S4.
In step S10, the iteration is ended, and the optimal position coordinates of the sink layer nodes and the transmission powers of all the sensing layer nodes are output.
As shown in fig. 3, a convergence curve comparison graph of the TMPA algorithm adopted in the TMPA algorithm-based hierarchical wireless sensor network topology optimization method provided in the embodiment of the present invention and the MPA algorithm adopted in the tradition is shown;
as shown in fig. 4, a network data connection diagram is optimized by using a TMPA algorithm in the TMPA algorithm-based hierarchical wireless sensor network topology optimization method provided in the embodiment of the present invention;
as shown in fig. 5, a comparison diagram of a layered wireless sensor network deployed by using a TMPA algorithm and a layered wireless sensor network deployed by using a uniform algorithm in a conventional layered wireless sensor network topology optimization method based on a TMPA algorithm provided in an embodiment of the present invention is shown.
The embodiment of the invention has the following beneficial effects:
the invention introduces Tent chaotic sequences into the MPA algorithm, effectively improves the local search capability of the optimal solution position of the MPA algorithm by utilizing the characteristics of the chaotic sequences, ensures the connection rate to be 100 percent, and ensures that the sum of the transmission power of all the sensing layer nodes is minimum and the standard deviation of the transmission power among all the sensing nodes is as small as possible by jointly optimizing the deployment positions of all the convergent layer nodes and the transmission power of all the sensing layer nodes, thereby achieving the purposes of saving energy, balancing energy consumption and prolonging the service life of a network.
It will be understood by those skilled in the art that all or part of the steps in the method for implementing the above embodiments may be implemented by relevant hardware instructed by a program, and the program may be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc.
While the invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not to be limited to the disclosed embodiment, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.

Claims (1)

1. A TMPA algorithm-based hierarchical wireless sensor network topology optimization method is characterized by comprising the following steps:
step S1, acquiring an arrangement area of the wireless sensor network, and setting obstacles and attenuation values thereof in the arrangement area; the sensing layer nodes and the convergence layer nodes are randomly distributed in the arrangement area;
step S2, setting the position coordinates (x, y, z) of the nodes of the convergence layer and the transmitting power p of the nodes of the perception layer as calculation variables, and setting algorithm related parameters; the step of setting the algorithm-related parameters is specifically to define the position coordinate (x, y, z) of the aggregation layer node as one group in the group, wherein x, y, z respectively correspond to one individual in the group, the number of the groups is n, and the maximum iteration number of optimization of the algorithm is Mmax(ii) a Definition of jth prey population as { Xj k，Yj k，Zj k}，
Figure FDA0003567944030000011
Coordinate vectors of the nodes of the convergence layer are obtained;
step S3, initializing a prey matrix, which is calculated by the following formula:
Figure FDA0003567944030000012
wherein L isx，Ly，LzThe length, width and height of the arrangement area are respectively;
step S4, in the k iteration, according to the positions of the current sensing layer node and the aggregation layer node, calculating the lowest transmitting power P of each sensing layer node under the condition that the connection rate is 1 through a formula (2)kAnd make an order
Figure FDA0003567944030000013
Is PkMean of all elements of the vector, randomTo a sum PkVectors of the same dimension
Figure FDA0003567944030000014
And further the amount of orientation
Figure FDA0003567944030000015
The larger data in the position corresponding to the vector P constitutes the vector PminSubstituting into the fitness function formula (3) to calculate:
Figure FDA0003567944030000027
Figure FDA0003567944030000021
wherein p isi，kRepresents the transmit power of the i-th sensing layer node at the k-th iteration, and pi，k＝β+αo，i＝1，2，K，ns；
Wherein eta is a penalty factor which does not satisfy the constraint condition and does not satisfy the constraint condition
Figure FDA0003567944030000022
In any of the three constraints, the penalty factor η is a sufficiently large positive integer, and conversely is 0; constraint C1The current sensing layer node and the convergence layer node are constrained to realize full connection; c2The emission power of all sensing layer nodes is restricted to reach energy balance; c3The transmission power range of all sensing layer nodes is restricted;
wherein p isiThe transmission power of the ith sensing layer node is obtained;
wherein L isΩIs the connection rate of the network, and LΩWhen the sensing layer is 1, the full connection is realized;
Figure FDA0003567944030000023
Figure FDA0003567944030000024
the distance from the ith sensing layer node to the jth aggregation layer node is calculated; gamma is a path loss exponent, representing the rate of increase of path loss with distance; dαIs a reference distance; alpha is a reference distance dαA lower received power; beta is aoA signal attenuation value caused for an obstacle;
wherein σpIs a standard deviation of transmission power of the node of the sensing layer, and
Figure FDA0003567944030000025
Figure FDA0003567944030000026
epsilon is a given standard deviation threshold;
step S5, according to the value of the fitness function, the population with the optimal fitness is set as the top predator TtopAnd using formula (4) to pair top predators TtopPerforming Tent chaos one-dimensional disturbance strategy, and replacing top-level predators T with individuals subjected to Tent chaos sequence disturbancetopAfter the data of the corresponding position in the database are obtained, the fitness of the individual before and after replacement is respectively calculated, and a group of a fitness optimal solution is further selected;
XI i，j＝Xmin j+Rt·(Xmax j-Xmin j) (4)；
wherein, XI i，jRandomly acquiring one-dimensional data from the top predators, and performing one-dimensional chaotic traversal on the acquired position to obtain a final solution; xmin jAnd Xmax jRespectively a lower bound and an upper bound of a jth dimension solution space; rtIs the t-th element in the Tent sequence, and
Figure FDA0003567944030000031
num represents the total number of elements in the Tent sequence; mod1 represents the value after the divisor decimal point is taken;
step S6, theTop predator TtopCopying n times to construct a predator matrix;
s7, optimizing the position of the node of the convergence layer by using a preset MPA algorithm;
step S8, solving the group with the optimal fitness again according to the optimized position of the convergence layer node and the position of the corresponding sensing layer node, and setting the group as a top-level predator TtopAnd using formula (4) to pair top predators TtopPerforming Tent chaos one-dimensional disturbance strategy, and replacing top-level predators T with individuals subjected to Tent chaos sequence disturbancetopAfter the data of the corresponding position in the database are obtained, the fitness of the individual before and after replacement is respectively calculated, and a group of a fitness optimal solution is further selected;
step S9, executing FADs effect of MPA algorithm, and k is k + 1; if k is less than or equal to MmaxThen return to step S4; otherwise, the iteration is ended;
and S10, outputting the optimal position coordinates of the nodes of the convergence layer and the transmitting power of all the nodes of the sensing layer.
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